The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979-present. This study introduces a supplemental and improved set of land surface hydrological fields (''MERRA-Land'') generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.

Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day 21 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

17 This paper evaluates the simulation of snow by the Community Land Model version 4 (CLM4), 18 the land model component of the Community Earth System Model (CESM1.0.4). We ran CLM4 19 in an offline mode forced with the corrected Modern-Era Retrospective Analysis for Research 20 and Applications meteorological (MERRA-Land) forcing and evaluated the output for the period 21 January 2001 to January 2011 over the Northern Hemisphere poleward of 30°N. Simulated snow 22 cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a 23 set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) 24 SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the 25 Canadian Meteorological Center (CMC) daily snow analysis products, snow depth from the U.S. 26 National Weather Service Cooperative Observer Program (COOP), and snowpack telemetry 27 (SNOTEL) SWE observations. CLM4 SCF was converted into snow cover extent (SCE) to 28 compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and 29 average bias of -1.54 x 10 2 km 2 . Overall, CLM4 agreed well with IMS snow cover, the 30 percentage of correctly modeled snow/no snow being 94%. CLM4 snow depth and SWE agreed 31 reasonably well with the CMC product, the average bias (and RMSE) of snow depth and SWE 32 being 0.044 m (0.19 m) and −0.010 m (0.04 m), respectively. CLM4 underestimated SNOTEL 33 SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow 34 estimates, and constitutes a benchmark against which improvement of the model through data 35 assimilation can be measured. 36 37 Keywords: snow model, snow depth, snow water equivalent, simulation, snow density. 38 39 40 Land surface models (LSMs) suffer from one of or a combination of the following: 1) errors in 64 forcing data, 2) improper model parameters, 3) simplified physical processes, and 4) simplified 65 numerical solutions or methods. The performance of the snow scheme in the Community Land 66 Model version 4 (CLM4), which is the land component of CESM, is unknown because there has 67 not been a comprehensive validation against observations. A few studies have investigated some 68 aspects of the CLM4 snow model including the snow cover fraction (SCF) estimates, the snow 69 albedo parameterizations, and the water budget. Swenson and Lawrence (2012) demonstrated 70 that the parameterization used to determine SCF based snow depth in CLM4 exhibits a bias 71 towards early melt when compared to satellite-observed SCF, and they proposed a new SCF 72 parameterization as a function of snow water equivalent (SWE) instead. The results showed an 73 improvement of the surface energy budget in snow-covered areas. Thackeray et al. (2014) 74 recently showed that the weak simulated snow albedo feedback over the boreal forest was 75 attributable to a poor parameterization of the CLM4 mechanism of snow removal from the forest 76 canopy. CLM4 was also assessed against its older version (CLM3.5) (Lawre...