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How To Write A Research Summary

It’s a common perception that writing a research summary is a quick and easy task. After all, how hard can jotting down 300 words be? But when you consider the weight those 300 words carry, writing a research summary as a part of your dissertation, essay or compelling draft for your paper instantly becomes daunting task.

A research summary requires you to synthesize a complex research paper into an informative, self-explanatory snapshot. It needs to portray what your article contains. Thus, writing it often comes at the end of the task list.

Regardless of when you’re planning to write, it is no less of a challenge, particularly if you’re doing it for the first time. This blog will take you through everything you need to know about research summary so that you have an easier time with it.

What is a Research Summary?

A research summary is the part of your research paper that describes its findings to the audience in a brief yet concise manner. A well-curated research summary represents you and your knowledge about the information written in the research paper.

While writing a quality research summary, you need to discover and identify the significant points in the research and condense it in a more straightforward form. A research summary is like a doorway that provides access to the structure of a research paper's sections.

Since the purpose of a summary is to give an overview of the topic, methodology, and conclusions employed in a paper, it requires an objective approach. No analysis or criticism.

Research summary or Abstract. What’s the Difference?

They’re both brief, concise, and give an overview of an aspect of the research paper. So, it’s easy to understand why many new researchers get the two confused. However, a research summary and abstract are two very different things with individual purpose. To start with, a research summary is written at the end while the abstract comes at the beginning of a research paper.

A research summary captures the essence of the paper at the end of your document. It focuses on your topic, methods, and findings. More like a TL;DR, if you will. An abstract, on the other hand, is a description of what your research paper is about. It tells your reader what your topic or hypothesis is, and sets a context around why you have embarked on your research.

Getting Started with a Research Summary

Before you start writing, you need to get insights into your research’s content, style, and organization. There are three fundamental areas of a research summary that you should focus on.

While deciding the contents of your research summary, you must include a section on its importance as a whole, the techniques, and the tools that were used to formulate the conclusion. Additionally, there needs to be a short but thorough explanation of how the findings of the research paper have a significance.
To keep the summary well-organized, try to cover the various sections of the research paper in separate paragraphs. Besides, how the idea of particular factual research came up first must be explained in a separate paragraph.
As a general practice worldwide, research summaries are restricted to 300-400 words. However, if you have chosen a lengthy research paper, try not to exceed the word limit of 10% of the entire research paper.

How to Structure Your Research Summary

The research summary is nothing but a concise form of the entire research paper. Therefore, the structure of a summary stays the same as the paper. So, include all the section titles and write a little about them. The structural elements that a research summary must consist of are:

It represents the topic of the research. Try to phrase it so that it includes the key findings or conclusion of the task.

The abstract gives a context of the research paper. Unlike the abstract at the beginning of a paper, the abstract here, should be very short since you’ll be working with a limited word count.

Introduction

This is the most crucial section of a research summary as it helps readers get familiarized with the topic. You should include the definition of your topic, the current state of the investigation, and practical relevance in this part. Additionally, you should present the problem statement, investigative measures, and any hypothesis in this section.

Methodology

This section provides details about the methodology and the methods adopted to conduct the study. You should write a brief description of the surveys, sampling, type of experiments, statistical analysis, and the rationality behind choosing those particular methods.

Create a list of evidence obtained from the various experiments with a primary analysis, conclusions, and interpretations made upon that. In the paper research paper, you will find the results section as the most detailed and lengthy part. Therefore, you must pick up the key elements and wisely decide which elements are worth including and which are worth skipping.

This is where you present the interpretation of results in the context of their application. Discussion usually covers results, inferences, and theoretical models explaining the obtained values, key strengths, and limitations. All of these are vital elements that you must include in the summary.

Most research papers merge conclusion with discussions. However, depending upon the instructions, you may have to prepare this as a separate section in your research summary. Usually, conclusion revisits the hypothesis and provides the details about the validation or denial about the arguments made in the research paper, based upon how convincing the results were obtained.

The structure of a research summary closely resembles the anatomy of a scholarly article . Additionally, you should keep your research and references limited to authentic and scholarly sources only.

Tips for Writing a Research Summary

The core concept behind undertaking a research summary is to present a simple and clear understanding of your research paper to the reader. The biggest hurdle while doing that is the number of words you have at your disposal. So, follow the steps below to write a research summary that sticks.

1. Read the parent paper thoroughly

You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

a. Scan: In the first read, go through it to get an understanding of its basic concept and methodologies.

b. Read: For the second step, read the article attentively by going through each section, highlighting the key elements, and subsequently listing the topics that you will include in your research summary.

c. Skim: Flip through the article a few more times to study the interpretation of various experimental results, statistical analysis, and application in different contexts.

Sincerely go through different headings and subheadings as it will allow you to understand the underlying concept of each section. You can try reading the introduction and conclusion simultaneously to understand the motive of the task and how obtained results stay fit to the expected outcome.

2. Identify the key elements in different sections

While exploring different sections of an article, you can try finding answers to simple what, why, and how. Below are a few pointers to give you an idea:

What is the research question and how is it addressed?
Is there a hypothesis in the introductory part?
What type of methods are being adopted?
What is the sample size for data collection and how is it being analyzed?
What are the most vital findings?
Do the results support the hypothesis?

Discussion/Conclusion

What is the final solution to the problem statement?
What is the explanation for the obtained results?
What is the drawn inference?
What are the various limitations of the study?

3. Prepare the first draft

Now that you’ve listed the key points that the paper tries to demonstrate, you can start writing the summary following the standard structure of a research summary. Just make sure you’re not writing statements from the parent research paper verbatim.

Instead, try writing down each section in your own words. This will not only help in avoiding plagiarism but will also show your complete understanding of the subject. Alternatively, you can use a summarizing tool (AI-based summary generators) to shorten the content or summarize the content without disrupting the actual meaning of the article.

SciSpace Copilot is one such helpful feature! You can easily upload your research paper and ask Copilot to summarize it. You will get an AI-generated, condensed research summary. SciSpace Copilot also enables you to highlight text, clip math and tables, and ask any question relevant to the research paper; it will give you instant answers with deeper context of the article..

4. Include visuals

One of the best ways to summarize and consolidate a research paper is to provide visuals like graphs, charts, pie diagrams, etc.. Visuals make getting across the facts, the past trends, and the probabilistic figures around a concept much more engaging.

5. Double check for plagiarism

It can be very tempting to copy-paste a few statements or the entire paragraphs depending upon the clarity of those sections. But it’s best to stay away from the practice. Even paraphrasing should be done with utmost care and attention.

Also: QuillBot vs SciSpace: Choose the best AI-paraphrasing tool

6. Religiously follow the word count limit

You need to have strict control while writing different sections of a research summary. In many cases, it has been observed that the research summary and the parent research paper become the same length. If that happens, it can lead to discrediting of your efforts and research summary itself. Whatever the standard word limit has been imposed, you must observe that carefully.

7. Proofread your research summary multiple times

The process of writing the research summary can be exhausting and tiring. However, you shouldn’t allow this to become a reason to skip checking your academic writing several times for mistakes like misspellings, grammar, wordiness, and formatting issues. Proofread and edit until you think your research summary can stand out from the others, provided it is drafted perfectly on both technicality and comprehension parameters. You can also seek assistance from editing and proofreading services , and other free tools that help you keep these annoying grammatical errors at bay.

8. Watch while you write

Keep a keen observation of your writing style. You should use the words very precisely, and in any situation, it should not represent your personal opinions on the topic. You should write the entire research summary in utmost impersonal, precise, factually correct, and evidence-based writing.

9. Ask a friend/colleague to help

Once you are done with the final copy of your research summary, you must ask a friend or colleague to read it. You must test whether your friend or colleague could grasp everything without referring to the parent paper. This will help you in ensuring the clarity of the article.

Once you become familiar with the research paper summary concept and understand how to apply the tips discussed above in your current task, summarizing a research summary won’t be that challenging. While traversing the different stages of your academic career, you will face different scenarios where you may have to create several research summaries.

In such cases, you just need to look for answers to simple questions like “Why this study is necessary,” “what were the methods,” “who were the participants,” “what conclusions were drawn from the research,” and “how it is relevant to the wider world.” Once you find out the answers to these questions, you can easily create a good research summary following the standard structure and a precise writing style.

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Home » Research Summary – Structure, Examples and Writing Guide

Research Summary – Structure, Examples and Writing Guide

Table of Contents

Research Summary

Definition:

A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings. It is often used as a tool to quickly communicate the main findings of a study to other researchers, stakeholders, or decision-makers.

Structure of Research Summary

The Structure of a Research Summary typically include:

Introduction : This section provides a brief background of the research problem or question, explains the purpose of the study, and outlines the research objectives.
Methodology : This section explains the research design, methods, and procedures used to conduct the study. It describes the sample size, data collection methods, and data analysis techniques.
Results : This section presents the main findings of the study, including statistical analysis if applicable. It may include tables, charts, or graphs to visually represent the data.
Discussion : This section interprets the results and explains their implications. It discusses the significance of the findings, compares them to previous research, and identifies any limitations or future directions for research.
Conclusion : This section summarizes the main points of the research and provides a conclusion based on the findings. It may also suggest implications for future research or practical applications of the results.
References : This section lists the sources cited in the research summary, following the appropriate citation style.

How to Write Research Summary

Here are the steps you can follow to write a research summary:

Read the research article or study thoroughly: To write a summary, you must understand the research article or study you are summarizing. Therefore, read the article or study carefully to understand its purpose, research design, methodology, results, and conclusions.
Identify the main points : Once you have read the research article or study, identify the main points, key findings, and research question. You can highlight or take notes of the essential points and findings to use as a reference when writing your summary.
Write the introduction: Start your summary by introducing the research problem, research question, and purpose of the study. Briefly explain why the research is important and its significance.
Summarize the methodology : In this section, summarize the research design, methods, and procedures used to conduct the study. Explain the sample size, data collection methods, and data analysis techniques.
Present the results: Summarize the main findings of the study. Use tables, charts, or graphs to visually represent the data if necessary.
Interpret the results: In this section, interpret the results and explain their implications. Discuss the significance of the findings, compare them to previous research, and identify any limitations or future directions for research.
Conclude the summary : Summarize the main points of the research and provide a conclusion based on the findings. Suggest implications for future research or practical applications of the results.
Revise and edit : Once you have written the summary, revise and edit it to ensure that it is clear, concise, and free of errors. Make sure that your summary accurately represents the research article or study.
Add references: Include a list of references cited in the research summary, following the appropriate citation style.

Example of Research Summary

Here is an example of a research summary:

Title: The Effects of Yoga on Mental Health: A Meta-Analysis

Introduction: This meta-analysis examines the effects of yoga on mental health. The study aimed to investigate whether yoga practice can improve mental health outcomes such as anxiety, depression, stress, and quality of life.

Methodology : The study analyzed data from 14 randomized controlled trials that investigated the effects of yoga on mental health outcomes. The sample included a total of 862 participants. The yoga interventions varied in length and frequency, ranging from four to twelve weeks, with sessions lasting from 45 to 90 minutes.

Results : The meta-analysis found that yoga practice significantly improved mental health outcomes. Participants who practiced yoga showed a significant reduction in anxiety and depression symptoms, as well as stress levels. Quality of life also improved in those who practiced yoga.

Discussion : The findings of this study suggest that yoga can be an effective intervention for improving mental health outcomes. The study supports the growing body of evidence that suggests that yoga can have a positive impact on mental health. Limitations of the study include the variability of the yoga interventions, which may affect the generalizability of the findings.

Conclusion : Overall, the findings of this meta-analysis support the use of yoga as an effective intervention for improving mental health outcomes. Further research is needed to determine the optimal length and frequency of yoga interventions for different populations.

References :

Cramer, H., Lauche, R., Langhorst, J., Dobos, G., & Berger, B. (2013). Yoga for depression: a systematic review and meta-analysis. Depression and anxiety, 30(11), 1068-1083.
Khalsa, S. B. (2004). Yoga as a therapeutic intervention: a bibliometric analysis of published research studies. Indian journal of physiology and pharmacology, 48(3), 269-285.
Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: a review of comparison studies. The Journal of Alternative and Complementary Medicine, 16(1), 3-12.

Purpose of Research Summary

The purpose of a research summary is to provide a brief overview of a research project or study, including its main points, findings, and conclusions. The summary allows readers to quickly understand the essential aspects of the research without having to read the entire article or study.

Research summaries serve several purposes, including:

Facilitating comprehension: A research summary allows readers to quickly understand the main points and findings of a research project or study without having to read the entire article or study. This makes it easier for readers to comprehend the research and its significance.
Communicating research findings: Research summaries are often used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public. The summary presents the essential aspects of the research in a clear and concise manner, making it easier for non-experts to understand.
Supporting decision-making: Research summaries can be used to support decision-making processes by providing a summary of the research evidence on a particular topic. This information can be used by policymakers or practitioners to make informed decisions about interventions, programs, or policies.
Saving time: Research summaries save time for researchers, practitioners, policymakers, and other stakeholders who need to review multiple research studies. Rather than having to read the entire article or study, they can quickly review the summary to determine whether the research is relevant to their needs.

Characteristics of Research Summary

The following are some of the key characteristics of a research summary:

Concise : A research summary should be brief and to the point, providing a clear and concise overview of the main points of the research.
Objective : A research summary should be written in an objective tone, presenting the research findings without bias or personal opinion.
Comprehensive : A research summary should cover all the essential aspects of the research, including the research question, methodology, results, and conclusions.
Accurate : A research summary should accurately reflect the key findings and conclusions of the research.
Clear and well-organized: A research summary should be easy to read and understand, with a clear structure and logical flow.
Relevant : A research summary should focus on the most important and relevant aspects of the research, highlighting the key findings and their implications.
Audience-specific: A research summary should be tailored to the intended audience, using language and terminology that is appropriate and accessible to the reader.
Citations : A research summary should include citations to the original research articles or studies, allowing readers to access the full text of the research if desired.

When to write Research Summary

Here are some situations when it may be appropriate to write a research summary:

Proposal stage: A research summary can be included in a research proposal to provide a brief overview of the research aims, objectives, methodology, and expected outcomes.
Conference presentation: A research summary can be prepared for a conference presentation to summarize the main findings of a study or research project.
Journal submission: Many academic journals require authors to submit a research summary along with their research article or study. The summary provides a brief overview of the study’s main points, findings, and conclusions and helps readers quickly understand the research.
Funding application: A research summary can be included in a funding application to provide a brief summary of the research aims, objectives, and expected outcomes.
Policy brief: A research summary can be prepared as a policy brief to communicate research findings to policymakers or stakeholders in a concise and accessible manner.

Advantages of Research Summary

Research summaries offer several advantages, including:

Time-saving: A research summary saves time for readers who need to understand the key findings and conclusions of a research project quickly. Rather than reading the entire research article or study, readers can quickly review the summary to determine whether the research is relevant to their needs.
Clarity and accessibility: A research summary provides a clear and accessible overview of the research project’s main points, making it easier for readers to understand the research without having to be experts in the field.
Improved comprehension: A research summary helps readers comprehend the research by providing a brief and focused overview of the key findings and conclusions, making it easier to understand the research and its significance.
Enhanced communication: Research summaries can be used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public, in a concise and accessible manner.
Facilitated decision-making: Research summaries can support decision-making processes by providing a summary of the research evidence on a particular topic. Policymakers or practitioners can use this information to make informed decisions about interventions, programs, or policies.
Increased dissemination: Research summaries can be easily shared and disseminated, allowing research findings to reach a wider audience.

Limitations of Research Summary

Limitations of the Research Summary are as follows:

Limited scope: Research summaries provide a brief overview of the research project’s main points, findings, and conclusions, which can be limiting. They may not include all the details, nuances, and complexities of the research that readers may need to fully understand the study’s implications.
Risk of oversimplification: Research summaries can be oversimplified, reducing the complexity of the research and potentially distorting the findings or conclusions.
Lack of context: Research summaries may not provide sufficient context to fully understand the research findings, such as the research background, methodology, or limitations. This may lead to misunderstandings or misinterpretations of the research.
Possible bias: Research summaries may be biased if they selectively emphasize certain findings or conclusions over others, potentially distorting the overall picture of the research.
Format limitations: Research summaries may be constrained by the format or length requirements, making it challenging to fully convey the research’s main points, findings, and conclusions.
Accessibility: Research summaries may not be accessible to all readers, particularly those with limited literacy skills, visual impairments, or language barriers.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Summary: What is it & how to write one

The Research Summary is used to report facts about a study clearly. You will almost certainly be required to prepare a research summary during your academic research or while on a research project for your organization.

If it is the first time you have to write one, the writing requirements may confuse you. The instructors generally assign someone to write a summary of the research work. Research summaries require the writer to have a thorough understanding of the issue.

This article will discuss the definition of a research summary and how to write one.

What is a research summary?

A research summary is a piece of writing that summarizes your research on a specific topic. Its primary goal is to offer the reader a detailed overview of the study with the key findings. A research summary generally contains the article’s structure in which it is written.

You must know the goal of your analysis before you launch a project. A research overview summarizes the detailed response and highlights particular issues raised in it. Writing it might be somewhat troublesome. To write a good overview, you want to start with a structure in mind. Read on for our guide.

Why is an analysis recap so important?

Your summary or analysis is going to tell readers everything about your research project. This is the critical piece that your stakeholders will read to identify your findings and valuable insights. Having a good and concise research summary that presents facts and comes with no research biases is the critical deliverable of any research project.

We’ve put together a cheat sheet to help you write a good research summary below.

Research Summary Guide

Why was this research done? – You want to give a clear description of why this research study was done. What hypothesis was being tested?
Who was surveyed? – The what and why or your research decides who you’re going to interview/survey. Your research summary has a detailed note on who participated in the study and why they were selected.
What was the methodology? – Talk about the methodology. Did you do face-to-face interviews? Was it a short or long survey or a focus group setting? Your research methodology is key to the results you’re going to get.
What were the key findings? – This can be the most critical part of the process. What did we find out after testing the hypothesis? This section, like all others, should be just facts, facts facts. You’re not sharing how you feel about the findings. Keep it bias-free.
Conclusion – What are the conclusions that were drawn from the findings. A good example of a conclusion. Surprisingly, most people interviewed did not watch the lunar eclipse in 2022, which is unexpected given that 100% of those interviewed knew about it before it happened.
Takeaways and action points – This is where you bring in your suggestion. Given the data you now have from the research, what are the takeaways and action points? If you’re a researcher running this research project for your company, you’ll use this part to shed light on your recommended action plans for the business.

LEARN ABOUT: Action Research

If you’re doing any research, you will write a summary, which will be the most viewed and more important part of the project. So keep a guideline in mind before you start. Focus on the content first and then worry about the length. Use the cheat sheet/checklist in this article to organize your summary, and that’s all you need to write a great research summary!

But once your summary is ready, where is it stored? Most teams have multiple documents in their google drives, and it’s a nightmare to find projects that were done in the past. Your research data should be democratized and easy to use.

We at QuestionPro launched a research repository for research teams, and our clients love it. All your data is in one place, and everything is searchable, including your research summaries!

Authors: Prachi, Anas

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How to Write a Summary | Guide & Examples

How to Write a Summary | Guide & Examples

Published on 25 September 2022 by Shona McCombes . Revised on 12 May 2023.

Summarising , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.

There are five key steps that can help you to write a summary:

Read the text
Break it down into sections
Identify the key points in each section
Write the summary
Check the summary against the article

Writing a summary does not involve critiquing or analysing the source. You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).

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When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, frequently asked questions.

There are many situations in which you might have to summarise an article or other source:

As a stand-alone assignment to show you’ve understood the material
To keep notes that will help you remember what you’ve read
To give an overview of other researchers’ work in a literature review

When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.

But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyse or critique it.

In any case, the goal of summarising is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.

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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:

Scan the article quickly to get a sense of its topic and overall shape.
Read the article carefully, highlighting important points and taking notes as you read.
Skim the article again to confirm you’ve understood the key points, and reread any particularly important or difficult passages.

There are some tricks you can use to identify the key points as you read:

Start by reading the abstract . This already contains the author’s own summary of their work, and it tells you what to expect from the article.
Pay attention to headings and subheadings . These should give you a good sense of what each part is about.
Read the introduction and the conclusion together and compare them: What did the author set out to do, and what was the outcome?

To make the text more manageable and understand its sub-points, break it down into smaller sections.

If the text is a scientific paper that follows a standard empirical structure, it is probably already organised into clearly marked sections, usually including an introduction, methods, results, and discussion.

Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.

Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?

Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.

In a scientific article, there are some easy questions you can ask to identify the key points in each part.

If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.

In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.

Now that you know the key points that the article aims to communicate, you need to put them in your own words.

To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.

The best way to do this is to put the article aside and write out your own understanding of the author’s key points.

Examples of article summaries

Let’s take a look at an example. Below, we summarise this article , which scientifically investigates the old saying ‘an apple a day keeps the doctor away’.

An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.

For example, in a literature review or research paper, you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.

Citing the source you’re summarizing

When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.

You can easily create your citations and references in APA or MLA using our free citation generators.

APA Citation Generator MLA Citation Generator

Finally, read through the article once more to ensure that:

You’ve accurately represented the author’s work
You haven’t missed any essential information
The phrasing is not too similar to any sentences in the original.

If you’re summarising many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.

A summary is a short overview of the main points of an article or other source, written entirely in your own words.

Save yourself some time with the free summariser.

A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarising, and on the purpose of the summary.

With the summariser tool you can easily adjust the length of your summary.

You might have to write a summary of a source:

As a stand-alone assignment to prove you understand the material
For your own use, to keep notes on your reading
To provide an overview of other researchers’ work in a literature review
In a paper , to summarise or introduce a relevant study

To avoid plagiarism when summarising an article or other source, follow these two rules:

Write the summary entirely in your own words by paraphrasing the author’s ideas.
Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarise the whole text, not just introduce it.

An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarise a source in a paper , in a literature review , or as a standalone assignment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, May 12). How to Write a Summary | Guide & Examples. Scribbr. Retrieved 12 March 2024, from https://www.scribbr.co.uk/working-sources/how-to-write-a-summary/

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Finding and summarizing research articles - apa format, introduction.

Writing a summary or abstract teaches you how to condense information and how to read an article more effectively and with better understanding. Research articles usually contain these parts: Title/Author Information, Abstract, Introduction, Methodology, Result or Findings, Discussion or Conclusion, and References. To gain a better understanding of an article, try reading the abstract and the discussion or conclusion first and then read the entire article.

Finding an Article

PsycINFO Research Database The American Psychological Association’s (APA) renowned resource for abstracts of scholarly journal articles, book chapters, books, and dissertations, the largest resource devoted to peer-reviewed literature in behavioral science and mental health.

PsycINFO Tutorial

Journal Article Request If you can't find the free full text version of a research article, please complete and submit this form. An Learning Commons staff member will then place an interlibrary loan request on your behalf.

