This powerful new land surface modeling system integrates data from advanced observing systems to support improved forecast model initialization and hydrometeorological investigations. Land surface temperature and wetness conditions affect and are affected by numerous climatological, meteorological, ecological, and geophysical phenomena. Therefore, accurate, high-resolution estimates of terrestrial water and energy storages are valuable for predicting climate change, weather, biological and agricultural productivity, and flooding, and for performing a wide array of studies in the broader biogeosciences. In particular, terrestrial stores of energy and water modulate fluxes between the land and atmosphere and exhibit persistence on diurnal, seasonal, and interannual time scales. Furthermore, because soil moisture, temperature, and snow are integrated states, biases in land surface forcing data and parameterizations accumulate as errors in the representations of these states in operational numerical weather forecast and climate models and their associated coupled data assimilation systems. That leads to incorrect surface water and energy partitioning, and, hence, inaccurate predictions. Reinitialization of land surface states would mollify this problem if the land surface fields were reliable and available globally, at high spatial resolution, and in near-real time.A Global Land Data Assimilation System (GLDAS) has been developed jointly by scientists at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) in order to produce such fields. GLDAS makes use of the new generation of groundand space-based observation systems, which provide data to constrain the modeled land surface states. Constraints are applied in two ways. First, by forcing the land surface models (LSMs) with observationbased meteorological fields, biases in atmospheric model-based forcing can be avoided. Second, by employing data assimilation techniques, observations of land surface states can be used to curb unrealistic model states. Through innovation and an ever-improving conceptualization of the physics underlying earth system processes, LSMs have continued to evolve and to display an improved ability to simulate complex phenomena. Concurrently, increases in computing power and affordability are allowing global simulations to be run more routinely and with less processing time, at spatial resolutions that could only be simulated using supercomputers five years ago. GLDAS harnesses this low-cost computing power to integrate observationbased data products from multiple sources within a sophisticated, global, high-resolution land surface modeling framework.What makes GLDAS unique is the union of all of these qualities: it is a global, high-resolution, offline (uncoupled to the atmosphere) terrestrial modeling system that incorporates satellite-and ground-based observations in order to produce opt...

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.