Evidence map›Paper›PMID 41779862›Full record

ArticleScience advances2026

Archeological data with AI- and physics-based modeling explain typhoon-induced disasters in inland China around 3000 yr B.P.

Ke Ding, Siyang Li, Aijun Ding, Houyuan Lu, Jianping Zhang, Dazhi Xi, Xin Huang, Sijia Lou, Xiaodong Tang, Xin Qiu and 9 more

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

19 authors.

Ke DingState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0002-6436-6226
Siyang LiInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0003-4362-8394
Aijun DingState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0003-4481-5386
Houyuan LuState Key Laboratory of Lithospheric and Environmental Coevolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.ORCID 0000-0001-5721-6679
Jianping ZhangState Key Laboratory of Lithospheric and Environmental Coevolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.ORCID 0000-0003-0815-009X
Dazhi XiDepartment of Earth Science, The University of Hong Kong, Hong Kong 999077, China.ORCID 0000-0002-4096-8441
Xin HuangState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0003-0922-5014
Sijia LouState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0002-9958-8494
Xiaodong TangState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0002-2845-8422
Xin QiuState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0001-5500-7486
Lejun HeSchool of History, Nanjing University, Nanjing 210023, China.
Yue MaState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0002-6174-2484
Haoxian LinState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0009-0005-8345-6875
Shiyan ZhangInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0002-2585-0584
Derong ZhouState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
Xiaolu ZhouSchool of History, Nanjing University, Nanjing 210023, China.
Zhe-Min TanState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0002-2227-6900
Congbin FuState Key Laboratory of Severe Weather Meteorological Science and Technology, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.ORCID 0000-0001-9686-4337
Quansheng GeInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0001-8712-8565

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change-related extreme events during the mid-late Holocene, especially around 3000 years before the present (yr B.P.), severely threatened human survival and cultural development at various locations. However, although marked social change during this period in China have also been reported to coincide with extreme disasters, the causes and impacts of these events remain unclear. Here, we aligned paleoclimate reconstructions with quantitative analyses of archeological evidence, including oracle bone scripts, together with artificial intelligence- and physics-based model simulations to uncover the causes. We found that intensified typhoon activities exerted considerable impacts on climate extremes and social change in inland China around 3000 yr B.P. These findings underscore the urgent need to improve preparedness for today's typhoon-induced disasters in the context of accelerating climate change.

Identifiers

PMID41779862
PMCPMC12959396

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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.