Summarizing an Article

The following websites offer advice and instruction on summarizing articles:

Andrews University: Guidelines for Writing an Article Summary

UConn: How to Summarize a Research Article

Resources for APA Style

APA (7th ed.) Formatting and Style Guide Purdue Online Writing Lab (OWL)

APA Style Website American Psychological Association

Books in the Learning Commons

Publication Manual of the American Psychological Association (7th ed.): BF76.7 .P83

Sample APA Citations

In-text citation.

If the author’s name is included within the text, follow the name with (year)

Example: Jones (2009) found that diabetes symptoms improve with exercise.

If the author’s name is not included within the text, follow the sentence with (Last Name, year).

Example: Increased exercise resulted in diminished diabetes symptoms (Jones, 2009).

Reference Citation

Author’s last name, A. A., & Author’s last name, B.B. (year).Title of article. Title of Journal , volume (issue), page number – page number. https://doi.org/xxxxx

Iscoe, K. E., & Riddell, M. C. (2011). Continuous moderate-intensity exercise with or without intermittent high-intensity work: Effects on acute and late glycaemia in athletes with Type 1 diabetes mellitus. Diabetic Medicine , 28 (7), 824-832. https://doi.org/10.1111/j.1464-5491.2011.03274.x

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A Complete Guide to Writing a Research Summary

A summary is a key part of any research. So, how should you go about writing one?

You will find many guides on the Internet about writing research. But, any article seldom covers the prospect of writing a research summary. While many things are shortened versions of the original article, there’s much more to research summaries.

From descriptive statistics to writing scientific research, a summary plays a vital role in describing the key ideas within. So, it begs a few questions, such as:

What exactly is a research summary?
How do you write one?
What are some of the tips for writing a good research summary ?

In this guide, we’ll answer all of these questions and explore a few essential factors about research writing. So, let’s jump right into it.

What is a Research Summary?

A research summary is a short, concise summary of an academic research paper. It is often used to summarize the results of an experiment, summarize the major findings and conclusions, and provide a brief overview of the methods and procedures used in the study.

The purpose of a research summary is to provide readers with enough information about an article to decide whether they want to read it in its entirety. It should be no more than two paragraphs long and should include:

A brief introduction summarizing why the article was written
The main idea of the article
The major findings and conclusions
An overview of how the study was conducted

In order to write effective research summaries, it is important that you can capture the essential points of the research and provide a concise overview. The key step in writing a good summary is to read through the article and make notes of the key points.

This can be done by underlining or highlighting key phrases in the article. One essential thing is to organize these points into an outline format, which includes an introduction and conclusion paragraph.

Another best and quick way to generate a precise summary of your research paper is to take assistance from the online text summarizer, like Summarizer.org .

The online summarizing tool gets the research paper and creates a precise summary of it by taking the important points.

Finally, you must edit your work for grammar and spelling errors before submitting it for grading.

The purpose of the research summary is to provide a comprehensive sum of everything that’s in the research. This includes a summarization of scientific/literal research, as well as of the writer’s aim and personal thoughts.

As for the summary length, it shouldn’t be more than 10% of the entire content. So, if your research is around 1000-words or so, then your summary should be 100-words. But, considering how most research papers are around 3000-4000 words, it should be 300-400 words.

Key pillars of a Research Summary

The summary of any research doesn’t just include the summarized text of the entire research paper. It includes a few other key things, which we’ll explore later on in this article. But, the purpose of a summary is to give proper insights to the reader, such as:

The writer’s intention
sources and bases of research
the purpose & result.

That’s why it’s important to understand that the summary should tell your reader all these elements. So, the fundamentals of any summary include:

Write a section and state the importance of the research paper from your perspective. In this section, you will have to describe the techniques, tools, and sources you employed to get the conclusion.
Besides that, it’s also meant to provide a brief and descriptive explanation of the actionable aspect of your research. In other words, how it can be implemented in real life.
Treat your research summary like a smaller article or blog. So, each important section of your research should be written within a subheading. However, this is highly optional to keep things organized.
As mentioned before, the research summary shouldn’t exceed 300-400 words. But, some research summaries are known to surpass 10000-words. So, try to employ the 10% formula and write one-tenth of the entire length of your research paper.

These four main points allow you to understand how a research summary is different from the research itself. So, it’s like a documentary where research and other key factors are left to the science (research paper), while the narration explains the key points (research summary)

How do you write a Research Summary?

Writing a research summary is a straightforward affair. Yet, it requires some understanding, as it’s not a lengthy process but rather a tricky and technical one. In a research summary, a few boxes must be checked. To help you do just that, here are 6 things you should tend to separately:

A summary’s title can be the same as the title of your primary research. However, putting separate titles in both has a few benefits. Such as:

A separate title shifts attention towards the conclusion.
A different title can focus on the main point of your research.
Using two different titles can provide a better abstract.

Speaking of an abstract, a summary is the abstract of your research. Therefore, a title representing that very thought is going to do a lot of good too. That’s why it’s better if the title of your summary differs from the title of your research paper.

2. Abstract

The abstract is the summarization of scientific or research methods used in your primary paper. This allows the reader to understand the pillars of the study conducted. For instance, there has been an array of astrological research since James Webb Space Telescope started sending images and data.

So, many research papers explain this Telescope’s technological evolution in their abstracts. This allows the reader to differentiate from the astrological research made by previous space crafts, such as Hubble or Chandra .

The point of providing this abstract is to ensure that the reader grasps the standards or boundaries within which the research was held.

3. Introduction

This is the part where you introduce your topic. In your main research, you’d dive right into the technicalities in this part. However, you’ll try to keep things mild in a research summary. Simply because it needs to summarize the key points in your main introduction.

So, a lot of introductions you’ll find as an example will be extensive in length. But, a research summary needs to be as concise as possible. Usually, in this part, a writer includes the basics and standards of investigation.

For instance, if your research is about James Webb’s latest findings , then you’ll identify how the studies conducted by this Telescope’s infrared and other technology made this study possible. That’s when your introduction will hook the reader into the main premise of your research.

4. Methodology / Study

This section needs to describe the methodology used by you in your research. Or the methodology you relied on when conducting this particular research or study. This allows the reader to grasp the fundamentals of your research, and it’s extremely important.

Because if the reader doesn’t understand your methods, then they will have no response to your studies. How should you tend to this? Include things such as:

The surveys or reviews you used;
include the samplings and experiment types you researched;
provide a brief statistical analysis;
give a primary reason to pick these particular methods.

Once again, leave the scientific intricacies for your primary research. But, describe the key methods that you employed. So, when the reader is perusing your final research, they’ll have your methods and study techniques in mind.

5. Results / Discussion

This section of your research needs to describe the results that you’ve achieved. Granted, some researchers will rely on results achieved by others. So, this part needs to explain how that happened – but not in detail.

The other section in this part will be a discussion. This is your interpretation of the results you’ve found. Thus, in the context of the results’ application, this section needs to dive into the theoretical understanding of your research. What will this section entail exactly? Here’s what:

Things that you covered, including results;
inferences you provided, given the context of your research;
the theory archetype that you’ve tried to explain in the light of the methodology you employed;
essential points or any limitations of the research.

These factors will help the reader grasp the final idea of your research. But, it’s not full circle yet, as the pulp will still be left for the actual research.

6. Conclusion

The final section of your summary is the conclusion. The key thing about the conclusion in your research summary, compared to your actual research, is that they could be different. For instance, the actual conclusion in your research should bring around the study.

However, the research in this summary should bring your own ideas and affirmations to full circle. Thus, this conclusion could and should be different from the ending of your research.

5 Tips for writing a Research Summary

Writing a research summary is easy once you tend to the technicalities. But, there are some tips and tricks that could make it easier. Remember, a research summary is the sum of your entire research. So, it doesn’t need to be as technical or in-depth as your primary work.

Thus, to make it easier for you, here are four tips you can follow:

1. Read & read again

Reading your own work repeatedly has many benefits. First, it’ll help you understand any mistakes or problems your research might have. After that, you’ll find a few key points that stand out from the others – that’s what you need to use in your summary.

So, the best advice anyone can give you is to read your research again and again. This will etch the idea in your mind and allow you to summarize it better.

2. Focus on key essentials in each section

As we discussed earlier, each section of your research has a key part. To write a thoroughly encapsulating summary, you need to focus on and find each such element in your research.

Doing so will give you enough leverage to write a summary that thoroughly condenses your research idea and gives you enough to write a summary out of it.

3. Write the research using a summarizing tool

The best advice you can get is to write a summary using a tool. Condensing each section might be a troublesome experience for some – as it can be time-consuming.

To avoid all that, you can simply take help from an online summarizer. It gets the lengthy content and creates a precise summary of it by using advanced AI technology.

As you can see, the tool condenses this particular section perfectly while the details are light.

Bringing that down to 10% or 20% will help you write each section accordingly. Thus, saving precious time and effort.

4. Word count limit

As mentioned earlier, word count is something you need to follow thoroughly. So, if your section is around 200-word, then read it again. And describe it to yourself in 20-words or so. Doing this to every section will help you write exactly a 10% summary of your research.

5. Get a second opinion

If you’re unsure about quality or quantity, get a second opinion. At times, ideas are in our minds, but we cannot find words to explain them. In research or any sort of creative process, getting a second opinion can save a lot of trouble.

There’s your guide to writing a research summary, folks. While it’s not different from condensing the entire premise of your research, writing it in simpler words will do wonders. So, try to follow the tips, tools, and ideas provided in this article, and write outstanding summaries for your research.

How it works

Writing a Summary – Explanation & Examples

Published by Alvin Nicolas at October 17th, 2023 , Revised On October 17, 2023

In a world bombarded with vast amounts of information, condensing and presenting data in a digestible format becomes invaluable. Enter summaries.

A summary is a brief and concise account of the main points of a larger body of work. It distils complex ideas, narratives, or data into a version that is quicker to read and easier to understand yet still retains the essence of the original content.

Importance of Summaries

The importance of summarising extends far beyond just making reading more manageable. In academic settings, summaries aid students in understanding and retaining complex materials, from textbook chapters to research articles. They also serve as tools to showcase one’s grasp of the subject in essays and reports.

In professional arenas, summaries are pivotal in business reports, executive briefings, and even emails where key points need to be conveyed quickly to decision-makers. Meanwhile, summarising skills come into play in our personal lives when we relay news stories to friends, recap a movie plot, or even scroll through condensed news or app notifications on our smartphones.

Why Do We Write Summaries?

In our modern information age, the sheer volume of content available can be overwhelming. From detailed research papers to comprehensive news articles, the quest for knowledge is often met with lengthy and complex resources. This is where the power of a well-crafted summary comes into play. But what drives us to create or seek out summaries? Let’s discuss.

Makes Important Things Easy to Remember

At the heart of summarisation is the goal to understand. A well-written summary aids in digesting complex material. By distilling larger works into their core points, we reinforce the primary messages, making them easier to remember. This is especially crucial for students who need to retain knowledge for exams or professionals prepping for a meeting based on a lengthy report.

Simplification of Complex Topics

Not everyone is an expert in every field. Often, topics come laden with jargon, intricate details, and nuanced arguments. Summaries act as a bridge, translating this complexity into accessible and straightforward content. This is especially beneficial for individuals new to a topic or those who need just the highlights without the intricacies.

Aid in Researching and Understanding Diverse Sources

Researchers, writers, and academics often wade through many sources when working on a project. This involves finding sources of different types, such as primary or secondary sources , and then understanding their content. Sifting through each source in its entirety can be time-consuming. Summaries offer a streamlined way to understand each source’s main arguments or findings, making synthesising information from diverse materials more efficient.

Condensing Information for Presentation or Sharing

In professional settings, there is often a need to present findings, updates, or recommendations to stakeholders. An executive might not have the time to go through a 50-page report, but they would certainly appreciate a concise summary highlighting the key points. Similarly, in our personal lives, we often summarise movie plots, book stories, or news events when sharing with friends or family.

Characteristics of a Good Summary

Crafting an effective summary is an art. It’s more than just shortening a piece of content; it is about capturing the essence of the original work in a manner that is both accessible and true to its intent. Let’s explore the primary characteristics that distinguish a good summary from a mediocre one:

Conciseness

At the core of a summary is the concept of brevity. But being concise doesn’t mean leaving out vital information. A good summary will:

Eliminate superfluous details or repetitive points.
Focus on the primary arguments, events, or findings.
Use succinct language without compromising the message.

Objectivity

Summarising is not about infusing personal opinions or interpretations. A quality summary will:

Stick to the facts as presented in the original content.
Avoid introducing personal biases or perspectives.
Represent the original author’s intent faithfully.

A summary is meant to simplify and make content accessible. This is only possible if the summary itself is easy to understand. Ensuring clarity involves:

Avoiding jargon or technical terms unless they are essential to the content. If they are used, they should be clearly defined.
Structuring sentences in a straightforward manner.
Making sure ideas are presented in a way that even someone unfamiliar with the topic can grasp the primary points.

A jumble of ideas, no matter how concise, will not make for a good summary. Coherence ensures that there’s a logical flow to the summarised content. A coherent summary will:

Maintain a logical sequence, often following the structure of the original content.
Use transition words or phrases to connect ideas and ensure smooth progression.
Group related ideas together to provide structure and avoid confusion.

Steps of Writing a Summary

The process of creating a compelling summary is not merely about cutting down content. It involves understanding, discerning, and crafting. Here is a step-by-step guide to writing a summary that encapsulates the essence of the original work:

Reading Actively

Engage deeply with the content to ensure a thorough understanding.

Read the entire document or work first to grasp its overall intent and structure.
On the second read, underline or highlight the standout points or pivotal moments.
Make brief notes in the margins or on a separate sheet, capturing the core ideas in your own words.

Identifying the Main Idea

Determine the backbone of the content, around which all other details revolve.

Ask yourself: “What is the primary message or theme the author wants to convey?”
This can often be found in the title, introduction, or conclusion of a piece.
Frame the main idea in a clear and concise statement to guide your summary.

List Key Supporting Points

Understand the pillars that uphold the main idea, providing evidence or depth to the primary message.

Refer back to the points you underlined or highlighted during your active reading.
Note major arguments, evidence, or examples that the author uses to back up the main idea.
Prioritise these points based on their significance to the main idea.

Draft the Summary

Convert your understanding into a condensed, coherent version of the original.

Start with a statement of the main idea.
Follow with the key supporting points, maintaining logical order.
Avoid including trivial details or examples unless they’re crucial to the primary message.
Use your own words, ensuring you are not plagiarising the original content.

Fine-tune your draft to ensure clarity, accuracy, and brevity.

Read your draft aloud to check for flow and coherence.
Ensure that your summary remains objective, avoiding any personal interpretations or biases.
Check the length. See if any non-essential details can be removed without sacrificing understanding if it is too lengthy.
Ensure clarity by ensuring the language is straightforward, and the main ideas are easily grasped.

The research done by our experts have:

Precision and Clarity
Zero Plagiarism
Authentic Sources

Dos and Don’ts of Summarising Key Points

Summarising, while seemingly straightforward, comes with its nuances. Properly condensing content demands a balance between brevity and fidelity to the original work. To aid in crafting exemplary summaries, here is a guide on the essential dos and don’ts:

Use your Own Words

This ensures that you have truly understood the content and are not merely parroting it. It also prevents issues of plagiarism.

Tip: After reading the original content, take a moment to reflect on it. Then, without looking at the source, write down the main points in your own words.

Attribute Sources Properly

Giving credit is both ethical and provides context to readers, helping them trace back to the original work if needed. How to cite sources correctly is a skill every writer should master.

Tip: Use signal phrases like “According to [Author/Source]…” or “As [Author/Source] points out…” to seamlessly incorporate attributions.

Ensure Accuracy of the Summarised Content

A summary should be a reliable reflection of the original content. Distorting or misrepresenting the original ideas compromises the integrity of the summary.

Tip: After drafting your summary, cross-check with the original content to ensure all key points are represented accurately and ensure you are referencing credible sources .

Avoid Copy-Pasting Chunks of Original Content

This not only raises plagiarism concerns but also shows a lack of genuine engagement with the material.

Tip: If a particular phrase or sentence from the original is pivotal and cannot be reworded without losing its essence, use block quotes , quotation marks, and attribute the source.

Do not Inject your Personal Opinion

A summary should be an objective reflection of the source material. Introducing personal biases or interpretations can mislead readers.

Tip: Stick to the facts and arguments presented in the original content. If you find yourself writing “I think” or “In my opinion,” reevaluate the sentence.

Do not Omit Crucial Information

While a summary is meant to be concise, it shouldn’t be at the expense of vital details that are essential to understanding the original content’s core message.

Tip: Prioritise information. Always include the main idea and its primary supports. If you are unsure whether a detail is crucial, consider its impact on the overall message.

Examples of Summaries

Here are a few examples that will help you get a clearer view of how to write a summary.

Example 1: Summary of a News Article

Original Article: The article reports on the recent discovery of a rare species of frog in the Amazon rainforest. The frog, named the “Emerald Whisperer” due to its unique green hue and the soft chirping sounds it makes, was found by a team of researchers from the University of Texas. The discovery is significant as it offers insights into the biodiversity of the region, and the Emerald Whisperer might also play a pivotal role in understanding the ecosystem balance.

Summary: Researchers from the University of Texas have discovered a unique frog, termed the “Emerald Whisperer,” in the Amazon rainforest. This finding sheds light on the region’s biodiversity and underscores the importance of the frog in ecological studies.

Example 2: Summary of a Research Paper

Original Paper: In a study titled “The Impact of Urbanisation on Bee Populations,” researchers conducted a year-long observation on bee colonies in three urban areas and three rural areas. Using specific metrics like colony health, bee productivity, and population size, the study found that urban environments saw a 30% decline in bee populations compared to rural settings. The research attributes this decline to factors like pollution, reduced green spaces, and increased temperatures in urban areas.

Summary: A study analysing the effects of urbanisation on bee colonies found a significant 30% decrease in bee populations in urban settings compared to rural areas. The decline is linked to urban factors such as pollution, diminished greenery, and elevated temperatures.

Example 3: Summary of a Novel

Original Story: In the novel “Winds of Fate,” protagonist Clara is trapped in a timeless city where memories dictate reality. Throughout her journey, she encounters characters from her past, present, and imagined future. Battling her own perceptions and a menacing shadow figure, Clara seeks an elusive gateway to return to her real world. In the climax, she confronts the shadow, which turns out to be her own fear, and upon overcoming it, she finds her way back, realising that reality is subjective.

Summary: “Winds of Fate” follows Clara’s adventures in a surreal city shaped by memories. Confronting figures from various phases of her life and battling a symbolic shadow of her own fear, Clara eventually discovers that reality’s perception is malleable and subjective.

Frequently Asked Questions

How long is a summary.

A summary condenses a larger piece of content, capturing its main points and essence. It is usually one-fourth of the original content.

What is a summary?

A summary is a concise representation of a larger text or content, highlighting its main ideas and points. It distils complex information into a shorter form, allowing readers to quickly grasp the essence of the original material without delving into extensive details. Summaries prioritise clarity, brevity, and accuracy.

When should I write a summary?

Write a summary when you need to condense lengthy content for easier comprehension and recall. It’s useful in academic settings, professional reports, presentations, and research to highlight key points. Summaries aid in comparing multiple sources, preparing for discussions, and sharing essential details of extensive materials efficiently with others.

How can I summarise a source without plagiarising?

To summarise without plagiarising: Read the source thoroughly, understand its main ideas, and then write the summary in your own words. Avoid copying phrases verbatim. Attribute the source properly. Use paraphrasing techniques and cross-check your summary against the original to ensure distinctiveness while retaining accuracy. Always prioritise understanding over direct replication.

What is the difference between a summary and an abstract?

A summary condenses a text, capturing its main points from various content types like books, articles, or movies. An abstract, typically found in research papers and scientific articles, provides a brief overview of the study’s purpose, methodology, results, and conclusions. Both offer concise versions, but abstracts are more structured and specific.

1 - Read the paper

In order to successfully summarize the whole research, you should understand it thoroughly. Read the paper carefully to understand its purpose, research design, methodology, results, and conclusions. You can also take a look at a research summary example to figure out what elements you should focus on while reading.

💡Tip #1 - Try a 3-stage reading method of Scan - Read - Skim. First, scan the paper to get an understanding of the concept. Then, read the paper attentively by focusing on elements you will include in your summary. Lastly, skim one last time to study the various elements.

2 - Identify the key points

Once you read the entirety of the paper, try to pinpoint the key findings and research questions. It would help you to work with a set of questions to ask yourself when analyzing the paper. The questions you can try to answer could be these:

What is the main research question?
Is there a hypothesis that is proposed in the introduction part?
What kind of methods are used in the study?
What is the sample size for data collection?
Do the results support the hypothesis?
What are the most major findings?
What are some limitations of the study?
What is the final conclusion?

3 - Make notes as you read

It can help you tremendously to make notes as you read the paper. You can put simple sentences in each or most paragraphs to capture to the most important part. Or, you can highlight various findings, elements, and sentences. This will help you figure out what is important and what is not in the end.

4 - Prepare a draft

Once you gathered all the key points and highlighted sections, you can start preparing your draft. You should use a structured plan for your summary. Also, try rewriting important elements in your own words to avoid plagiarism. When you are preparing the draft, always be mindful of the word count limit.

💡Tip #2 - Keep it 10% or shorter One of the most crucial aspects of a research summary is the fact that it must be SHORT. Therefore, make sure the length of the summary is 10% or less of the original length of the parent paper.

5 - Finalize the summary

When you have the draft ready, proofread it to make sure everything is correct. And make sure the summary is objective, precise, and factually correct. You can also find additional literature to support your study and add that to the result section as well.

💡Tip #3 - Do not add anything new Never add new information or opinions to the summary that is not mentioned in the parent paper.

Why do you need to summarize your research results?

The purpose of a research summary is to give a brief overview of the study to the readers. A reader who is trying to find appropriate research to go through can easily get through the central ideas. It is also a great way to elaborate on the significance of the findings, and it reminds the reader of the strengths of your main arguments.

Having a good summary is almost as important as writing a research paper.

Wrapping it up

Having a good summary is almost as important as writing a research paper. A research paper involves statistical analysis, factual findings, and theories. And the summary of the paper briefly explains the main concepts and ideas. A person reading the summary of a paper should clearly understand the discussion and conclusion of the research study .

In this article, we have gathered a step-by-step guide to writing a research summary and useful tips to keep in mind. Next time, make sure your summary is to the point and faithful to the original paper. If you are planning to write your own research summary, you can get started with useful and easy-to-use survey templates of forms.app!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

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USC Libraries
Research Guides

Organizing Your Social Sciences Research Paper

Executive Summary
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

An executive summary is a thorough overview of a research report or other type of document that synthesizes key points for its readers, saving them time and preparing them to understand the study's overall content. It is a separate, stand-alone document of sufficient detail and clarity to ensure that the reader can completely understand the contents of the main research study. An executive summary can be anywhere from 1-10 pages long depending on the length of the report, or it can be the summary of more than one document [e.g., papers submitted for a group project].

