Search NASA⌕ Search

DOE OSTI · 1853005

Greenland Surface Melt Dominated by Solar and Sensible Heating

Abstract

The Greenland Ice Sheet is the primary source of global Barystatic sea-level rise, and at least half of its recent mass-loss acceleration is caused by surface meltwater runoff. Previous studies on surface melt have examined various thermodynamic and dynamic drivers, yet their contributions are not compared using unified observations. We use decade-long in-situ measurements from automatic weather stations throughout the ablation zone to assess energy components and identify the leading physical processes in this area. Large melt events exceeding 3σ contribute only ~2% to total surface melt since 2007. The day-to-day variability of all melt is dominated by sensible heat exchange (31 ± 7%) and shortwave radiation (28 ± 5%). Sensible and solar heating correlate with the occurrence of dry and fast gravity-driven winds. These katabatic winds increase sensible heating of the surface mainly by enhancing vertical mixing that reduces the temperature inversion. The concomitant low humidity and clear skies yield increased solar heating.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wang, Wenshan, Zender, Charles S., van As, Dirk, Fausto, Robert S., Laffin, Matthew K.. 2021-03-27. Greenland Surface Melt Dominated by Solar and Sensible Heating. https://doi.org/10.1029/2020gl090653

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