Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal

Krishnan, Athira; Bhaskaran, Prasad K.

doi:10.1007/s00382-020-05406-z

Cited by 39 publications

(10 citation statements)

References 81 publications

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“…Figure 4 displays the prediction results of PME after deviation correction on the surface wind speed trends in the 21st century under different emission scenarios. The results show that the mean wind speed is increasing under SSP1-2.6 and decreasing under SSP2-4.5, SSP3-7.0, and SSP5-8.5, with a downward rate of −0.0007~−0.0072 m/s/10a, and the annual wind speed changes more significantly under SSP3-7.0 and SSP5-8.5, which is consistent with the study by Krishnan et al [38].…”

Section: Temporal Trendsupporting

confidence: 92%

Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

Long

Liu

et al. 2021

Remote Sensing

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Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of p = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What’s more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter.

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Section: Temporal Trendsupporting

confidence: 92%

Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

Long

Liu

et al. 2021

Remote Sensing

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“…The quality of the observation data is a critical factor influencing our understanding of the performance of models. Satellite, reanalysis, and meteorological station data are broadly used in climate model evaluations (Vignesh et al 2020;Krishnan and Bhaskaran 2020;Li et al 2020b). In this study, the ERA5 reanalysis data were used as a reference.…”

Section: Discussionmentioning

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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“…Yuan et al, 2019;Wu et al, 2020a), and wind or storms (e.g. Krishnan and Bhaskaran, 2020;Emanuel and Center, 2020). An overview of global climate projections (CMIP) and regional climate projections (CORDEX) is provided by ECMWF (2020).…”

Section: Model-based Projectionsmentioning

confidence: 99%

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Slater

Anderson

Buechel

et al. 2021

Hydrol. Earth Syst. Sci.

156

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Abstract. Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or storms have devastating effects each year. One of the key challenges for society is understanding how these extremes are evolving and likely to unfold beyond their historical distributions under the influence of multiple drivers such as changes in climate, land cover, and other human factors. Methods for analysing hydroclimatic extremes have advanced considerably in recent decades. Here we provide a review of the drivers, metrics, and methods for the detection, attribution, management, and projection of nonstationary hydroclimatic extremes. We discuss issues and uncertainty associated with these approaches (e.g. arising from insufficient record length, spurious nonstationarities, or incomplete representation of nonstationary sources in modelling frameworks), examine empirical and simulation-based frameworks for analysis of nonstationary extremes, and identify gaps for future research.

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Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal

Cited by 39 publications

References 81 publications

Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

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

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

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