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80 Todorovic, Zelimir William and Marietta Wolczacka Frye. “Writing Effective Executive Summaries: An Interdisciplinary Examination.” In United States Association for Small Business and Entrepreneurship. Conference Proceedings . (Decatur, IL: United States Association for Small Business and Entrepreneurship, 2009): pp. 662-691.

Importance of a Good Executive Summary

Although an executive summary is similar to an abstract in that they both summarize the contents of a research study, there are several key differences. With research abstracts, the author's recommendations are rarely included, or if they are, they are implicit rather than explicit. Recommendations are generally not stated in academic abstracts because scholars operate in a discursive environment, where debates, discussions, and dialogs are meant to precede the implementation of any new research findings. The conceptual nature of much academic writing also means that recommendations arising from the findings are distributed widely and not easily or usefully encapsulated. Executive summaries are used mainly when a research study has been developed for an organizational partner, funding entity, or other external group that participated in the research . In such cases, the research report and executive summary are often written for policy makers outside of academe, while abstracts are written for the academic community. Professors, therefore, assign the writing of executive summaries so students can practice synthesizing and writing about the contents of comprehensive research studies for external stakeholder groups.

When preparing to write, keep in mind that:

An executive summary is not an abstract.
An executive summary is not an introduction.
An executive summary is not a preface.
An executive summary is not a random collection of highlights.

Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Clayton, John. "Writing an Executive Summary that Means Business." Harvard Management Communication Letter (July 2003): 2-4; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; Murphy, Herta A., Herbert W. Hildebrandt, and Jane P. Thomas. Effective Business Communications . New York: McGraw-Hill, 1997; Vassallo, Philip. "Executive Summaries: Where Less Really is More." ETC.: A Review of General Semantics 60 (Spring 2003): 83-90 .

Structure and Writing Style

Writing an Executive Summary

Read the Entire Document This may go without saying, but it is critically important that you read the entire research study thoroughly from start to finish before you begin to write the executive summary. Take notes as you go along, highlighting important statements of fact, key findings, and recommended courses of action. This will better prepare you for how to organize and summarize the study. Remember this is not a brief abstract of 300 words or less but, essentially, a mini-paper of your paper, with a focus on recommendations.

Isolate the Major Points Within the Original Document Choose which parts of the document are the most important to those who will read it. These points must be included within the executive summary in order to provide a thorough and complete explanation of what the document is trying to convey.

Separate the Main Sections Closely examine each section of the original document and discern the main differences in each. After you have a firm understanding about what each section offers in respect to the other sections, write a few sentences for each section describing the main ideas. Although the format may vary, the main sections of an executive summary likely will include the following:

An opening statement, with brief background information,
The purpose of research study,
Method of data gathering and analysis,
Overview of findings, and,
A description of each recommendation, accompanied by a justification. Note that the recommendations are sometimes quoted verbatim from the research study.

Combine the Information Use the information gathered to combine them into an executive summary that is no longer than 10% of the original document. Be concise! The purpose is to provide a brief explanation of the entire document with a focus on the recommendations that have emerged from your research. How you word this will likely differ depending on your audience and what they care about most. If necessary, selectively incorporate bullet points for emphasis and brevity. Re-read your Executive Summary After you've completed your executive summary, let it sit for a while before coming back to re-read it. Check to make sure that the summary will make sense as a separate document from the full research study. By taking some time before re-reading it, you allow yourself to see the summary with fresh, unbiased eyes.

Common Mistakes to Avoid

Length of the Executive Summary As a general rule, the correct length of an executive summary is that it meets the criteria of no more pages than 10% of the number of pages in the original document, with an upper limit of no more than ten pages [i.e., ten pages for a 100 page document]. This requirement keeps the document short enough to be read by your audience, but long enough to allow it to be a complete, stand-alone synopsis. Cutting and Pasting With the exception of specific recommendations made in the study, do not simply cut and paste whole sections of the original document into the executive summary. You should paraphrase information from the longer document. Avoid taking up space with excessive subtitles and lists, unless they are absolutely necessary for the reader to have a complete understanding of the original document. Consider the Audience Although unlikely to be required by your professor, there is the possibility that more than one executive summary will have to be written for a given document [e.g., one for policy-makers, one for private industry, one for philanthropists]. This may only necessitate the rewriting of the introduction and conclusion, but it could require rewriting the entire summary in order to fit the needs of the reader. If necessary, be sure to consider the types of audiences who may benefit from your study and make adjustments accordingly. Clarity in Writing One of the biggest mistakes you can make is related to the clarity of your executive summary. Always note that your audience [or audiences] are likely seeing your research study for the first time. The best way to avoid a disorganized or cluttered executive summary is to write it after the study is completed. Always follow the same strategies for proofreading that you would for any research paper. Use Strong and Positive Language Don’t weaken your executive summary with passive, imprecise language. The executive summary is a stand-alone document intended to convince the reader to make a decision concerning whether to implement the recommendations you make. Once convinced, it is assumed that the full document will provide the details needed to implement the recommendations. Although you should resist the temptation to pad your summary with pleas or biased statements, do pay particular attention to ensuring that a sense of urgency is created in the implications, recommendations, and conclusions presented in the executive summary. Be sure to target readers who are likely to implement the recommendations.

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80; Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Executive Summaries. Writing@CSU. Colorado State University; Clayton, John. "Writing an Executive Summary That Means Business." Harvard Management Communication Letter , 2003; Executive Summary. University Writing Center. Texas A&M University; Green, Duncan. Writing an Executive Summary. Oxfam’s Research Guidelines series ; Guidelines for Writing an Executive Summary. Astia.org; Markowitz, Eric. How to Write an Executive Summary. Inc. Magazine, September, 15, 2010; Kawaski, Guy. The Art of the Executive Summary. "How to Change the World" blog; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; The Report Abstract and Executive Summary. The Writing Lab and The OWL. Purdue University; Writing Executive Summaries. Effective Writing Center. University of Maryland; Kolin, Philip. Successful Writing at Work . 10th edition. (Boston, MA: Cengage Learning, 2013), p. 435-437; Moral, Mary. "Writing Recommendations and Executive Summaries." Keeping Good Companies 64 (June 2012): 274-278; Todorovic, Zelimir William and Marietta Wolczacka Frye. “Writing Effective Executive Summaries: An Interdisciplinary Examination.” In United States Association for Small Business and Entrepreneurship. Conference Proceedings . (Decatur, IL: United States Association for Small Business and Entrepreneurship, 2009): pp. 662-691.

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Research Summary

A research paper analyzes a perspective or argues a point. It is an expanded essay based on your interpretation, evaluation or argument about a certain topic.

According to Sunny Empire State College , “When you write a research paper you build upon what you know about the subject and make a deliberate attempt to find out what experts know. A research paper involves surveying a field of knowledge in order to find the best possible information in that field.” Whatever type of research paper you choose to write, it should present your own ideas backed with others’ (especially experts on the field) information and data.

Every research paper has a research summary. A research summary is a brief overview of what the whole research is about. It is a professional piece of writing that describes your research to the readers. It concisely yet perfectly captures the essence of the research as a whole. You may also see What Should Be in an Executive Summary of a Report?

Fundamentals of a Research Summary

Having a good template for a research summary is nothing if you don’t know its importance and basic function. Before you start writing your research summary, you should first know its fundamentals on the areas you need to pay attention to such as its content, style and organization.

The content of your research summary must briefly discuss the techniques and tools used in the research and the importance of the research as a whole. Explain how the research can be of benefit for the people.
To organize your research summary, each topic must be discussed in separate paragraphs. How you came up with a factual research must be briefly explained in a separate paragraph.
If you have a lengthy research paper, try not to write not more than 10% of the entire paper. If it’s not as lengthy, you should not write more than 300 words in your summary.

However, rules may vary according to your research professor’s standards. This is just the basic fundamentals on how to write your research summary. Also see Thesis Outline Examples

How to Write a Research Summary

It is apparent that a research summary is a condensed version of the main idea of your research paper. Because of this, it is advised that the summary of your paper is written after you are done with your entire research. This is to ensure that all the added information in your research can be written in your summary as well and all of those that removed can be edited out. Here are a few steps on how to write a research summary:

Read your paper

It should be a fact you should know beforehand; the importance of reading your entire research paper thoroughly to write an effective research summary. Along the way, take notes of the important details and key findings that you want to highlight in your paper. This will help you organize your summary better. Remember that your research summary is a mini-paper of your study and it should contain the main ideas of your entire research.

Write a draft

For your first draft, focus on the content rather than the length of your summary. Your draft is your first outline on what to include in the final summary. Writing a draft ensures you write a clear, thorough and coherent summary of your research paper. Also see How to Write a Rough Outline

Identify main points

Within your research paper, you must identify the major points that will encourage prospective readers to go through your research paper. These major points must thoroughly and completely explain what the paper is trying to convey.

Separate sections

Identify the differences of the main section in your paper. Write a few sentences describing the main ideas of each section. In short, you should be able to present and thoroughly describe what each main section is focused on. It should have these basic sections:

Introduction, brief opening statement
Purpose of the study
Data gathering method
Summary of findings
Description of recommendations with actual justification.

Combine Information

All the information you have gathered must be then used to make your summary. Remember that your summary is just an overview of your research paper as a whole. It should be not be more than 10% of your whole paper. Also see 5 Summary Writing Examples and Samples

Making The First Draft

After establishing the basic way of writing a research summary, it is a must to write a first draft. It should follow the flow of the original paper. Here’s a few steps on how to make a first draft:

First, state the research question in the introduction of your summary. This holds the ground as to the summary’s direction. Provide an explanation why your research is interesting and how it can help your target recipients.

Second, state the hypothesis you wish to prove. This will help you and your readers stay grounded on the topic at hand.

Third, briefly discuss the methodology used in your research. Discuss and describe the procedure, materials, participants, design, etc. The analysis of your data must also be included. You may also see How to Write a Successful Thesis Proposal

Fourth, describe the results and significance of your research. And lastly, briefly discuss the key implications of your research. The results and its interpretation should directly coincide with your hypothesis.

Editing your Research Summary

A research paper is a formal piece of writing. Your summary should be tailored to your expected readers. Say for example the prospective readers are your classmates, so the style of your paper should be clearly understood by them.

Eliminate wordiness. Avoid using unnecessary adjectives and adverbs. Write in a way it would be easier for your readers to understand. It is common for research papers to establish a word count. Avoid elongating your sentences when it has shorter versions.

Being vague in describing and explaining the points of your paper might lead to confusion in your readers part. Use specific, concrete language when presenting results. Use reliable and specific examples and references as well. You should also use scientifically accurate language to help support your claims. Avoid informal words and adjectives to describe the results of your research.

Paraphrase the information you want to include in your research paper. Direct quoting the information you have read from a different source is not oftenly used in formal writings. To give the exact credit for the information you paraphrased, follow the citation format required by your professor.

Reread your paper and let others read it as well. This way minor errors you were not able to notice can be quickly pointed out and corrected.

Research Summary Writing Tips

Your research summary should not be more than 10 pages long or not more than 10% of your original document. This keeps your research summary concise and compact. It should be short enough for your readers to read through but long enough for you to clearly explain your study.

Copy and paste

Avoid simply copy and pasting different parts of your paper into your summary. You should paraphrase parts that you want to include. As most research advisers read through all of your paper, it can easily be identified if you have copy-pasted parts from your research and might give you a bad grade.

Consider the readers

Although not a requirement from your professor, catering your summary to what the readers need is sometimes required. As some studies are given out to different influential people in the field, writing a summary that caters to the readers’ necessities might be required.

Research Article Summary Template

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Research Report Executive Summary Template

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Research Summary Example

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Research Summary Sample

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Research Writing Summary Tips (continuation)

Clarity and organization.

One of the common mistakes in writing a research is publishing an unclear and unpolished summary. Bear in mind that your readers are likely reading about the topic of your research for the first time, avoid unclear and uncertain explanations and a disorganized summary.

Use strong and positive language

Use precise and strong words to help strengthen the foundation of your summary. Your summary should be able to stand alone despite it being a part of the research paper. Once you have convinced your readers with the recommendations regarding the topic of your paper, the readers should be able to find concrete evidence and explanations within your summary. Avoid pleas and biased statements in your summary, but make sure you are able to relay the sense of urgency for the recommendations you have given.

Divide into parts

To make things easier for you, divide your paper into different sections and headings, much like creating an outline. With this in mind, every point should be explained limited to its essence. In this way, you avoid writing too much information about your paper in your summary.

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How to Write a Summary of an Article: Brevity in Brilliance

Table of contents

1 What Is an Article Summary?
2 Difference Between Abstract and Research Summary Writing
3.1 Preparing for Summarizing
3.2 Identifying Main Ideas
3.3 Writing The Summary
4.1 Introduction
4.2 Methods
4.3 Results
4.4 Discussion
4.5.1 Structure Types
5 Summary Writing Tips and Best Practices
6 Common Mistakes to Avoid
7 Examples of Article Summaries

Writing a review or a critique is often more difficult than it seems, so students and writers alike are often wondering about how to summarize an article. We know how challenging a task this can be, so this guide will give you a clear perspective and the main points on how to write a summary of an article.

Here’s a brief overview of the main points the article will cover before we start:

The essence of an article summary and how to approach writing it;
Three main steps for a successful research summary;
Tips and strategies for outlining the main idea;
Examples of good and bad short summaries for inspiration;
Common mistakes to avoid when writing a research article summary.

The steps outlined in this post will help you summarize an article in your own words without sacrificing the original text message and ideas.

What Is an Article Summary?

An article summary is a concise and condensed version of a longer piece of writing, often an article, research paper, or news report. Its purpose is to capture the main ideas, crucial points, and key arguments found in the original text, providing a brief and easily understandable overview.

These summaries are composed in the author’s own words, distilling the essential information to help readers quickly grasp the content without having to read the entire article. They serve as a helpful tool to offer a snapshot of the most important aspects of the content, making it simpler for readers to decide whether they wish to delve into the complete article.

A common goal of academic summary writing is to improve critical thinking skills , and they serve as great practice for academic writers to improve their own writing skills. There are several main goals of writing a synopsis of an article:

This paper’s main goal is to provide a comprehensive yet brief descriptive comment on a particular article, telling your readers about the author’s topic sentence and important points in his work and the key points of it.
It serves to outline a laconic reader’s perspective on the paper while keeping the main point.
Identifies all the crucial segments from each of the paper’s sections.

A proper article summary can help do your college essays the right way because it provides a great, concise view of the source article. Especially if you are often facing writing tasks like academic papers, knowing how to write a good synopsis can upgrade your writing skills.

Difference Between Abstract and Research Summary Writing

Things get confusing when someone wants to define their place and purpose inside the text. To be more precise, the abstract appears first in the academic article, whereas the summary appears last.

Many students cannot distinguish between a summary and an abstract of a research paper. While these have certain similarities, they are not the same. Therefore, you must be aware of the subtleties before beginning a research article.

On the one hand, both components have a limited scope. Their goal is to provide a thorough literature assessment of the research paper’s main ideas. When you write a research summary, focus on your topic, methods, and findings.

Below you can find more differences between the abstract and research article summary for your project:

Abstracts provide a succinct synopsis of your work and showcase your writing style.
Abstracts lay out the background information and clarify the primary hypothesis thesis statement, while the summary emphasizes your research methodology, highlighting the important elements.

Finally, you must submit the abstract before actual publication. On the other hand, article summaries come with the finished piece of paper.

Steps to Write a Summary for an Article

In the world of effective communication, the skill of crafting short yet informative summaries is invaluable. Whether you’re a student dealing with academic articles, a professional simplifying complex reports, or simply someone looking to grasp the essence of an interesting read, mastering the art of summarization is crucial. This summarizing guidelines will lead you through the steps to write a compelling piece.

These steps will empower you to extract core ideas and key takeaways, making it easier to understand and share information efficiently.

Preparing for Summarizing

Before you start writing your summary of the article, you’ll have to read the piece a few times first as a base for further understanding. It’s recommended that you read the paper without taking any notes first because this gives you some room to create your own perspective of the work.

After the first reading, you should be able to tell the author’s perspective and the type of audience they are focusing on. Subsequently, you should get ready for the second read with a paper to write notes on as you get into the arguments of the post.

Identifying Main Ideas

As you come to the second read of the article, you should focus on the thesis statement, main ideas, and important details laid out in the piece. If you look at the headings and sections individually, you should be able to get some material for the summarizing by taking out the crucial events or a topic sentence from each part.

While writing down the main arguments of the post, make sure to ask the five “W” questions. If you think about the “Who” , “Why” , “When” , “Where” , and “What” , you should be able to construct a layout for the summary based on the main ideas.

Writing The Summary

Once you lay down the article’s main ideas and answer the key questions about it, you’ll have an outline for writing. The next move is to keep an eye out on the structure of the summary and use the material in your notes to write your short take on these essential points.

The steps for writing article summaries can be similar to the main steps of article review writing . Therefore, it’s necessary to discuss the structure next so we can set you in the right direction with summary-specific format tips.

Outline Your Research Summary

To summarize research papers, you must be aware of the basic structure. You may know how to cite sources and filter the ideas, but you’ll also have to organize your findings in a concise academic structure.

The following components are essential for a summary paper format:

Introduction

Your research article’s introduction is a brief overview of your work. Outlining important ideas or presenting the state of the topic under research seeks to make the issue easier for your audience to comprehend.

The Methods section includes tests, databases, experiments, surveys, questionnaires, sampling, or statistical analysis, used to conduct a research study. However, for a solid research paper summary example, you should avoid getting bogged down in the specifics and just discuss the tools you utilized and how you conducted your study.

This part the summary of research, presents all of the data you gathered from your investigations and analysis. Therefore, incorporate any information you learned by watching your target and the supporting theories.

This stage requires you to summarize research paper, evaluate the result in light of the pertinent background, and determine how it reacts to the prevailing trends. You need to identify the subject’s advantages and disadvantages once you have provided an explanation using theoretical models. You may also recommend more research in the area.

Use this last part to support or refute your theories in light of the data collection and analysis, though, if your mentor insists on it being in a separate paragraph.

Here’s a research summary example outlining the topic “The Impact of Social Media on Mental Health Among Adolescents”:

I. Introduction.

Brief overview of the rise of social media.
Importance of studying its impact on mental health.
Statement of the problem.
Purpose of the study.

II. Literature Review.

Statistics on social media penetration.
Common platforms and their features.
Studies supporting a negative and/or a positive impact.
Gaps and inconsistencies in existing literature.

III. Methodology.

Quantitative approach.
Cross-sectional survey.
Survey instrument details.
Ethical considerations.

IV. Data Analysis.

Descriptive statistics.
Inferential statistics (e.g., regression analysis).
Tables and figures.
Key findings.

V. Discussion.

Correlation between social media usage and mental health.
Identification of patterns and trends.
Practical implications for parents, educators, and policymakers.
Suggestions for future research.

VI. Conclusion.

Summary of key findings.
Final remarks on the study’s contribution to the field.

The given research article summary example depicts how the text can be structured in a laconic and effective way.

Structure Types

So, now you can see the best practices and structure types for writing both empirical and argumentative summaries. The only thing left to discuss is to go through our example outlined above and divide its structure into distinctive parts, which you could use when writing your own summary.

The best way to start is by mentioning the title and the author of the article. It’s best to keep it straightforward: “ In “Who Will Be In Cyberspace”, author Langdon Winner takes a philosophical approach…”

The next part is critical for writing a good summary since you’ll want to captivate the reader with a short and concise one-point thesis. If you look at our example, you’ll see that the first sentence or two contains the main point, along with the title and the author’s name.

So, that’s an easy way to get straight to the point while also sounding professional, and this works for all the essay structure types. You should briefly point out the main supportive points as well – “ He supports this through the claims that people working in the information industry should be more careful about newly developed technologies…”

The key is to keep it neutral and not overcomplicate things with supportive claims. Try to make them as precise as possible and provide examples that directly support the main thesis.

Unless it’s a scientific article summary where you are requested to provide your take as a researcher, it’s also best to avoid using personal opinions. You can conclude the summary by once again mentioning the main thought of the article, and this time you can make the connection between the main thesis and supporting points to wrap up.

Summary Writing Tips and Best Practices

The way in which you’ll approach writing a summary depends on the type and topic of the original article, but there are some common points to keep in mind. Whether you are trying to summarize a research article or a journal piece, these tips can help you stay on topic:

Be concise – The best way to summarize an article quickly is to be straightforward. In practice, it means making it all in a few sentences and no longer than one-fourth of the size of the original article.
Highlight the study’s most significant findings – For your summary paper, prioritize presenting results that have the most substantial impact or contribute significantly to the field.
Create a reverse outline – On the other hand, you can also remove the supporting writing to end up with a reverse essay outline and these are the ideas you can expand on through your summary.
Use your own words – In most cases, a paper summary will be scanned for plagiarism, so you need to make sure you are using your words to express the main point uniquely. This doesn’t mean you have to provide your perspective on the topic. It just means your summary needs to be original.
Make sure to follow the tone – Summarizing an article means you’ll also need to reflect on the tone of the original piece. To properly summarize an article, you should address the same tone in which the author is addressing the audience.
Use author tags – Along with the thesis statement, you also have to express the author’s take through author tags. This means you need to state the name of the author and piece title at the beginning, and keep adding these “tags” like “he” or “she” or simply refer to the author by name when expressing their ideas.
Avoid minor details – To ensure you stay on topic, it’s recommended that you avoid repetition, any minor details, or descriptive elements. Try to keep the focus on key points, main statements and ideas without being carried away in thought.
Steer clear of interpretations or personal opinions – Avoid personal interpretations or opinions when you write a summary for a research paper. Remember to stick to presenting facts and findings without injecting subjective views.
Highlight the research context – Focus on explaining to the readers why research is important. Your summary of research paper must not repeat the previous studies. Find the gap in the existing literature it could fill. When you write a summary of a research article, try to help readers understand the significance of your study within the broader academic or practical context. Use a paraphraser if you need a fresh perspective on your writing style.

Common Mistakes to Avoid

Just like it’s important to avoid plagiarism in your text , there are a few other mistakes that commonly occur. The whole point is to summarize article pieces genuinely, with a focus on the author’s argument and writing in your own words.

We’ve often seen college graduates do an article summary and misrepresent the author’s idea or take, so that’s an important piece of advice. You should avoid drifting away from the author’s main idea throughout the summary and keep it precise but not too short.

Quotes shouldn’t be used directly within the piece, and by that, we mean both quotes from the author and quotes from other summaries on the same topic since it would qualify as plagiarism. Finally, you shouldn’t state your opinion unless you are doing a summary of a novel or short story with a specific academic goal of writing from your perspective.

