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The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides the long‐term soil moisture (SM) products and this study conducts a comprehensive assessment of SM products of multiple CMIP6 model simulations over conterminous China. Both near‐surface (0–10 cm) SM simulations from 40 models and root‐zone (0–100 cm) SM from 25 models are compared with a set of in situ and a satellite‐based data set in the growing season for 1992–2013. The long‐term trend and interannual variability of near‐surface SM for 1961–2014 are further evaluated with an offline land surface modeling data set. Simulations from most models broadly capture the spatial characteristics of observation, and the multimodel mean (MME) well reproduces seasonal variations over the majority of regions, regardless of the large spread across models. SM from CMIP6 models from the same institution likely manifests similar performances, which indicates that the land‐surface scheme plays a dominant role in the CMIP6 SM reproduction. The majority of CMIP6 models well simulate the overall drying trend in China as a whole and the signs of a SM trend are highly consistent across different models, but the areas with significant wetting/drying trends vary with models. The spatial patterns of SM interannual variability are model‐dependent. MME is overall superior to the simulation of individual model and may have potential applications in future research. The heterogeneity of SM performances across CMIP6 models reveals the complexity and uncertainty in modeling land surface variables, suggesting the need for improving representations of land surface processes in the coupled models. Plain Language Summary: Soil moisture influences the exchange of energy, water, and carbon within the soil‐vegetation‐atmosphere system. Climate models are extensively applied to simulate soil moisture (SM) that is required to be assessed before applications. In this study, we comprehensively evaluate the SM simulations in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) historical experiments with a set of station observations, a satellite‐based data set, and a high‐quality pre‐derived SM product in China as the reference data sets. The results show that the spatial and temporal characteristics of SM simulated by the majority of models are consistent with reference data sets and the multimodel mean (MME) well reproduces SM seasonal variations in most regions. Resemble performances are found for models from the same institution. For China as a whole during 1961–2014, the drying trend is reproduced by most models. There are high consistencies of trend patterns across the country among models, although the magnitudes of the trend are model‐dependent. The interannual variability of SM varies with models in terms of spatial patterns, for which MME overall shows better performance than the simulation of the individual model. This study provides a basic reference for selecting SM products in CMIP6 for future applications. Key Points: Station‐based, satellite‐based, and pre‐derived soil moisture (SM) data sets in China are used to assess the Coupled Model Intercomparison Project multimodel simulationsThe SM simulations are similar to models in the same institutions and most models reproduce the recent drying trend in ChinaThe multimodel mean performs overall superior to the individual model, which is suggested for future applications [ABSTRACT FROM AUTHOR]

A Regional Extreme Climatic Change Index (RECCI), simultaneously considering the changes in intensity, frequency and interannual variability of three major extreme climatic variables (i.e., precipitation, temperature and wind speed), is constructed to represent regional changes of climate extremes in response to global warming. First, the daily outputs from 13 models in the Coupled Model Intercomparison Project phase 5 project in both historical and future simulations under the Representative Concentration Pathway 8.5 scenario are used to compute the extreme climatic indices. Second, the RECCI is computed on both annual and seasonal time scales during three periods (i.e., 2016–2035, 2046–2065 and 2080–2099) over 26 subregions. Finally, the spatiotemporal change of the RECCI is investigated, and then, the 26 subregions are classified into four categories for each period. The first category with the largest RECCI value is very sensitive to global warming, which is called hot spots of climate extremes. The results show that most hot spots are not time invariant on annual and seasonal time scales with some exceptions. On the annual time scale, the Amazon Basin is the only persistent hot spot in all three periods. For the seasonal time scale in March‐April‐May, the climate extremes in the Amazon Basin always display the strongest responsiveness to global warming, and the Eastern Africa is the only persistent hot spot in June‐July‐August in three periods. Similar results are also found for the other two seasons and periods. In addition, the change in extreme temperature is crucial over the East Asia with change in frequency prominent. Key Points: The majority of the regions in different categories of climate extremes are not time invariant on annual and seasonal time scalesThe Amazon Basin is the only persistent hot spot of climate extremes in 2016–2035, 2046–2065 and 2080–2099The Regional Extreme Climatic Change Index is a robust metric that is not sensitive to the actual models chosen [ABSTRACT FROM AUTHOR]

Abstract: Change in the near‐surface soil freeze/thaw cycle is critical for assessments of hydrological activity, ecosystems, and climate change. Previous studies investigated the near‐surface soil freeze/thaw cycle change mostly based on in situ observations and satellite monitoring. Here numerical simulation method is tested to estimate the long‐term change in the near‐surface soil freeze/thaw cycle in response to recent climate warming for its application to predictions. Four simulations are performed at 0.5° × 0.5° resolution from 1979 to 2009 using the Community Land Model version 4.5, each driven by one of the four atmospheric forcing data sets (i.e., one default Climate Research Unit‐National Centers for Environmental Prediction [CRUNCEP] and three newly developed Modern Era Retrospective‐Analysis for Research and Applications, Climate Forecast System Reanalysis, and European Centre for Medium‐Range Weather Forecasts Reanalysis Interim). The observations from 299 weather stations in both Russia and China are employed to validate the simulated results. The results show that all simulations reasonably reproduce the observed variations in the ground temperature, the freeze start and end dates, and the freeze duration (the correlation coefficients range from 0.47 to 0.99, and the Nash‐Sutcliffe efficiencies range from 0.19 to 0.98). Part of the simulations also exactly simulate the trends of the ground temperature, the freeze start and end dates, and the freeze duration. Of the four simulations, the results from the simulation using the CRUNCEP data set show the best overall agreement with the in situ observations, indicating that the CRUNCEP data set could be preferentially considered as the basic atmospheric forcing data set for future prediction. The simulated area‐averaged annual freeze duration shortened by 8.03 days on average from 1979 to 2009, with an uncertainty (one standard deviation) of 0.67 days caused by the different atmospheric forcing data sets. These results address the performance of numerical model in simulating the long‐term changes in the near‐surface soil freeze/thaw cycle and the role of different atmospheric forcing data sets in the simulation, which are useful for the prediction of future freeze/thaw dynamics. [ABSTRACT FROM AUTHOR]

Numerical simulation is of great importance to the investigation of changes in frozen ground on large spatial and long temporal scales. Previous studies have focused on the impacts of improvements in the model for the simulation of frozen ground. Here the sensitivities of permafrost simulation to different atmospheric forcing data sets are examined using the Community Land Model, version 4.5 (CLM4.5), in combination with three sets of newly developed and reanalysis-based atmospheric forcing data sets (NOAA Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-I), and NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA)). All three simulations were run from 1979 to 2009 at a resolution of 0.5° × 0.5° and validated with what is considered to be the best available permafrost observations (soil temperature, active layer thickness, and permafrost extent). Results show that the use of reanalysis-based atmospheric forcing data set reproduces the variations in soil temperature and active layer thickness but produces evident biases in their climatologies. Overall, the simulations based on the CFSR and ERA-I data sets give more reasonable results than the simulation based on the MERRA data set, particularly for the present-day permafrost extent and the change in active layer thickness. The three simulations produce ranges for the present-day climatology (permafrost area: 11.31-13.57 × 106 km2; active layer thickness: 1.10-1.26 m) and for recent changes (permafrost area: −5.8% to −9.0%; active layer thickness: 9.9%-20.2%). The differences in air temperature increase, snow depth, and permafrost thermal conditions in these simulations contribute to the differences in simulated results. [ABSTRACT FROM AUTHOR]