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Land surface albedo plays a critical role in climate, hydrological and biogeochemical modeling, and weather forecasting. It is often assigned in models and satellite retrievals by albedo climatology look‐up tables using land cover type and other variables; however, there are considerable differences in albedo simulations among models, which partially result from uncertainty in obsolete albedo climatology. Therefore, this study introduces a new global 500 m daily blue‐sky land surface albedo climatology data set under both actual and snow‐free surface conditions utilizing 20‐year Moderate Resolution Imaging Spectroradiometer products from Google Earth Engine. In situ measurements from 38 long‐term‐maintained sites were utilized to validate the accuracies of different albedo climatology datasets. The root‐mean‐square error, bias, and correlation coefficient of the new climatology are 0.031, −0.003, and 0.96, respectively, which are more accurate than the Global Land Surface Satellite, GlobAlbedo, and 16 model datasets. Data intercomparison suggests that ERA5 exhibits better performance than Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and 14 Coupled Model Intercomparison Projects Phase 6 models. However, it contains positive biases in the snow‐free season, while MERRA2 underestimates the snow albedo. Global albedo variation associated with basic surface plant functional types was also characterized, and snow impact was considered separately. Temporal variability analysis indicates that traditional climatology datasets with coarser temporal resolutions (≥8 days) cannot capture albedo variation over areas with distinct snow seasons, especially in central Eurasia and boreal regions. These results confirm the high reliability and robustness of the new albedo climatology in model assessment, data assimilation, and satellite product retrievals. Plain Language Summary: Surface albedo represents the Earth's surface ability to reflect solar energy, and thus directly influences atmospheric and water circulations by controlling available energies. As an essential variable estimated in current climate models, surface albedo is assigned by searching look‐up tables based on the land cover and soil types. However, there are large differences in simulated albedo among various models, partially because the albedo climatology in these tables was generated in the 1980s and needs to be updated. As global surface albedo has been accurately retrieved from satellite data for decades, we were able to produce a new albedo climatology data set under actual and snow‐free conditions with high spatiotemporal resolutions. Ground validation and data comparison indicate that the new data set performs with higher accuracy compared with other datasets. Moreover, assessment by the new climatology data set shows that albedo from advanced climate models and reanalysis datasets has clear uncertainties, especially over high latitudes and the Tibetan Plateau. Temporal variation in global albedo is better characterized at basic vegetation types. The new albedo climatology data set shows great potential for use in model assessment, modeling improvement, and satellite retrieval of other variables. Key Points: A global 500 m daily blue‐sky land surface albedo climatology data set in actual and snow‐free surface conditions was produced from Moderate Resolution Imaging SpectroradiometerSite validation and data comparison reveal its reliability and robustness in model assessment, data assimilation, and satellite retrievalsSimulated albedo data by state‐of‐art reanalysis and Coupled Model Intercomparison Projects Phase 6 models still have a clear bias in polar regions and the Tibetan Plateau [ABSTRACT FROM AUTHOR]

Abstract: Several groups routinely produce gridded land surface air temperature (LSAT) data sets using station measurements to assess the status and impact of climate change. The Intergovernmental Panel on Climate Change Fifth Assessment Report suggests that estimated global and hemispheric mean LSAT trends of different data sets are consistent. However, less attention has been paid to the intercomparison at local/regional scales, which is important for local/regional studies. In this study we comprehensively compare four data sets at different spatial and temporal scales, including Berkley Earth Surface Temperature land surface air temperature data set (BEST‐LAND), Climate Research Unit Temperature Data Set version 4 (CRU‐TEM4v), National Aeronautics and Space Administration Goddard Institute for Space Studies data (NASA‐GISS), and data provided by National Oceanic and Atmospheric Administration National Center for Environmental Information (NOAA‐NCEI). The mean LSAT anomalies are remarkably different because of the data coverage differences, with the magnitude nearly 0.4°C for the global and Northern Hemisphere and 0.6°C for the Southern Hemisphere. This study additionally finds that on the regional scale, northern high latitudes, southern middle‐to‐high latitudes, and the equator show the largest differences nearly 0.8°C. These differences cause notable differences for the trend calculation at regional scales. At the local scale, four data sets show significant variations over South America, Africa, Maritime Continent, central Australia, and Antarctica, which leads to remarkable differences in the local trend analysis. For some areas, different data sets produce conflicting results of whether warming exists. Our analysis shows that the differences across scales are associated with the availability of stations and the use of infilling techniques. Our results suggest that conventional LSAT data sets using only station observations have large uncertainties across scales, especially over station‐sparse areas. In developing future LSAT data sets, the data uncertainty caused by limited and unevenly distributed station observations must be reduced. [ABSTRACT FROM AUTHOR]

Abstract: Earth surface net radiation (Rn) characterizes the surface radiation budget and plays a critical role in ecological, biogeochemical, and hydrological processes. The Rn products from remote sensing and reanalysis have not been validated comprehensively. In this study, four Rn products (Clouds and the Earth's Radiant Energy System [CERES], ERA‐Interim, Modern‐Era Retrospective analysis for Research and Applications version 2, and Japanese 55‐year Reanalysis) were validated using global ground measurements on monthly (255 sites) and annual (172 sites) timescales. These products have similar accuracies, with average root‐mean‐square error (RMSE) ranges of 5.35 W m−2 (monthly) and 2.30 W m−2 (annually). However, varying accuracies and systemic biases exist across different climatic zones. The annual land Rn intercomparison illustrates that large uncertainty exists over polar regions and deserts. A significantly negative annual anomaly in the CERES product for the 2001–2008 period is identified when examining annual Rn anomalies over the global land surface. Detailed uncertainty analysis indicates that the global CERES Rn anomaly is mainly due to different versions of input data such as aerosol optical thickness and atmospheric profiles (in 2006 and 2008) and cloud properties (in 2002). This work demonstrates that temporal analysis provides powerful quality control for global time series satellite products when the validation using ground measurements fails to capture potential issues. [ABSTRACT FROM AUTHOR]

