Seasonal variations of hydrological cycle components in the Mississippi River basin from a MAPS version with snow and frozen soil physics

A coupled atmospheric/land-surface model covering the conterminous United States with an associated 1-hour atmospheric data assimilation cycle, the Mesoscale Analysis and Prediction System (MAPS), has been improved to include snow and frozen soil physics. The new aspects of the land-surface model are described in this paper, along with detailed one-dimensional (1-D) tests. These tests show that the MAPS 1-D soil/vegetation/snow model is capable of providing accurate simulations over multi-year periods at locations with significant snow and frozen soil processes. The performance of the full 3-D model/assimilation system running at a 40-km resolution over a 9-month period from November 1997 through July 1998 is then examined. Soil moisture and temperature at multiple levels have been cycled in MAPS since April 1996 and snow water equivalent depth and snow temperature since March 1997. Cycling of these fields gives estimates that are physically consistent with the evolution of atmospheric fields over this fairly long period and are vastly improved over climatological estimates. Precipitation and surface temperature fields show good agreement with monthly analyses from the National Climatic Data Center (NCDC) in the 9month comparison. Soil moisture fields and hydrological cycle components such as precipitationminus-evaporation, snow accumulation, snow melt, and surface runoff are also examined from the MAPS cycle and are qualitatively reasonable and mutually consistent. Some needed improvements for MAPS are indicated by these experiments, including reducing moderate spin-up in 0-1 hour precipitation forecasts by improving initial cloud/moisture fields and eliminating biases in convective precipitation over warm oceans and nearby land areas. Overall, the results indicate a system with continuous cycling of soil and snow fields and frequent data assimilation, accurate model physics, and good overall model performance, can provide good seasonal as well as short-range estimates of unobserved components of the hydrological cycle. .