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Improving the land-surface components of Climate Forecast System Reanalysis (CFSR)

Global reanalyses, starting with the NCEP/NCAR reanalysis in the mid-1990s, are widely used as surrogates for space time observations in both the atmosphere and the land surface (and, to a lesser extent, the oceans). Early (e.g. NCEP/NCAR) reanalysis land surface products had many problems, including discontinuities near the beginning of the satellite era in the 1970s, and unrealistic land surface variables (such as soil moisture) resulting from updates to some land surface variables intended to resolve deficiencies in atmospheric moisture and moisture transport profiles. These issues have been mitigated to some extent in more recent reanalyses (e.g., Climate Forecast System Reanalysis (CFSR), ERA-40 and ERA-Interim and the North American Regional Reanalysis, NARR). Nonetheless, the fidelity of land variables from land atmosphere reanalyses remains questionable. We believe that the land data assimilation system used in upcoming reanalyses can be enhanced via improved (1) land characterization data sets (e.g. vegetation type and soil texture class, and the characterization of urban areas, etc.), (2) atmospheric forcing data sets (e.g. precipitation, downward solar and longwave radiation), (3) assimilation of near-real time land states (e.g. surface skin temperature, albedo, soil moisture, snow extent, vegetation greenness and density), (4) land-model spin-up procedures, and (5) downscaling techniques for forcing data and land states. We intend to investigate options for making these improvements, in the context of an enhanced CFSR framework. Additionally, currently missing in reanalyses is the inclusion of some key variables in the land surface water budget, such as groundwater, streamflow (routed to the mouths of major rivers), and lakes, reservoirs (managed), and wetlands. All of these are needed to complete the water cycle for a fully-coupled system, and to account for feedbacks to the atmosphere and coupling between the terrestrial and atmospheric budgets (e.g. over long periods river discharge from a region equals net atmospheric convergence into the region, so errors in river flow predictions must manifest themselves in the atmospheric moisture fields). We intend to investigate inclusion of such representations in the context of the new Noah-MP land surface modeling framework, which will be the land surface scheme for NCEP’s next generation reanalyses. Co-PIs Lettenmaier and Wood have extensive experience in representation of the above variables in the context of the VIC land model, and some of the VIC parameterizations may be transferred to Noah-MP as needed. Furthermore, all co-Is have extensive experience in development of model evaluation data sets, through programs like NLDAS and GLDAS, which we will draw from.

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