Improvement of snowpack simulations in a regional climate model

Abstract

Improvement of snowpack simulations in a regional climate model Jiming Jin 1 and Norman L. Miller 2 * Departments of Watershed Sciences and Plants, Soils, and Climate and Utah Climate Center, Utah State University, Logan, UT, USA Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. *Miller’s work is partially supported by the U.S. Department of Energy and LBNL under Contract No. DE- AC02-05CH11231. Abstract: To improve simulations of regional-scale snow processes and related cold-season hydroclimate, the Community Land Model version 3 (CLM3), developed by the National Center for Atmospheric Research (NCAR), was coupled with the Pennsylvania State University/NCAR fifth-generation Mesoscale Model (MM5). CLM3 physically describes the mass and heat transfer within the snowpack using five snow layers that include liquid water and solid ice. The coupled MM5–CLM3 model performance was evaluated for the snowmelt season in the Columbia River Basin in the Pacific Northwestern United States using gridded temperature and precipitation observations, along with station observations. The results from MM5–CLM3 show a significant improvement in the SWE simulation, which has been underestimated in the original version of MM5 coupled with the Noah land-surface model. One important cause for the underestimated SWE in Noah is its unrealistic land-surface structure configuration where vegetation, snow and the topsoil layer are blended when snow is present. This study demonstrates the importance of the sheltering effects of the forest canopy on snow surface energy budgets, which is included in CLM3. Such effects are further seen in the simulations of surface air temperature and precipitation in regional weather and climate models such as MM5. In addition, the snow-season surface albedo overestimated by MM5–Noah is now more accurately predicted by MM5–CLM3 using a more realistic albedo algorithm that intensifies the solar radiation absorption on the land surface, reducing the strong near-surface cold bias in MM5–Noah. The cold bias is further alleviated due to a slower snowmelt rate in MM5–CLM3 during the early snowmelt stage, which is closer to observations than the comparable components of MM5–Noah. In addition, the over-predicted precipitation in the Pacific Northwest as shown in MM5–Noah is significantly decreased in MM5 CLM3 due to the lower evaporation resulting from the longer snow duration. KEY WORDS: land-surface model; regional climate model; snow; vegetation INTRODUCTION In the western United States (WUS), seasonally accumulated snow mass has become increasingly important to water resources, because of the rapidly increasing demand for water to supply the expanding economy and population. Over the last decade, scientists have made a significant effort to understand and forecast snowmass variability in the WUS (Miller et al., 1999; Serreze et al., 1999; Knowles and Cayan, 2002; Jin and Miller, 2007). Observational studies have found a downward trend in snowpack over the last five decades, which is consistent with the smaller ratio of snowfall to rainfall and earlier snowmelt in the WUS, a finding consistent with global warming observations (Mote, 2003; Stewart et al., 2004). Snowmelt timing is also related to winter and spring temperature variability (Dettinger and Cayan, 1995; Cayan et al., 2001). McCabe and Clark (2005) examined eighty-four rivers in the WUS to better understand snowmelt runoff variability and associated climate processes. They found that earlier snowmelt has a high correlation with increased spring and early summer atmospheric pressure and temperature over the WUS, indicating that snowmelt trend and variability is tightly linked to atmospheric forcings. On the other hand, snow mass in the WUS also affects atmospheric processes. Lo and Clark (2002) found that an inverse correlation exists between winter snow cover and subsequent summer monsoon rainfall in the WUS; a similar relationship was previously found in Eurasia by Bamzai and Shukla (1999). This correlation indicates that the land surface often warms up more quickly with a lower snow depth and smaller snow cover area (or conversely, it warms up more slowly with a higher snow depth and larger snow cover area) when compared with normal conditions, suggesting that anomalous snow depth and cover can significantly affect atmospheric circulation patterns. Thus, accurate forecasting of accumulated snow mass and snowmelt timing is essential to both understanding climate variability and managing water resources in the WUS. However, it is very difficult to obtain the detailed observations throughout the WUS, which would allow a full understanding of snow mass and cover variations, as well as related processes. Numerical models are important tools for investigating and quantifying interactions between snow and climate, as well as for the advance of