In 2007, Denmark launched the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) to assess changes in the mass balance of the ice sheet. The two major contributors to the ice sheet mass loss are surface melt and a larger production of icebergs through faster ice flow. PROMICE is focused on both processes. Ice movement and discharge is tracked by satellites and GPSs. The surface mass balance is monitored by a network of weather stations in the melt zone of the ice sheet, providing ground truth data to calibrate mass budget models.

The Greenland Climate Network (GC-Net) was established in 1995 by Prof. Konrad Steffen at CIRES, to obtain knowledge of the mass gain and climatology of the ice sheet. The programme was funded by the USA until 2020, at which point Denmark assumed responsibility for the operation and maintenance of the weather station network. The snowfall and climatology are monitored by a network of weather stations in the accumulation zone of the ice sheet, supplemented by satellite-derived data products.

Together, the two monitoring programmes deliver data about the mass balance of the Greenland ice sheet in near real-time. Explore our project dataverses and datasets below.
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Network Common Data Form - 38.4 MB - MD5: a83c7556971f7f3a0e7f8e1aa5aecdf7
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Comma Separated Values - 81.8 KB - MD5: de74c6268f83a4a1350342b67688f17f
Uploaded with pyDataverse 2025-04-03 15:33
Network Common Data Form - 400.5 KB - MD5: e96b07686f364572f5b93327cf021c65
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Comma Separated Values - 128.5 KB - MD5: 1c28bb13abf5a0d0419e0f304dc0329a
Uploaded with pyDataverse 2025-04-03 16:11
Network Common Data Form - 446.2 KB - MD5: 61af096f148c83062d07b4da685f1f43
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Comma Separated Values - 2.9 MB - MD5: 5b0947f91825ff860d9e49d880c13fd5
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Network Common Data Form - 2.2 MB - MD5: d693c1f54c1275c1d21649b92a9d2312
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Comma Separated Values - 5.5 KB - MD5: e5749e5839932f37dab9f8340b0281b0
Uploaded with pyDataverse 2025-04-03 16:13
Network Common Data Form - 352.3 KB - MD5: 5f8eac09ee56a239993b08417232394c
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Comma Separated Values - 2.6 MB - MD5: 0a48f56a22c2e27c27254de0a0a66654
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