The second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) land surface temperature (LST) dataset has been widely used for permafrost mapping in specific areas; however, its accuracy and application need to be evaluated over China. In this study, the MERRA-2 LST was evaluated against meteorological observations and three other reanalysis datasets including the first-generation MERRA, Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), using multiple statistical methods over the period from 1980 to 2018. The results revealed that the MERRA-2 LST generally exhibited cold bias compared to meteorological observations while performing better than the JRA55, ERA-Interim, and MERRA datasets in China, particularly in high-altitude permafrost regions. The comparison indicated that the time series trends for the MERRA-2 LST was consistent with that observed until 2000, and noticeably amplified cold bias, particularly for the period after 2005, was observed. Moreover, two correction methods were proposed and compared to reduce the error range for the MERRA-2 dataset, which was caused by the difference in elevation and land cover types. Calibrated results demonstrated that the linear regression method (Method1) between the elevation difference and mean bias error (MBE) for the LST performed well with root mean square error (RMSE) ranged from 2.15 to 5.97 ?C to 1.09-2.53 ?C. Moreover, in comparison with the MODIS LST dataset, the results showed that the adjusted MERRA-2 LST was in good agreement with smaller RMSEs against the observations. The surface frost number model was used for mapping the permafrost distribution over China based on the daily adjusted MERRA-2 LST dataset. According to the simulation results, the permafrost extent had a slightly continued degradation trend with a rate of 3-5% per decade over the past 39 years. The simulated permafrost area over China for the years 2010-2018 was approximately 1.63 x 106 km2, which accounts for 16.9% of mainland China. Thus, the adjusted MERRA-2 LST with high spatial-temporal consistency is the optimal choice to investigate permafrost distribution on a large scale.

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERAS-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i. e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m(3)/m(3), respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m(3)/m(3), respectively) performed better than ERAS-Land (R = 0.46; ubRMSE = 0.03 m(3)/m(3)). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERAS-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i. e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m(3)/m(3), respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m(3)/m(3), respectively) performed better than ERAS-Land (R = 0.46; ubRMSE = 0.03 m(3)/m(3)). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

Long-term and high-resolution gridded products of precipitation and temperature data are highly important to study the changes in climate and environment under global warming. Considering the uncertainties of these products in mountainous areas, it is necessary to evaluate the data reliability. This study evaluates the performances of the CMFD (China Meteorological Forcing Dataset) and ERA5-Land in simulating precipitation and temperature in the Qilian Mountains over the period of 1980-2018. We use the observation data of 28 basic meteorological stations in the Qilian Mountains to compare with the reanalysis products. Error metrics (the correlation coefficient (CC), the root mean square error (RMSE), the mean absolute error (MAE), and the relative bias (BIAS)) are used to quantify the monthly differences in existence between the observed data and reanalysis data. Our findings indicate that both CMFD and ERA5-Land could well reproduce the spatial distribution of mean monthly precipitation and temperature in the region. A good correlation is found between CMFD and OBS under different amounts of monthly precipitation conditions. The monthly average temperatures of CMFD and ERA5-Land reveal a high correlation with the observed results. Moreover, the CC values of CMFD and ERA5-Land precipitation products are the highest in autumn and the lowest in winter, and the CC values of both CMFD and ERA5-Land temperature products are higher in spring and autumn. However, we find that both reanalysis products underestimate the temperature to varying degrees, and the amount of precipitation is overestimated by ERA5-Land. The results of the evaluation show that the errors in precipitation yielded by CMFD as a whole are distinctly fewer than those yielded by ERA5-Land, while the errors in air temperature yielded by both ERA5-Land and CMFD are nearly identical to each other. Overall, ERA5-Land is more suitable than CMFD for studying the trends of temperature changes in the Qilian Mountains. As for simulation of precipitation, CMFD performs better in the central and eastern parts of the Qilian Mountains, whereas ERA5-Land performs better in the western part of the Qilian Mountains.