Examples of Article Summaries

While our guide and tips can be used for a variety of different types of written pieces, there are various types of articles. From professional essay writing to informative article synopsis, options can vary.

We will give you an example of a summary of the different article types that you may run upon, so you can see exactly what we mean by those standardized instructions and tips:

The question of how to summarize an article isn’t new to students or even writers with more experience, so we hope this guide will shed some light on the process. The most important piece of advice we can give you is to stay true to the main statement and key points of the article and express the synopsis in your original way to avoid plagiarism.

As for the structure, we are certain you’ll be able to use our examples and layouts for different types of summaries, so make sure to pay extra attention to the structure, quotes, and author tags.

What is a good way to start a summary?

To begin a summary effectively, start by briefly introducing the article’s topic and the main points the author discusses. Capture the reader’s attention with a concise yet engaging opening sentence. Provide context and mention the author’s name and the article’s title. Convey the essence of the article’s content, highlighting its significance or relevance to the reader. This initial context-setting sentence lays the foundation for a clear and engaging summary that draws the reader in.

What is the difference between summarizing and criticizing an article?

Summarizing an article entails condensing its main points objectively and neutrally, presenting the essential information to readers. In contrast, critiquing an article involves a more in-depth evaluation, assessing its strengths and weaknesses, methodology, and overall quality, often including the expression of personal opinions and judgments. Summarization offers a snapshot of the content, while critique delves deeper, offering a comprehensive assessment.

When summarizing a text, focus on these critical questions:

“What’s the main point?” Find the core message or argument.
“What supports the main point?” Identify key supporting details and evidence.
“Who’s the author?” Consider their qualifications and potential bias.
“Who’s the intended audience?” Understand the expected reader’s knowledge level.
“Why is it important?” Explain the text’s relevance and significance within its context. Addressing these questions ensures a thorough and effective summary.

How long is a summary and how many paragraphs does a summary have?

A summary typically ranges from one to three paragraphs in length, depending on the complexity and length of the original text. The goal is to concisely present the main points or essence of the source material, usually resulting in a summary that is significantly shorter than the original.

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Planning to Write

Q: How to write the summary of chapter one of a research project?

In my research project, I am not sure how to write chapter one summary.

Asked by Feshter Moyo on 01 May, 2019

Your question is unclear. Are you referring to the "Thesis summary" that is often a part of the first chapter of a thesis along with the introduction? Or do you want to know how to write the summary of the first chapter of your thesis?

If it's the latter, you would need generic inputs on how to write the summary of any chapter of a book or a thesis. Basically, a summary of any chapter would just highlight the main points that have been discussed in that chapter. It should be a highly condensed version of whatever you have covered in that chapter.

If you wish to know about the "Thesis Summary," I would suggest you first go through the guidelines provided by your univeristy/institution. Each university has different requirements and while some require a chapter-wise summary of the entire thesis, others focus on the main elements, such as problem statement, methodology, reults, conclusion and implication. You can also clarisy your doubts with your supervisor before you start writing.

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Open access
Published: 06 March 2024

Association and mediation between educational attainment and respiratory diseases: a Mendelian randomization study

Guohui Lan 1 na1 ,
Mengying Xie 2 na1 ,
Jieli Lan 3 na1 ,
Zelin Huang 1 na1 ,
Xiaowei Xie 4 ,
Mengdan Liang 1 ,
Zhehui Chen 1 ,
Xiannuan Jiang 1 ,
Xiaoli Lu 1 ,
Xiaoying Ye 1 ,
Tingting Xu 1 ,
Yiming Zeng 5 &
Xiaoxu Xie 1 , 3 , 5

Respiratory Research volume 25 , Article number: 115 ( 2024 ) Cite this article

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Respiratory diseases are a major health burden, and educational inequalities may influence disease prevalence. We aim to evaluate the causal link between educational attainment and respiratory disease, and to determine the mediating influence of several known modifiable risk factors.

We conducted a two-step, two-sample Mendelian randomization (MR) analysis using summary statistics from genome-wide association studies (GWAS) and single nucleotide polymorphisms (SNPs) as instrumental variables for educational attainment and respiratory diseases. Additionally, we performed a multivariable MR analysis to estimate the direct causal effect of each exposure variable included in the analysis on the outcome, conditional on the other exposure variables included in the model. The mediating roles of body mass index (BMI), physical activity, and smoking were also assessed.

MR analyses provide evidence of genetically predicted educational attainment on the risk of FEV1 (β = 0.10, 95% CI 0.06, 0.14), FVC (β = 0.12, 95% CI 0.07, 0.16), FEV1/FVC (β = − 0.005, 95% CI − 0.05, 0.04), lung cancer (OR = 0.54, 95% CI 0.45, 0.65) and asthma (OR = 0.86, 95% CI 0.78, 0.94). Multivariable MR dicated the effect of educational attainment on FEV1 (β = 0.10, 95% CI 0.04, 0.16), FVC (β = 0.07, 95% CI 0.01, 0.12), FEV1/FVC (β = 0.07, 95% CI 0.01, 0.01), lung cancer (OR = 0.55, 95% CI 0.42, 0.71) and asthma (OR = 0.88, 95% CI 0.78, 0.99) persisted after adjusting BMI and cigarettes per day. Of the 23 potential risk factors, BMI, smoking may partially mediate the relationship between education and lung disease.

High levels of educational attainment have a potential causal protective effect on respiratory diseases. Reducing smoking and adiposity may be a target for the prevention of respiratory diseases attributable to low educational attainment.

Key messages

What is already known on this topic.

Several observational studies have revealed that people with a higher education attainment is associated with a lower risk of developing respiratory diseases. However, observational studies are susceptible to reverse causation and confounding factors. Also, the role of genetic factors in the study remains unknown.

What this study adds

In this study, by leveraging data from the recently published genome-wide association studies, we found a significant genetic correlation between educational attainment and respiratory disease. We further confirmed that the causal relationship between educational attainment and respiratory disease is partially mediated by smoking and obesity.

How this study might affect research, practice or policy

Our study highlights the importance of early detection and prevention of respiratory disease, including lung function, lung cancer and asthma, amongst low education group. Moreover, our findings might provide new understandings for the mechanisms associated with educational attainment and respiratory disease.

Introduction

Deaths from chronic respiratory diseases constituted 7% of all deaths globally in 2019, with the prevalent diseases including chronic obstructive pulmonary disease (COPD), asthma, and lung cancer [ 1 ]. Identifying potential risk factors is crucial to safeguarding public health and preventing the emergence of diseases. Lung function is an important predictor of quality of life and longevity [ 2 ].

Socioeconomic disparities in health have been documented. Individuals with lower socioeconomic status have higher mortality and morbidity risks compared to individuals with higher socioeconomic status. There has been research into the impact of socioeconomic factors on health outcomes [ 3 , 4 , 5 , 6 , 7 ]. Among many socio-economic indicators, educational attainment (EA) has been identified as a social determinant of health through various mechanisms, such as neurodevelopment, health behavior, and health literacy [ 8 ].

Several studies have examined the association between EA and respiratory diseases. Previous studies have employed cross-sectional designs to investigate the complex relationship between EA and lung function [ 9 ] and lung cancer [ 10 ]. There have been several studies that have examined the effects of education on pulmonary health, and they have also identified potential mechanisms or mediators that may explain these effects. The finding suggest that this association may be mediated by some modifiable factors related to both exposure and outcome, such as BMI, physical activity and smoking. Nevertheless, these studies were observational in nature and thus prone to methodological limitations, including confounding and reverse causality, as well as failure to consider mediating factors. Therefore, the relationship between EA and respiratory disease, as well as lung function is unclear.

Mendelian randomization (MR) is a method for inferring causal relationships based on genetics, utilizing single-nucleotide polymorphism (SNP) as a surrogate of exposure, evaluating observed data and correlating causal relationships through a statistical relationship between genotypes and phenotypes [ 11 ]. Under several assumptions, an MR study should produce results which avoid the potential biases associated with observational studies, such as confounding, reverse causation, and measurement errors, which are common in observational studies. Multivariable Mendelian randomization (MVMR) is a rapidly evolving analytical method that estimates the effect of each exposure variable on the model results while also considering the effects of other exposure variables that may affect the model results [ 12 ]. This method is based on Mendelian inheritance law, randomly grouping multiple variables simultaneously to create a random distribution of variables between the groups, thereby enhancing the reliability and accuracy of the experiment.

The correlation between EA and asthma, COPD, and lung cancer has been reported in previous studies [ 9 , 10 , 13 ], the causal mechanism is not clear and these did not assess mediation by modifiable factors. In addition, recent GWAS on compared with the previously reported GWAS, a newly published GWAS for EA comes from a large sample of population data, and the results are more accurate. Therefore, using the latest GWAS data, it is possible to update the results of studies on the relationship between EA and respiratory diseases, in order to better understand this association. Potential confounding factors were also included in the MVMR analysis to control for their effects and obtain more accurate estimates of the direct causal effect of each exposure on the outcome.

In this report, the MR method was used to assess the causal association between EA and lung function, asthma and lung cancer, and a two-step Mendelian randomization was used to assess its mediated proportion in association for 23 potential mediating factors. Ultimately these causal conclusions will support the development of prevention policies.

Study design

In Mendelian randomization research, genetic information is usually used as an instrumental variable (IV) due to their random distribution in humans and robust associations with the exposure and outcome variables being investigated. EA was assessed causally associated with lung cancer, FEV1, FVC, FEV1/FVC and asthma using two-step Mendelian randomization analysis. All GWAS summary statistics were gathered from a public GWAS website ( https://gwas.mrcieu.ac.uk/ ) for the purposes of these analyses. Data summarized from GWAS are presented in Table 1 .

Education attainment

The genetic instruments for EA were selected from a meta-analysis comprising 71 GWAS discovery cohorts that included 1, 131, 881 European ancestral participants. To facilitate the classification and conversion of educational levels into standardized units for better cross-country and cross-regional comparisons, the International Standard Classification of Education (ISCED) 2011 was employed, utilizing 4.2 years of education as the unit within the educational systems of the UK and the US. In a study with the identifier ieu-a-1239, conducted on a sample of more than 1.1 million individuals, a genetic association analysis of EA was performed. This analysis identified 1271 independent SNPs that exhibited significant associations with EA [ 14 ].

Outcome-respiratory disease

The outcomes used in this study were lung function indicators and related lung diseases (lung cancer and asthma).

Lung function

We selected respiratory function indicators to assess lung function in 400,102 European ancestry individuals. We identified 139 new signals related to lung function, including forced expiratory volume in one second (FEV1), lower forced vital capacity (FVC), and the FEV1-to-FVC ratio [ 15 ]. ID: ebi-a-GCST007431 (FEV1/FVC), ebi-a-GCST007429 (FVC), ebi-a-GCST007432 (FEV1).

Lung cancer

The International Lung Cancer Consortium (ILCCO) conducted a GWAS analysis on lung cancer and identified 259 SNPs (with a significance level of P < 5 × 10 –8 ) in a study involving 11,348 lung cancer cases and 15,861 controls [ 16 ]. ID: ieu-a-966.

Valette et al.[ 17 ] used genetic instruments from the UK Biobank in a study that employed a broad definition of asthma. The study included 56,167 asthma cases and 352,255 controls. ID: ebi-a-GCST90014325.

Based on our review of the literature, we selected 23 candidate mediators of modifiable risk factors (please refer to Additional file 1 : Fig. S1 in the supplementary materials for an overview of the process of identifying the candidate mediators). The mediators involved in the relationship between EA and respiratory disease were selected based on the following criteria for inclusion in the analysis: (1) Exposure and mediating factors are related in a causal way; (2) There was an association between mediating factors and outcomes, whether or not exposure factors were corrected. Ultimately, we identified three risk factors that met the criteria, including BMI [ 18 ], physical activity [ 19 ] and cigarettes per day [ 20 ], were included in the mediation analysis to assess the role of mediation between EA and lung function, lung cancer, or asthma. ID: ieu-b-40 (BMI), ebi-a-GCST90012791 (physical activity), ieu-b-25 (cigarettes per day).

SNP selection

To conduct Mendelian randomization, we selected the instrumental variables (IVs) as follows. Firstly, we selected SNPs that were significantly associated with educational attainment for each MR analysis, excluding genetic instruments with P values greater than 5 × 10 –8 in relation to the exposure. Secondly, as part of the MR analysis, we utilized independent SNPs as genetic instruments when genetic associations were identified for both the exposure and the outcome of interest. Then, the clumping process ( r 2 < 0.001 within 10,000-kb windows) was employed to determine whether the included SNPs are in linkage disequilibrium (LD). If no SNPs related to exposure were identified in the results, we did not utilize proxy SNPs. Finally, to ensure that there was no direct correlation between the instrumental variables used in the analysis and the outcome, excluding genetic instruments with P values < 5 × 10 –8 in relation to the outcome.

Statistical analysis

Based on three critical assumptions, the MR method was developed: (1) The genetic variation must be closely related to the exposure in the MR analysis; (2) Genetic variation cannot be associated with confounding factors between exposure and outcome; (3) Exposure must be the mechanism through which genetic variables influence outcomes [ 21 ].

To assess whether potential mediators mediate between exposure and outcome, a two-step Mendelian randomization was used to assess the effect. The first step involved estimating the effect sizes of the exposure on lung function, lung cancer, asthma and mediators respectively. We use IVW as our primary method, which is characterized by regression without considering the presence of intercept terms and fitting with the reciprocal of the outcome variance as a weighting factor [ 22 ]. Additionally, we used MR-PRESSO, MR-Egger, and weighted median tests to estimate the effects. Subsequently, MVMR was used to determine the effect of each mediator on the outcome, taking into account how each instrument was genetically influenced [ 23 ].

Direct and indirect effects are both part of the total effect. Direct effect refers to the impact of the exposure factor on the outcome, independent of intermediary variables. Indirect effect, on the other hand, refers to the impact of the exposure factor on the outcome through intermediary variables [ 24 ]. The overall effect of EA on outcome was thus decomposed into two distinct components: (i) the indirect effect through each mediator individually, indicating the influence of education As a primary method for testing whether a mediated effect was present and its magnitude, we used the Sobel test (a × b in Fig. 1 ), and (ii) the direct effect of education on outcome after adjusting for each mediator (c' in Fig. 1 ) [ 25 ]. By using this statistical technique, we can explore complex relationships between variables and understand how intermediary variables impact exposure-outcome relationships.

Diagrams illustrating associations examined in this study. A The total effect of exposure on outcome, c, was derived using univariable MR. B The total effect was decomposed into: (i) indirect effect using a two-step MR (where a is the total effect of exposure on mediator, b is the effect of mediator on outcome adjusting for exposure and the mediating effect is calculated using the product method ( a × b )); (ii) direct effect ( c' = c – a × b ). C For mediation by both smoking and BMI combined (arrows represent their bidirectional causal relationship), the indirect effect was derived using the difference method ( c – c' ). Proportion mediated was the indirect effect divided by the total effect

To derive the indirect effect of combining multiple mediations, the difference method (c–c') is used, where c' indicates that multiple mediating factors are adjusted in the MVMR model. The delta method is used to calculate the confidence interval when the indirect effect is divided by the total effect (RMediation (shinyapps.io)), the proportion of the mediating effect can be quantified for one mediator or a combination of mediations. For each genetic instruments, we set P < 5 × 10 –8 to selected genome-wide significant SNPs. To address the issue of linkage disequilibrium, we applied LD thresholds pairwise from the original GWAS for each mediator, with SNPs for each mediator adhering to an LD cut-off of r 2 < 0.01 within a window of 1 MB.

Sensitivity analysis

UVMR's IVW method can be examined for its robustness using two methods. Weighted medians are used in UVMR as well as Egger methods in MR Egger to assess the robustness of the IVW method, and Egger methods are used in MVMR to assess the robustness of the MVMR-IVW method. The MR-Egger method can determine whether horizontal pleiotropism exists in the instrumental variable to prevent violating the instrumental variable assumption. In addition, the Cochran's Q test is often used as an indicator of heterogeneity in meta-analysis, with a P-value less than 0.05 indicating the presence of heterogeneity in the study. An assessment of the strength of the genetic instrumental variables used in the study was conducted by using conditional F-statistics. A commonly used threshold for an "acceptable" F-statistic is 10, indicating that the instruments explain at least 10 times as much variance as the residual variance. However, this threshold may vary depending on the study design and sample size. In addition, we performed a “leave-one-out” sensitivity assessment to determine whether or not a certain SNP had too much influence on the results, and these SNPs were excluded from the MR analysis. Only when the IVW estimate agrees with at least one sensitivity analysis in direction and statistical significance, and there is no evidence of pleiotropy, is it considered to have a causal association.

The MR analyses were all performed using R (version 4.0.2) with the “TwoSampleMR” and “MRPRESSO” R package [ 26 , 27 ].

Patient and public involvement

The patient and public were not involved in the design or reporting of this study.

Effect of education attainment on lung function, lung cancer and asthma

The results of analyses found that increased genetically predicted EA was significantly related to enhanced FEV1 (β = 0.10, 95% CI 0.06, 0.14), improved FVC (β = 0.12, 95% CI 0.07, 0.16), and a less favorable FEV1/FVC ratio (β = -0.005, 95% CI − 0.05, 0.04). Furthermore, this heightened EA was also associated with a reduced risk of lung cancer (OR = 0.54, 95% CI 0.45, 0.65) and asthma (OR = 0.86, 95% CI 0.78, 0.94) (Fig. 2 ).

MR-estimated effects of educational attainment on each outcome separately, presented as β/OR with 95% CI. EA educational attainment, FEV1 forced expiratory volume in one second, FVC forced vital capacity, FEV1/FVC forced expiratory volume in one second / forced vital capacity

Effect of education attainment on mediators

Table 2 shows the impact of education predicted by genetics on various mediators. A UVMR analysis revealed that for each extra 1-SD year of education are associated with lower BMI (IVW = − 0.16, 95% CI − 0.22, − 0.10), fewer cigarettes smoked per day (IVW = − 0.32, 95% CI − 0.40, − 0.24), and higher physical activity levels (IVW = 0.20, 95% CI 0.16, 0.23).

Effect of mediators on lung function, lung cancer and asthma after adjusting education attainment

According to Fig. 3 , each mediator significantly predicted lung function and lung cancer after adjusting for EA. In this study, we excluded physical activity from our analysis because there was only one SNP available, which would lead to a large bias in the results. In the MVMR results, a 1-SD increase in BMI was associated with an increased risk of FEV1/FVC (β = 0.11, 95% CI 0.09, 0.14); lung cancer (OR = 1.12, 95% CI 0.98, 1.28); asthma (OR = 1.15, 95% CI 1.08, 1.22), and a 1-SD increase in genetically predicted cigarettes per day was associated with a higher risk of lung cancer (OR = 1.41, 95% CI 1.14, 1.74) and asthma (OR = 1.05, 95% CI 0.98,1.12). By contrast, each 1-SD unit higher BMI was associated with a reduced risk of FEV1 (β = − 0.09, 95% CI − 0.12, − 0.06) and FVC (β = − 0.17, 95% CI − 0.20, − 0.14), and a 1-SD lower genetically predicted cigarettes per day was associated with a decreased risk of FEV1 (β = − 0.08, 95% CI − 0.12, − 0.04), FVC (β = − 0.07, 95% CI − 0.11, − 0.02) and FEV1/FVC (β = − 0.04, 95% CI − 0.08, − 0.004).

Effect of one standard deviation (SD) increase in exposure on outcome in multivariable models. EA, educational attainment; BMI body mass index, FEV1 forced expiratory volume in one second, FVC forced vital capacity, FEV1/FVC forced expiratory volume in one second/forced vital capacity

Mediating effect of mediators in the association between education attainment and lung function and respiratory diseases

In the MVMR analysis of the impact of EA to lung function through the consumption of cigarettes per day, the direct effect of EA on FEV1, FVC and FEV1/FVC was β = 0.08 (95% CI 0.04, 0.13), 0.09 (95% CI 0.05, 0.14) and β = − 0.01 (95% CI − 0.05, 0.03) after adjusting for the number of cigarettes smoked per day (Fig. 3 ). The direct effect of BMI on FEV1, FVC and FEV1/FVC was − 0.09 (95% CI − 0.12, − 0.06), − 0.17 (95% CI − 0.02, − 0.14) and 0.11 (95% CI 0.09, 0.14), respectively, after accounting for EA. The proportion mediated of FEV1, FVC and FEV1/FVC by BMI was 15%, 23% and 379%, respectively (Table 3 ).

The MVMR analysis revealed that the direct effect of EA on lung cancer and asthma through cigarette consumption per day was 0.62 (95% CI 0.51, 0.76) and 0.85 (95% CI 0.78, 0.93) after adjusting for cigarettes smoked per day (Fig. 3 ). The direct effect of cigarettes per day on lung cancer and asthma was OR = 1.41 (95% CI 1.14, 1.74) and OR = 1.05 (95% CI 0.98, 1.12) after accounting for EA. The proportion mediated of lung cancer and asthma by cigarettes per day was 18% and 10% (Table 3 ).

Both smoking and BMI were included in the FEV1 outcome MVMR model when considered simultaneously, effect sizes for EA (β = 0.10, 95% CI 0.04, 0.16), BMI (β = − 0.08, 95% CI − 0.11, − 0.05) and cigarettes per day (β = − 0.04, 95%CI − 0.09, 8e−06) (Fig. 3 ). Combined BMI and smoking mediated 44% of the effect of EA on FVC (Table 3 ). When BMI was the mediator, the effects of education on lung function and lung disease were shown in Fig. 3 and Table 3 .

MR sensitivity analyses

According to the Cochran's Q test, the instrumental variables from education attainment to lung cancer did not show any heterogeneity, but there was heterogeneity in the other instrumental variables of the analysis which demonstrated a trend for the other instrumental variables (Table 4 ). In order to assess whether SNP has a horizontal pleiotropy, MR-Egger regression was used, which provided a valuable assessment of whether there was horizontal pleiotropy (Fig. 4 ). In the sensitivity analysis results, there was no significant evidence of directional pleiotropy ( P > 0.05, Table 5 ). Furthermore, a further consistency between MR-weighted median and MR-IVW is in the direction of the distribution (Additional file 1 : Table S1, Table 2 ). In reverse MR analyses between mediators and education attainment, the significant correlation between BMI and education attainment was found, but this reverse association could be due to horizontal pleiotropy (Egger intercept = − 0.0018; P = 0.0003). In terms of education attainment, Physical Activity and Cigarettes per day did not appear to have a causal effect (Additional file 1 : Table S2). Moreover, leave-one-out analysis revealed that no SNP drove the results, and funnel plots were symmetrical (Fig. 4 ), indicating that the causal relationship has not been violated (Fig. 4 ). All SNPs have F-statistic ranging from 29.69 to 240.25. F -statistics > 10 considered suggestive of adequate instrument strength (Detailed information about SNPs is shown in Additional file 2 : Table S3).

