| Summary: | The Greenland Ice Sheet contains nearly 3 million cubic kilometers of glacial ice. If the entire ice sheet completely melted, sea level would raise by nearly 7 meters. There is thus considerable interest in monitoring the mass balance of the Greenland Ice Sheet. Each year, the ice sheet gains ice from snowfall and loses ice through iceberg calving and surface melting. In this thesis, we develop, validate and apply a physics based numerical model to estimate current and future surface mass balance of the Greenland Ice Sheet. The numerical model consists of a coupled surface energy balance and englacial model that is simple enough that it can be used for long time scale model runs, but unlike previous empirical parameterizations, has a physical basis. The surface energy balance model predicts ice sheet surface temperature and melt production. The englacial model predicts the evolution of temperature and meltwater within the ice sheet. These two models can be combined with estimates of precipitation (snowfall) to estimate the mass balance over the Greenland Ice Sheet. We first compare model performance with in-situ observations to demonstrate that the model works well. We next evaluate how predictions are degraded when we statistically downscale global climate data. We find that a simple, nearest neighbor interpolation scheme with a lapse rate correction is able to adequately reproduce melt patterns on the Greenland Ice Sheet. These results are comparable to those obtained using empirical Positive Degree Day (PDD) methods. Having validated the model, we next drove the ice sheet model using the suite of atmospheric model runs available through the CMIP5 atmospheric model inter-comparison, which in turn built upon the RCP 8.5 (business as usual) scenarios. From this exercise we predict how much surface melt production will increase in the coming century. This results in 4--10 cm sea level equivalent, depending on the CMIP5 models. Finally, we try to bound melt water production from CMIP5 data with the model by ...
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