Abstract
Abstract. The Greenland ice sheet (GrIS) has been the focus of climate studies due to its considerable impact on sea level rise. Accurate estimates of surface mass fluxes would contribute to understanding the cause of its recent changes and would help to better estimate the past, current and future contribution of the GrIS to sea level rise. Though the estimates of the GrIS surface mass fluxes have improved significantly over the last decade, there is still considerable disparity between the results from different methodologies (e.g., Rae et al., 2012; Vernon et al., 2013). The data assimilation approach can merge information from different methodologies in a consistent way to improve the GrIS surface mass fluxes. In this study, an ensemble batch smoother data assimilation approach was developed to assess the feasibility of generating a reanalysis estimate of the GrIS surface mass fluxes via integrating remotely sensed ice surface temperature measurements with a regional climate model (a priori) estimate. The performance of the proposed methodology for generating an improved posterior estimate was investigated within an observing system simulation experiment (OSSE) framework using synthetically generated ice surface temperature measurements. The results showed that assimilation of ice surface temperature time series were able to overcome uncertainties in near-surface meteorological forcing variables that drive the GrIS surface processes. Our findings show that the proposed methodology is able to generate posterior reanalysis estimates of the surface mass fluxes that are in good agreement with the synthetic true estimates. The results also showed that the proposed data assimilation framework improves the root-mean-square error of the posterior estimates of runoff, sublimation/evaporation, surface condensation, and surface mass loss fluxes by 61, 64, 76, and 62 %, respectively, over the nominal a priori climate model estimates.
Highlights
Introduction and backgroundThe Greenland ice sheet (GrIS) has recently experienced thinning of the marginal ice (e.g., Straneo and Heimbach, 2013; Khan et al, 2014), thickening of its interior (e.g., Johannessen et al, 2005; Fettweis et al, 2007), acceleration and increase of ice discharge from many of Greenland’s outlet glaciers (e.g., Rignot et al, 2008; Wouters et al, 2013), and enhanced surface melt (e.g., Tedesco et al, 2013; Vernon et al, 2013)
The prior estimates were derived from an offline surface module (CROCUS) forced by an ensemble of meteorological forcing fields that were based on a nominal regional climate model simulation
A posterior estimate was generated by conditioning the forcings on the synthetically generated ice surface temperature (IST) measurements using an ensemble batch smoother (EnBS) approach
Summary
The Greenland ice sheet (GrIS) has recently experienced thinning of the marginal ice (e.g., Straneo and Heimbach, 2013; Khan et al, 2014), thickening of its interior (e.g., Johannessen et al, 2005; Fettweis et al, 2007), acceleration and increase of ice discharge from many of Greenland’s outlet glaciers (e.g., Rignot et al, 2008; Wouters et al, 2013), and enhanced surface melt (e.g., Tedesco et al, 2013; Vernon et al, 2013). Many studies (e.g., Van de Wal et al, 2012) have taken advantage of in situ measurements to provide a direct pointscale estimate of the surface mass balance (SMB, i.e., the difference between accumulation and ablation) With these limited in situ measurements alone, large-scale mapping of the GrIS surface mass fluxes (i.e., precipitation, evaporation, sublimation, condensation, and runoff) is impossible. We provide an example of taking advantage of information in the relevant data streams to provide a better spatiotemporal characterization of the model outputs (i.e., the GrIS surface mass fluxes) This can be done using a data assimilation approach which attempts to merge model estimates with measurements in an optimal way (Evensen, 2009)
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