Using MODIS land surface temperatures and the Crocus snow model to understand the warm bias of ERA-Interim reanalyses at the surface in Antarctica

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 T s 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 T s 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 T s 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 MediumRange 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.

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

“…Despite a few deviations from the observations, Crocus captured well the variations of SSA in response to meteorological conditions and metamorphism at Dome C. Since metamorphism strongly depends on the temperature profile close to the surface, this suggests that Crocus successfully resolves the energy budget of the snowpack close to the surface, as already pointed out by Brun et al (2011) andFréville et al (2014). It also proves that the metamorphism parameterization of Flanner and Zender (2006) is appropriate to study snow on the Antarctic Plateau, although this parameterization cannot be strictly assessed in complex conditions as encountered at Dome C. This is promising for larger-scale studies over the Antarctic Plateau and puts Crocus as an appropriate tool to investigate the spatial pattern of SSA over the Antarctic continent (Scambos et al, 2007), which probably results from the combined effects of precipitation, snow drift and metamorphism.…”

“…ERA-Interim data were already used by Fréville et al (2014) to simulate snow surface temperature on the Antarctic Plateau. As detailed in Libois et al (2014a), precipitation rate was multiplied by 1.5 to ensure that simulated annual snow accumulation matches observations at Dome C. On the contrary, ERA-Interim wind was found in good agreement with measurements performed on the 40 m high instrumented tower at Dome C (Genthon et al, 2013).…”

“…In addition, the few studies dedicated to the simulation of snow physical properties on the Antarctic Plateau focus on the evolution of the snowpack internal and surface temperatures (e.g. Brun et al, 2011;Fréville et al, 2014) or on punctual profiles (Dang et al, 1997;Groot Zwaaftink et al, 2013), rather than on temporal evolution of snow properties. Nevertheless, correctly simulating SSA evolution remains crucial to better understand the sensitivity of this region to future changes in precipitation and air temperature (Krinner et al, 2006).…”

Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

“…The surface temperature products of the Greenland Ice Sheet are used as a baseline to investigate future warming trends (e.g. Hall et al 2012), to monitor melt events on the ice sheet (Hall et al, 2013), and as input for surface mass balance or snowpack modeling (Fréville et al, 2014;Shamir and Georgakakos, 2014;Navari et al, 2016). A number of validation studies present results acquired over various time scales and in different locations to 130 determine the accuracy of the MODIS surface temperature products in the cryosphere (Hall et al, 2004(Hall et al, , 2008Koenig and Hall, 2010;Westermann et al, 2012;Hachem et al, 2012;Shuman et al, 2014;Østby et al, 2014;Shamir and Georgakakos, 2014;Hall et al, 2015;Williamson et al, 2017).…”

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

“…ERA-Interim overestimates the sea ice surface temperature compared with the SHEBA observations. A recent study also found that the ERA-Interim simulates a warmer surface temperature over the Antarctic ice sheet due to an overestimation of the surface turbulent fluxes under very stable conditions (Fréville et al, 2014;Jones and Lister, 2014). Because both WRF-Noah and WRF-HIGHTSI apply the same physics schemes for radiation, microphysics, cumulation and the boundary layer, and use the spectral nudging technique to constrain the atmospheric circulation, the differences in simulating the downward radiation and turbulent flux by WRF-Noah and WRF-HIGHTSI are small.…”