Evaluation of CORDEX regional climate models in simulating temperature and precipitation over the Tibetan Plateau

Guo, Dali; Sun, Jun; Yu, Enyan

doi:10.1080/16742834.2018.1451725

Cited by 46 publications

(28 citation statements)

References 22 publications

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“…Using weather stations biased to eastern regions of the TP has been shown to overestimate the warming trend of the plateau as a whole and magnify inter-annual variability in 2 m air temperature (Figure 11). Reanalysis data and model output can expand analysis to the scale of the entire TP, but both have relatively coarse spatial resolution (Wang and Zeng, 2012;Su et al, 2013;Hu et al, 2014;Maussion et al, 2014;Jiang et al, 2016;Giorgi and Gao, 2018;Guo et al, 2018;Gao et al, 2018b). The accuracy of reanalysis data depends on the quantity of surface observations in the region (Dee et al, 2011), and there are therefore known inaccuracies in the reanalyses over the TP (Wang and Zeng, 2012).…”

Section: Discussionmentioning

confidence: 99%

Satellite data reveal southwestern Tibetan plateau cooling since 2001 due to snow‐albedo feedback

Guo

Sun

Yang

et al. 2019

Intl Journal of Climatology

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Given the threats that climate change poses to solid water reservoirs on the Tibetan plateau (TP), there is significant interest in understanding spatial patterns of climate change and their causes. Weather station observations have been extensively examined, but are scarce, resulting in an incomplete understanding of climate change across the TP, particularly in the west. Using recent (2001–2015) satellite‐based data sets (2 m air temperature, land surface temperature, albedo and snow cover), this study reveals that mean annual 2 m air temperature in the southwestern TP has decreased by 0.15°C/decade in contrast to overall warming (+0.18°C/decade) on the rest of the TP. Up to 45% (74%) of the variance in the annual (spring) 2 m air temperature can be explained by simultaneous change in snow‐induced albedo in the southwestern TP. The free atmosphere column over this region and Northwest India is cooling, providing a favourable environment for the decrease in 2 m air temperature observed. Moreover, the anomalous water vapour transport into the southwestern TP is advantageous for increased snowfall and the associated decrease in 2 m air temperature. The implications of this anomalous cooling under global warming have yet to be fully considered, in particular for the futures of glaciers and snowpack over the Himalayan Mountains in the southwestern TP.

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Section: Discussionmentioning

confidence: 99%

Satellite data reveal southwestern Tibetan plateau cooling since 2001 due to snow‐albedo feedback

Guo

Sun

Yang

et al. 2019

Intl Journal of Climatology

Self Cite

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“…The GMFD was originally developed as the atmospheric forcing data set for offline land surface models. This data set has been evaluated and widely used in climate change, model validation, and data intercomparison studies over China (Chen et al, 2017;Gao et al, 2013;Guo et al, 2018;Guo & Wang, 2016). The CN05.1 data set is constructed using daily observations from more than 2,400 meteorological stations in China.…”

Section: Methodsmentioning

confidence: 99%

“…The CN05.1 shows large uncertainty over western China where sparse observation stations are available, especially over regions from northern Qinghai-Tibet Plateau (TP) to Kunlun Mountains and Taklimakan Desert (Wu & Gao, 2013). This data set has been evaluated and widely used in climate change, model validation, and data intercomparison studies over China (Chen et al, 2017;Gao et al, 2013;Guo et al, 2018;Guo & Wang, 2016). Figure 2a shows the spatial distribution of DMT in CN05.1 for 1961-2005.…”

Section: Methodsmentioning

confidence: 99%

Application of the Bias Correction and Spatial Downscaling Algorithm on the Temperature Extremes From CMIP5 Multimodel Ensembles in China

Wang

2019

Earth and Space Science

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The Bias Correction and Spatial Downscaling (BCSD) is a trend-preserving statistical downscaling algorithm, which has been widely used to generate accurate and high-resolution data set. We employ the BCSD technique to statistically downscale projected daily maximum temperature (DMT) over China from 13 general circulation models in Coupled Model Intercomparison Project Phase 5 (CMIP5) project to supplement the National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections data set under the Representative Concentration Pathway 2.6 (RCP2.6) scenario. We then compare the differences of DMT and four DMT-related indices (i.e., summer days (SU), annual maximum value of DMT (TXx), intensity, and frequency of heat wave) between before and after downscaling over eight subregions of China. The results indicate that the BCSD method reduces the cool bias of the DMT over the whole China compared with original CMIP5 simulations, especially over the Qinghai-Tibet plateau. The SU increases after downscaling for both China as a whole and most subregions except for South China. The BCSD also affects the mean value of TXx, intensity, and frequency of heat wave at subregional scales, although it shows little impact on China as a whole. Besides, the BCSD reduces the temporal variability of most indices except for the heat wave frequency. The most striking finding is that the intermodel spreads of DMT, SU, TXx, and heat wave intensity are dramatically reduced after downscaling compared with raw CMIP5 simulations. In summary, the BCSD method shows significant improvements to original CMIP5 climate projections under RCP2.6 scenario.

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“…For precipitation, particularly wet conditions are simulated by the COSMO-CLM over the Tibetan Plateau. Again, this bias seems to be common to several RCMs for areas characterized by complex topography (GUO et al, 2018;Gao et al, 2015;Feng and Fu, 2006) and is likely related to an overestimation of orographic precipitation enhancement in the models (Gerber et al, 2018) and/or to an incorrect simulation of the planetary boundary layer (Xu et al, 2016). Additionally, in the COSMO-CLM simulations a significant dry bias occurs over arid and desertic regions, especially in summer.…”

Section: Discussionmentioning

confidence: 96%

“…Importantly, an additional cold bias, in some cases lower than -10°C, is present for every season over the Tibetan Plateau. Other regional climate models suffer from a similar bias (GUO et al, 2018;Meng et al, 2018). This could likely be related to a bad representation of the albedo for highly complex topographies.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity studies with the Regional Climate Model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

Russo¹,

Kirchner²,

Schaap³

et al. 2019

Preprint

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Abstract. Due to its extension, geography and the presence of several under-developed or developing economies, the Central Asia domain of the Coordinated Regional climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, Regional Climate Models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for present-days, testing the effects of a set of singular physical parameterizations and their mutual interaction on the simulation of monthly and seasonal values of three variables that are important for impact studies: 2-meter temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo taking into consideration the ratio of forest fractions and the soil conductivity taking into account the ratio of liquid water and ice in the soil, allows to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The results for the mean climate appear to be independent of the observational dataset used for evaluation and of the boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

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Evaluation of CORDEX regional climate models in simulating temperature and precipitation over the Tibetan Plateau

Cited by 46 publications

References 22 publications

Satellite data reveal southwestern Tibetan plateau cooling since 2001 due to snow‐albedo feedback

Satellite data reveal southwestern Tibetan plateau cooling since 2001 due to snow‐albedo feedback

Application of the Bias Correction and Spatial Downscaling Algorithm on the Temperature Extremes From CMIP5 Multimodel Ensembles in China

Sensitivity studies with the Regional Climate Model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

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