The Role of Atmospheric Teleconnections and Local Forcings in Predicting Greenland Ice Sheet Surface Mass Loss

In recent decades, the Arctic climate has experienced substantial climactic change, including significant decreases in both sea ice extent and Greenland Ice Sheet (GrIS) surface mass balance. These trends are overlain by substantial interannual variability in atmospheric circulation driven by large-scale atmospheric teleconnection patterns. In addition, there is evidence to suggest that the removal of Arctic sea ice can alter local atmospheric circulation through increased air temperature, clouds, and water vapor, which may contribute to increased surface melting on the GrIS. Here, we seek to characterize how these processes are linked to Greenland Ice Sheet surface mass loss and constrain how the representation of these forcings can impact the prediction of meltwater runoff within the NASA Goddard Earth Observing System Model (GEOS) seasonal-to-subseasonal forecasting system (S2S v2.1). To do this, we use a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis product, retrospective seasonal forecasts from the GEOS S2S v2.1, and independent GEOS simulations. Results from MERRA-2 reanalysis indicate that the negative phase of the North Atlantic Oscillation (NAO) results in warm surface air temperatures and reduced precipitation across Greenland, both of which act to enhance summer ice surface mass losses. When compared with MERRA-2, retrospective forecasts from the GEOS S2S v2.1 system effectively reproduce the pattern of summer GrIS surface mass loss and demonstrate reasonable skill in predicting the magnitude of meltwater runoff at leads of 1 to 3 months. However, during periods with a strong negative NAO, ice sheet surface mass balance is substantially underestimated. This pattern is also associated with an underprediction of the Greenland Blocking Index height and over prediction of sea ice extent, suggesting that both local and non-local forcings may play a role in the reduced prediction skill during these periods. Using both retrospective forecasts and independent simulations, we characterize the relative importance of local and non-local mechanisms in driving summer GrIS