Mendelian randomization scatterplots and funnel plots of educational attainment to each mediator and outcome association. BMI body mass index, FEV1 forced expiratory volume in one second, FVC forced vital capacity, FEV1/FVC forced expiratory volume in one second/forced vital capacity

In this MR study, the casual relationship between ET and respiratory functions and diseases was identified. To delve deeper into the mechanisms behind this association, we have identified three potential mediators from a pool of 23 modifiable risk factors. Our study findings reveal that education plays a crucial role in safeguarding lung function, preventing lung cancer, and mitigating the risk of asthma. An additional 4.2 years of schooling was associated with higher FEV1 and FVC values and lower lung cancer and asthma rates.

This is the first time that two-step MR analysis has been used to study the mediating relationship between EA and respiratory disease. Higher educational attainment is protective against respiratory disease, consistent with traditional observational findings. Actually, previous studies have shown that higher educational attainment has a protective effect on a range of health outcomes including lung cancer, artery stroke, type 2 diabetes. It is worth noting that this protective effect decreases as smoking and BMI are adjusted [ 28 ]. For example, smoking mediated 28% of the causal relationship between education and myocardial infarction, and BMI mediated 18% [ 29 ]. This shows that the implementation of public health measures to reduce smoking and obesity has wide-ranging benefits in preventing the occurrence of disease.

In this study, although the protective effect of education on respiratory diseases was verified, the mediating factor of choice explained only one quarter of the effect of education, leaving a significant portion unexplained. There are a number of other factors that may explain the remaining associations, including poverty, employment, diet, psychosocial factors, and access to healthcare [ 30 , 31 , 32 , 33 , 34 ]. However, since many of these factors are not heritable and cannot be obtained in GWAS, they cannot be included in this study.

In the UK, it has been proven that raising the age at which students leave school can have an impact on EA and lead to improvements in population health and a decrease in mortality rates[ 35 ]. Although EA has often been used as a proxy for socioeconomic status in previous studies, it is important to acknowledge that interventions solely targeting educational attainment may not offer an optimal solution for alleviating the burden of respiratory disease. In this study, a two-stage MR study was conducted to demonstrate that some risk factors mediate the relationship between EA and respiratory disease, and that these factors are more likely to change than EA.

In comparison to prior investigations, this study encompasses the following commendable attributes: (1) The study uses SNPs as genetic instruments can capture the impact of genetic variation on the phenotype or disease of interest. This approach effectively mitigates the confounding effects of reverse causality and errors. Due to allele random assignment at the time of conception, MR results that are insensitive to reverse causation. Additionally, using SNP as a tool variable can also improve the reliability and accuracy of MR analysis. (2) Exposure and outcome summary statistics in the study were obtained from the largest and most recent GWAS. (3) In order to improve the statistical power, a rigorous screening process was carried out for IVs (4) As part of the research process, multiple sensitivity analyses were performed in order to improve the results' accuracy. Furthermore, the MR analysis results align with those of observational studies, thereby reinforcing the robustness of the conclusions.

Notwithstanding the aforementioned strengths, this study is subject to some limitations that warrant consideration. Firstly, the GWAS used in the study exclusively featured on European populations. Thus, the generalization of results is not suitable for non-European people. Therefore, newer GWAS studies should focus on non-European populations. Secondly, given that EA has sex differences with respiratory diseases, associations and mediations may also differ between the sexes. However, as GWAS summary data were used, the effects of sex and age on outcomes could not be studied. A sex-stratified GWAS study may be used in future MR studies to address this issue. Thirdly, since lung cancer and asthma are binary variables, log-odds should be used in MR Analyses. The optimality of this approach is not achieved since the odds ratios do not collapse, i.e. marginal ORs are not equivalent to conditional odds ratios. Fourthly, the GWAS summary data used in this article comes from different repositories, in which case there is some heterogeneity between the data. This is inevitable, however, because when different data sources are selected, the bias of instrumental variables is reduced and the reliability of the results is improved. Finally, there is a possibility that GWAS results may be biased by sample overlap between studies.

Elevated levels of EA may potentially exert a protective effect on respiratory diseases, with modifiable risk factors such as BMI and cigarettes per day mediating this relationship. Interventions to reduce smoking and adiposity may reduce much of this risk, which assumes even greater significance for individuals with respiratory disease. However, most of the remaining effects of EA on the relationship between respiratory disease remain unexplained. As such, there is a pressing need for enhanced preventive measures to address socioeconomic and educational disparities, as well as further research into other modifiable risk factors.

Availability of data and materials

The datasets analyzed in the current study are available in a public GWAS website ( https://gwas.mrcieu.ac.uk/ ).

GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–1222. https://doi.org/10.1016/S0140-6736(20)30925-9 (Erratum in: Lancet. 2020;396(10262):1562).

Mannino DM, et al. Lung function and mortality in the United States: data from the First National Health and Nutrition Examination Survey follow up study. Thorax. 2003;58(5):388–93.

Article CAS PubMed PubMed Central Google Scholar

Blane D, et al. Association of cardiovascular disease risk factors with socioeconomic position during childhood and during adulthood. BMJ. 1996;313(7070):1434–8.

Davey Smith G, et al. Education and occupational social class: which is the more important indicator of mortality risk? J Epidemiol Community Health. 1998;52(3):153–60.

Article CAS PubMed Google Scholar

Marang-vandeMheen PJ, et al. Socioeconomic differentials in mortality among men within Great Britain: time trends and contributory causes. J Epidemiol Community Health. 1998;52(4):214–8.

Article CAS Google Scholar

Martikainen P, et al. Educational differences in lung cancer mortality in male smokers. Int J Epidemiol. 2001;30(2):264–7.

Smith GD, et al. Lifetime socioeconomic position and mortality: prospective observational study. BMJ. 1997;314(7080):547–52.

Cohen AK, Syme SL. Education: a missed opportunity for public health intervention. Am J Public Health. 2013;103(6):997–1001.

Article PubMed PubMed Central Google Scholar

Zhang DD, et al. Association between socioeconomic status and chronic obstructive pulmonary disease in Jiangsu province, China: a population-based study. Chin Med J (Engl). 2021;134(13):1552–60.

Article PubMed Google Scholar

Swaminathan R, et al. Education and cancer incidence in a rural population in south India. Cancer Epidemiol. 2009;33(2):89–93.

Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22.

Sanderson E. Multivariable mendelian randomization and mediation. Cold Spring Harb Perspect Med. 2021;11(2):a038984. https://doi.org/10.1101/cshperspect.a038984 .

Li Y, et al. Evaluating the causal association between educational attainment and asthma using a Mendelian randomization design. Front Genet. 2021;12: 716364.

Lee JJ, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50(8):1112–21.

Shrine N, et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat Genet. 2019;51(3):481–93.

Wang Y, et al. Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer. Nat Genet. 2014;46(7):736–41.

Valette K, et al. Prioritization of candidate causal genes for asthma in susceptibility loci derived from UK Biobank. Commun Biol. 2021;4(1):700.

Yengo L, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼ 700000 individuals of European ancestry. Hum Mol Genet. 2018;27(20):3641–9.

Tyrrell J, et al. Genetic predictors of participation in optional components of UK Biobank. Nat Commun. 2021;12(1):886.

Article ADS CAS PubMed PubMed Central Google Scholar

Liu M, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51(2):237–44.

Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(2):195–208.

Article Google Scholar

Lawlor DA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63.

Article MathSciNet PubMed Google Scholar

Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181(4):251–60.

Carter AR, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021;36(5):465–78.

VanderWeele TJ. Mediation analysis: a practitioner’s guide. Annu Rev Public Health. 2016;37:17–32.

Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. https://doi.org/10.7554/eLife.34408

Dean CB, Nielsen JD. Generalized linear mixed models: a review and some extensions. Lifetime Data Anal. 2007;13(4):497–512.

Article MathSciNet CAS PubMed Google Scholar

Yuan S, Xiong Y, Michaëlsson M, Michaëlsson K, Larsson SC. Genetically predicted education attainment in relation to somatic and mental health. Sci Rep. 2021;11(1):4296. https://doi.org/10.1038/s41598-021-83801-0 .

Carter AR, et al. Understanding the consequences of education inequality on cardiovascular disease: mendelian randomisation study. BMJ. 2019;365:1855.

Belot A, et al. Association between age, deprivation and specific comorbid conditions and the receipt of major surgery in patients with non-small cell lung cancer in England: a population-based study. Thorax. 2019;74(1):51–9.

Brundisini F, et al. Chronic disease patients’ experiences with accessing health care in rural and remote areas: a systematic review and qualitative meta-synthesis. Ont Health Technol Assess Ser. 2013;13(15):1–33.

CAS PubMed PubMed Central Google Scholar

Collins PF, et al. Influence of deprivation on health care use, health care costs, and mortality in COPD. Int J Chron Obstruct Pulmon Dis. 2018;13:1289–96.

Scoditti E, Massaro M, Garbarino S, Toraldo DM. Role of diet in chronic obstructive pulmonary disease prevention and treatment. Nutrients. 2019;11(6):1357. https://doi.org/10.3390/nu11061357 .

Watson A, Wilkinson TMA. Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research. Ther Adv Respir Dis. 2022;16:17534666221075492.

Davies NM, et al. The causal effects of education on health outcomes in the UK Biobank. Nat Hum Behav. 2018;2(2):117–25.

Article MathSciNet PubMed PubMed Central Google Scholar

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Acknowledgements

Our gratitude goes out to the consortia and participants of the GWAS that provided us with the summary statistical data, including Social Science Genetic Association Consortium, International Lung Cancer Consortium, Genetic Investigation of Anthropometric Traits, and GWAS & Sequencing Consortium of Alcohol and Nicotine use.

This research was supported by National Natural Science Foundation of China Youth Program (82203989), Natural Science Foundation of Fujian Province (2021J01729), and Fujian medical university talent research funding (XRCZX2019031). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Guohui Lan, Mengying Xie, Jieli Lan and Zelin Huang have contributed equally to this work.

Authors and Affiliations

Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China

Guohui Lan, Zelin Huang, Mengdan Liang, Zhehui Chen, Xiannuan Jiang, Xiaoli Lu, Xiaoying Ye, Tingting Xu & Xiaoxu Xie

The Second Clinical Medical School, Nanchang University, Nanchang, China

Mengying Xie

Clinical Research Unit, The Second Affiliated Hospital, Fujian Medical University, Quanzhou, China

Jieli Lan & Xiaoxu Xie

The First Clinical Medical School, Shanxi Medical University, Taiyuan, China

Xiaowei Xie

Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine of Fujian Province, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China

Yiming Zeng & Xiaoxu Xie

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XXX and YZ conceptualized the study design and interpreted the results. GL, MX, JL, and ZH analyzed the data and drafted the manuscript. XWX, ML, ZC, XJ, XL, XY, and TX provided the methodological suggestions and revised the manuscript.

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Supplementary Information

Additional file 1: figure s1..

Overview of the process of identifying the mediators. Table S1. Mendelian randomization analysis of the effect of educational attainment on lung function and disease. Table S2. Reverse MR analysis of mediators to education attainment.

Additional file 2: Table S3.

All instrumental variables used in Mendelian randomization analysis.

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Lan, G., Xie, M., Lan, J. et al. Association and mediation between educational attainment and respiratory diseases: a Mendelian randomization study. Respir Res 25 , 115 (2024). https://doi.org/10.1186/s12931-024-02722-4

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Review Article
Published: 05 March 2024

Projections of an ice-free Arctic Ocean

Alexandra Jahn ORCID: orcid.org/0000-0002-6580-2579 1 , 2 ,
Marika M. Holland ORCID: orcid.org/0000-0001-5621-8939 3 &
Jennifer E. Kay ORCID: orcid.org/0000-0002-3625-5377 1 , 4

Nature Reviews Earth & Environment volume 5 , pages 164–176 ( 2024 ) Cite this article

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Climate and Earth system modelling
Cryospheric science
Projection and prediction

Observed Arctic sea ice losses are a sentinel of anthropogenic climate change. These reductions are projected to continue with ongoing warming, ultimately leading to an ice-free Arctic (sea ice area <1 million km 2 ). In this Review, we synthesize understanding of the timing and regional variability of such an ice-free Arctic. In the September monthly mean, the earliest ice-free conditions (the first single occurrence of an ice-free Arctic) could occur in 2020–2030s under all emission trajectories and are likely to occur by 2050. However, daily September ice-free conditions are expected approximately 4 years earlier on average, with the possibility of preceding monthly metrics by 10 years. Consistently ice-free September conditions (frequent occurrences of an ice-free Arctic) are anticipated by mid-century (by 2035–2067), with emission trajectories determining how often and for how long the Arctic could be ice free. Specifically, there is potential for ice-free conditions in May–January and August–October by 2100 under a high-emission and low-emission scenario, respectively. In all cases, sea ice losses begin in the European Arctic, proceed to the Pacific Arctic and end in the Central Arctic, if becoming ice free at all. Future research must assess the impact of model selection and recalibration on projections, and assess the drivers of internal variability that can cause early ice-free conditions.

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Introduction

Arctic sea ice cover — including sea ice area (SIA) 1 , sea ice extent (SIE) 2 and sea ice thickness 3 , 4 — has declined conspicuously since the beginning of satellite observations in 1978. Although losses have occurred in all seasons 5 , reductions are greatest during summer, with SIA 6 declining by −0.078 million km 2 year −1 between 1979 and 2023. However, these reductions are not temporally consistent: summertime SIA losses between 1996–2012 are more than twice those over 1979–2023, reaching −0.17 million km 2 year −1 . Spatial variability also contributes to sea ice loss heterogeneity 7 , with the largest reductions seen in the shelf seas of the Arctic Ocean (the Barents, Kara, Laptev, East Siberian and Chukchi Seas).

Given observed and projected warming 8 , these sea ice reductions are set to continue such that the Arctic could become ice free. Indeed, climate models from the late 1970s already predicted the possibility of reaching summer ice-free conditions under sufficient warming 9 , with current climate models suggesting that September is likely to be ice free before mid-century 10 . However, internal variability 11 , 12 , physical differences between the models 13 and evolving definitions of ‘ice free’ 12 complicate accurate predictions, as demonstrated by the timing of ice-free conditions differing by more than 20 years owing to internal variability 12 , by more than 100 years across models 10 , 14 or by decades depending on the definition used 12 .

Regardless of prediction uncertainties, the transition to an ice-free Arctic signifies a regime shift from a perennial sea ice cover to a seasonal sea ice cover, or from a white summer Arctic to a blue Arctic 15 (Fig. 1 ). Such changes have probably not occurred for at least 80,000 years 16 (Box 1 ) and will have important impacts on the local and global climate and on ecological systems. For instance, replacing sea ice cover with open water modifies the radiation balance via reductions in albedo 17 , in turn, accelerating and amplifying anthropogenic warming 18 , especially in the Arctic 19 , 20 , 21 , 22 . Moreover, open-water areas and ice-free conditions allow for a larger fetch 23 , increasing wave heights 24 , 25 and, thereby, coastal erosion around the Arctic Ocean 26 , 27 , 28 . From an ecosystem perspective, the transition towards a summer ice-free Arctic threatens the survival of sea ice-dependent mammals such as polar bears and seals 29 , 30 , 31 , leads to increasing ocean productivity 32 , and allows for the potential migration of some fish species from the sub-polar seas into the Arctic Ocean 33 , 34 . Economic activity in the Arctic could also increase owing to enhanced accessibility for shipping 35 and resource exploration 36 . Due to the multitude of impacts on an ice-free Arctic, it is important to understand the timing of when the Arctic could become ice free.

a , Pan-Arctic September sea ice concentration with a sea ice area (SIA) of 5.5 million km 2 , typical for the 1980s. b , The same as in part a , but for 3.3 million km 2 , typical for 2015–2023. c , The same as in part a , but for sea ice area of <1 million km 2 , referred to as an ice-free Arctic. d , The climatological sea ice area seasonal cycles for 1980–1999 from satellite-derived observations 121 using the bootstrap 122 (solid red line) and NASA team 123 (dashed red line) algorithms, for 1980–1999 from select CMIP6 models 10 (black), and for a predicted ice-free September in select CMIP6 ensemble mean (blue). Red shading indicates uncertainty in the observed sea ice area (bounded by the seasonal cycle from the two algorithms), grey shading the CMIP6 ensemble spread for 1980–1999, and light blue shading the CMIP6 ensemble spread during the decade when the ensemble mean first goes ice free. Although sea ice area is reduced in all months of the year in the future, the loss is predicted to be greatest in September, but winter sea ice returns even after ice-free conditions are reached.

In this Review, we summarize the current understanding of an ice-free Arctic. We begin by discussing the drivers of sea ice loss, followed by available methodological approaches and corresponding uncertainties. Next, we outline predictions of an ice-free Arctic, including for September, months beside September and regional variability. We end with an outlook of future research needs. To quantify ice-free projections, we analyse monthly sea ice from select 10 Climate Model Intercomparison Project 6 (CMIP6) 37 models, hereafter referred to as ‘select models’, chosen on the basis of observations falling within the ensemble spread of each model for two key metrics 10 : the 2005–2014 September mean sea ice area and the observed sensitivity of sea ice area to cumulative CO 2 emissions over 1979–2014 (Supplementary Table 1 ). These select models are supplemented by large ensemble simulations from CMIP5 (ref. 38 ) and CMIP6.

Box 1 The history of ice-free conditions in the Arctic

Although sea ice has been a defining feature of the Arctic Ocean since the Eocene (47 million years ago) 16 , with perennial sea ice first appearing during the Miocene (around 13–14 million years ago) 16 , 124 , 125 , 126 , ice-free conditions are not a first for the Arctic when assessed over the geological record. For example, before the Arctic became ice-covered, early ancestors of tropical plants and crocodiles thrived in the Arctic during the Cretaceous (over 70 million years ago) 127 , 128 , 129 . Moreover, proxy evidence suggest a return to ice-free summers in the Central Arctic Ocean during the late Miocene (approximately 5 million years ago) 130 .

There is also evidence for ice-free conditions in the more recent geological past. For example, the last ice-free conditions in the Arctic likely occurred during the Eemian — the warmest period of the warmest quaternary interglacials — including marine isotope stage 5e (MIS 5e) (between 130,000 and 115,000 years ago) and potentially MIS 5a (around 80,000 years ago). At these times, proxy records indicate open water north of Greenland 131 , 132 , 133 , 134 , 135 and a northward shift of the tree line by hundreds of kilometres in Alaska and Russia 126 , 136 ; note that paleo evidence for these changes is stronger for MIS 5e than MIS 5a.

By contrast, during the Holocene (the current interglacial that started 11,000 years ago), the Arctic Ocean likely retained its perennial sea ice cover 137 , 138 . However, there is evidence for regionally ice-free conditions in the Arctic during the mid-Holocene warm period that peaked around 6,000 years before present, particularly in the shelf seas of the eastern Arctic 16 , 138 , 139 . Thus, perennial sea ice was probably much reduced in the summer during the mid-Holocene and restricted to north of Greenland 138 where the oldest and thickest ice is found today 140 , 141 .

Thus, when pan-Arctic ice-free conditions occur again in the next few decades, it will probably be a first for at least 80,000 years 132 , 133 , if not for over 115,000 years 135 . The occurrence of pan-Arctic winter ice-free conditions, predicted to occur in the twenty-third century under extreme warming 115 , would be a first for 47 million years, since the Arctic became sea ice covered in the Eocene 16 .

Drivers of Arctic sea ice loss

Arctic sea ice changes are linked to a multitude of interconnected processes and feedbacks (Fig. 2 ). Atmospheric and oceanic heat transport into the Arctic are two processes that vary because of internal climate variability and externally forced changes 39 , 40 . Within the Arctic, various feedbacks are also at play 41 . In the case of forced anthropogenic changes, the majority of these local feedbacks are positive feedbacks , amplifying Arctic sea ice loss and warming 42 , but their magnitude is uncertain and varies across models 43 , 44 . Dominant examples include the albedo feedback and lapse rate feedback. Negative feedbacks , such as the influence of ice thickness on ice growth rates 45 , can somewhat mitigate ice loss, but not enough to counteract declining trends. The strength of these feedbacks can be climate state dependent 46 , 47 , which means their relative strength will vary as sea ice changes.

The highly coupled processes and feedbacks that affect Arctic sea ice in response to anthropogenic warming.

The observed September sea ice loss is attributable to forced change from anthropogenic emissions 48 , 49 , reinforced by internal variability 50 . Historical model simulations that apply subsets of external forcings (only natural forcings, only anthropogenic aerosol forcings, only greenhouse gas forcings) have enabled the attribution of forced changes in the climate, demonstrating that greenhouse gas emissions drove considerable ice loss, modestly offset by the cooling effects of anthropogenic aerosol emissions 51 . Thus, the magnitude of observed sea ice loss would not have been possible without anthropogenic greenhouse gas emissions 48 (Supplementary Fig. 1 ). Although CO 2 emissions were the most impactful drivers, the radiative effects of chlorofluorocarbons account for about 48% of forced September sea ice loss from 1979–2005 (ref. 52 ). Hence, the Montreal Protocol delayed the occurrence of the first ice-free Arctic by about 10 years (ref. 53 ). Although observed sea ice loss has a roughly linear relationship with global mean surface temperature 54 , 55 and with the cumulative carbon dioxide emissions 1 , these relationships might not hold for the future given changes in the mix of external forcings that contribute to forced changes in regional Arctic warming and Arctic sea ice loss.

Internal variability has enhanced this observed sea ice loss 50 . Specifically, internal variability in atmospheric circulation is estimated to have reinforced the observed September ice loss by up to 50% (refs. 50 , 56 , 57 ). Atmospheric variability, thereby, overall accounts for about 75% of Arctic sea ice internal variability 58 . Ocean heat fluxes into the Arctic, however, are also important and might have helped stabilize September SIA between 2007 and 2023 (ref. 59 ).

Internal variability combined with forced sea ice loss and local positive feedback can lead to large multi-year changes in the Arctic sea ice, referred to as rapid ice loss events (RILEs) 60 . As Arctic sea ice becomes thinner, large areas of the ice pack are susceptible to melt out, resulting in increased summer ice area variability 61 , 62 and a higher likelihood of RILEs. These RILEs are driven by ocean heat transport variations 60 , 63 , atmospheric circulation anomalies 64 or a combination of the two 65 . The surface albedo feedback and fall cloud feedbacks reinforce these events 66 . Notably, periods of limited ice loss or even increasing sea ice are also possible when internal variability counteracts anthropogenically forced change 50 . The evolution of these high-ice-loss and low-ice-loss events affects the trajectory by which Arctic summer ice-free conditions will be reached. The potential occurrence of high ice-loss events allows for the possibility of reaching ice-free conditions within a few years when starting from the average sea ice cover in the early 2020s.

