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Rongqian Yang
,
Michael Ek
, and
Jesse Meng

Abstract

Surface water and energy budgets from the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) Atmospheric Model Intercomparison Project (AMIP-II) Global Reanalysis 2 (GR2), the North American Regional Reanalysis (NARR), and the NCEP Climate Forecast System Reanalysis (CFSR) are compared here with each other and with available observations over the Mississippi River basin. The comparisons in seasonal cycle, interannual variation, and annual mean over a 31-yr period show that there are a number of noticeable differences and similarities in the large-scale basin averages. Warm season precipitation and runoff in the GR2 are too large compared to the observations, and seasonal surface water variation is small. By contrast, the precipitation in both NARR and CFSR is more reasonable and in better agreement with the observation, although the corresponding seasonal runoff is very small. The main causes of the differences in both surface parameterization and approach used in assimilating the observed precipitation datasets and snow analyses are then discussed. Despite the discrepancies in seasonal water budget components, seasonal energy budget terms in the three reanalyses are close to each other and to available observations. The interannual variations in both water and energy budgets are comparable. This study shows that the CFSR achieves a large improvement over the GR2, indicating that the CFSR dataset can be used in climate variability studies. Nonetheless, improved land surface parameterization schemes and data assimilation techniques are needed to depict the surface water and energy climates better, in particular, the variation in seasonal runoff.

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Jesse Meng
,
Rongqian Yang
,
Helin Wei
,
Michael Ek
,
George Gayno
,
Pingping Xie
, and
Kenneth Mitchell

Abstract

The NCEP Climate Forecast System Reanalysis (CFSR) uses the NASA Land Information System (LIS) to create its land surface analysis: the NCEP Global Land Data Assimilation System (GLDAS). Comparing to the previous two generations of NCEP global reanalyses, this is the first time a coupled land–atmosphere data assimilation system is included in a global reanalysis. Global observed precipitation is used as direct forcing to drive the land surface analysis, rather than the typical reanalysis approach of using precipitation assimilating from a background atmospheric model simulation. Global observed snow cover and snow depth fields are used to constrain the simulated snow variables. This paper describes 1) the design and implementation of GLDAS/LIS in CFSR, 2) the forcing of the observed global precipitation and snow fields, and 3) preliminary results of global and regional soil moisture content and land surface energy and water budgets closure. With special attention made during the design of CFSR GLDAS/LIS, all the source and sink terms in the CFSR land surface energy and water budgets can be assessed and the total budgets are balanced. This is one of many aspects indicating improvements in CFSR from the previous NCEP reanalyses.

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