Abstract
Greenland Ice Sheet (GrIS) surface melt has contributed to the global sea-level rise and the ongoing warming is expected to promote this process. This study provides a new strategy for the quantitative estimate of GrIS daily surface melt at enhanced resolution (3.125 km) from a remote sensing perspective beyond traditional regional climate models (RCMs). Daily melt flux is estimated from spaceborne radiometer observations with a back-propagation neural network model. The network is trained with melt fluxes that are calculated using detailed in-situ atmospheric and snow observations and a surface energy balance model. Our results provide details about the extreme melt in mid-July 2012 when surface melt occurred at Summit and the meltwater volume exceeded 20 Gt as a result of anomalous warming. Meltwater volume from the satellite is very close to that from RCMs.
| Original language | English |
|---|---|
| Article number | e2021GL096690 |
| Journal | Geophysical Research Letters |
| Volume | 49 |
| Issue number | 6 |
| DOIs | |
| State | Published - 28 Mar 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Greenland Ice Sheet
- back-propagation neural network
- energy balance model
- melt flux
- remote sensing
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