Search

This paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change. [ABSTRACT FROM AUTHOR]

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]

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. Key Points: The cool bias of the daily maximum temperature would reduce after statistical downscalingThe BCSD affects the mean value of extreme temperature indices at subregional scalesThe BCSD method would reduce the intermodel spreads of extreme temperature indices [ABSTRACT FROM AUTHOR]

• The seasonal cycles of simulated runoff show good agreement with the UNH/GRDC composite runoff. • VIC-CN05.1 generally reproduces the monthly time series of latent heat fluxes well at flux tower sites in China. • The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements. • The spatial patterns of simulated snow variables are generally similar to observations in northeastern and northwestern China. • Except for the northwest basin, the simulated monthly TWSC match well with that from the GRACE. This paper describes a daily 0.25° × 0.25° terrestrial hydrological dataset (VIC-CN05.1) for China during 1961–2017. The dataset is simulated by the latest variable infiltration capacity (VIC) model and driven by pure station-based atmospheric forcings and high-resolution soil parameters based on field surveys. Multiple observational (in situ and remote sensing) products and five statistical metrics, including biases, relative error (RE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R), are used to validate the model outputs. The results indicate that the seasonal cycles of simulated total runoff and composite runoff from the University of New Hampshire and Global Runoff Data Centre (UNH/GRDC) show good agreement in the Yangtze, southeast, and Pearl River basins, with positive REs ≤ 7% and NSEs ≥ 0.87. At the Changbaishan, Haibei Shrubland, and Qianyanzhou flux towers, VIC-CN05.1 reproduces the monthly latent heat fluxes well during 2003–2005. The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements (438 stations) during 2003–2016, with mean R = 0.80. The spatial patterns of simulated and observed snow depth, snow water equivalent (SWE), and snow cover in winter are generally consistent in the northeastern and northwestern China. Except for the northwest basin, the simulated monthly terrestrial water storage changes in most river basins (particularly the Yangtze, Huai, and Yellow River basins) match well with observations based on the Gravity Recovery and Climate Experiment (GRACE) during 2003–2016, with positive NSEs and mean R = 0.41. This terrestrial hydrology dataset is valuable for hydroclimatic research in China, such as model evaluations and long-term trend analyses. [ABSTRACT FROM AUTHOR]