Improving surface mass balance estimation of the Greenland ice sheet through assimilation of multi-sensor satellite products and ground measurements into a regional climate model

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The Greenland Ice Sheet (GIS) plays a major role in Arctic climate and is a major consideration in projections of sea level rise. Diagnosing the surface mass balance of the GIS is a critical objective that continues to involve large uncertainties from errors in modeled precipitation and errors related to sub-grid-scale process representation. To date, there has been limited work in integrating remote sensing techniques and ground-based data with modeling. In this project, a data assimilation approach will provide a rigorous framework for merging these disparate sources of information in a consistent way (based on their associated uncertainty) to obtain an optimal surface mass balance posterior estimate comprising maps in space and time. This project will focus on implementing a synergistic modeling/observation framework with the final outcome being improved estimates of the mean and uncertainty of the surface states and fluxes associated with the surface mass balance. Broader impacts include a significant contribution to understanding of GIS influence on sea level rise. Sponsor: CUNY City College, Convent Ave at 138th St, New York, NY 10031-9101 Award Number: 0909388 NSF Program: Arctic Natural Sciences