Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Wu, Jie; Shi, Ying; Xu, Ying

doi:10.1029/2020jd033611

Cited by 74 publications

(51 citation statements)

References 71 publications

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“…Except for the AMME, which shows a small deviation, the CMCC-CM2-SR5, MIROC6, HadGEM3-GC31-LL, UKESM1-0-LL, KACE-1-0-G, and ACCESS-CM2 models show smaller bias (with an absolute value of relative deviation of <0.2) in all seasons and on the annual basis than that of the other models, while the CanESM5, MIROC-ES2L, BCC-CSM2-MR, MRI-ESM2-0, FGOALS-f3-L, and FGOALS-g3 models all show poor performance. Compared to another study [27]…”

Section: Climatologymentioning

confidence: 72%

“…Overall, the CRMSEs of the CMCC-CM2-SR5, CESM2-WACCM, NorESM2-LM, NorESM2- Taylor diagrams that depict the spatial correlation coefficient (SCC), normalized standard deviation (STD), and centered RMSE (CRMSE) of the climatological annual and seasonal NWS over China simulated by each of the CMIP6 GCMs and AMME against the observations for the period 1975-2014 are presented in Figure 3. Wu et al [27] have pointed out that the multi-model ensemble of CMIP6 generally performs better in simulating the spatial distribution of NWS over China than CMIP5. In our paper, it can be seen that the SCC of each models varies in the range of 0.38-0.82, except that of INM-CM4-8 and INM-CM5-0 in summer, indicating that most simulated fields of annual and seasonal NWS match well with those based on the observations.…”

Section: Climatologymentioning

confidence: 99%

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Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models

Deng

Hua

Fan³

2021

Atmosphere

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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).

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Section: Climatologymentioning

confidence: 72%

Section: Climatologymentioning

confidence: 99%

Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models

Deng

Hua

Fan³

2021

Atmosphere

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“…For example, by using CMIP data, researchers have evaluated the future trends of wind speed and wind energy for the Northern Hemisphere 6 , including Europe 11 , China [12][13] 10 and India 13 .…”

Section: Evaluating the Cmip-predicted Wind Speeds In Chinamentioning

confidence: 99%

“…Yet , a decline of wind speed 6 due to global climate change in the north hemisphere might have a profound impact on wind power development and even global carbon neutrality. At present, Coupled Model Intercomparison Project (CMIP) 7 climate data are widely used to predict future climate trends [8][9][10] , including those of wind speed 6,[11][12][13] . However, past research on the uncertainty and accuracy of CMIP models has typically focused on temperature and precipitation, etc.…”

Section: Introductionmentioning

confidence: 99%

Severely underestimated rate of wind speed decline jeopardizes China's carbon peak emission target

Feng

Wang

Song

et al. 2022

Preprint

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The development of wind energy is indispensable in the pursuit of global carbon neutrality. Following decades of climate change, China's annual average wind speed has shown a clear decline, but the rate of this decline and its potential impacts on the need for wind power development in China have not been quantified. Here, we reveal that China's observed wind speed has declined significantly at -0.169 m/s/10 yr, 33.33 times the rate predicted by the Coupled Model Intercomparison Project (CMIP) of World Climate Research Programme, indicating a severe underestimation by those models. We attribute this underestimation to CMIP neglecting the atmospheric boundary layer height and greatly underestimating the Arctic amplification effect on wind speed. Scenario analyses demonstrate that China’s future wind power installed capacity, investment costs, and land area occupied based on the observed trend scenario will increase by approximately 53% compared to any trend of three (high, medium, and low) CMIP emission scenarios in order to meet China’s wind-generated electricity target and carbon peak emission goal in 2030. Hence, formulating its wind energy development plans based on these CMIP scenarios’ trends will prevent China from meeting its low-carbon electricity generation of carbon peak emission target by 2030 and delaying the 2060 goal of carbon neutrality, emphasizing that the CMIP models urgently need to be improved. These findings should serve as a warning to countries throughout the Northern Hemisphere to formulate wind power development plans in consideration of the climate change impacts in the pursuit of global carbon neutrality.

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“…The simulated annual precipitation in CMIP6 also has significant improvements and good agreement with observations, which is attributed to the finer resolution and improved physical parameterizations (Eyring et al 2016). Additionally, CMIP6 models underestimate the annual surface wind speed, and they have poor performance for reproducing the decreasing trend (Wu et al 2020). However, from the simulation of sea surface temperature over the Indian Ocean, CMIP6 models underestimate the warming trend and display little improvement compared to the CMIP5 models (Li and Su 2020a).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Performance of CMIP6 Model Simulations for the Asian-Pacific Region: Perspectives from Multiple Dimensions

Song

Zhang

Yan

2022

Preprint

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A comprehensive climate model assessment from multiple dimensions is critical for model selection to reduce uncertainties. Here, we evaluated the performance of seven models involved in the Coupled Model Intercomparison Project-6 (CMIP6) by comparing the simulated meteorological variables at the near-surface (sea ice cover and sea surface temperature) and pressure levels (air temperature, specific humidity, zonal wind, meridional wind, and geopotential height at 850 hPa, 500 hPa, and 200 hPa) to those using ERA5 reanalysis data for the 1995-2014 period from the perspectives of climatological and interannual variability. Then, a comprehensive rating index approach was applied to rank the models. The results show that the CMIP6 models could mostly reproduce the spatial variability of the climatology. However, there were also systematic biases. The sea ice cover and sea surface temperature exhibited noticeable biases of approximately -13.2% and 0.6 °C, respectively. Additionally, all models underestimated air temperature and geopotential height, while overestimating specific humidity in the middle troposphere and zonal and meridional wind speeds in the upper troposphere. Regarding interannual variability, the CMIP6 models performed well for the variables at a pressure level of 850 hPa and with sea ice cover. Taken together, all variables showed that NorESM2-MM exhibited good performance in terms of climatology, while MPI-ESM1-2-LR exhibited good interannual variability. Overall, in regard to the comprehensive rate index, MPI-ESM1-2-LR performed the best among the seven models. This study provides valuable scientific references for selecting the available CMIP6 models as lateral boundary conditions towards dynamical downscaling over Asian-Pacific area.

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Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Cited by 74 publications

References 71 publications

Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models

Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models

Severely underestimated rate of wind speed decline jeopardizes China's carbon peak emission target

Evaluation of the Performance of CMIP6 Model Simulations for the Asian-Pacific Region: Perspectives from Multiple Dimensions

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