Abstract. Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation (P ), evapotranspiration (ET), runoff (R), and the total water storage change (TWSC) at 0.5 • spatial resolution globally and to obtain water budget closure (i.e., to enforce P − ET − R − TWSC = 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term , monthly 0.5 • resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High‐Resolution Infrared Radiometric Sounder retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.

Abstract. Closing the terrestrial water budget is necessary to providing consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in-situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g. in-situ observation, satellite remote sensing, land surface model and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. In this study, a systematic method is developed to optimally combine multiple available data sources for precipitation (P), evapotranspiration (ET), runoff (R) and the total water storage change (TWSC) at 0.5° spatial resolution globally and to obtain water budget closure (i.e. to enforce P − ET − R − TWSC = 0) through a Constrained Kalman Filter (CKF) data assimilation technique. The resulting long-term (1984–2010), monthly, 0.5° resolution global terrestrial water cycle Climate Data Record (CDR) dataset is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This dataset serves to bridge the gap between sparsely gauged regions and the regions with sufficient in-situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in-situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS) and ET from FLUXNET. The dataset is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

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Sensible heat flux is a turbulent flux driving interactions between the Earth’s surface and the atmosphere, propelling local and regional climate. While turbulent fluxes are measured in situ, global scales require estimates at larger spatial scales, which can be made using remotely sensed satellite data. This study uses a first-order approximation to calculate the unconstrained hourly, terrestrial, 0.5°-resolution sensible heat flux using a land surface temperature consistent with the High Resolution Infrared Radiation Sounder (HIRS) retrievals, six reanalysis-based air temperature products, and a dataset of Zilitinkevich empirical constant Czil values. This sensible heat flux dataset is constrained using the daily Bowen ratio and available energy, to produce nine constrained, daily products. All resulting global, terrestrial averages are within the uncertainty range of ±6.3 W m−2 from the 38.8 W m−2 global annual average previously reported in the literature. The product constrained with the net radiation using the Moderate Resolution Infrared Spectroradiometer (MODIS) albedo and air temperature from the National Centers for Environmental Protection (NCEP) Climate Forecast System Reanalysis (CFSR) performs closest to the FLUXNET ground observations in the monthly analysis. These sensible heat flux estimates should be used for benchmarking global climate models at monthly or annual scales, and improvements should be made to the accuracy of input variables, particularly the temperature gradient, Czil estimates, and the roughness length.

Sensible heat flux directly influences local and regional climate and can be estimated using remotely sensed satellite observations. Although significant efforts have been made to estimate sensitivity and uncertainty in energy flux estimates at the local and regional scales using both models and algorithms compatible with remotely sensed satellite data, few studies quantify the sensitivity or uncertainty at the global scale, enabling a global comparison among uncertainty drivers. This study uses the 10 percentile change from the mean value in the empirical cumulative distribution function for the distribution of each input data set to calculate the sensitivity of the unconstrained, terrestrial sensible heat flux to change in the input data sets and uses this sensitivity in a first‐order analysis of the uncertainty in the sensible heat flux. The largest sensitivities to the Zilitinkevich empirical constant (Czil) are in the Amazon, northern Australia, and the plains of North America, while the sensitivity of the sensible heat flux to the temperature gradient is largest in dry regions of shorter vegetation. The Czil contributes most to the uncertainty of over 50–100 W/m2 in the Amazon and Indonesia, while the temperature gradient contributes most to the uncertainty elsewhere, producing an overall global average uncertainty of 24.8 W/m2. Future work should reduce the uncertainties in the temperature gradient and the Czil to reduce the uncertainty in sensible heat flux estimates.

Land surface temperature (LST) is a critical state variable for surface energy exchanges as it is one of the controls on emitted radiation at Earth’s surface. LST also exerts an important control on turbulent fluxes through the temperature gradient between LST and air temperature. Although observations of surface energy balance components are widely accessible from in situ stations in most developed regions, these ground-based observations are not available in many underdeveloped regions. Satellite remote sensing measurements provide wider spatial coverage to derive LST over land and are used in this study to form a high-resolution, long-term LST data product. As selected by the Global Energy and Water Exchanges project (GEWEX) Data and Assessments Panel (GDAP) for development of internally consistent datasets, the High Resolution Infrared Radiation Sounder (HIRS) data are used for the primary satellite observations because of the data record length. The final HIRS-consistent, hourly, global, 0.5° resolution LST dataset for clear and cloudy conditions from 1979 to 2009 is developed through merging the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) LST estimates with the HIRS retrievals using a Bayesian postprocessing procedure. The Baseline Surface Radiation Network (BSRN) observations are used to validate the HIRS retrievals, the CFSR LST estimates, and the final merged LST dataset. An intercomparison between the original retrievals and CFSR LST datasets, before and after merging, is also presented with an analysis of the datasets, including an error assessment of the final LST dataset.

Environmental Prediction (NCEP) Stage IV radar data set (Lin and Mitchell 2005), which is a 3-hourly, 0.25° gridded product available from 50°N−50°S. (2) In situ observation The GPCC product (Schneider et al. 2014) is gridded rain gauge-analysis from in-situ observations which are collected by the World Meteorological Organization (WMO) at monthly, 0.5°. A number of approximate 67000 ground gauge stations over the globe are interpolated in GPCC. (3) Hybrid data sets The PGF dataset used in this study, which is an updated version of the PGF described in (Sheffield et al. 2006), provides near-surface meteorological data for driving land surface models and other terrestrial modeling systems. For the precipitation, the PGF combines reanalysis from National Centers for Environmental Prediction-National Center for Atmospheric Research (NECP-NCAR), satellite remote sensing from TRMM and Global Precipitation Climatology Project (GPCP), gauge observation from Climate Research Unit (CRU), and then disaggregates in time and space. The dataset is currently available at 0.25 degree, 3-hourly globally for 1948-2010. CHIRPS (Funk et al. 2014) combines reanalysis from Climate Forecast System Version 2 (CFSv2), satellite remote sensing infrared (IR) from Climate Prediction Center (CPC) and National Climatic Data Center (NCDC), satellite remote sensing precipitation from TRMM, monthly precipitation climatology (CHPClim) with ground observations from a variety of sources. CHIRPS is currently available at 0.05° resolution from 1981 to near present at quasi-global (50°N−50°S, 180°N−180°S). In this study the global monthly CHIRPS at 0.5° is applied in order to keep a uniform spatial and temporal resolution with other products.