The European Center for Medium-Range Weather Forecasts (ECMWF) released its latest reanalysis dataset named ERA5 in 2017. To assess the performance of ERA5 in Antarctica, we compare the near-surface temperature data from ERA5 and ERA-Interim with the measured data from 41 weather stations. ERA5 has a strong linear relationship with monthly observations, and the statistical significant correlation coefficients (p < 0.05) are higher than 0.95 at all stations selected. The performance of ERA5 shows regional differences, and the correlations are high in West Antarctica and low in East Antarctica. Compared with ERA5, ERA-Interim has a slightly higher linear relationship with observations in the Antarctic Peninsula. ERA5 agrees well with the temperature observations in austral spring, with significant correlation coefficients higher than 0.90 and bias lower than 0.70 degrees C. The temperature trend from ERA5 is consistent with that from observations, in which a cooling trend dominates East Antarctica and West Antarctica, while a warming trend exists in the Antarctic Peninsula except during austral summer. Generally, ERA5 can effectively represent the temperature changes in Antarctica and its three subregions. Although ERA5 has bias, ERA5 can play an important role as a powerful tool to explore the climate change in Antarctica with sparse in situ observations.

It is important to assess the freezing and thawing condition of ground surface for understanding the impacts of frozen ground on surface and subsurface hydrology, the surface energy and moisture balance, ecosystem conservation, and engineering construction on the Qinghai-Tibet Plateau (QTP). However, assessing the changes of ground surface freezing and thawing condition on the QTP still remains a challenge owing to data sparseness and discontinuous observations. The annual ground surface freezing index (GFI) and ground surface thawing index (GTI) could be used to predict changes of the thermal regime of permafrost and can be good indicators of climate change on the QTP, which has important engineering applications. In this study, we first calibrated the reanalysis ground surface temperature (GST) data using the methods of elevation correction on the QTP. After calibration, the quality of reanalysis data has been improved significantly. For the annual time series, the root mean square error decreased from 7.7 to 1.6 degrees C, the absolute value of mean bias error decreased from 7.5 to 0.0 degrees C, and the correlation coefficient increased from 0.62 to 0.86. Second, we estimated the annual and seasonal spatial distributions of GST. The spatial distribution of spring and autumn GST closely resembled the annual mean pattern. The long-term mean GFI and GTI from the calibrated reanalysis dataset were 1322.3 and 2027.9 degrees C/day, respectively. The GFI and GTI were presented as latitude and elevation zonation; it can also be seen that permafrost mostly occurred in the high GFI and low GTI regions. Estimating the GFI and GTI precisely will be utilized to model the permafrost distribution and estimate active layer thickness in the future.