To develop a finescale dataset for the purpose of analyzing historical climatic change over the Tibet Plateau (TP), a high-resolution regional climate simulation for 1979–2011 was conducted using the Weather Research and Forecasting (WRF) Model driven by the ERA-Interim (ERA-Int). This work evaluates the high-resolution (30 km) WRF simulation in terms of annual variation, spatial structure, and 33-yr temporal trends of surface air temperature (Tair) and precipitation (Prec) over the TP, with reference to station observations. Another focus is on the examination of the height–temperature relationship. Inheriting from its forcing, the WRF simulation presents an apparent cold bias in the TP. The cold bias is largely reduced by a lapse rate correction of the simulated surface air temperature with help of the station and model elevations. ERA-Int presents the same sign of Tair and Prec trends as the observations, but with smaller magnitude, especially in the dry season. Compared to its forcing, the WRF simulation improves the simulation of the annual cycles and temporal trends of Tair and Prec in the wet season. In the dry season, however, there is hardly any improvement. The observed Tair presents a downward linear trend in the lapse rate. This feature is examined in the WRF simulation in comparison to ERA-Int. The WRF simulation captures the observed lapse rate and its temporal trend better than ERA-Int. The decreasing lapse rate over time confirms that Tair change in the TP is elevation dependent.

Desertification in the Tibetan Plateau (TP) has drawn increasing attention in the recent decades. It has been postulated as a consequence of increasing climate aridity due to the observed warming. This study quantifies the aridity changes in the TP and attributes the changes to different climatic factors. Using the ratio of precipitation to potential evapotranspiration (P/PET) as an aridity index, we used observed meteorological records at 83 stations in the TP to calculate PET using the Penman-Monteith algorithm and the ratio. Spatial and temporal changes of P/PET in 1979-2011 were analyzed. Results show that stations located in the arid and semi-arid northwestern TP are becoming significantly wetter, and half of the stations in the semi-humid eastern TP are becoming drier, though not significantly, in the recent three decades. The aridity change patterns are significantly correlated with the change patterns of precipitation, sunshine duration and diurnal temperature range. Temporal correlations between the annual P/PET ratio and other meteorological variables confirm the significant correlation between aridity and the three variables, with precipitation being the dominant driver of P/PET changes at the interannual time scale. Annual PET are insignificantly but negatively correlated with P/PET in the cold season. In the warm season, however, the correlation between PET and P/PET is significant at the confidence level of 99.9% when the cryosphere near the surface melts. Significant correlation between annual wind speed and aridity occurs in limited locations and months. Consistency in the climatology pattern and linear trends in surface air temperature and precipitation calculated using station data, gridded data, and nearest grid-to-stations for the TP average and across sub-basins indicate the robustness of the trends despite the large spatial heterogeneity in the TP that challenge climate monitoring.

Available observations below 5000 m altitude suggest that some mountain regions are undergoing accelerated elevationdependent warming (EDW) in response to global or regional climate change. We address the question of whether EDW exists above 5000 m altitude, which is the elevation of much of the mountainous portion of the Tibetan Plateau, and headwaters to most of Asia's major rivers. We analyzed four data sources: in situ observations, gridded observations, ERA-Interim reanalysis, and Weather Research and Forecasting (WRF) regional climate model output over the portion of the Tibetan Plateau above 5000 m elevation. We also analyzed the relative contributions of changes in water vapor, diabatic heating, snow, and surface energy changes to EDW in WRF simulations and ERA-Interim. Gridded observations over the Tibetan Plateau show EDW below 5000 m, in apparent consistency with studies elsewhere. However, the gridded observations above 5000 m are essentially entirely extrapolated from lower elevations. Despite differences in details, neither ERA-Interim nor WRF indicate EDW above 5000 m. The WRF simulation produces more realistic temperature profiles at elevations where observations exist, which are also consistent with the simulated profiles of factors contributing to vertical heating. Furthermore, WRF projects no EDW above 5000 m in future climate projections (with CCSM4 boundary conditions) for RCP 4.5 and 8.5 global emission scenarios. Therefore, we conclude that EDW above 5000 m over the Tibetan Plateau is not occurring, nor is it likely to occur in future decades.

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Simulation domain and topography in the dynamical downscaling and different resolution of the topography over the Tibetan Plateau (a) in the Community Climate System Model (CCSM) (b) and Weather Research and Forecasting (WRF) model (c).

Quality of a downscaling depends primarily on the quality of the driving global climate model (GCM). In this study, historical atmospheric conditions simulated by 14 GCMs in CMIP5 are evaluated for downscaling applications centred over the Tibetan Plateau (TP) with ERA‐Interim reanalysis as reference. Another reanalysis NCEP‐DOE is also used to estimate the uncertainty associated with the reanalyses. Performances of six frequently used GCM variables, involving atmospheric circulation, air temperature and humidity, are evaluated in terms of biases, spatial correlation coefficient, mean absolute error as well as distinct seasonal features. To detect distributional biases, the two‐sample Kolmogorov–Smirnov test (KS test) is applied to both the original time series and their anomalies on the monthly scale. A spatial ranking scheme is finally applied to objectively quantify overall relative merits of the GCMs over this region. We found that differences between two reanalysis datasets are negligible over this region. Regarding the GCMs' performances, the biases of the simulated variables show remarkable differences among models. Sea level pressure and 500 hPa geopotential height are well simulated by all the GCMs, whereas specific humidity at 600 hPa has a significant dry bias and temperature at 500 hPa has a sizable cold bias. The spatial pattern of the upper‐tropospheric circulation is relatively poorly simulated. The KS test suggests that the climatic mean and higher order moments play about an equal role in causing the errors. According to the ranking scores, CCSM4, CNRM‐CM5, MPI‐ESM‐LR, NorESM1‐M, MIROC4h, MPI_ESM_MR and CSIRO‐MK are relatively superior to other GCMs for this region.

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