Methods for predicting an ice-free Arctic

Predictions of an ice-free Arctic use different definitions or methods, each with their own inherent uncertainties. These approaches are now discussed.

Contrasting definitions

The definition of an ‘ice-free Arctic’ has varied over time. Early on, it referred to the nearly complete disappearance of all sea ice, or zero SIE (refs. 9 , 60 , 67 ). However, as thick sea ice remains north of Greenland and the Canadian Arctic Archipelago more than a decade after the rest of the Arctic Ocean becomes ice free in September 60 , 68 , a SIE threshold of 1 million km 2 became commonplace 48 .

This 1 million km 2 threshold, however, can also introduce differences depending on the sea ice metrics it is applied to. For instance, an ice-free Arctic occurs earlier when the threshold is used with SIA rather than SIE (ref. 69 ) (Fig. 3a ). Specifically, for the select CMIP6 models 10 , ice-free conditions occur 0–47 years earlier (mean, mode and standard deviation of 8, 3 and 10 years, respectively) when using SIA instead of SIE. Moreover, differences occur when SIA calculations use a minimum threshold of 15% sea ice concentration 70 , 71 , producing even earlier ice-free dates compared with using the standard SIA.

a , Year of the earliest ice-free September for ‘selected CMIP6 models’ 10 (Supplementary Table 1 ) based on different emission scenarios and definitions of ice free: ‘Earliest ice-free conditions’ use unsmoothed monthly sea ice area or sea ice extent (monthly SIA and SIE, respectively); ‘Consistently ice-free conditions’ use 5-year or 20-year smoothed sea ice area, or the first year after which the Arctic is ice free for 5 years for unsmoothed sea ice area (5-year mean, 20-year mean and 5 years in a row, respectively). Numbers on the right y -axis indicate the number of models that do not go ice free by 2100 for a given model, definition or scenario. b , The fraction of CMIP6 models that have reached ice-free conditions at least once in the monthly mean September sea ice area by a given year under a given forcing scenario — the cumulative probability of first ice-free conditions — and their likelihood according to the Intergovernmental Panel on Climate Change (IPCC) definitions. c , The same as in part b , but for the selected CMIP6 models in part a . d , The same as in part c , but the fraction of selected CMIP6 models that are ice free for a given temperature anomaly (using a 5-year smoothed mean to reflect the level of forced warming rather than individual year temperatures), with the anomaly calculated relative to each of the models 1850–1899 global temperature. e , The fraction of selected CMIP6 models that are ice free in a given year, smoothed by a 20-year running mean. Although definition differences and model selection influence the specifics of ice-free predictions, they all indicate that ice-free conditions tend to occur at least once by 2050 under all assessed scenarios, and become a frequent occurrence thereafter under all scenarios except SSP1-1.9.

The temporal aspect of the underlying sea ice data also impacts the definition of ice free. Collectively, two clear definition categories emerge: predictions of the earliest ice-free conditions and predictions of consistently ice-free conditions (Fig. 3a ), emphasizing internal variability and forced responses, respectively. Earliest ice-free conditions are obtained using unsmoothed monthly sea ice time series. This category focuses on the earliest possible occurrence of ice-free conditions, which could be a single event caused by internal variability once the mean sea ice state is low enough. By contrast, consistently ice-free conditions use smoothed data and, thus, focus on the likely occurrence of ice-free conditions based on the forced response. This category is heterogeneous, with examples using 5-year running means 54 , 67 , 72 , 73 , using ensemble means 74 , using five consecutive ice-free years 12 , 14 , 75 , 76 , or likely ice-free conditions based on cumulative probabilities 77 , 78 . With all these methods, the predicted occurrence of first ice-free conditions is delayed compared with the earliest ice-free conditions (Fig. 3a ). This diversity of definitions causes challenges in comparing existing ice-free predictions (Table 1 ), as definition differences affect the timing of ice-free conditions, ranging from a few years to well over a decade (Fig. 3a ).

Cumulative probabilities provide a useful way to provide insight into both first ice-free and consistently ice-free conditions in a comprehensive manner. Indeed, when predictions of an ice-free Arctic are given in terms of cumulative probabilities, both the occurrence of the first possible ice-free Arctic (any percentage above zero) and consistently ice-free conditions can be inferred 76 , 77 , 79 , 80 . For the latter, different thresholds can be used to define consistently ice free, for example, >66% corresponding to the start of the ‘likely’ cumulative probability (Fig. 3 , part a versus part c ).

For regional ice-free conditions, it is not the 1 million km 2 threshold that is used to determine ice-free conditions, but instead a regional average sea ice concentration threshold. However, again there are differences in the threshold chosen. Specifically, a region has been considered ice free when the area-averaged sea ice concentration in the region falls below 15% (ref. 81 ) or reaches 6% (ref. 75 ).

Different prediction methods

In addition to definition choices, predictions of an ice-free Arctic can also be made using different methodological approaches, namely, using climate models or statistical models. Most Arctic ice-free predictions are made using projections from climate models 9 , 10 , 48 , 67 , 76 , 80 , 82 , 83 . Climate models explicitly simulate the evolution of sea ice, including dynamical and thermodynamical processes, albeit in an incomplete way owing to limited scientific understanding and/or computational constraints. Their model output can provide predictions of both early and consistently ice-free conditions depending on how the model output is analysed.

Statistical methods have also been used to provide predictions of an ice-free Arctic. Most of these predictions are based on observed linear relationships between global or Arctic temperature and sea ice cover 54 , 77 , 82 , 84 , 85 , or CO 2 and sea ice cover 1 , 5 . Another approach is to use non-linear statistical relationships to make ice-free predictions 86 . Although useful, these statistical models possess several limitations. For example, the models typically assume that observed relationships will continue into the future, an assumption that might not be correct. Furthermore, they typically rely on linear relationships that represent the response of sea ice to forcing and, thereby, usually only provide predictions of consistently ice-free conditions and not early ice-free conditions. Inclusion of a statistical representation of internal variability can overcome this latter limitation 77 , 84 , 85 . In these cases, internal variability is usually based on standard deviations from observations or models, which means representation of rare sea ice loss events is dependent on how internal variability is estimated; using ±3 σ (ref. 77 ) accounts for 99.7% of internal variability and, hence, excludes only truly rare events (0.3%), whereas using ±1 σ (ref. 85 ) or ±2 σ (ref. 84 ) excludes 32% or 5% internal variability, respectively, delaying the predicted occurrence of the earliest ice-free conditions.

Inherent uncertainties of predictions

Predictions based on climate models and statistical models each have uncertainties that are important to recognize. For climate model predictions, internal variability uncertainty, scenario uncertainty and model uncertainty are key considerations 87 , whereas for statistical models, internal variability uncertainty (or neglecting internal variability uncertainty), scenario uncertainty, observational uncertainty and observed relationship uncertainty are important 85 .

Internal variability prediction uncertainty is caused by the chaotic nature of the climate system 88 . The magnitude of internal variability uncertainty is around 20 years for predictions of a first ice-free Arctic 11 , 12 (but can be even larger for some models 89 ) and slightly reduced by 8 years on average (Supplementary Fig. 2 ) for consistently ice-free conditions, as some internal variability is averaged out. This internal variability uncertainty cannot be eliminated, even with improvements in models and/or methodology, but could potentially be reduced by better understanding the underlying drivers of internal variability and refining predictions based on the potential predictability of those drivers 90 . Initial-value predictability (the predictability that arises from knowledge of an initial state) of sea ice might also allow for more precise predictions as the time of an ice-free Arctic comes closer, but this predictability is limited to seasonal–interannual timescales 91 .

Scenario uncertainty is related to the evolution of future net emissions of greenhouse gases from all sectors, including land use. Given that these scenarios depend on future societal and policy decisions, it is an uncertainty that is not reducible. However, predictions based on degrees of anthropogenic warming 10 , 54 , 55 , 80 , 84 (Fig. 3d ) or cumulative CO 2 emissions 1 , 10 , rather than time, remove dependency on the specific emission scenario used.

Model uncertainty arises from structural differences in climate models — the choices made when building individual climate model components. These model (or structural) uncertainties are the largest source of uncertainty when predicting an ice-free Arctic 10 , 14 , 92 . Indeed, the ice-free prediction range due to model uncertainty in non-refined projections is over 100 years 10 , 14 (Fig. 3b ). These model uncertainties are those that have the largest potential for reductions through model improvements. Yet, large multi-model spread has persisted for nearly two decades 10 despite improvements in sea ice model physics, highlighting that such improvements do not always yield immediate improvements in predictions.

Observational uncertainties in large-scale sea ice products refer to those associated with remote-sensing techniques. Depending on the methodology used, these uncertainties are linked to atmospheric interference, algorithmic uncertainties and the spatial resolution of sensors 93 . Comparing different products allows the magnitude of observational uncertainty to be estimated 10 , 85 , 93 , which for September SIA is about 0.9 million km 2 over 1980–1999 (Fig. 1d ).

Finally, uncertainties in observed relationships occur because of short time series 94 and/or uncertainty in whether historical relationships will continue in the future. For instance, extrapolating a short 12-year (1996–2007) observed linear relationship into the future led to prediction of the earliest possible ice-free Arctic in 2016 ± 3 years 95 . This prediction was not realized because the observed rate of sea ice decline is not constant in time, illustrating why linear extrapolation, especially of short time series, is not a reliable prediction method.

Refining model spread

Given the large uncertainties from structural model differences, there have been substantial efforts to reduce multi-model spread in ice-free projections. These approaches include using model selection 10 , 14 , 48 , 71 , 96 , 97 , 98 , model weighting 13 , 74 , 92 , emergent constraints 70 , 73 , and model recalibration or constrained estimation 55 , 78 , 99 , although no consensus on the best approach exists yet 14 , 55 .

Model selection describes the use of a subset of the best models, whereas model weighting includes all models but weights the best models more heavily. Various metrics have been used for model selection or weighting, largely the mean, seasonal cycle and trends of SIA or SIE, the rates of warming or cumulative CO 2 emissions 10 , 14 , 48 , 92 , and sea ice-based emergent constraints 70 , 73 . However, other metrics also show promise. For instance, model selection based on the relationship between summer SIA and April sea ice thickness narrowed CMIP6 projection uncertainty more than any previously used sea ice metrics 71 . Similarly, northward ocean heat flux as a selection parameter moved predictions 10 years earlier compared with sea ice-based parameters alone 98 . The importance of other oceanic variables in model weighting and selection also needs to be assessed, particularly Arctic Ocean stratification 100 , 101 and the properties of underlying warm Atlantic water 100 , 102 which have known biases in CMIP6 models.

Constrained model projections, also referred to as model recalibration, refer to the adjustment of climate model projections using observations. Thus, rather than selecting some models and using those as they are, model recalibrations modify the model-produced projections. Different recalibration methods influence the projected timing of ice-free conditions, as demonstrated by earlier ice-free dates when scaling the simulated SIA responses to greenhouse gas forcing 78 , whereas a recalibration of the SIE sensitivity to atmospheric circulation leads to later ice-free dates than unconstrained projections 99 (Table 1 ).

Owing to differences in the underlying data and the definition of used ice-free condition, it is not currently possible to directly compare the effect of different model selection or refinement methods on ice-free projections. Thus, there is a need for dedicated intercomparisons to assess such effects. These efforts would allow the creation of a common set of metrics to use to select and/or refine sea ice projections, as well as establish a common ice-free definition to use going forward. As part of that process, it is crucial to not confuse precision with accuracy, as more precise projections are not by default better and can indeed be worse if, for example, the influence of internal variability is neglected.

Predictions of an ice-free Arctic

Considering definition differences and corresponding uncertainties, pan-Arctic ice-free predictions for September, ice-free conditions for months outside of September, and regional ice-free conditions are now discussed.

Pan-Arctic predictions for September

Most predictions for an ice-free Arctic focus on September, the month of lowest seasonal SIA and, thus, the first to reach ice-free conditions. These predictions indicate that the earliest ice-free conditions could potentially occur in the 2020s to 2030s and are likely going to occur by 2050 (ref. 10 ) (Table 1 ; Fig. 3c ). However, there is large variability in these predictions, ranging from the 2010s to >2100 (refs. 10 , 14 ). Refined projections — through model selection, weighting and constraining — reduce this uncertainty to 2015–2050 (refs. 10 , 74 , 78 , 96 ). In terms of temperature, the earliest ice-free conditions could occur for warming >1.3 °C, are likely to occur for warming of 1.8 °C (Table 1 ; Fig. 3d ), and exhibit a range of 0.9–3.2 °C (ref. 10 ) that can be refined to 1.3–2.9 °C (refs. 10 , 74 , 78 , 96 ).

For these earliest September ice-free conditions, there is no influence of emission scenario 10 , 69 , 74 . Indeed, all scenarios exhibit the possibility of earliest ice-free conditions from the 2010s or from a warming of 1.3 °C (Fig. 3c,d ). This consistency arises owing to the short lead time and resulting small difference between emission trajectories 103 , 104 . Accordingly, the occurrence of the earliest ice-free conditions will be determined by internal climate variability 12 once the mean sea ice state is low enough. For example, conditions similar to those that caused the record 2007 (ref. 105 ) and 2012 (ref. 106 ) September minimums could lead to the drop of sea ice below the 1 million km 2 threshold once mean SIA is ≤2 million km 2 . Early ice-free conditions could also be the result of a multi-year RILE (refs. 60 , 63 ) that could lead to ice-free conditions from an even higher mean sea ice state. However, internal variability (and resulting large single-year or multi-year events) can either enhance or oppose the forced response 50 and, hence, could delay the occurrence of ice-free conditions past the predicted earliest ice-free conditions 12 .

Despite no impact of emission scenarios on the timing of an earliest ice-free Arctic, there remains a small chance that ice-free conditions can be avoided. In particular, if warming is limited to <1.5 °C or only exceeds 1.5 °C for a short time, there is a <10% chance that the Arctic does not become ice free 76 , 79 , 80 , 85 , 107 , 108 , as in Shared Socioeconomic Pathway (SSP) 1-1.9 (Fig. 3d ).

Warming levels also affect the frequency of ice-free conditions re-occurring after a first ice-free September 76 , 80 (Fig. 3e ). For instance, if ice-free conditions occur for warming of ≤1.5 °C, they would likely not re-occur for several decades 76 , 107 . Yet, for warming >2 °C and >3 °C, September ice-free conditions would likely re-occur every 2–3 years 76 , 80 , 107 or almost every year 76 , 80 , respectively; in the latter case, these changes are comparable to what is seen for the select CMIP6 models under SSP2-4.5 and SSP5-8.5 (Fig. 3e ). Notably, if temperatures decrease again, probabilities of ice-free conditions in a given year will also decrease, as evident for SSP1-1.9 (Fig. 3e ). Hence, no irreversible sea ice tipping point exists for summer Arctic sea ice 109 , 110 , 111 .

Consistently ice-free conditions are expected by mid-century, potentially under all warming scenarios 78 . Predictions for consistently ice-free conditions range from 2023 to 2085, with refined projections of 2035–2067 (Table 1) ; the reduced uncertainty compared with the earliest ice-free conditions is linked to the averaging out of some internal variability (Supplementary Fig. 2 ). In terms of warming, these conditions begin to occur at a global temperature increase of ≥1.8 °C (Table 1) . Although consistently ice-free conditions potentially occur under all scenarios 78 (Fig. 3c ), the strength of the forcing has some effect on the timing of consistently ice-free conditions 78 and, hence, also the interval between the earliest and consistently ice-free conditions (Fig. 3a ). For example, although the difference between predictions of the earliest ice-free conditions and consistently ice-free conditions is around 10 years for SSP5-8.5, it is at least 15 years for SSP1-2.6 (Fig. 3a ).

All previous predictions of ice-free conditions used monthly means as their underlying base data. Yet, the first time SIA dips below the 1 million km 2 threshold will be detected in daily satellite observations. SIA-based calculations from the CESM2-LE (ref. 112 ) suggest that the first occurrence of daily ice-free conditions happens, on average, 4 years prior to the ice-free September monthly mean (Supplementary Fig. 3 ), with a range of 0–18 years. Of the CESM2-LE members, 56% exhibit daily ice-free conditions earlier than monthly ice-free conditions (Supplementary Fig. 3b ), whereas 44% of the CESM2-LE members experience daily and monthly ice-free conditions for the first time during the same year. Differences of 10 years or more, thereby, occur in 20% of the CESM2-LE members, with the largest differences occurring for ensemble members that have relatively late monthly mean ice-free conditions (Supplementary Fig. 3b ). The earliest ice-free conditions in daily observations could, thus, occur even earlier than predicted based on monthly analysis of CMIP6 models and, hence, potentially in the 2020s (Fig. 3c ).

Seasonality of reaching ice-free conditions

Although ice-free conditions are first expected in September, they could extend into other months 5 , 76 , 78 , 84 , 85 . The duration of this ice-free period has direct bearing on the resulting impacts: ice-free conditions that begin earlier in the summer strengthen the ice albedo feedback 47 , increase early open-water areas and, thereby, ocean heat uptake, subsequently delaying fall freeze-up 113 and extending the ice-free season into late fall 5 , 76 , 84 .

Generally, ice-free duration beyond September exhibits pronounced sensitivity to the warming level and, thus, emission scenario. For example, there is a possibility of occasional ice-free conditions in August and October with <2 °C warming (or SSP1-1.9) (refs. 76 , 85 ), extending into July with ≥2.5 °C warming 85 (or SSP1-2.6) and into November with ≥3.5 °C warming 76 (or SSP2-4.5) (Fig. 4 ). In some select CMIP6 models under SSP5-8.5, first ice-free conditions also occur in December, January, May and June during the second half of the twenty-first century (Fig. 4d ) when warming exceeds 3.5 °C (ref. 114 ).

a , The probability of ice-free conditions in a given year and month without any smoothing for selected CMIP6 models 10 forced with SSP1-1.9. The probability is given using the IPCC terms and percentage values. The earliest ice-free conditions can be inferred when any probability of ice-free conditions exists, whereas consistently ice-free conditions start to exist when the probability in a given year reaches the likely category. b , The same as in part a , but for SSP1-2.6. c , The same as in part a , but for SSP2-4.5. d , The same as in part a , but for SSP5-8.5. There are large differences in how likely an ice-free Arctic is to occur in the months of a given year depending on the forcing scenario, with the possibility of ice-free conditions limited to 3 months under SSP1-2.6 and SSP1-1.9, 5 months under SSP2-4.5 and 9 months under SSP5-8.5.

Intuitively, consistently ice-free conditions exhibit the same temperature sensitivity as first ice-free conditions but with delayed emergence. Consistently ice-free conditions likely emerge in August with ≥2.5 °C warming, October with ≥3.5 °C warming and November with ≥4 °C warming 76 , 85 . These sensitivities translate to differences across scenarios. For the selected CMIP6 models forced with SSP2-4.5, the ice-free season is expected to span 3 months per year by 2100 (as determined by continuous likely (>66%) ice-free conditions): ice-free conditions emerge in August by approximately 2055 and in October by approximately 2080 (Fig. 4c ). In contrast, the likely ice-free season is expected to span 6 months for SSP5-8.5: beyond September, continuous ice-free conditions emerge in August by approximately 2050, in October by approximately 2055, in November by approximately 2070, in July by approximately 2075 and in December by approximately 2090 (ice-free conditions in July to October become very likely or virtually certain by 2100) (Fig. 4d ). Consistently ice-free conditions are not expected beyond September for SSP1-1.9 (Fig. 4a ) or SSP1-2.6 (Fig. 4b ). In terms of CO 2 emissions, consistently ice-free conditions are predicted to begin to occur in July to October for an additional 1,400 Gt CO 2 relative to 2016 levels, and in November for around 3,000 Gt CO 2 (refs. 5 , 84 ).

With further warming, the Arctic could become ice free year-round. However, consistently ice-free conditions year-round are not anticipated until atmospheric CO 2 levels reach approximately 1,900 ppm (ref. 115 ), which are not expected until the twenty-third century under the strongest emission scenarios.

Regional variations of ice-free Arctic conditions

In addition to the seasonal sensitivity of ice-free projections, regional variability in ice-free timings are also expected. However, there are limited explicit predictions of these regional ice-free conditions, and those that do exist focus on consistently ice-free metrics 75 , 81 . In addition, regional assessments possess uncertainties greater than the pan-Arctic given larger internal variability (as averaging over smaller regions) and a reduced chance for compensating biases 75 , 81 . Accordingly, any projected dates of regional ice-free conditions are quite dependent on the underlying models and on whether model selection was performed, as well as on the exact definition of what constitutes ice-free conditions 75 , 81 . Thus, differences are apparent between CMIP5 and CMIP6, with earlier regional ice-free dates in CMIP6, potentially at least partially because of requiring only >85% open water 81 versus 94% open water 75 to consider a region ice free.

Despite uncertainty in the timing, CMIP5 and CMIP6 models generally exhibit the same progression of consistently ice-free conditions around the Arctic 75 , 81 . Across all scenarios, September ice-free conditions start in the European Arctic shelf seas, with the Barents Sea and Kara Sea followed by the Laptev Sea, proceed to the Chukchi Sea, East Siberian Sea and Beaufort Sea (the Pacific Arctic), and end in the central Arctic 75 , 81 . Specifically, September regional ice-free conditions in the Barents Sea and Kara Sea are simulated to exist from August to October prior to 2015, with the Laptev Sea, East Siberian Sea and Chukchi Sea following in the 2020s and 2030s, followed by the Beaufort Sea in the 2030s and 2040s, under both SSP1-2.6 and SSP5-8.5 (ref. 81 ). The central Arctic could become ice free in September between 2040 and 2060 under SSP5-8.5 (ref. 81 ) and in the 2060s to 2100 in SSP2-4.5, while avoiding consistently regional ice-free conditions in the central Arctic under SSP1-2.6 and SSP1-1.9 (Fig. 5 ).

a , Year sea ice is consistently ice free for July to November for SSP1-1.9, calculated as the first time sea ice concentration (SIC) in each grid is below 15% in a given month in the ensemble mean 81 of the selected CMIP6 models 10 . Bright white areas indicate regions that retain ice cover with more than 15% SIC in 2100, whereas dark blue areas indicate regions that became ice free before 2020 or that never had ice cover. b , The same as in part a , but for SSP1-2.6. c , The same as in part a , but for SSP2-4.5. d , The same as in part a , but for SSP5-8.5. Forcing scenarios have a big impact on the regional sea ice loss, with no ice-covered regions expected to remain between July and November by 2090 under SSP5-8.5, but some ice-covered regions remaining for every month under SSP1-1.9.

