Abstract
The Greenland ice sheet has experienced significant melt over the past six decades, with extreme melt events covering large areas of the ice sheet. Melt events are typically analysed using summary statistics, but the nature and characteristics of the events themselves are less frequently analysed. Our work examines melt events from a statistical perspective by modelling 19 years of Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature data using a Gaussian mixture model. We use a mixture model with separate model components for ice and meltwater temperatures at 1139 locations spaced across the ice sheet. By considering the uncertainty of the ice surface temperature measurements, we use the two categories of model components to define a probability of melt for a given observation rather than using a fixed melt threshold. This probability can then be used to estimate the expected number of melt events at a given location. Furthermore, the model can be used to estimate temperature quantiles at a given location, and analyse temperature and melt trends over time by fitting the model to subsets of time. Fitting the model to data from 2001–2009 and 2010–2019 shows increases in melt probability for significant portions of the ice sheet, as well as the yearly expected maximum temperatures.
Highlights
The Greenland ice sheet has experienced significant melt over the past six decades (Fettweis et al, 2011) and has had an overall accelerating contribution to sea-level rise from a combination of melt and dynamical discharge, in particular over the last 18 15 years (Rignot et al, 2018)
Our work examines melt events from a statistical perspective by modelling 19 years of Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature data using a Gaussian mixture 5 model
There are many ways to study the temperature of the ice sheet, including through observations from space (Zhengming and Dozier, 1989), Automatic Weather Stations (AWSs) (Tedesco et al, 2013), 25 and using models e.g. Global Climate Models (GCMs) (Smith et al, 2007)
Summary
The Greenland ice sheet has experienced significant melt over the past six decades (Fettweis et al, 2011) and has had an overall accelerating contribution to sea-level rise from a combination of melt and dynamical discharge, in particular over the last 18 15 years (Rignot et al, 2018). Whilst in-situ observations are often considered to provide the most accurate measurements for a given location, and GCMs output allows consideration of temperatures under different climate scenarios, satellite data has comparable accuracy to in-situ measurements (Hall et al, 2008) with far higher spatial coverage, providing the most comprehensive overall view of the ice sheet. Extreme melt events are likely to become more common as overall temperatures on the Greenland ice sheet increase, contributing to increasing amounts of melt. Our model is sufficiently generalise-able as to be useful for pixels not explicitly used to generate the model, regardless of elevation, distance from the coast, or location, as demonstrated by the well-distributed range of pixels used for the modelling We use this model to investigate time trends in the observation period and to quantify both the frequency and magnitude of 45 temperature events that are likely to result in ice melt
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