Abstract
Abstract. Moderate-Resolution Imaging spectroradiometer (MODIS) land surface temperatures in Antarctica were processed in order to produce a gridded data set at 25 km resolution, spanning the period 2000–2011 at an hourly time step. The Aqua and Terra orbits and MODIS swath width, combined with frequent clear-sky conditions, lead to very high availability of quality-controlled observations: on average, hourly data are available 14 h per day at the grid points around the South Pole and more than 9 h over a large area of the Antarctic Plateau. Processed MODIS land surface temperatures, referred to hereinafter as MODIS Ts values, were compared with in situ hourly measurements of surface temperature collected over the entirety of the year 2009 by seven stations from the Baseline Surface Radiation Network (BSRN) and automatic weather stations (AWSs). In spite of an occasional failure in the detection of clouds, MODIS Ts values exhibit a good performance, with a bias ranging from −1.8 to 0.1 °C and errors ranging from 2.2 to 4.8 °C root mean square at the five stations located on the plateau. These results show that MODIS Ts values can be used as a precise and accurate reference to test other surface temperature data sets. Here, we evaluate the performance of surface temperature in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis known as ERA-Interim reanalysis. During conditions detected as cloud free by MODIS, ERA-Interim shows a widespread warm bias in Antarctica in every season, ranging from +3 to +6 °C on the plateau. This confirms a recent study which showed that the largest discrepancies in 2 m air temperature between ERA-Interim and the global temperature data set HadCRUT4 compiled by the Met Office Hadley Centre and the University of East Anglia's Climatic Research Unit occur in Antarctica. A comparison with in situ surface temperature shows that this bias is not strictly limited to clear-sky conditions. A detailed comparison with stand-alone simulations by the Crocus snowpack model, forced by ERA-Interim, and with the ERA-Interim/land simulations, shows that the warm bias may be due primarily to an overestimation of the surface turbulent fluxes in very stable conditions. Numerical experiments with Crocus show that a small change in the parameterization of the effects of stability on the surface exchange coefficients can significantly impact the snow surface temperature. The ERA-Interim warm bias appears to be likely due to an overestimation of the surface exchange coefficients under very stable conditions.
Highlights
Ice-sheet melt is the largest potential source of uncertainties for future sea level rise, which has led to a growing interest in the observation and modeling of the interactions between the ice sheets and their environment
We focused on the ERA-Interim skin temperature, hereafter ERA-i Ts, which forms the interface between the soil and the atmosphere in the Integrated Forecast System (IFS) (European Centre for Medium-Range Weather Forecasts (ECMWF), http://www.ecmwf.int)
In the rest of the paper, we focus on the Antarctic Plateau, where hourly Moderate-Resolution Imaging spectroradiometer (MODIS) Ts values are more frequent and of better quality than in coastal regions
Summary
Ice-sheet melt is the largest potential source of uncertainties for future sea level rise, which has led to a growing interest in the observation and modeling of the interactions between the ice sheets and their environment. Fréville et al.: Antarctic surface temperature that the Greenland Ice Sheet loses mass from both enhanced discharge and decreasing surface mass balance due to increased surface melting (Rignot et al, 2011), the changes in Antarctica in the recent decades have a complex signature In this context, it is of vital importance to monitor and understand the processes controlling the surface heat and mass exchanges between the Antarctic Ice Sheet and the atmosphere. In situ observations that were not used in the re-analyses are difficult to come by, limiting our ability to evaluate the reanalyzed variables
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