With only a small scenario impact on the timing of consistently ice-free conditions in the shelf seas, the main impacts of scenario differences are the duration of ice-free conditions in the shelf seas and whether and for how long the Central Arctic becomes ice free (Fig. 5) . Specifically, the ice-free season is limited to 3 months a year by 2100 under SSP1-2.6 in the Laptev, East Siberian, Chukchi and Beaufort Sea, but lasts 7–8 months under SSP5-8.5 (ref. 81 ). In the Kara Sea, the difference between these two scenarios is 5 versus 9 months, whereas in the Barents Sea, it is 9 months versus ice free year-round 81 . Regional ice-free conditions in the Central Arctic only occur for SSP5-8.5 and SSP2-4.5, with the consistently ice-free season limited to August and September in SSP2-4.5, but extending for over 5 months under SSP5-8.5 (Fig. 5 ).

Summary and future perspectives

Arctic sea ice has declined substantially since the beginning of the satellite observations in 1978, and is projected to continue to do so into the future. Indeed, earliest ice-free conditions, defined as a single occurrence of ice-free conditions in the monthly mean data, might occur in the 2020s or 2030s for September, and are likely to occur by mid-century 10 independent of emission scenario 10 , 76 , 78 , 80 . Consistently ice-free conditions, which refers to the transition to a frequently ice-free Arctic, are expected to occur between 2035–2067 under the high-emission scenarios, with a small delay possible for lower-emission scenarios. At the regional scale, these losses will begin in the shelf seas of the European Arctic, proceeding into the Pacific Arctic, and, under SSP2-4.5 and SSP5-8.5, end in the central Arctic. Ice loss is also expected beyond the months of September, particularly the shoulder months of August and October, but with marked temperature sensitivity. Thus, greenhouse gas mitigation strongly affects ice-free conditions, determining how often, for how long and where the Arctic will lose its sea ice cover. In particular, under the low-warming scenarios (SSP1-2.6), with warming remaining well below 2 °C, ice-free conditions could remain an exception rather than the new normal 76 . Furthermore, sea ice recovers quickly when temperatures drop 109 , 110 , 111 , so if the world reaches sufficient negative emissions to lead to a global warming of less than 1.5 °C, ice-free conditions could disappear again in the future.

As the earliest possible date of an ice-free Arctic approaches, clear communication is key. Predictions must differentiate between those of consistently ice-free conditions (or likely (>66%) ice-free conditions) because of the forced response, and predictions of the earliest possible ice-free conditions that could occur over a decade earlier because of internal variability 12 . Cumulative probabilities or the probability of ice-free conditions in a given year both provide opportunities to present both types of predictions (Fig. 3b–d ), with the additional benefit of highlighting that ice-free predictions are always probabilistic. In addition, any communications must make it clear what thresholds and approaches are used, as demonstrated by SIA-based assessments leading to earlier ice-free conditions in comparison to SIE (ref. 69 ) (Fig. 3a ). It also needs to be clearly communicated that currently published ice-free predictions focus on monthly averaged values, yet ice-free conditions will probably occur earlier when daily values are considered (in one model, 4 years earlier on average). Further projections using daily data are needed to assess whether such long daily–monthly offsets apply to other models.

Another important issue to consider is when the Arctic sea ice community will consider that an ice-free Arctic has been reached. Deciding on these criteria ahead of reaching ice free conditions is prudent given the various definitions as well as observational uncertainty in satellite-derived sea ice products (Fig. 1d ). As such, it is possible that the 1 million km 2 ice-free threshold will be crossed in some SIA or SIE products under some definitions but not in others. Clarity on how this issue will be handled will facilitate communication around the occurrence of the first ice-free Arctic when it occurs.

Most predictions have focused on pan-Arctic ice-free conditions, yet this transition will occur regionally. Regional ice-free predictions, however, have been rare 75 , 81 . Efforts are needed to develop methods that better constrain regional sea ice projections and reduce their uncertainties. For example, how well existing model selection, weighting and recalibration perform for sea ice projections in different regions of the Arctic should be assessed. New methods might need to be developed to better constrain regional sea ice projections from climate models, always accounting for the irreducible internal variability uncertainty.

Given that climate model and statistical ice-free predictions are always probabilistic, it is also important to assess whether seasonal sea ice predictions have the skill needed to predict the first ice-free conditions at shorter lead times. Given that seasonal sea ice predictions often perform least well when the decline in a given year is far from that expected from the long-term trend, predictions of earliest ice-free conditions are potentially going to be challenging for current seasonal prediction systems 116 . Seasonal prediction experiments initialized with climate model conditions several months prior to a simulated early ice-free state could provide useful insights. Of course, these prediction assessments have their limitations, particularly associated with resolution and absent processes in large-scale climate models, but they might, nonetheless, provide useful insights into the skills of seasonal ice-free predictions.

To better constrain predictions of an ice-free Arctic, and of Arctic sea ice loss in general, dedicated intercomparisons of different model selection, weighting and recalibration methods are required. Currently, too many parameters (models, ensemble members, emission scenarios and ice-free definitions) differ to be able to identify the impact of an individual approach. Furthermore, defining best practice for skilfully reducing sea ice projection uncertainty would be very valuable, including deciding on the best set of metrics to base such methods on to improve projection accuracy and reduce projection uncertainty. Considering sea ice thickness 71 and ocean heat fluxes 98 as selection criteria should be part of that discussion. Additionally, biases in models should be used as an opportunity to better understand the real world 117 . For example, by analysing what drives features not seen in models but present in observations, progress can be made on improving models.

Finally, there is an urgent need to gain a better understanding of the impacts of an ice-free Arctic and the processes that could lead to an early ice-free Arctic, especially drivers of internal variability that contribute to ensemble spread. Such research could provide answers as to what is or what is not predictable, regionally and in the pan-Arctic mean. In terms of impacts, priorities should be given to understand ice-free effects on marine ecosystems, the global energy budget, wave height and coastal erosion. In particular, understanding the nuances of the impacts of occasional daily ice-free conditions versus frequent monthly ice-free conditions versus ice-free conditions that occur for several months a year is needed to assess the true impact of what the transition of the Arctic sea ice cover into its new seasonal sea ice regime means in a warming world.

Data availability

The CMIP6 sea ice area data is the same as analysed in ref. 10 . The underlying SIC data, also used for the spatial plot (Fig. 5) , is available on the Earth System Grid Federation (ESGF, https://esgf-node.llnl.gov/search/cmip6/ ). The data for the CESM2-LE (ref. 112 ) is available at https://www.cesm.ucar.edu/projects/cvdp-le/data-repository . The data for the CLIVAR Large Ensemble Archive 38 is available at https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.CLIVAR_LE.html .

Notz, D. & Stroeve, J. Observed Arctic sea-ice loss directly follows anthropogenic CO 2 emission. Science 354 , 747–750 (2016).

Article ADS CAS PubMed Google Scholar

Cavalieri, D. J., Parkinson, C. L., Gloersen, P., Comiso, J. C. & Zwally, H. J. Deriving long-term time series of sea ice cover from satellite passive-microwave multisensor data sets. J. Geophys. Res. 104 , 15803–15814 (1999).

Article ADS Google Scholar

Kwok, R. & Rothrock, D. A. Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008. Geophys. Res. Lett. 36 , L15501 (2009).

Kacimi, S. & Kwok, R. Arctic snow depth, ice thickness, and volume from ICESat-2 and CryoSat-2: 2018–2021. Geophys. Res. Lett. 49 , e2021GL097448 (2022).

Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13 , 103001 (2018).

Meier, W., Fetterer, F., Windnagel, A. K. & Stewart, J. S. Climate data record of passive microwave sea ice concentration, version 4. National Snow and Ice Data Center https://nsidc.org/data/G02202/versions/4 (2021).

England, M., Jahn, A. & Polvani, L. Nonuniform contribution of internal variability to recent Arctic sea ice loss. J. Clim. 32 , 4039–4053 (2019).

Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ. 3 , 168 (2022).

Parkinson, C. L. & Kellogg, W. Arctic sea ice decay simulated for a CO 2 -induced temperature rise. Clim. Change 2 , 149–162 (1979).

Article ADS CAS Google Scholar

Notz, D. & SIMIP Community. Arctic sea ice in CMIP6. Geophys. Res. Lett. 47 , e2019GL086749 (2020).

Article Google Scholar

Notz, D. How well must climate models agree with observations? Phil. Trans. R. Soc. A 373 , 20140164 (2015).

Article ADS PubMed PubMed Central Google Scholar

Jahn, A., Kay, J., Holland, M. & Hall, D. How predictable is the timing of a summer ice-free Arctic? Geophys. Res. Lett. 43 , 9113–9120 (2016).

Knutti, R. et al. A climate model projection weighting scheme accounting for performance and interdependence. Geophys. Res. Lett. 44 , 1909–1918 (2017).

Massonnet, F. et al. Constraining projections of summer Arctic sea ice. Cryosphere 6 , 1383–1394 (2012).

Newton, R. et al. White Arctic vs. blue Arctic: a case study of diverging stakeholder responses to environmental change. Earths Future 4 , 396–405 (2016).

Polyak, L. et al. History of sea ice in the Arctic. Quat. Sci. Rev. 29 , 1757–1778 (2010).

Pistone, K., Eisenman, I. & Ramanathan, V. Observational determination of albedo decrease caused by vanishing Arctic sea ice. Proc. Natl Acad. Sci. USA 111 , 3322–3326 (2014).

Article ADS CAS PubMed PubMed Central Google Scholar

Pistone, K., Eisenman, I. & Ramanathan, V. Radiative heating of an ice-free Arctic Ocean. Geophys. Res. Lett. 46 , 7474–7480 (2019).

Holland, M. & Bitz, C. Polar amplification of climate change in coupled models. Clim. Dyn. 21 , 221–232 (2003).

Screen, J. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464 , 1334–1337 (2010).

Dai, A., Luo, D., Song, M. & Liu, J. Arctic amplification is caused by sea-ice loss under increasing CO 2 . Nat. Commun. 10 , 121 (2019).

Jenkins, M. & Dai, A. The impact of sea-ice loss on Arctic climate feedbacks and their role for Arctic amplification. Geophys. Res. Lett. 48 , e2021GL094599 (2021).

Casas-Prat, M. & Wang, X. Sea ice retreat contributes to projected increases in extreme Arctic Ocean surface waves. Geophys. Res. Lett. 47 , e2020GL088100 (2020).

Waseda, T. et al. Correlated increase of high ocean waves and winds in the ice-free waters of the Arctic Ocean. Sci. Rep. 8 , 4489 (2018).

Li, J., Ma, Y., Liu, Q., Zhang, W. & Guan, C. Growth of wave height with retreating ice cover in the Arctic. Cold Reg. Sci. Technol. 164 , 102790 (2019).

Overeem, I. et al. Sea ice loss enhances wave action at the Arctic coast. Geophys. Res. Lett . https://doi.org/10.1029/2011GL048681 (2011).

Nielsen, D., Pieper, P. & Barkhordarian, A. Increase in Arctic coastal erosion and its sensitivity to warming in the twenty-first century. Nat. Clim. Change 12 , 263–270 (2022).

Irrgang, A. M. et al. Drivers, dynamics and impacts of changing Arctic coasts. Nat. Rev. Earth Environ. 3 , 39–54 (2022).

Learmonth, J. A. et al. Potential effects of climate change on marine mammals. Oceanogr. Mar. Biol. 44 , 431 (2006).

Google Scholar

Regehr, E. V., Lunn, N. J., Amstrup, S. C. & Stirling, I. Effects of earlier sea-ice breakup on survival and population size of polar bears in western Hudson Bay. J. Wildl. Manag. 71 , 2673–2683 (2007).

Laidre, K. L. et al. Quantifying the sensitivity of Arctic marine mammals to climate-induced habitat change. Ecol. Appl. 18 , S97–S125 (2008).

Article PubMed Google Scholar

Renaut, S., Devred, E. & Babin, M. Northward expansion and intensification of phytoplankton growth during the early ice-free season in Arctic. Geophys. Res. Lett. 45 , 10,590–10,598 (2018).

Hollowed, A. B., Planque, B. & Loeng, H. Potential movement of fish and shellfish stocks from the sub-Arctic to the Arctic Ocean. Fish. Oceanogr. 22 , 355–370 (2013).

Ingvaldsen, R. B. et al. Physical manifestations and ecological implications of Arctic Atlantification. Nat. Rev. Earth Environ. 2 , 874–889 (2021).

Melia, N., Haines, K. & Hawkins, E. Sea ice decline and 21st century trans-Arctic shipping routes. Geophys. Res. Lett. 43 , 9720–9728 (2016).

Schiermeier, Q. The great Arctic oil race begins. Nature 482 , 13–14 (2012).

Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9 , 1937–1958 (2016).

Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10 , 277–286 (2020).

Mewes, D. & Jacobi, C. Heat transport pathways into the Arctic and their connections to surface air temperatures. Atmos. Chem. Phys. 19 , 3927–3937 (2019).

Hahn, L. C., Armour, K. C., Battisti, D. S., Donohoe, A. & Fajber, R. Seasonal changes in atmospheric heat transport to the Arctic under increased CO 2 . Geophys. Res. Lett. 50 , e2023GL105156 (2023).

Goosse, H. et al. Quantifying climate feedbacks in polar regions. Nat. Commun. 9 , 1919 (2018).

Kay, J. E. et al. Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators. J. Clim. 25 , 5190–5207 (2012).

Pithan, F. & Mauritsen, T. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci. 7 , 181–184 (2014).

Hahn, L. C., Armour, K. C., Zelinka, M. D., Bitz, C. M. & Donohoe, A. Contributions to polar amplification in CMIP5 and CMIP6 models. Front. Earth Sci. https://doi.org/10.3389/feart.2021.710036 (2021).

Bitz, C. M. & Roe, G. H. A mechanism for the high rate of sea ice thinning in the Arctic Ocean. J. Clim. 17 , 3623–3632 (2004).

Massonnet, F. et al. Arctic sea-ice change tied to its mean state through thermodynamic processes. Nat. Clim. Change 8 , 599–603 (2018).

Holland, M. M. & Landrum, L. The emergence and transient nature of Arctic amplification in coupled climate models. Front. Earth Sci. 9 , 719024 (2021).

Wang, M. & Overland, J. E. A sea ice free summer Arctic within 30 years? Geophys. Res. Lett . https://doi.org/10.1029/2009GL037820 (2009).

Notz, D. & Marotzke, J. Observations reveal external driver for Arctic sea-ice retreat. Geophys. Res. Lett . https://doi.org/10.1029/2012GL051094 (2012).

Kay, J. E., Holland, M. M. & Jahn, A. Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world. Geophys. Res. Let . https://doi.org/10.1029/2011GL048008 (2011).

Mueller, B., Gillett, N., Monahan, A. & Zwiers, F. Attribution of Arctic sea ice decline from 1953 to 2012 to influences from natural, greenhouse gas, and anthropogenic aerosol forcing. J. Clim. 31 , 7771–7787 (2018).

Polvani, L. et al. Substantial twentieth-century Arctic warming caused by ozone-depleting substances. Nat. Clim. Change 10 , 130–133 (2020).

England, M. R. & Polvani, L. M. The Montreal Protocol is delaying the occurrence of the first ice-free Arctic summer. Proc. Natl Acad. Sci. USA 120 , e2211432120 (2023).

Article CAS PubMed PubMed Central Google Scholar

Mahlstein, I. & Knutti, R. September Arctic sea ice predicted to disappear near 2 °C global warming above present. Geophys. Res. Lett . https://doi.org/10.1029/2011JD016709 (2012).

Stroeve, J. & Notz, D. Insights on past and future sea-ice evolution from combining observations and models. Glob. Planet. Change 135 , 119–132 (2015).

Ding, Q. et al. Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Clim. Change 7 , 289–295 (2017).

Roach, L. & Blanchard-Wrigglesworth, E. Observed winds crucial for Arctic sea ice loss. Geophys. Res. Lett . 49 , e2022GL097884 (2022).

Olonscheck, D., Mauritsen, T. & Notz, D. Arctic sea-ice variability is primarily driven by atmospheric temperature fluctuations. Nat. Geosci. 12 , 430–434 (2019).

Polyakov, I. V. et al. Fluctuating Atlantic inflows modulate Arctic Atlantification. Science 381 , 972–979 (2023).

Holland, M. M., Bitz, C. M. & Tremblay, B. Future abrupt reductions in the summer Arctic sea ice. Geophys. Res. Lett . https://doi.org/10.1029/2006GL028024 (2006).

Goosse, H., Arzel, O., Bitz, C. M., de Montety, A. & Vancoppenolle, M. Increased variability of the Arctic summer ice extent in a warmer climate. Geophys. Res. Lett . https://doi.org/10.1029/2009GL040546 (2009).

Mioduszewski, J. R., Vavrus, S., Wang, M., Holland, M. & Landrum, L. Past and future interannual variability in Arctic sea ice in coupled climate models. Cryosphere 13 , 113–124 (2019).

Auclair, G. & Tremblay, L. B. The role of ocean heat transport in rapid sea ice declines in the Community Earth System Model Large Ensemble. J. Geophys. Res. Oceans 123 , 8941–8957 (2018).

Döscher, R. & Koenigk, T. Arctic rapid sea ice loss events in regional coupled climate scenario experiments. Ocean Sci. 9 , 217–248 (2013).

Paquin, J.-P., Döscher, R., Sushama, L. & Koenigk, T. Causes and consequences of mid-21st-century rapid ice loss events simulated by the Rossby Centre regional atmosphere-ocean model. Tellus A Dyn. Meteorol. Oceanogr. https://doi.org/10.3402/tellusa.v65i0.19110 (2013).

Vavrus, S., Holland, M. & Bailey, D. Changes in Arctic clouds during intervals of rapid sea ice loss. Clim. Dyn. 36 , 1475–1489 (2011).

Boe, J., Hall, A. & Qu, X. September sea-ice cover in the Arctic Ocean projected to vanish by 2100. Nat. Geosci. 2 , 341–343 (2009).

Pfirman, S., Haxby, W. F., Colony, R. & Rigor, I. Variability in the Arctic sea ice drift. Geophys. Res. Lett . https://doi.org/10.1029/2004GL020063 (2004).

DeRepentigny, P., Jahn, A., Holland, M. M. & Smith, A. Arctic sea ice in two configurations of the CESM2 during the 20th and 21st centuries. J. Geophys. Res. Oceans 125 , e2020JC016133 (2020).

Wang, B., Zhou, X., Ding, Q. & Liu, J. Increasing confidence in projecting the Arctic ice-free year with emergent constraints. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ac0b17 (2021).

Zhou, X., Wang, B. & Huang, F. Evaluating sea ice thickness simulation is critical for projecting a summer ice-free Arctic Ocean. Environ. Res. Lett. 17 , 114033 (2022).

Landrum, L. & Holland, M. Extremes become routine in an emerging new Arctic. Nat. Clim. Change 10 , 1108–1115 (2020).

Thackeray, C. & Hall, A. An emergent constraint on future Arctic sea-ice albedo feedback. Nat. Clim. Change 9 , 972–978 (2019).

Snape, T. J. & Forster, P. M. Decline of Arctic sea ice: evaluation and weighting of CMIP5 projections. J. Geophys. Res. Atmos. 119 , 546–554 (2014).

Laliberté, F., Howell, S. E. L. & Kushner, P. J. Regional variability of a projected sea ice-free Arctic during the summer months. Geophys. Res. Lett. 43 , 256–263 (2016).

Jahn, A. Reduced probability of ice-free summers for 1.5 °C compared to 2 °C warming. Nat. Clim. Change 8 , 409–413 (2018).

Bonan, D. B., Schneider, T., Eisenman, I. & Wills, R. C. J. Constraining the date of a seasonally ice-free Arctic using a simple model. Geophys. Res. Lett. 48 , e2021GL094309 (2021).

Kim, Y., Min, S., Gillett, N., Notz, D. & Malinina, E. Observationally-constrained projections of an ice-free Arctic even under a low emission scenario. Nat. Commun. 14 , 3139 (2023).

Screen, J. A. & Williamson, D. Ice-free Arctic at 1.5 °C? Nat. Clim. Change 7 , 230–231 (2017).

Sigmond, M., Fyfe, J. C. & Swart, N. C. Ice-free Arctic projections under the Paris Agreement. Nat. Clim. Change 8 , 404–408 (2018).

Arthun, M., Onarheim, I. H., Dörr, J. & Eldevik, T. The seasonal and regional transition to an ice-free Arctic. Geophys. Res. Lett. 48 , e2020GL090825 (2021).

Ridley, J. K. & Blockley, E. W. Brief communication: solar radiation management not as effective as CO 2 mitigation for Arctic sea ice loss in hitting the 1.5 and 2 °C COP climate targets. Cryosphere 12 , 3355–3360 (2018).

Stroeve, J. C. et al. Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett . https://doi.org/10.1029/2012GL052676 (2012).

Notz, D. & Stroeve, J. The trajectory towards a seasonally ice-free Arctic Ocean. Curr. Clim. Change Rep. 4 , 407–416 (2018).

Article PubMed PubMed Central Google Scholar

Niederdrenk, A. L. & Notz, D. Arctic sea ice in a 1.5 °C warmer world. Geophys. Res. Lett. 45 , 1963–1971 (2018).

Diebold, F. X. & Rudebusch, G. D. Probability assessments of an ice-free Arctic: comparing statistical and climate model projections. J. Econom. 231 , 520–534 (2022).

Article MathSciNet Google Scholar

Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc. 90 , 1095–1107 (2009).

Lorenz, E. N. Deterministic nonperiodic flow. J. Atmos. Sci. 20 , 130–141 (1963).

Holland, M. & Hunke, E. A review of Arctic sea ice climate predictability in large-scale earth system models. Oceanography 35 , 20–27 (2022).

Screen, J. A. & Deser, C. Pacific Ocean variability influences the time of emergence of a seasonally ice-free Arctic Ocean. Geophys. Res. Lett. 46 , 2222–2231 (2019).

Blanchard-Wrigglesworth, E., Bitz, C. M. & Holland, M. M. Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys. Res. Lett . https://doi.org/10.1029/2011GL048807 (2011).

Senftleben, D., Lauer, A. & Karpechko, A. Constraining uncertainties in CMIP5 projections of September Arctic sea ice extent with observations. J. Clim. 33 , 1487–1503 (2020).

Meier, W. N. & Stewart, J. S. Assessing uncertainties in sea ice extent climate indicators. Environ. Res. Lett. 14 , 035005 (2019).

Swart, N. C., Fyfe, J. C., Hawkins, E., Kay, J. E. & Jahn, A. Influence of internal variability on Arctic sea-ice trends. Nat. Clim. Change 5 , 86â89 (2015).

Maslowski, W., Kinney, J. C., Higgins, M. & Roberts, A. The future of Arctic sea ice. Annu. Rev. Earth Planet. Sci. 40 , 625–654 (2012).

Wang, M. & Overland, J. E. A sea ice free summer Arctic within 30 years: an update from CMIP5 models. Geophys. Res. Lett. https://doi.org/10.1029/2012GL052868 (2012).

Liu, J., Curry, J. A., Wang, H. & Horton, R. M. Impact of declining Arctic sea ice on winter snowfall. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1114910109 (2012).

