{"title":"Change of global land extreme temperature in the future","authors":"Xinlong Zhang , Taosheng Huang , Weiping Wang , Ping Shen","doi":"10.1016/j.gloplacha.2024.104583","DOIUrl":null,"url":null,"abstract":"<div><p>Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.</p></div>","PeriodicalId":55089,"journal":{"name":"Global and Planetary Change","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global and Planetary Change","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921818124002303","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.
期刊介绍:
The objective of the journal Global and Planetary Change is to provide a multi-disciplinary overview of the processes taking place in the Earth System and involved in planetary change over time. The journal focuses on records of the past and current state of the earth system, and future scenarios , and their link to global environmental change. Regional or process-oriented studies are welcome if they discuss global implications. Topics include, but are not limited to, changes in the dynamics and composition of the atmosphere, oceans and cryosphere, as well as climate change, sea level variation, observations/modelling of Earth processes from deep to (near-)surface and their coupling, global ecology, biogeography and the resilience/thresholds in ecosystems.
Key criteria for the consideration of manuscripts are (a) the relevance for the global scientific community and/or (b) the wider implications for global scale problems, preferably combined with (c) having a significance beyond a single discipline. A clear focus on key processes associated with planetary scale change is strongly encouraged.
Manuscripts can be submitted as either research contributions or as a review article. Every effort should be made towards the presentation of research outcomes in an understandable way for a broad readership.