Recent recovery of the boreal spring sensible heating over the Tibetan Plateau will continue in CMIP6 future projections

Wang, Meirong; Wang, Jun; Chen, Deliang; Duan, Anmin; Liu, Yimin; Zhou, Siyang; Guo, Dong; Wang, Hengmao; Ju, Weimin

doi:10.1088/1748-9326/ab57a3

Cited by 35 publications

(23 citation statements)

References 69 publications

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“…Moreover, compared with those in CMIP5, GCMs participating in CMIP6 have finer resolutions and more complicated physical processes. With the availability of the state‐of‐the‐art GCMs, some researchers have analyzed the simulations in China, including the mean climate and climate extremes (Wang et al, 2019; Xin et al, 2020; Zhou et al, 2020). However, few comprehensive studies on the surface wind speed over China have been conducted.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

2020

JGR Atmospheres

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Surface wind speed has great impacts on the economy, environment, and society around the world. Based on 24 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6), this paper assesses the historical surface wind speed over China, and quantifies the advancements of CMIP6 over CMIP5. In addition, future changes of surface wind speed under the Shared Socioeconomic Pathway scenarios of 1-2.6, 2-4.5, and 5-8.5 (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are also provided, by using 18 out of the 24 models with all three scenarios. Results show that multimodel ensemble mean of CMIP6 can well capture the spatial distributions of the annual, summer, and winter surface wind speed and generally performs better than both the median of the individual CMIP6 GCMs and multimodel ensemble mean of CMIP5 in monthly, seasonal, and annual wind climatology. However, CMIP6 GCMs fail to reproduce the observed decreasing trends, in terms of magnitude and/or sign. Meanwhile, the annual cycle of the surface wind speed simulated by CMIP6 GCMs falsely demonstrates the maximum in winter instead of spring. In the context of global warming, surface wind speed is projected to decrease in most parts of China in the middle and late 21st century under the three scenarios. Moreover, the trends of wind speed averaged over China will decrease significantly in both annual and winter under all three scenarios, but it will increase significantly in summer under SSP5-8.5.

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Section: Introductionmentioning

confidence: 99%

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

Shi

2020

JGR Atmospheres

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“…The negative values are mostly found in the relatively dry western part of the region, where increasing the temperature could lead to higher evaporation and drier conditions, limiting vegetation growth. The positive coefficients occur more frequently in the relatively humid east, where higher temperature could lead to better thermal conditions and longer sun duration for photosynthesis, hence promoting vegetation growth [59,60].…”

Section: Climate Model Evaluationmentioning

confidence: 99%

Projecting Future Vegetation Change for Northeast China Using CMIP6 Model

Yuan

Hou

et al. 2021

Remote Sensing

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Northeast China lies in the transition zone from the humid monsoonal to the arid continental climate, with diverse ecosystems and agricultural land highly susceptible to climate change. This region has experienced significant greening in the past three decades, but future trends remain uncertain. In this study, we provide a quantitative assessment of how vegetation, indicated by the leaf area index (LAI), will change in this region in response to future climate change. Based on the output of eleven CMIP6 global climates, Northeast China is likely to get warmer and wetter in the future, corresponding to an increase in regional LAI. Under the medium emissions scenario (SSP245), the average LAI is expected to increase by 0.27 for the mid-century (2041–2070) and 0.39 for the late century (2071–2100). Under the high emissions scenario (SSP585), the increase is 0.40 for the mid-century and 0.70 for the late century, respectively. Despite the increase in the regional mean, the LAI trend shows significant spatial heterogeneity, with likely decreases for the arid northwest and some sandy fields in this region. Therefore, climate change could pose additional challenges for long-term ecological and economic sustainability. Our findings could provide useful information to local decision makers for developing effective sustainable land management strategies in Northeast China.

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“…The median of the multi‐reanalysis products is computed, which can reduce the influence by extremely large and small values (Wang et al ., 2019). Relationships ( r ) between climate variables (DSR, DLR, SCF, WS, and BLH) and Δ T within the same reanalysis product are detected using the Pearson correlation analysis.…”

Section: Data and Evaluation Strategymentioning

confidence: 99%

A climatology of surface–air temperature difference over the Tibetan Plateau: Results from multi‐source reanalyses

Wang

Chen

Pang

et al. 2020

Intl Journal of Climatology

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The Tibetan Plateau (TP), known as earth's “Third Pole,” influences regional and even global weather and climate systems through its mechanical and thermal‐dynamical forcing. Near‐surface (2 m) air temperature (Ta) and surface (skin) temperature (Ts) are two crucial parameters of land–atmosphere interactions and climate change. Their difference (ΔT = Ts − Ta) determines the heating source over the TP that drives the Asian summer monsoon. This study focuses on climatology, inter‐annual variability, and long‐term trend of ΔT over the TP in the last four decades (1979–2018), based on four latest reanalysis datasets including ERA‐Interim, ERA5, MERRA2, and JRA55, along with observational data. We show that ΔT‐based different datasets display fairly different climatology in terms of seasonality, spatial distribution, and long‐term trend. ΔT exhibits a clear seasonality with negative value in winter and positive ones in summer despite different strengths and timings presented by the reanalyses. Along with global warming, all reanalyses except JRA55 exhibit obvious downwards trends of ΔT in a spatially non‐uniform way. The median ΔT among the four reanalyses features uniform decreases in all seasons, with the most distinct area on the northern TP, as well as the largest and least decreases in autumn and spring, respectively. Further analysis shows that the differences in ΔT are most likely associated with discrepancies in radiation forcing, snow cover, wind speed, and boundary layer height within the reanalyses. The present findings highlight the difficulty for the state‐of‐the‐art reanalyses to represent the climate change over the TP and point to possible factors behind the deficiencies identified.

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Recent recovery of the boreal spring sensible heating over the Tibetan Plateau will continue in CMIP6 future projections

Cited by 35 publications

References 69 publications

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

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

Projecting Future Vegetation Change for Northeast China Using CMIP6 Model

A climatology of surface–air temperature difference over the Tibetan Plateau: Results from multi‐source reanalyses

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