Docquier, D. & Koenigk, T. Observation-based selection of climate models projects Arctic ice-free summers around 2035. Commun. Earth Environ. 2 , 144 (2021).

Topal, D. & Ding, Q. Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections. Nat. Clim. Change 13 , 710–718 (2023).

Khosravi, N. et al. The Arctic Ocean in CMIP6 models: biases and projected changes in temperature and salinity. Earths Future 10 , e2021EF002282 (2022).

Muilwijk, M. et al. Divergence in climate model projections of future Arctic Atlantification. J. Clim. 36 , 1727–1748 (2023).

Heuzè, C., Zanowski, H., Karam, S. & Muilwijk, M. The deep Arctic Ocean and Fram Strait in CMIP6 models. J. Clim. 36 , 2551–2584 (2023).

O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9 , 3461–3482 (2016).

Bonan, D. B., Lehner, F. & Holland, M. M. Partitioning uncertainty in projections of Arctic sea ice. Environ. Res. Lett. 16 , 044002 (2021).

Lindsay, R. W., Zhang, J., Schweiger, A., Steele, M. & Stern, H. Arctic sea ice retreat in 2007 follows thinning trend. J. Clim. 22 , 165–176 (2009).

Parkinson, C. L. & Comiso, J. C. On the 2012 record low Arctic sea ice cover: combined impact of preconditioning and an August storm. Geophys. Res. Lett. 40 , 1356–1361 (2013).

Sanderson, B. et al. Community climate simulations to assess avoided impacts in 1.5 °C and 2 °C futures. Earth Syst. Dyn. 8 , 827–847 (2017).

Screen, J. Arctic sea ice at 1.5 and 2 °C. Nat. Clim. Change 8 , 362–363 (2018).

Tietsche, S., Notz, D., Jungclaus, J. H. & Marotzke, J. Recovery mechanisms of Arctic summer sea ice. Geophys. Res. Lett. https://doi.org/10.1029/2010GL045698 (2011).

Armour, K. C., Eisenman, I., Blanchard-Wrigglesworth, E., McCusker, K. E. & Bitz, C. M. The reversibility of sea ice loss in a state of the art climate model. Geophys. Res. Lett . https://doi.org/10.1029/2011GL048739 (2011).

Li, C., Notz, D., Tietsche, S. & Marotzke, J. The transient versus the equilibrium response of sea ice to global warming. J. Clim. 26 , 5624–5636 (2013).

Rodgers, K. B. et al. Ubiquity of human-induced changes in climate variability. Earth Syst. Dyn. 12 , 1393–1411 (2021).

Lebrun, M., Vancoppenolle, M., Madec, G. & Massonnet, F. Arctic sea-ice-free season projected to extend into autumn. Cryosphere 13 , 79–96 (2019).

Tebaldi, C. et al. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn. 12 , 253–293 (2021).

Jahn, A. & Holland, M. M. Implications of Arctic sea ice changes for North Atlantic deep convection and the meridional overturning circulation in CCSM4-CMIP5 simulations. Geophys. Res. Let. 40 , 1206–1211 (2013).

Hamilton, L. & Stroeve, J. 400 predictions: the SEARCH Sea Ice Outlook 2008–2015. Polar Geogr. 39 , 274–287 (2016).

Ding, Q. et al. Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nat. Geosci. 12 , 28–33 (2019).

Stroeve, J., Holland, M. M., Meier, W., Scambos, T. & Serreze, M. Arctic sea ice decline: faster than forecast. Geophys. Res. Lett . https://doi.org/10.1029/2007GL029703 (2007).

Liu, J., Song, M., Horton, R. M. & Hu, Y. Reducing spread in climate model projections of a September ice-free Arctic. Proc. Natl Acad. Sci. USA 110 , 12571–12576 (2013).

Rosenblum, E. & Eisenman, I. Faster Arctic sea ice retreat in CMIP5 than in CMIP3 due to volcanoes. J. Clim. 29 , 9179–9188 (2016).

Dörr, J., Notz, D. & Kern, S. UHH sea ice area product. Universität Hamburg https://doi.org/10.25592/uhhfdm.8559 (2021).

Comiso, J. SSM/I Concentrations Using the Bootstrap Algorithm (NASA, 1995).

Cavalieri, D., Gloersen, P. & Campbell, W. Determination of sea ice parameters with the Nimbus 7 SMMR. J. Geophys. Res. 89 , 5355–5369 (1984).

Krylov, A. et al. A shift in heavy and clay mineral provenance indicates a middle Miocene onset of a perennial sea ice cover in the Arctic Ocean. Paleoceanogr. Paleoclimatol. https://doi.org/10.1029/2007PA001497 (2008).

Darby, D. Arctic perennial ice cover over the last 14 million years. Paleoceanogr. Paleoclimatol. https://doi.org/10.1029/2007PA001479 (2008).

Miller, G. et al. Temperature and precipitation history of the Arctic. Quat. Sci. Rev. 29 , 1679–1715 (2010).

Tarduno, J. A. et al. Evidence for extreme climatic warmth from late Cretaceous Arctic vertebrates. Science 282 , 2241–2243 (1998).

Jenkyns, H., Forster, A., Schouten, S. & Damsté, J. S. S. High temperatures in the late Cretaceous Arctic Ocean. Nature 432 , 888–892 (2004).

Nathorst, A. G. Ueber die reste eines Brotfruchtbaums Artocarpus dicksonii n. sp., aus den cenomanen Kreideablagerungen Grönlands. Kongl. Svenska Vetenskaps-Akad. Hand. 24 , 2–9 (1890).

Stein, R. et al. Evidence for ice-free summers in the late Miocene central Arctic Ocean. Nat. Commun. 7 , 11148 (2016).

Nøgaard-Pedersen, N., Mikkelsen, N. & Kristoffersen, Y. Arctic Ocean record of last two glacial-interglacial cycles off North Greenland/Ellesmere Island — implications for glacial history. Mar. Geol. 244 , 93–108 (2007).

Nørgaard-Pedersen, N., Mikkelsen, N., Lassen, S. J., Kristoffersen, Y. & Sheldon, E. Reduced sea ice concentrations in the Arctic Ocean during the last interglacial period revealed by sediment cores off Northern Greenland. Paleoceanogr. Paleoclimatol. 22 , PA1218 (2007).

ADS Google Scholar

Adler, R. E. et al. Sediment record from the western Arctic Ocean with an improved late quaternary age resolution: HOTRAX core HLY0503-8JPC, Mendeleev Ridge. Glob. Planet. Change 68 , 18–29 (2009).

Sime, L. C., Sivankutty, R., Vallet-Malmierca, I., de Boer, A. M. & Sicard, M. Summer surface air temperature proxies point to near-sea-ice-free conditions in the Arctic at 127 ka. Clim. Past 19 , 883–900 (2023).

Vermassen, F. et al. A seasonally ice-free Arctic Ocean during the last interglacial. Nat. Geosci. 16 , 723–729 (2023).

Lozhkin, A. V. & Anderson, P. M. The last interglaciation in northeast Siberia. Quat. Res. 43 , 147–158 (1995).

Tremblay, L. B., Schmidt, G. A., Pfirman, S., Newton, R. & DeRepentigny, P. Is ice-rafted sediment in a North Pole marine record evidence for perennial sea-ice cover? Phil. Trans. Roy. Soc. A 373 , 2052 (2015).

de Vernal, A. et al. Natural variability of the Arctic Ocean sea ice during the present interglacial. Proc. Natl Acad. Sci. USA 117 , 26069–26075 (2020).

Jakobsson, M., Long, A., Ingoĺfsson, O., Kjær, K. H. & Spielhagen, R. F. New insights on Arctic quaternary climate variability from palaeo-records and numerical modelling. Quat. Sci. Rev. 29 , 3349–3358 (2010).

Pfirman, S., Fowler, C., Tremblay, B. & Newton, R. The last Arctic sea ice refuge. Circle 4 , 6–8 (2009).

Newton, R., Pfirman, S., Tremblay, L. B. & DeRepentigny, P. Defining the “ice shed” of the Arctic Ocean’s last ice area and its future evolution. Earths Future 9 , e2021EF001988 (2021).

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Acknowledgements

A.J. was supported by an Alexander von Humboldt Fellowship and NSF CAREER award 1847398. M.M.H. acknowledges support from NSF awards 2138788 and 2040538. J.E.K. was supported by NASA PREFIRE award 849K995 and NSF award 2233420. We thank J. Dörr for sharing the sea ice area data calculated for the SIMIP analysis 10 and C. Wyburn-Powell for the assistance with regridding of the CMIP6 models for the spatial analysis. We also thank the participants at the Interagency Arctic Research Policy Committee (IARPC) webinar on an ice-free Arctic for the helpful discussions. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making their model output available, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. We also acknowledge the US Climate and Ocean: Variability, Predictability and Change (CLIVAR) Working Group on Large Ensembles, the modelling centres that contributed to the CLIVAR Large Ensemble project, and the CESM2-LE project.

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Supplementary information.

The fraction of incoming shortwave solar radiation that is reflected by a surface, ranging between 0 and 1.

The variability in the climate system attributable to the chaotic nature of the climate system.

Dampening feedbacks in the climate system, reducing an initial perturbation.

Amplifying feedbacks in the climate system, enhancing an initial perturbation.

(SIA). The total area of sea ice present, without any threshold, calculated as sea ice concentration multiplied by grid area and summed over all Northern Hemisphere grid boxes. Note that sometimes, sea ice area is calculated only for grid cells with at least 15% sea ice cover.

(SIE). The area of all grid boxes that have at least 15% sea ice concentration, calculated as sea ice concentration multiplied by the area of all grid boxes with 15% or more sea ice concentration.

The change in sea ice area divided by the change in global or Arctic temperature or cumulative CO 2 emissions over the same time period.

(SSP). A forcing scenario that is part of the Scenario Model Intercomparison Project of CMIP6.

An irreversible change in an environmental condition.

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Jahn, A., Holland, M.M. & Kay, J.E. Projections of an ice-free Arctic Ocean. Nat Rev Earth Environ 5 , 164–176 (2024). https://doi.org/10.1038/s43017-023-00515-9

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Excess Deaths During the COVID-19 Pandemic

Discoveries & impact (march 2024).

Most analyses regarding the excess risk of death during the COVID-19 pandemic have relied on summary data. However, a recent study published in the International Journal of Epidemiology instead analyzed individual patient-level data based on medical records from the largest integrated healthcare system in the United States.

The research involved a cohort of over five million patients receiving care through the Department of Veterans Affairs (VA) for two years before and two years after the pandemic. Overall, there were a total of 103,164 excess deaths observed during the pandemic. The team found that the frailest patients and those with the highest comorbidities had the highest absolute excess death rates. Meanwhile, the least frail patients and those with the lowest comorbidities were found to have the largest relative increases in death rates compared to pre-pandemic levels.

These findings underscore the importance of individual-level data in understanding COVID-19's impact and highlight how different groups may have been affected by the pandemic.

To learn more, read the article: “Excess mortality in US Veterans during the COVID-19 pandemic: an individual-level cohort study”

Weinberger DM, Bhaskaran K, Korves C, et al. Excess mortality in US Veterans during the COVID-19 pandemic: an individual-level cohort study. Int J Epidemiol. Published online October 6, 2023. doi:10.1093/ije/dyad136

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Children Surpass a Year of HIV Remission after Treatment Pause

Nih-funded trial shows promising outcomes with treatment started promptly after birth.

March 6, 2024

Small spheres clustered along the edge of dappled sloping surface.

Colorized transmission electron micrograph of HIV-1 virus particles (yellow) budding and replicating from an H9 T cell (dark blue). The virus particles are in various stages of maturity, which accounts for differences in shape.

Four children have remained free of detectable HIV for more than one year after their antiretroviral therapy (ART) was paused to see if they could achieve HIV remission, according to a presentation today at the 2024 Conference on Retroviruses and Opportunistic Infections (CROI) in Denver. The children, who acquired HIV before birth, were enrolled in a clinical trial funded by the National Institutes of Health in which an ART regimen was started within 48 hours of birth and then closely monitored for drug safety and HIV viral suppression. The outcomes reported today follow planned ART interruptions once the children met predefined virological and immunological criteria.

“These findings are clear evidence that very early treatment enables unique features of the neonatal immune system to limit HIV reservoir development, which increases the prospect of HIV remission,” said NIAID Director Jeanne Marrazzo, M.D., M.P.H. “The promising signals from this study are a beacon for future HIV remission science and underscore the indispensable roles of the global network of clinicians and study staff who implement pediatric HIV research with the utmost care.”

Advances in ART have significantly reduced perinatal HIV transmission, when a child acquires HIV while in the uterus, during birth, or through consumption of milk from a lactating person. If transmission does occur, children must take lifelong ART to control replication of the virus and protect their immune systems from life-threatening complications. Typically, interruption in treatment will lead to rapid resumption of HIV replication and detectable virus in the blood within weeks. However, in 2013, a case report described an infant born with HIV in Mississippi who initiated treatment at 30 hours of life, was taken off their ART at 18 months of age and remained in remission with no evidence of detectable HIV for 27 months.

( Access audio-described version of the video here )

Building on the observation that very early ART initiation may limit HIV’s ability to establish reservoirs of dormant virus in infants researchers began an early-stage proof-of-concept study of very early ART in infants conducted in Brazil, Haiti, Kenya, Malawi, South Africa, Tanzania, Thailand, Uganda, the United States, Zambia, and Zimbabwe. Previous publications from the clinical study showed that ART initiated within hours of birth was safe and effective at achieving and maintaining HIV suppression. A small subset of children achieved sustained HIV suppression and met other predefined study criteria for interrupting ART. These criteria include a durable absence of HIV replication from 48 weeks of ART initiation onward, no detectable antibodies near the time of ART interruption, and having a CD4+ T-cell count (the main immune cell target of HIV) similar to those of a child without HIV. Children who met these criteria, were older than 2 years and had stopped consuming human milk were eligible to interrupt ART.

Data presented at CROI summarized the experience of six children, all aged 5 years, who were eligible for ART interruption with close health and safety monitoring. Four of the six children experienced HIV remission, defined as the absence of replicating virus for at least 48 weeks off ART. One of them experienced remission for 80 weeks, but then their HIV rebounded to detectable levels. Three others have been and remain in remission for 48, 52 and 64 weeks, respectively. However, two children did not experience remission, and their HIV became detectable within three and eight weeks after ART interruption, respectively. The two children whose HIV returned at eight and 80 weeks experienced mild acute retroviral syndrome (ARS) with symptoms including headache, fever, rash, swollen lymph nodes, tonsillitis, diarrhea, nausea and vomiting. One child had markedly low white blood cells, which are a type of immune cell. Both the ARS and white blood deficiency resolved either prior to or soon after restarting ART. The three children who experienced viral rebound resumed HIV suppression within six, eight and 20 weeks of restarting ART.

“This is the first study to rigorously replicate and expand upon the outcomes observed in the Mississippi case report,” said lead study virologist Deborah Persaud, M.D., professor of pediatrics at the Johns Hopkins University School of Medicine, and director of the Division of Pediatric Infectious Diseases at Johns Hopkins Children's Center, Baltimore. “These results are groundbreaking for HIV remission and cure research, and they also point to the necessity of immediate neonatal testing and treatment initiation in health care settings for all infants potentially exposed to HIV in utero.”

The latest findings show that very early ART initiation has varying but favorable outcomes on control of HIV. While ARS was generally mild and resolved in both cases, the authors cautioned that close monitoring for this potential event is needed in ongoing and future HIV remission research involving ART interruption. The children participating in this study took ART regimens with medicines that have been part of standard first-line therapy for decades. Further research is planned or underway to understand how these observations could differ in children receiving newer, more potent generations of antiretroviral drugs, and to identify biomarkers to predict the likelihood of HIV remission or rebound following ART interruption. Additional studies are also needed to understand the mechanisms by which neonatal immunity and very early ART initiation limited the formation of HIV reservoirs and contributed to the remission observed in this study.

“ART shifted the HIV care paradigm, but treatment is a long road, especially for children as lifetime HIV survivors” said Adeodata Kekitiinwa, MBChB, MMed, emeritus clinical associate professor in the Department of Pediatrics at Baylor College of Medicine, study investigator of record and clinical research site leader in Kampala, Uganda. “This trial takes us a step closer to realizing another paradigm shift in which our approach to ART could be so effective that it might be used for a season of life, rather than its entirety.”

This ongoing research is being conducted by the International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) Network, which is funded by the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health, with co-funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), and the National Institute of Mental Health (NIMH).

The research was led by study co-chairs Ellen Chadwick, M.D., professor of pediatrics at Northwestern University Feinberg School of Medicine, and Yvonne Bryson, M.D., professor of pediatrics at the David Geffen School of Medicine and Mattel Children’s Hospital at UCLA, and director of the Los Angeles Brazil AIDS Consortium. Dr. Kekitiinwa, Boniface Njau, M.S., study coordinator at Kilimanjaro Christian Medical Centre in Tanzania and Teacler Nematadzira, MBChB, site investigator at the University of Zimbabwe-University of California San Francisco Collaborative Research Program continue to lead the study teams overseeing care of children who experienced HIV remission. Jennifer Jao, M.D., M.P.H., professor of pediatrics at Northwestern University Feinberg School of Medicine has since assumed a study co-chair role with Dr. Chadwick. The full IMPAACT P1115 study team consists of hundreds of staff across 30 NIAID- and NICHD-supported sites in the 11 study countries.

NIH is grateful to the research sites and study participants, and to the families, caregivers, and communities that continue to support their involvement in HIV science.

For more information about the trial, known as IMPAACT P1115, please see ClinicalTrials.gov using the identifier NCT02140255 .

Submit a Media Request

Contact the NIAID News & Science Writing Branch.

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Research Summary
Research Summary. A research paper analyzes a perspective or argues a point. It is an expanded essay based on your interpretation, evaluation or argument about a certain topic. ... Remember that your research summary is a mini-paper of your study and it should contain the main ideas of your entire research. Write a draft.
A Guide to Writing a Research Summary: Steps, Structure, and Tips
A research summary or a research article comprehensively covers a topic. It is a brief overview of a study typically from a peer-reviewed journal. Many universities and colleges give such assignments and assess the students' performance based on how they interpret the scientific knowledge and data presented in the written academic article. If ...
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Research Summary
A research paper summary, as the name implies, is a copy that summarizes the research. Research, in this case, refers to a study or an article from an established academic journal. As students climb up the academic ladders during college, they need to become proficient with academic writing.
Q: How to write the summary of chapter one of a research project?
Basically, a summary of any chapter would just highlight the main points that have been discussed in that chapter. It should be a highly condensed version of whatever you have covered in that chapter. If you wish to know about the "Thesis Summary," I would suggest you first go through the guidelines provided by your univeristy/institution.
How To Write an Executive Summary for a Research Paper (With ...
The executive summary briefly describes the study's key points and suggests changes, actions and implementation strategies for the business. You can use the following steps to write an executive summary for a research paper: 1. Read the entire research paper.
Online Summarizing Tool
Saves you hundreds of hours. Scholarcy, the online article summarizer tool, reads your research articles, reports and book chapters in seconds and breaks them down into bite-sized sections - so you can quickly assess how important any document is to your work. By identifying key information such as study participants, data analyses, main findings and limitations, researchers and students ...
Use AI To Summarize Scientific Articles
SciSummary uses GPT-3.5 and GPT-4 models to provide summaries of any scientific articles or research papers. The technology learns as it goes as our team of PhDs analyze requested summaries and guides the training of the model. SciSummary makes it easy to stay up-to-date with the latest scientific breakthroughs and research findings, without ...
Association and mediation between educational attainment and
How this study might affect research, practice or policy. Our study highlights the importance of early detection and prevention of respiratory disease, including lung function, lung cancer and asthma, amongst low education group. ... Exposure and outcome summary statistics in the study were obtained from the largest and most recent GWAS. (3) In ...
Projections of an ice-free Arctic Ocean
In this Review, we summarize the current understanding of an ice-free Arctic. We begin by discussing the drivers of sea ice loss, followed by available methodological approaches and corresponding ...
Collaborative Management of Water‐Energy‐Food‐Ecosystems Nexus in
In summary, much research has been conducted based on "nexus" concepts to plan water-related nexus systems. However, some research gaps still need to be filled. First, previous studies mainly used single-level and/or bi-level programming to manage water-centered nexus systems; few works focused on multi-level programming to hierarchically ...
Milk provisioning in oviparous caecilian amphibians
Although lactation is a mammalian attribute, a few nonmammalian species have been documented as producing nutrients primarily directed to offspring feeding in a form that is functionally similar to mammalian milk, including spiders (), cockroaches (), and certain fishes (8, 9) and birds (10, 11).Among amphibians, peculiar parental food provisioning modes have been previously documented in ...
Excess Deaths During the COVID-19 Pandemic < Humanitarian Research Lab
Most analyses regarding the excess risk of death during the COVID-19 pandemic have relied on summary data. However, a recent study published in the International Journal of Epidemiology instead analyzed individual patient-level data based on medical records from the largest integrated healthcare system in the United States.. The research involved a cohort of over five million patients ...
Brice Wilson, Ph.D.
RESEARCH SUMMARY. The Assay Development and Screening Section (ADSS) develops, optimizes, and implements high throughput screening campaigns for the discovery of biologically active agents for the study and potential treatment of cancer. These campaigns involve close collaboration with CCR Principal Investigators who have targets of interest ...
Children Surpass a Year of HIV Remission after Treatment Pause
The research was led by study co-chairs Ellen Chadwick, M.D., professor of pediatrics at Northwestern University Feinberg School of Medicine, and Yvonne Bryson, M.D., professor of pediatrics at the David Geffen School of Medicine and Mattel Children's Hospital at UCLA, and director of the Los Angeles Brazil AIDS Consortium. ...

How To Write A Research Summary

What is a Research Summary?

Research summary or Abstract. What’s the Difference?

Getting Started with a Research Summary

How to Structure Your Research Summary

Introduction

Methodology

Tips for Writing a Research Summary

1. Read the parent paper thoroughly

2. Identify the key elements in different sections

Discussion/Conclusion

3. Prepare the first draft

4. Include visuals

5. Double check for plagiarism

6. Religiously follow the word count limit

7. Proofread your research summary multiple times

8. Watch while you write

9. Ask a friend/colleague to help

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Research Summary

Structure of Research Summary

How to Write Research Summary

Example of Research Summary

Purpose of Research Summary

Characteristics of Research Summary

When to write Research Summary

Advantages of Research Summary

Limitations of Research Summary

About the author

Muhammad Hassan

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A Complete Guide to Writing a Research Summary

What is a Research Summary?

Key pillars of a Research Summary

How do you write a Research Summary?

2. Abstract

3. Introduction

4. Methodology / Study

5. Results / Discussion

6. Conclusion

5 Tips for writing a Research Summary

1. Read & read again