Abstract
AbstractEstimating the Greenland ice sheet surface mass balance (SMB) is an important component of current and future projections of sea level rise. Given the lack of in situ information, imperfect models, and underutilized remote sensing data, it is critical to combine the available observations with a physically based model to better characterize the spatial and temporal variation of the Greenland ice sheet SMB. This work proposes a data assimilation framework that yields SMB estimates that benefit from a state‐of‐the‐art snowpack model (Crocus) and a 16‐day albedo product. Comparison of our results against in situ SMB measurements from the Kangerlussuaq transect shows that assimilation of 16‐day albedo product reduces the root‐mean‐square error of the posterior estimates of SMB from 1,240 millimeter water equivalent per year (mmWE/yr) to 230 mmWE/yr and reduces the bias from 1,140 mmWE/yr to −20 mmWE/yr.
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