Evidence map›Paper›PMID 36470927›Full record

ArticleScientific reports2022

The trend and spatial spread of multisectoral climate extremes in CMIP6 models.

Oluwafemi E Adeyeri, Wen Zhou, Xuan Wang, Ruhua Zhang, Patrick Laux, Kazeem A Ishola, Muhammad Usman

Erratum issuedAbstract read
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Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Oluwafemi E AdeyeriSchool of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong, SAR, China.
Wen ZhouDepartment of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China. wen_zhou@fudan.edu.cn.
Xuan WangSchool of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong, SAR, China.
Ruhua ZhangDepartment of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China.
Patrick LauxInstitute for Meteorology and Climate Research Atmospheric Environmental Research, Karlsruhe Institute of Technology, Campus Alpine, Germany.
Kazeem A IsholaIrish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Ireland.
Muhammad UsmanSchool of Engineering, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979-2014), near future (2025-2060) and far future (2065-2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions.

Indexed as

Climate ChangeGreenhouse GasesAfricaForecastingGreenhouse Gases

Identifiers

PMID36470927
PMCPMC9722700

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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.