Feasibility of improving a priori regional climate model estimates of Greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures

摘要

The Greenland ice sheet (GrIS) has been the focus of climate studies due toits considerable impact on sea level rise. Accurate estimates of surfacemass fluxes would contribute to understanding the cause of its recentchanges and would help to better estimate the past, current and futurecontribution of the GrIS to sea level rise. Though the estimates of the GrISsurface mass fluxes have improved significantly over the last decade, thereis still considerable disparity between the results from differentmethodologies (e.g., Rae et al., 2012; Vernon et al., 2013). The dataassimilation approach can merge information from different methodologies ina consistent way to improve the GrIS surface mass fluxes. In this study, anensemble batch smoother data assimilation approach was developed to assessthe feasibility of generating a reanalysis estimate of the GrIS surface massfluxes via integrating remotely sensed ice surface temperature measurementswith a regional climate model (a priori) estimate. The performance of theproposed methodology for generating an improved posterior estimate wasinvestigated within an observing system simulation experiment (OSSE)framework using synthetically generated ice surface temperaturemeasurements. The results showed that assimilation of ice surfacetemperature time series were able to overcome uncertainties in near-surfacemeteorological forcing variables that drive the GrIS surface processes. Ourfindings show that the proposed methodology is able to generate posteriorreanalysis estimates of the surface mass fluxes that are in good agreementwith the synthetic true estimates. The results also showed that the proposeddata assimilation framework improves the root-mean-square error ofthe posterior estimates of runoff, sublimation/evaporation, surfacecondensation, and surface mass loss fluxes by 61, 64, 76, and62?%, respectively, over the nominal a priori climate model estimates.