A method for the detection of the severe rain‐on‐snow event on Banks Island, October 2003, using passive microwave remote sensing

Abstract

Severe wintertime rain‐on‐snow (ROS) events create a strong ice layer (or layers) in the snow on arctic tundra that act as a barrier to ungulate grazing. They are linked with large‐scale ungulate (reindeer, caribou, elk, and musk‐ox) herd declines via starvation and reduced calf production rate when the animals are unable to penetrate the resulting subsnowpack ice layer. ROS events also produce considerable perturbation in the mean wintertime soil temperature under the snowpack. ROS is a sporadic but well‐known and significant phenomenon that is currently very poorly documented. Characterization of the distribution and occurrence of severe ROS events is based only on anecdotal evidence, indirect observations of carcasses found adjacent to iced snowpacks, and irregular detection by a sparse observational weather network. We have analyzed in detail a particular ROS event that took place on Banks Island in early October 2003 that resulted in the death of 20,000 musk oxen. We make use of multifrequency passive microwave imagery from the Special Sensor Microwave Imager satellite sensor suite in conjunction with a strong‐fluctuation‐theory (SFT) emissivity model. We show that a combination of time series analysis and cluster analysis based on microwave spectral gradients and polarization ratios provides a means to detect the stages of the ROS event resulting from the modification of the vertical structure of the snowpack, specifically wetting the snow, the accumulation of liquid water at the base of the snow during the rain event, and the subsequent modification of the snowpack after refreezing. SFT model analysis provides quantitative confirmation of our interpretation of the evolution of the microwave properties of the snowpack as a result of the ROS event. In addition to the grain coarsening owing to destructive metamorphism, we detect the presence of the internal water and ice layers, directly identifying the physical properties producing the hazardous conditions. This analysis offers the potential to characterize both the frequency and global distribution of ROS using multifrequency satellite passive microwave imagery.