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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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 |
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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 |
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