Painter

Understanding and forecasting changes in High Mountain Asia Snow Hydrology via a Novel Bayesian Reanalysis and Modeling Approach

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Snow and ice of High Mountain Asia provide critical freshwater supply to over a billion people and provide climate influence through higher albedo and lower thermal conductivity. High Mountain Asia holds the greatest amount of ice outside of Earth’s polar region and as such has great potential to contribute to sea level rise. Snow cover and glaciers have been in general negative trend across the Anthropocene, yet there are large uncertainties in the scale of that retreat, the magnitude of the resulting contribution to sea-level rise, and in particular the causes. We propose to use NASA remote sensing retrievals of snow, ice, aerosols, and soil properties to constrain cutting-edge mesoscale modeling to improve our understanding of the controls on snowmelt in High Mountain Asia. Our overarching science goal is to better understand the physical processes that are driving changes in High Mountain Asia snow and ice. To address this goal, we must fill the void in quantitative knowledge of snowmelt of the Himalaya and its forcing by global warming, seasonal temperature variation, regional atmospheric heating by dust and BC and snow and ice darkening by dust and BC, and the relative contribution of these forcings to snowmelt.

This leads to our science objectives: OBJ1: Quantify and document seasonal variation of fractional snow covered area, snow grain size, and radiative forcing by light-absorbing particles in the HMA area OBJ2: Validate physically-based mesoscale modeling with remote sensing and determine optimal calibration to minimize errors OBJ3: Understand relative contributions of variation in energy balance components to variation in snow and ice melt across the HMA in present day OBJ4: Understand the future changes to snowmelt across HMA under scenarios of increased GHGs and changes in BC and dust emissions.

In this project, we will address these science objectives with unique satellite remote sensing retrievals of snow and ice properties from MODIS and VIIRS, and use these to constrain the Weather Research and Forecasting (WRF) model coupled with a chemistry component (Chem), the land surface scheme of the Community Land Model (CLM) and the snow, ice, and aerosol radiative transfer model SNICAR. The two unique suite of remote sensing products are (1) the MODIS Snow Covered Area and Grain size (MODSCAG), (2) the MODIS Dust Radiative Forcing in Snow (MODDRFS). These two datasets will be generated for 2000-2016 and will provide provides unprecedented access to snow cover variation, snow grain size, radiative forcing by particulates in snow, and annually resolved minimum snow and ice cover. After the model is carefully evaluated and calibrated using remote sensing, in situ measurements, and other available reanalysis data, the coupled model WRF-Chem-CLM-SNICAR (WCCS) will be run over the entire HMA domain for both present and future with predicted economic development scenarios.

This investigation directly addresses the A.48 call by being based on satellite remote sensing retrievals, in situ measurements, and mesoscale modeling. The investigation will contribute to the development and refinement of remote sensing and modeling tools to foster the interdisciplinary research, forecast change in the HMA region, and support policy development such as dust and pollution mitigation and improving runoff forecasting for water management. The remote sensing and modeling tools are interoperable and can directly link and support the Glacial Melt Toolbox (GMELT).

Our development of key components of the GMELT will broadly support needs of the High Mountain Asia Team (HiMAT). Moreover, PI Painter will propose to be the HiMAT Team Leader based on his expertise in mountain remote sensing and modeling, scientific partnerships with US and Asian scientists, and extensive project management.