feasibility of improving a priori regional climate model estimates of greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures
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2016
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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.
| Reference Key |
navari2016thefeasibility
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|---|---|
| Authors | ;M. Navari;S. A. Margulis;S. M. Bateni;M. Tedesco;P. Alexander;X. Fettweis |
| Journal | journal of applied polymer science |
| Year | 2016 |
| DOI |
10.5194/tc-10-103-2016
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| URL | |
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