Comparing Greenland Ice Sheet Melt Variability From Different Satellite Passive Microwave Remote Sensing Products Over a Common 5-year Record

Abstract

Satellite microwave brightness temperature (Tb) observations over the Greenland Ice Sheet permit determination of melted/frozen snow conditions at spatial and temporal scales that are uniquely suited for climate model validation and metrics of ice sheet change. Strong microwave sensitivity to the presence of liquid water in the snowpack is clear. Yet, a host of unique microwave-derived melt products covering the ice sheet are available, each based on different methodology, and with unknown inter-product agreement. Here, we compared five different published microwave melt products over a common 5-year (2003–2007) record to establish compatibility between products and agreement with in situ observations from a network of on-ice weather stations (AWS) spanning the ice sheet. A sixth product, leveraging both Tb seasonal trends and diurnal variability, was also introduced and included in the comparison. We found variable agreement between products and observations, with melt estimates based on microwave emissions modeling and the newly presented Adaptive Threshold (ADT) algorithm showing the best performance for AWS sites with more than 1-day average annual melt period (e.g., 68.9% of ADT melt days consistent with AWS observations; 31.1% of ADT frozen days contrasting with AWS observed melt). Spatial patterns of melting also varied between products. The different products showed substantial spread in melt occurrence even for products with the best AWS agreement. Product differences were generally larger under higher melt conditions; whereby, the fraction of the ice sheet experiencing ≥25 days of melting each year ranged from 4 to 25% for different products. While long-term satellite records have consistently shown increasing decadal trends in melt extent, our results imply that the melt frequency at any given location, particularly in the ice sheet interior where melting is less prevalent, is still subject to significant uncertainty.