From Snowflake to Snowpack: How Do Cloud Microphysical Process Representations Influence Hydrologic Response?
Alejandro N. Flores1* (firstname.lastname@example.org), William J. Rudisill1,2, Rosemary W. H. Carroll3, Daniel Feldman2, Hans-Peter Marshall1, James P. McNamara1, Annareli Morales4, Alan Rhoades2, Allison N. Vincent1, Erica Siirila-Woodburn2, Zexuan Xu2
1Boise State University, Boise, ID; 2Lawrence Berkeley National Laboratory, Berkeley, CA; 3Desert Research Institute, Reno, NV; 4NOAA Physical Sciences Laboratory, Boulder, CO
The volume of water stored within seasonal snowpacks in the Upper Colorado River Basin is a fundamental constraint on downstream water availability. Climate change is already altering the partitioning of precipitation between rain and snow. Precipitation delivered as rain instead of snow bypasses a natural reservoir that delays its release and transits through fundamentally different pathways, having profound consequences for runoff production and biogeochemical cycling. Coupled land-atmosphere models are important tools that scientists use to examine effects of climate and climate change on precipitation. An important facet of modeling rain-snow partitioning in atmospheric models is how cloud microphysics are parameterized in these models, which simulate mass and the energy balance of hydrometeors based on important assumptions on their shape, size distribution, growth characteristics, and other properties. Here the project reports on numerical experiments examining the degree to which a variety of cloud microphysical process representations in the Weather Research and Forecasting (WRF) model leads to variability in spatiotemporal predictions of precipitation in the Upper Colorado River Basin. Within the East River Watershed, corresponding predictions of snow water equivalent and depth were created using the WRF-derived forcing scenarios as input to a land model and compared simulated snow conditions with retrievals from the Airborne Snow Observatory (ASO). Generally, more sophisticated microphysics parameterizations produce better predictions of precipitation and snow water storage when compared to available precipitation and ASO data. Differences in simulated precipitation can, in part, be attributed to how vertical hydrometeor structure in the atmosphere is resolved. This study highlights the importance of atmospheric microphysics research for surface and subsurface hydrology and the role that snowpack, and in general, surface observations can play in constraining atmospheric processes and advancing atmospheric science research. Field campaigns—like DOE’s Watershed Function science focus area—that collect intensive and temporally extensive hydrologic and critical zone data are collocated with campaigns like the Surface-Atmosphere Integrated Laboratory, which collects intensive near-surface atmosphere data, hold promise for co-producing scientific insights that are mutually beneficial to both fields.