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David P. Schneider and David B. Reusch


This study examines the biases, intermodel spread, and intermodel range of surface air temperature (SAT) across the Antarctic ice sheet and Southern Ocean in 26 structurally different climate models. Over the ocean (40°–60°S), an ensemble-mean warm bias peaks in late austral summer concurrently with the peak in the intermodel range of SAT. This warm bias lags a spring–summer positive bias in net surface radiation due to weak shortwave cloud forcing and is gradually reduced during autumn and winter. For the ice sheet, inconsistencies among reanalyses and observational datasets give low confidence in the ensemble-mean bias of SAT, but a small summer warm bias is suggested in comparison with nonreanalysis SAT data. The ensemble mean hides a large intermodel range of SAT, which peaks during the summer insolation maximum. In summer on the ice sheet, the SAT intermodel spread is largely associated with the surface albedo. In winter, models universally exhibit a too-strong deficit in net surface radiation related to the downward longwave radiation, implying that the lower atmosphere is too stable. This radiation deficit is balanced by the transfer of sensible heat toward the surface (which largely explains the intermodel spread in SAT) and by a subsurface heat flux. The winter bias in downward longwave radiation is due to the longwave cloud radiative effect, which the ensemble mean underestimates by a factor of 2. The implications of these results for improving climate simulations over Antarctica and the Southern Ocean are discussed.

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David B. Reusch and Richard B. Alley


Automatic weather stations (AWSs) currently provide the only year-round, continuous direct measurements of near-surface weather on the West Antarctic ice sheet away from the coastal manned stations. Improved interpretation of the ever-growing body of ice-core-based paleoclimate records from this region requires a deeper understanding of Antarctic meteorology. As the spatial coverage of the AWS network has expanded year to year, so has the meteorological database. Unfortunately, many of the records are relatively short (less than 10 yr) and/or incomplete (to varying degrees) due to the vagaries of the harsh environment. Climate downscaling work in temperate latitudes suggests that it is possible to use GCM-scale meteorological datasets (e.g., ECMWF reanalysis products) to address these problems in the AWS record and create a uniform and complete database of West Antarctic surface meteorology (at AWS sites). Such records are highly relevant to the improved interpretation of the expanding library of snow-pit and ice-core datasets.

Artificial neural network (ANN) techniques are used to predict 6-hourly AWS surface data (temperature, pressure) using large-scale features of the atmosphere (e.g., 500-mb geopotential height) from a region around the AWS. ANNs are trained with a calendar year of observed AWS data (possibly incomplete) and corresponding GCM-scale data. This methodology is sufficient both for high quality predictions within the training set and for predictions outside the training set that are at least comparable to the state of the art. For example, the results presented herein for temperature prediction are approximately equal to those from a satellite-based methodology but with no exposure to problems from surface melt events or sensor changes. Similarly, the significant biases seen in ECMWF surface temperatures are absent from the predictions here, resulting in an rms error that is half as large with respect to the original AWS observations.

These results support high confidence in the ANN-based predictions from the GCM-scale data for periods when AWS data are unavailable, for example, before installation. ANNs thus provide a means to expand the surface meteorological records significantly in West Antarctica.

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