Evidence map›Paper›PMID 41387684›Full record

ArticleNature communications2025

Super El Niño events drive climate regime shifts with enhanced risks under global warming.

Aoyun Xue, Xin Geng, Fei-Fei Jin, Yechul Shin, Mi-Kyung Sung, Jong-Seong Kug

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Aoyun XueBren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA.
Xin GengCIC-FEMD/KLME, State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China. gengxin@nuist.edu.cn.ORCID http://orcid.org/0000-0003-0970-8382
Fei-Fei JinDepartment of Atmospheric Sciences, University of Hawaii, Honolulu, HI, USA.ORCID http://orcid.org/0000-0001-5101-2296
Yechul ShinSchool of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea.ORCID http://orcid.org/0000-0002-4381-697X
Mi-Kyung SungCenter for Climate and Carbon Cycle Research, Korea Institute of Science and Technology, Seoul, Korea.ORCID http://orcid.org/0000-0002-7659-6571
Jong-Seong KugSchool of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea. jskug@snu.ac.kr.ORCID http://orcid.org/0000-0003-2251-2579

Funding

National Research Foundation of Korea (NRF) NRF-2022R1A3B1077622
6 · The paper itself

Abstract

Climate regime shifts (CRSs), characterized by abrupt and persistent transitions between alternative stable states in the climate system, pose serious threats to ecosystems and human well-being. Understanding the potential drivers of CRSs is crucial, particularly in a warming world where CRSs are becoming more frequent. Here, using multiple observations and model simulations, we find that the likelihood of CRS occurrence significantly increases in the context of super El Niño events due to their remarkable climate perturbations. This higher probability is detected across various climate elements, such as surface air temperature, sea surface temperature, and surface soil moisture. In addition, we suggest that this boost effect of super El Niño events on CRSs will be greatly amplified under future greenhouse warming. Our findings underscore a deeper and more persistent climate footprint of super El Niño events, suggesting that early warnings and proactive measures are crucial for mitigating their escalating risks.

Identifiers

PMID41387684
PMCPMC12717066

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.