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 - 525.4 KB - MD5: 6748ad96f6f1cceae46b12d38a7b0e15
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Comma Separated Values - 3.3 MB - MD5: be0980779f7046625f9c9ec2d7557e87
Uploaded with pyDataverse 2025-12-03 17:09
Network Common Data Form - 2.5 MB - MD5: 11b391509b14a7de7e721859b5700c69
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Comma Separated Values - 72.3 MB - MD5: bf793f292ecc0b7ec6fdb6303bb87f83
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Network Common Data Form - 30.8 MB - MD5: 11ac2d2e1359c12beba770712faff57f
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Comma Separated Values - 114.4 KB - MD5: a3f2823d07eb35757e3d68466c223aeb
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Network Common Data Form - 527.3 KB - MD5: 32e39f76fe86aad50e85174711235a0d
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Comma Separated Values - 3.5 MB - MD5: e9a2ab6af301bf588becac3a5efb4b5e
Uploaded with pyDataverse 2025-12-03 16:04
Network Common Data Form - 2.6 MB - MD5: ef0b498f0ffbfd557ffe5153b9f11e77
Uploaded with pyDataverse 2025-12-03 16:03
Comma Separated Values - 74.4 MB - MD5: 83ec85064698c1970830ffb3de655835
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