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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Sep 15, 2026 - pypromice v1.12.3
Python Source Code - 15.6 KB - MD5: 399014e41ca4d3b7932bb78e1e99e96d
Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
Network Common Data Form - 311.7 MB - MD5: 8d77fa5f64de51d3fc6701b44c8ca360
Data
PNG Image - 1.9 MB - MD5: cefc268e7a882ffe0ccf17680be8c594
Image
Adobe PDF - 101.9 KB - MD5: d3130a455cd0687bf1181379fdf3bc61
Uploaded with pyDataverse 2026-09-01 17:19
Comma Separated Values - 3.7 KB - MD5: 246c9a3d84fea2530bf3ade20a54f245
Uploaded with pyDataverse 2026-09-01 17:19
Comma Separated Values - 6.3 KB - MD5: 8b35b882fc7cdcc91e8df33621e617e6
Uploaded with pyDataverse 2026-09-01 17:19
Comma Separated Values - 2.8 MB - MD5: 32cd3eb429483b66044219ad71c7c5c3
Uploaded with pyDataverse 2026-09-01 16:32
Network Common Data Form - 2.0 MB - MD5: 8c203d67538c47ff3576c138eab99420
Uploaded with pyDataverse 2026-09-01 16:32
Comma Separated Values - 68.1 MB - MD5: c0f7944caec4e268f1487a46851f207a
Uploaded with pyDataverse 2026-09-01 16:30
Network Common Data Form - 27.2 MB - MD5: 1f0264922cbde1819e82e72e7ecfccb9
Uploaded with pyDataverse 2026-09-01 16:31
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