Cargando…

Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data

This study assesses present-day extreme climate changes over China by using a set of phase 6 of the Coupled Model Intercomparison Project (CMIP6) statistical downscaled data and raw models outputs. The downscaled data is produced by the adapted spatial disaggregation and equal distance cumulative di...

Descripción completa

Detalles Bibliográficos
Autores principales: Wei, Linxiao, Liu, Lyuliu, Jing, Cheng, Wu, Yao, Xin, Xiaoge, Yang, Baogang, Tang, Hongyu, Li, Yonghua, Wang, Yong, Zhang, Tianyu, Zhang, Fen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180870/
https://www.ncbi.nlm.nih.gov/pubmed/35681982
http://dx.doi.org/10.3390/ijerph19116398
_version_ 1784723626361094144
author Wei, Linxiao
Liu, Lyuliu
Jing, Cheng
Wu, Yao
Xin, Xiaoge
Yang, Baogang
Tang, Hongyu
Li, Yonghua
Wang, Yong
Zhang, Tianyu
Zhang, Fen
author_facet Wei, Linxiao
Liu, Lyuliu
Jing, Cheng
Wu, Yao
Xin, Xiaoge
Yang, Baogang
Tang, Hongyu
Li, Yonghua
Wang, Yong
Zhang, Tianyu
Zhang, Fen
author_sort Wei, Linxiao
collection PubMed
description This study assesses present-day extreme climate changes over China by using a set of phase 6 of the Coupled Model Intercomparison Project (CMIP6) statistical downscaled data and raw models outputs. The downscaled data is produced by the adapted spatial disaggregation and equal distance cumulative distribution function (EDCDF) method at the resolution of 0.25° × 0.25° for the present day (1961–2014) and the future period (2015–2100) under the Shared Socioeconomic Path-way (SSP) 2-4.5 than SSP5-8.5 emission scenario. The results show that the downscaling method improves the spatial distributions of extreme climate events in China with higher spatial pattern correlations, Taylor Skill Scores and closer magnitudes no matter single model or multi model ensemble (MME). In the future projections, large inter-model variability between the downscaled models still exists, particular for maximum consecutive 5-day precipitation (RX5). The downscaled MME projects that total precipitation (PTOT) and RX5, will increase with time, especially for the northwest China. The projected heavy precipitation days (R20) also increase in the future. The region of significant increase in R20 locates in the south of river Yangtze. Maxi-mum annual temperature (TXX) and percentage of warm days (TX90p) are projected to increase across the whole country with larger magnitude over the west China. Projected changes of minimum annual temperature (TNN) over the northeastern China is the most significant area. The higher of the emission scenario, the more significant of extreme climates. This reveals that the spatial distribution of extreme climate events will become more uneven in the future.
format Online
Article
Text
id pubmed-9180870
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91808702022-06-10 Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data Wei, Linxiao Liu, Lyuliu Jing, Cheng Wu, Yao Xin, Xiaoge Yang, Baogang Tang, Hongyu Li, Yonghua Wang, Yong Zhang, Tianyu Zhang, Fen Int J Environ Res Public Health Article This study assesses present-day extreme climate changes over China by using a set of phase 6 of the Coupled Model Intercomparison Project (CMIP6) statistical downscaled data and raw models outputs. The downscaled data is produced by the adapted spatial disaggregation and equal distance cumulative distribution function (EDCDF) method at the resolution of 0.25° × 0.25° for the present day (1961–2014) and the future period (2015–2100) under the Shared Socioeconomic Path-way (SSP) 2-4.5 than SSP5-8.5 emission scenario. The results show that the downscaling method improves the spatial distributions of extreme climate events in China with higher spatial pattern correlations, Taylor Skill Scores and closer magnitudes no matter single model or multi model ensemble (MME). In the future projections, large inter-model variability between the downscaled models still exists, particular for maximum consecutive 5-day precipitation (RX5). The downscaled MME projects that total precipitation (PTOT) and RX5, will increase with time, especially for the northwest China. The projected heavy precipitation days (R20) also increase in the future. The region of significant increase in R20 locates in the south of river Yangtze. Maxi-mum annual temperature (TXX) and percentage of warm days (TX90p) are projected to increase across the whole country with larger magnitude over the west China. Projected changes of minimum annual temperature (TNN) over the northeastern China is the most significant area. The higher of the emission scenario, the more significant of extreme climates. This reveals that the spatial distribution of extreme climate events will become more uneven in the future. MDPI 2022-05-24 /pmc/articles/PMC9180870/ /pubmed/35681982 http://dx.doi.org/10.3390/ijerph19116398 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wei, Linxiao
Liu, Lyuliu
Jing, Cheng
Wu, Yao
Xin, Xiaoge
Yang, Baogang
Tang, Hongyu
Li, Yonghua
Wang, Yong
Zhang, Tianyu
Zhang, Fen
Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data
title Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data
title_full Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data
title_fullStr Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data
title_full_unstemmed Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data
title_short Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data
title_sort simulation and projection of climate extremes in china by a set of statistical downscaled data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180870/
https://www.ncbi.nlm.nih.gov/pubmed/35681982
http://dx.doi.org/10.3390/ijerph19116398
work_keys_str_mv AT weilinxiao simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT liulyuliu simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT jingcheng simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT wuyao simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT xinxiaoge simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT yangbaogang simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT tanghongyu simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT liyonghua simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT wangyong simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT zhangtianyu simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata
AT zhangfen simulationandprojectionofclimateextremesinchinabyasetofstatisticaldownscaleddata