Assessment of Simulations of Snow Depth in the Qinghai-Tibetan Plateau Using CMIP5 Multi-Models

Wei, Zhigang; Dong, Wenjie

doi:10.1657/aaar0014-050

Cited by 32 publications

(28 citation statements)

References 30 publications

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“…The similar result may be primarily because the SnowModel input data included groundbased measured air temperature, precipitation, wind conditions, and in part snow depth. However, results from CMIP5 (Coupled Model Intercomparsion Project Phase 5; Terzago et al, 2014;Wei and Dong, 2015) overestimated snow depth over the TP and underestimated in forest regions. This implies that large uncertainties currently still exist in CMIP5 modeling snow depth.…”

Section: Comparisons With Previous Resultsmentioning

confidence: 97%

“…Using groundbased snow depth measurements over the Eurasian continent against snow depth obtained from passive microwave satellite remote sensing, Zheng et al (2015) found that the mean percentage error was greater than 50 % and can be up to 200 %. Apart from remote sensing, numerical modeling is often used to obtain spatially complete fields of snow depth and/or SWE (Liston and Hiemstra, 2011;Ji and Kang, 2013;Terzago et al, 2014;Wei and Dong, 2015). However, lowresolution satellite remote sensing data are used as input parameter, which can affect simulation accuracy and do not provide a sufficient time series length.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

et al. 2018

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Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term ground-based measurements from 1814 stations. Spatially, long-term (1971Spatially, long-term ( -2000 mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade −1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50 • N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

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Section: Comparisons With Previous Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

et al. 2018

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“…There is another narrow snow cover region located from the Karakorum Mountains in the western TP to the Himalayan Mountains in the southern TP. This is consistent with results of previous studies (Li, ; Wei and Dong, ). In the CMIP5 model results, the mean winter snow depth distinctly decreases from the northwest to the southeast.…”

Section: Climate Variables Related To Tp Permafrostmentioning

confidence: 99%

Estimation of permafrost on the Tibetan Plateau under current and future climate conditions using the CMIP5 data

Chang

Lyu

Luo

et al. 2018

Intl Journal of Climatology

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Permafrost has significant impacts on climate change through its strong interaction with the climate system. In order to better understand the permafrost variation and the role it plays in climate change, model outputs from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used in the present study to diagnose the near‐surface permafrost on the Tibetan Plateau (TP), assess the abilities of the models to simulate present‐day (1986–2005) permafrost and project future permafrost change on the TP under four different representative concentration pathways (RCPs). The results indicate that estimations of present‐day permafrost using the surface frost index (SFI) and the Kudryavtsev method (KUD) show a spatial distribution similar to that of the frozen soil map on the TP. However, the permafrost area calculated via the KUD is larger than that calculated via the SFI. The SFI produces a present‐day permafrost area of 127.2 × 104 km2. The results also indicate that the permafrost on the TP will undergo regional degradation, mainly at the eastern, southern and northeastern edges, during the 21st century. Furthermore, most of the sustainable permafrost will probably exist only in the northwestern TP by 2099. The SFI also indicates that the permafrost area will shrink by 13.3 × 104 km2 (9.7%) and 14.6 × 104 km2 (10.5%) under the RCP4.5 and RCP8.5 scenarios, respectively, in the next 20 years and by 36.7 × 104 km2 (26.6%) and 45.7 × 104 km2 (32.7%), respectively, in the next 50 years. The results are helpful for us to better understand the permafrost response to climate change over the TP, further investigate the physical mechanism of the freeze–thaw process and improve the model parameterization scheme.

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“…Su et al () also reported that there are cold biases of 1.1°–2.5 °C in the eastern part of TP in winter and spring from CMIP5 simulations during 1961–2005. Furthermore, Wei and Dong () pointed out that the annual means of snow depth in all CMIP5 models over TP are larger than those derived from in situ and satellite observations. These studies imply that an important physical process could be missing in most of state‐of‐the‐art general circulation models (GCMs).…”

Section: Introductionmentioning

confidence: 99%

Impact of 3‐D Radiation‐Topography Interactions on Surface Temperature and Energy Budget Over the Tibetan Plateau in Winter

Lee

Liou²,

Wang

et al. 2019

JGR Atmospheres

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We incorporate a parameterization to quantify the effect of three‐dimensional (3‐D) radiation‐topography interactions on the solar flux absorbed by the surfaces, including multiple reflections between surfaces and differences in sunward/shaded slopes, in the Community Climate System Model version 4 (CCSM4). A sensitivity experiment is carried out using CCSM4 with the prescribed sea surface temperature for year 2000 to investigate its impact on energy budget and surface temperature over the Tibetan Plateau (TP). The results show that the topographic effect reduces the upward surface shortwave flux and, at the same time, enhance snowmelt rate over the central and southern parts of TP. Comparing to observations and the ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5), we found that CMIP5 models have a strong cold bias of 3.9 K over TP, partially induced by the strong reflection of shortwave fluxes. We show that the inclusion of topographic effect reduces the substantial biases of upward shortwave fluxes and surface air temperatures over TP by 13% in the CCSM4 model.

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Assessment of Simulations of Snow Depth in the Qinghai-Tibetan Plateau Using CMIP5 Multi-Models

Cited by 32 publications

References 30 publications

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

Estimation of permafrost on the Tibetan Plateau under current and future climate conditions using the CMIP5 data

Impact of 3‐D Radiation‐Topography Interactions on Surface Temperature and Energy Budget Over the Tibetan Plateau in Winter

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