Cryospheric and Polar Processes Seminar
Modeling Snow in Western US: (i) improving and understanding dust-on-snow processes & (ii) developing a high resolution hydrologic modeling and data integration platform by Dr. Catalina Oaida, Postdoctoral Scholar, NASA Jet Propulsion Laboratory
In this talk I will focus on two main topics that share a common thread: snow in the Western U.S.
First, I will present land surface and regional climate modeling efforts from my PhD work, which involved improving snow processes in these numerical models (WRF-SSiB) by incorporating a more physically-based snow scheme (SNICAR) that accounts for snow grain growth as well as dust and black carbon in snow. We validate the model development at a site in the Upper Colorado River Basin where snow albedo, snow depth and dust concentrations were observed (among other quantities). We then run the modified WRF regional climate model for ten continuous years (2000-2009) over North America under two scenarios: (1) no aerosol deposition in snow, and (2) GOCART-modeled dust, black carbon, and organic carbon surface deposition in snow, and compare the two in order to investigate the impacts of aerosols in snow on the hydrologic cycle. Differences between these two cases reveal substantial changes, with dust and BC in snow causing 8.5 W/m2 additional spring net shortwave radiation absorbed at the surface over western US, about 0.5 °C average warming, with a reduction in regional-average snow water equivalent (SWE) of 12 mm.
More recently, during my postdoc appointment I have been involved with the Western States Water Mission (WSWM), which is a high resolution (1.75 km) hydrologic modeling and data integration platform, whose ultimate goal is to make hydrologic data available and accessible to users for both on-the-fly analysis and exploration, as well as for download. Two of the datasets coming out of this work are a historic (1981-2017) and an assimilated (2000-2017) SWE dataset. For the latter, MODSCAG fractional snow cover was assimilated using a batch smoother technique. Both SWE datasets are validated over the Tuolumne Basin in CA using the Airborne Snow Observatory measurements. A brief overview of the approach taken by WSWM will be presented, including the snow data assimilation framework and select data analytics tool capabilities.