Regional Greenland Accumulation Variability from Operation IceBridge Airborne Accumulation Rradar

• Published in: The Cryosphere
• Main Authors: Lewis, Gabriel, Osterberg, Erich, Hawley, Robert, Whitmore, Brian
• Format: Text
• Language: unknown
• Published: Dartmouth Digital Commons 2017
• Subjects: Earth Sciences; Glaciology; Physical Sciences and Mathematics; Greenland; Ice Sheet; North Atlantic; North Atlantic oscillation
• Online Access: https://digitalcommons.dartmouth.edu/facoa/2310; https://doi.org/10.5194/tc-11-773-2017; https://digitalcommons.dartmouth.edu/context/facoa/article/3314/viewcontent/tc_11_773_2017.pdf

The mass balance of the Greenland Ice Sheet (GrIS) in a warming climate is of critical interest to scientists and the general public in the context of future sea-level rise. An improved understanding of temporal and spatial variability of snow accumulation will reduce uncertainties in GrIS mass balance models and improve projections of Greenland's contribution to sea-level rise, currently estimated at 0.089 ± 0.03 m by 2100. Here we analyze 25 NASA Operation IceBridge accumulation radar flights totaling > 17 700 km from 2013 to 2014 to determine snow accumulation in the GrIS dry snow and percolation zones over the past 100–300 years. IceBridge accumulation rates are calculated and used to validate accumulation rates from three regional climate models. Averaged over all 25 flights, the RMS difference between the models and IceBridge accumulation is between 0.023 ± 0.019 and 0.043 ± 0.029 m w.e. a−1, although each model shows significantly larger differences from IceBridge accumulation on a regional basis. In the southeast region, for example, the Modèle Atmosphérique Régional (MARv3.5.2) overestimates by an average of 20.89 ± 6.75 % across the drainage basin. Our results indicate that these regional differences between model and IceBridge accumulation are large enough to significantly alter GrIS surface mass balance estimates. Empirical orthogonal function analysis suggests that the first two principal components account for 33 and 19 % of the variance, and correlate with the Atlantic Multidecadal Oscillation (AMO) and wintertime North Atlantic Oscillation (NAO), respectively. Regions that disagree strongest with climate models are those in which we have the fewest IceBridge data points, requiring additional in situ measurements to verify model uncertainties.