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.
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

21 to 30 of 4,113 Results
Sep 16, 2024 - pypromice v1.4.2
Python Source Code - 13.0 KB - MD5: 5c8506e6b10b36f86b1a0817e8069d33
Sep 16, 2024 - pypromice v1.4.2
Plain Text - 609 B - MD5: c596fe3cbc2630d89e6132f0b064f60d
Sep 16, 2024 - pypromice v1.4.2
Python Source Code - 1.7 KB - MD5: 53c5873378dfc53350b24ff0242183cc
Sep 16, 2024 - pypromice v1.4.2
Unknown - 4.3 KB - MD5: 87fb4c06d8aec45bd676497b29876747
Sep 16, 2024 - pypromice v1.4.2
Unknown - 5.0 KB - MD5: 326a859d4279ff07462eee8ba8c4cc12
Sep 16, 2024 - pypromice v1.4.2
Unknown - 2.8 KB - MD5: dafcfda8188b2ccc0b2c6448eeb8146d
Sep 16, 2024 - pypromice v1.4.2
Python Source Code - 0 B - MD5: d41d8cd98f00b204e9800998ecf8427e
Sep 16, 2024 - pypromice v1.4.2
Python Source Code - 30 B - MD5: 54e1c5e0a772348c3b2fc0d2d0c095ab
Sep 16, 2024 - pypromice v1.4.2
Python Source Code - 32 B - MD5: 3a688b3c335d6c88461121b693242407
Sep 16, 2024 - pypromice v1.4.2
Python Source Code - 0 B - MD5: d41d8cd98f00b204e9800998ecf8427e
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.