Projected Changes in Snow Water Equivalent over the Tibetan Plateau under Global Warming of 1.5° and 2°C

You, Qinglong; Wu, Fu‐Peng; Wang, Hongguo; Jiang, Zhihong; Pepin, Nick; Kang, Shichang

doi:10.1175/jcli-d-19-0719.1

Cited by 25 publications

(14 citation statements)

References 68 publications

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“…During the past decades, the TP has undergone rapid warming with its magnitude of temperature increase being larger than the rate of the global average (Guo & Wang, 2012;Liu & Chen, 2000;Yao et al, 2019). This trend is expected to continue in the future (e.g., Liu et al, 2009;You et al, 2020). Consequently, the snow depth over the TP may decrease, which would lead to more precipitation in the local and surrounding regions.…”

Section: Discussionmentioning

confidence: 99%

Impacts of Bias‐Corrected ERA5 Initial Snow Depth on Dynamical Downscaling Simulations for the Tibetan Plateau

Lin

Chen

et al. 2021

JGR Atmospheres

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The Tibetan Plateau (TP) is often called the “Water Tower of Asia,” which contains the largest amount of snow and glaciers outside the polar regions. As an important and variable feature of the land surface, snow coverage on the TP has great impacts on regional climate. However, the commonly used ERA5 reanalysis in dynamical downscaling largely overestimates the snow depth for the TP. To improve the representation of snow cover in ERA5, a new ERA5‐driven downscaling data set (High Asia Refined analysis version 2, HAR v2) was generated by the Weather Research and Forecasting (WRF) model with the bias‐corrected snow depth. This study aims to identify and better understand the impact of bias‐corrected initial snow conditions on simulated regional climate, by comparing the HAR v2 with a 5‐year ERA5 forced WRF simulation without bias correction of initial snow depth (referred to as WRF_ERA5). The results show that the bias correction significantly improves the simulation of 2 m air temperature (T2), with regional mean cold bias reduced by 0.2°C–2.4°C, but no significant improvement in precipitation simulation is found. Further comparative analysis reveals that higher snow depth in WRF_ERA5 leads to T2, mean daily precipitation, summer extreme precipitation, and contributions of convective precipitation to summer mean daily precipitation decrease by 0°C–4°C, 0%–60%, 0%–40%, and 0%–10%, respectively, in most areas of the TP. In addition, the bias‐corrected initial snow depth also has impacts on simulated diurnal cycles of precipitation and T2 and leads to peak hours one hour earlier. Overall, this study confirms the importance of snow cover for the climate in the TP.

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Section: Discussionmentioning

confidence: 99%

Impacts of Bias‐Corrected ERA5 Initial Snow Depth on Dynamical Downscaling Simulations for the Tibetan Plateau

Lin

Chen

et al. 2021

JGR Atmospheres

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“…In the different regions, the IVS values obtained in the NEC, NC, WNW, and ENW regions in spring were generally lower than those obtained in the SC region, while the IVS values obtained din the SC, SW, and JH regions in autumn were generally lower than those obtained in the NC region. The annual average IVS values of the TP region were higher than those of other regions, at 0.484 (GFDL-CM4) ~ 11.095 (CESM2-WACCM), likely because the models could hardly reproduce the EW events related to the complex topography (You et al 2020).…”

Section: Interannual Variabilitymentioning

confidence: 87%

Evaluation of events of extreme temperature change between neighboring days in CMIP6 models over China

Song

Yan

2022

Theor Appl Climatol

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In the context of global warming, the frequency and intensity of extreme weather and climate events are increasing. However, the impact of these changes that is directly felt by people is the day-to-day temperature change. Extreme temperature changes between neighboring days (ETCNs) carry substantial disease risks and socioeconomic impacts. Evaluative studies of ETCN events with global circulation models (GCMs) remain unknown in China. This study quantitatively evaluates the performances of 36 GCMs and the multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the extreme cooling (EC) and extreme warming (EW) events of two consecutive days as defined by relative thresholds. Moreover, we select the optimal models in different regions at the seasonal and annual scales in China, providing theoretical support for the frequency projection and modeling improvement of ETCN events. The results showed that from 1981 to 2013, the annual average EW events and EC events in China showed increasing but not statistically significant trends, and the frequency of EW events was higher than that of EC events. EW events mostly occurred in spring, while EC events mostly occurred in autumn.Additionally, the performances of the CMIP6 models are quite different between EC and EW events.The simulations of EC events are generally more reliable than those of EW events, and the models can also capture the annual cycle of EC events well. Furthermore, the CMIP6 models overestimate the frequencies of EW and EC events but underestimate the frequency of EC events in autumn. The CMIP6 models exhibit poor performance in simulating the trend of and interannual variability in ETCN events and can only simulate the decreasing trend in autumn. Finally, according to the overall ranking of the CMIP6 models, GFDL-ESSM4 and EC-Earth3-Veg-LR achieve the best performance in simulating EW and EC events, respectively. The CMIP6 MME only effectively improved the capabilities of the models to simulate winter EC events in the WNW region. In terms of the trend of and interannual variability in ETCN events, individual models exhibited better performances than the CMIP6 ensemble.

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“…Moreover, the role of SWE primarily depends on the water conditions of the regions. The seasonal water budget of a region has a closer relationship with SWE compared with other snow cover indicators (You et al, 2020; Zhang & Ma, 2018). The proportion of SCD‐dominated pixels for summer GPP decreased compared with spring GPP, whereas the proportion of SWE‐dominated pixels increased.…”

Section: Discussionmentioning

confidence: 99%

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

Liu,

Xiao,

Zhang

et al. 2023

Ecosphere

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Snow cover is an important control element in the Tibetan Plateau (TP) ecosystem; however, the impact of snow cover changes on gross primary productivity (GPP) is largely unknown, particularly under complex geographical conditions. In this study, we investigated the impacts of snow cover changes on different seasonal GPP and their mechanisms in different geographical zones using multisource remote sensing data from 1982 to 2018. Snow cover significantly affected GPP by nearly 15% of the TP region, and snow cover days (SCDs) were the dominant snow cover indicators affecting GPP variations compared with snow water equivalent (SWE) and snow cover end date (SCED). In general, an increase in snow cover leads to a significant increase in GPP in regions with low precipitation and temperature, but limits the accumulation of GPP in humid and warm regions. Furthermore, from the humid to the arid zone, the “moisture effect” of snow cover (by altering soil moisture) plays an increasingly important role in regulating GPP variations with increasing drought levels. This study elucidates the importance of snow cover in regulating different seasonal GPP variations and significantly improves our insight into the response of vegetation carbon uptake to snow cover changes in the TP.

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Projected Changes in Snow Water Equivalent over the Tibetan Plateau under Global Warming of 1.5° and 2°C

Cited by 25 publications

References 68 publications

Impacts of Bias‐Corrected ERA5 Initial Snow Depth on Dynamical Downscaling Simulations for the Tibetan Plateau

Impacts of Bias‐Corrected ERA5 Initial Snow Depth on Dynamical Downscaling Simulations for the Tibetan Plateau

Evaluation of events of extreme temperature change between neighboring days in CMIP6 models over China

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

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