Conference Objects

Developing a Glacial Surface Model for Greenland to Improve the Projections of Surface Runoff

Dong, LingLing; Porter, David F.; Alexander, Patrick M.; Tedesco, Marco

Over the past several decades, the Greenland Ice Sheet has been losing mass through a combination of increased surface runoff and accelerating ice flux to the ocean. Our understanding of the surface component is drawn heavily from satellite observations and climate models. The MAR (Modèle Atmosphère Régional) model is a 3D regional climate model used extensively in Greenland because of its proven record at simulating precipitation and firn and snowpack evolution over glaciated surfaces. Our study focuses on the surface snow and the ice down to 15-meter in depth. A light-weighted surface model for us to integrate the local observation data and force many simulations is needed. Our goal is to develop a surface-only model, derived from MAR, as a tool for understanding the glacial surface components, correlations, and MAR biases to improve projections of surface runoff. This model includes the ability to integrate observations from surface weather stations, translate the data into a model forcing format, force different simulations with various configurations or datasets, visualize model outputs, find key correlations between atmospheric drivers and modeled firn desertification.

In the model development, we extract the surface code from the full MAR for the simulations initialized and forced with the following snow and atmospheric fields: snow depth, temperature, density, water volume, and grain size. We then verify that the surface model generates the same outputs as the full MAR does if fetched with the identical data. The bias is checked with snowpack time-depth plots for multiple sites around Greenland, including Summit and Swiss Camp. We have found a very small bias when compared to the fully-coupled MAR. We perform quality control for the data inputs, such as replacing missing data from the station measurements, defining the max and min for each dataset, filtering out the data outliers by statistics standard deviations. As the result, our model software can provide multiple simulations in sequential and concurrent mode with user-friendly interfaces, and run robustly. The model’s first release is currently being deployed over different sites across Greenland to understand the importance of atmospheric forcing versus snow model biases in projections of future mass loss due to surface melt.

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More About This Work

Academic Units
Lamont-Doherty Earth Observatory
Marine Geology and Geophysics
Published Here
December 12, 2018