Spatial and temporal variability of Antarctic ice sheet microwave brightness temperatures

Abstract

Annual and interannual variability of 21 years of passive microwave brightness temperatures on the Antarctic ice sheet is documented through principal component analysis. The leading modes show that brightness temperatures are dominantly forced by the annual temperature cycle, with surface melting signals explaining about 5% of the total variance of the data. Analysis of the data with the annual cycle and melting signals removed results in two significant interannual modes. The first is consistent with the Antarctic Oscillation and its influence on Antarctic surface temperatures, while the second reflects regional scale variability that appears to be linked to sea‐ice anomalies and the El Niño‐Southern Oscillation. The expression of these characteristic patterns of Southern Hemisphere climate variability on the Antarctic continent provides a framework for interpreting modern climate records, as well as a basis for reconstructing past climate variability with ice cores.

Weekly gridded Aquarius L-band radiometer/scatterometer observations and salinity retrievals over the polar regions – Part 2: Initial product analysis

“…2. Several previous studies discuss these spatial distributions of microwave observations at higher frequencies and their correlation with geophysical properties (e.g., Long and Drinkwater, 2000;Schneider and Steig, 2002;Schneider et al, 2004;Picard et al, 2009;Brucker et al, 2010), which are also evident at L band.…”

Influence of meter-scale wind-formed features on the variability of the microwave brightness temperature around Dome C in Antarctica

“…The large-scale spatial variations of brightness temperature in Antarctica are well known (e.g., Schneider and Steig, 2002;Fahnestock et al, 2000). The spatial variations of brightness temperature are generally continuous, except in coastal and mountainous regions.…”

“…They have several advantages over other remote sensing techniques: high sensitivity to snow properties (temperature, grain size, density), subdaily coverage in the polar regions, and independence of cloud conditions and solar illumination. Typical applications for ice sheets aim to retrieve snow temperature (Shuman et al, 1995;Schneider and Steig, 2002;Schneider et al, 2004), snowmelt (e.g., Zwally, 1977;Abdalati and Steffen, 1995;Torinesi et al, 2003), snow accumulation (Vaughan et al, 1999;Arthern et al, 2006), grain size (Brucker et al, 2010;Picard et al, 2012), thermal properties (Koenig et al, 2007;Picard et al, 2009) or surface state (Shuman et al, 1993;Champollion et al, 2013). Passive microwave data are also widely used in assimilation schemes to constrain atmospheric analyses for which the surface emissivity is an issue, particularly over Antarctica (Guedj et al, 2010).…”

Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

“…These observations are available in these areas several times a day, are relatively insensitive to the atmosphere in many frequency bands, and are independent of the solar illumination. They are sensitive to several properties relevant for monitoring the snow cover and have been exploited in numerous algorithms to retrieve continental snow cover extent (Grody and Basist, 1996), snow depth and snow water equivalent on both land (Josberger and Mognard, 2002;Kelly and Chang, 2003;Derksen et al, 2003) and sea ice (Cavalieri et al, 2012;Brucker and Markus, 2013), snow accumulation on ice sheets (Abdalati and Steffen, 1998;Vaughan et al, 1999;Winebrenner et al, 2001;Arthern et al, 2006), melt events (Abdalati and Steffen, 1997;Picard and Fily, 2006), snow temperature (Shuman et al, 1995;Schneider, 2002;Schneider et al, 2004), and snow grain size (Brucker et al, 2010;Picard et al, 2012). Some of these studies are based on empirical relationships supported by physical interpretations (Koenig et al, 2007) and others directly use physical models and data assimilation techniques (Durand and Margulis, 2007;Picard et al, 2009;Takala et al, 2011;Toure et al, 2011;Huang et al, 2012).…”