Coupled cryosphere model development for regional climate study and the initialization of Purdue regional model with the land data assimilation system
Abstract
The presence of snow strongly affects the surface energy budget in mid-to-high latitudes during the transition period of winter to spring. Snow's high albedo dramatically reduces the amount of shortwave radiative energy available at the surface, and its low thermal conductivity significantly restricts exchanges of heat between soil and the atmosphere. Synoptic waves propagating over the Northern Plains are a major source of snow storms and flash flooding during this period. The presence of snow and its melt from winter to spring affects the propagation of synoptic waves and the amount of precipitation over this region. Here we investigate the effect of snow cover, soil frost, and snowmelt on the atmosphere over cold land and implement a multi-layer Soil-Snow-Vegetation Model (SSVM) coupled to an atmospheric Purdue Regional Model (PRM). We have applied a one-dimensional, multi-layer land surface model based on the conservations of heat and water substance inside the soil and snow for use with the regional climate model. Compared to the current land-surface scheme, the new SSVM shows significant differences in both moisture and temperature simulation for the months of March and April, which affects the surface energy budget and the hydrological cycle. This research has achieved the following goals: (a) a comprehensive and physically based surface model has been incorporated into the PRM, (b) a detailed description of a multi-layer snow and frozen soil was presented, (c) a flexible surface model that can be applicable for the study of both warm and cold seasons was developed and tested, and (d) the regional climate effect of cold season processes was studied to understand the interactions between the atmosphere and the land. In part two, analysis data derived from the Finite Volume Data Assimilation System (FVDAS, NASA Global Modeling and Assimilation Office) and the Land Data Assimilation System (LDAS, NASA Hydrological Sciences Branch) have been successfully implemented to PRM and are tested to provide initial conditions and lateral boundary forcing. Initialization of the PRM's weather forecast with FVDAS and LDAS high-resolution land surface and soil moisture data are compared with ECMWF reanalysis.
Degree
Ph.D.
Advisors
Sun, Purdue University.
Subject Area
Hydrologic sciences|Atmospheric sciences
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