Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models
The characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through compa...
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doaj-2a155488c6a44058ad8624d3a84144732021-08-26T13:31:46ZengMDPI AGAtmosphere2073-44332021-08-01121062106210.3390/atmos12081062Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 ModelsHao Deng0Wei Hua1Guangzhou Fan2School of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaPlateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu 610225, ChinaThe characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through comparison with observational NWS data acquired in China during a historical period (1975–2014), and projected future changes in NWS under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) based on an optimal multi-model ensemble. Results showed that most models reproduced the spatial pattern of NWS for all seasons and the annual mean, although the models generally overestimated NWS magnitude. All models tended to underestimate the trends of decline of NWS for all seasons and the annual mean. On the basis of a comprehensive ranking index, the KIOST-ESM, CNRM-ESM2-1, HadGEM3-GC31-LL, CMCC-CM2-SR5, and KACE-1-0-G models were ranked as the five best-performing models. In the projections of future change, nationally averaged NWS for all months was weaker than in the historical period, and the trends decreased markedly under all the different scenarios except the winter time series under SSP2-4.5. Additionally, the projected NWS over most regions of China weakened in both the early period (2021–2060) and the later period (2061–2100).https://www.mdpi.com/2073-4433/12/8/1062CMIP6near-surface wind speedChinaevaluationprojection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hao Deng Wei Hua Guangzhou Fan |
spellingShingle |
Hao Deng Wei Hua Guangzhou Fan Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models Atmosphere CMIP6 near-surface wind speed China evaluation projection |
author_facet |
Hao Deng Wei Hua Guangzhou Fan |
author_sort |
Hao Deng |
title |
Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models |
title_short |
Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models |
title_full |
Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models |
title_fullStr |
Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models |
title_full_unstemmed |
Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models |
title_sort |
evaluation and projection of near-surface wind speed over china based on cmip6 models |
publisher |
MDPI AG |
series |
Atmosphere |
issn |
2073-4433 |
publishDate |
2021-08-01 |
description |
The characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through comparison with observational NWS data acquired in China during a historical period (1975–2014), and projected future changes in NWS under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) based on an optimal multi-model ensemble. Results showed that most models reproduced the spatial pattern of NWS for all seasons and the annual mean, although the models generally overestimated NWS magnitude. All models tended to underestimate the trends of decline of NWS for all seasons and the annual mean. On the basis of a comprehensive ranking index, the KIOST-ESM, CNRM-ESM2-1, HadGEM3-GC31-LL, CMCC-CM2-SR5, and KACE-1-0-G models were ranked as the five best-performing models. In the projections of future change, nationally averaged NWS for all months was weaker than in the historical period, and the trends decreased markedly under all the different scenarios except the winter time series under SSP2-4.5. Additionally, the projected NWS over most regions of China weakened in both the early period (2021–2060) and the later period (2061–2100). |
topic |
CMIP6 near-surface wind speed China evaluation projection |
url |
https://www.mdpi.com/2073-4433/12/8/1062 |
work_keys_str_mv |
AT haodeng evaluationandprojectionofnearsurfacewindspeedoverchinabasedoncmip6models AT weihua evaluationandprojectionofnearsurfacewindspeedoverchinabasedoncmip6models AT guangzhoufan evaluationandprojectionofnearsurfacewindspeedoverchinabasedoncmip6models |
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1721194894953807872 |