The land surface thermal-infrared broadband emissivity (BBE) is a vital variable for estimating land surface radiation budgets (SRBs). We develop a framework for retrieving the 30 m BBE from Landsat surface reflectance data to estimate SRBs at finer scales and validate coarse resolution data. In the developed framework, the land surface is classified as bare soils and vegetated surfaces to allow different algorithms to be used for the BBE estimation. We propose a downscaling algorithm that uses the empirical relationship between the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) BBE and Landsat surface reflectance at 90 m to retrieve the 30 m BBE over bare soils. A look-up table (LUT)-based algorithm is proposed for vegetated surfaces. The BBE is interpolated from a LUT that is constructed from the 4SAIL radiative transfer model with inputs of the leaf BBE, the soil background BBE, and the leaf area index (LAI). Ground measurements that were collected at 11 relatively homogeneous sandy sites during three independent field campaigns are used to validate the proposed algorithm over bare soils. The average difference between the retrieved and field-measured BBEs is 0.012. We produce the land surface BBE of China in 2008 by using the developed framework and composited winter and summer seasonal BBE maps. The composited seasonal BBE maps are compared to the seasonal BBE maps derived from the ASTER emissivity product. The bias is within ±0.005 over bare soils and ranges from 0.012 to 0.019 over vegetated surfaces. Combined with the validated results in this study and published references, the comparison results demonstrate the good performance of the developed framework. This study provides a new perspective on estimating BBEs from sensors with only a thermal-infrared channel. [ABSTRACT FROM AUTHOR]

A broadband parameterization scheme for estimating surface solar irradiance (SSI, ~ 0.3–4.0 μm) is developed based on extensive radiative transfer simulations on a diversity of atmospheric conditions. In the new scheme, the accumulated SSI arriving at the surface is considered as a whole, but the different radiative attenuations, such as the ozone absorption, permanent gas absorption, water vapor absorption, Rayleigh scattering, aerosol absorption and scattering, and cloud absorption and scattering, are accounted for separately. In particular, for the cloud radiative attenuations, the single scattering and multiple scattering of clouds are parameterized respectively to improve the accuracy. Compared with the complicated spectral radiative transfer model of LibRadtran (Emde et al., 2016, https://doi.org/10.5194/gmd‐9‐1647‐2016), more than 90% of the SSI discrepancies are within ±5%, which means in theory the new parameterization scheme is very accurate. When the broadband parameterization scheme is practically applied to Moderate Resolution Imaging Spectroradiometer (MODIS) products, the validation results indicate that the high‐quality instantaneous SSI can be thus acquired. At eight sites over China, R2 ranges from 0.86 to 0.96 and all RMSEs lie within 100 W/m2, while at seven sites over the United States, R2 fluctuates within the range of 0.82–0.94 and the RMSEs at most sites are <90 W/m2, with the exception of Table Mountain. The performance of the new parameterization scheme is comparable to those of more complicated methods such as the look‐up‐table methods and the δ‐two‐stream approximation methods. But it is more attractive due to its simplicity and efficiency. Key Points: A new broadband SSI parameterization scheme is developedThe parameterization scheme is efficient and easy to implementValidation results show a comparable accuracy with other approaches [ABSTRACT FROM AUTHOR]

The MODerate‐resolution Imaging Spectroradiometer (MODIS) provides spatially contiguous measurements of terrestrial biophysical variables, which can be used to estimate the terrestrial latent heat flux (LE). MODIS‐derived shortwave infrared reflectance (SWIR) metrics (SWIRs) are sensitive to the soil moisture and vegetation water stress. In this study, we used the MODIS‐derived SWIRs with eddy covariance flux measurements obtained from 25 flux tower sites representing 10 different land cover types within China to evaluate the sensitivity of SWIRs to ground‐measured evaporation fraction and LE. The water constraint metrics determined using the MODIS‐derived SWIR generally corresponded better with the ground‐measured evaporation fraction values than those obtained without using SWIR. The MODIS‐derived SWIRs were used as proxies for the soil and vegetation water supply constraints in a revised Priestley‐Taylor algorithm to estimate the terrestrial LE. The estimated LE using the MODIS‐derived SWIRs generally corresponded well with the ground‐measured LE (0.56 ≤ R2 ≤ 0.97) for most of the flux tower sites. Regional algorithm sensitivity analysis using the MODIS‐derived SWIRs as water supply proxies demonstrated that water limitations reduce LE by more than 53% over China, and the atmospheric vapor pressure deficit and relative humidity are not sufficient to characterize both the atmosphere demand and water supply for LE estimation. Our results demonstrate the potential of using MODIS‐derived SWIRs to characterize soil and vegetation water supply factors for determining LE, where the relatively high spatial and temporal resolutions (500 m and daily) are closer to the scale of the eddy covariance ground measurements. Key Points: MODIS‐derived SWIR corresponded better with EF than those without using SWIRMODIS‐derived SWIRs were used as proxies for water supply to estimate LEMODIS‐derived SWIR can be used to detect the impacts of water stress on LE [ABSTRACT FROM AUTHOR]