Model sensitivities affecting long-term simulations of precipitation

We are coupling a series of models that address the multitude of physical processes and temporal and spatial scales important for understanding water resources within a basin. These models encompass the entire hydrologic cycle and include surface and subsurface hydrology models, a river model, and an atmospheric model. This coupled system will be able to simulate water resources for current and future scenarios in order to evaluate the regional impacts of climate change or increased water use, particularly in arid or semi-arid and mountainous regions. Our present focus is on the upper Rio Grande basin, which includes portions of the San Juan and Sangre de Cristo Mountain ranges, among others, in southern Colorado and New Mexico. The accumulated snow pack in these ranges is the primary contributor to the available surface water within the basin.

This paper presents simulations focusing on the atmospheric component of the coupled system, the Regional Atmospheric Modeling System, to determine the sensitivity of its long-term precipitation predictions to grid resolution and input data. Previous work has indicated that the simulation of precipitation, especially over elevated terrain, can be improved when grid spacing over the upper Rio Grande is increased from 20 km to 5 km. The current work examines whether this holds true in simulations of months to years, as required in regional climate simulations.

The paper also addresses other factors that may enhance the model's performance in simulating precipitation in regions with complex terrain. These include the importance of using observed sea surface temperatures, instead of climatological mean SSTs, for winter precipitation predictions in California and in the Rio Grande basin.