PROMICE and GC-Net automated weather station (AWS) data in Greenland. The data is available either as csv or netcdf files. Station locations, installation (if necessary, decommission) date, location type (tundra, ice sheet, local glacier), station type (one or two levels of measurements) are available in the AWS_readme.pdf file. The AWS_sites_metadata.csv is updated at each release but can have missing coordinates if the latest positions are not known. The list of variables and their descriptions is available in the AWS_variables.csv file. Please consult these documents before using the dataset. See https://github.com/GEUS-Glaciology-and-Climate/pypromice for detailed information on how the data product is produced. To raise issues regarding our data, please add an issue here: https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues For the latest (but unchecked) data files, updated every hour with new transmissions, please visit: https://thredds.geus.dk/ Terms of use If the data are presented or used to support results of any kind, please inform a member of the PROMICE team at GEUS, and: - Include the acknowledgement: ”Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) are provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www.promice.dk. ZAC, LYN, FRE and NUK_K stations are financially supported by the Glaciobasis programme as part of the Greenland Ecosystem Monitoring project (https://g-e-m.dk/). The NUK_K station is owned and maintained by Asiaq Greenland Survey. The WEG stations are funded and maintained by Jakob Abermann at the Department of Geography and Regional Science of the University of Graz. The RED_L station is funded and maintained by Rainer Prinz at the Department of Atmospheric and Cryospheric Sciences of the University of Innsbruck. The NUK_B station is funded and maintained by James Lea at the University of Liverpool. The SER_B station is funded and maintained by Anders Bjørk at the Department of Geosciences and Natural Resource Management of the University of Copenhagen.” - Include a reference to the peer-reviewed article presenting the PROMICE weather station data: Fausto, R. S., van As, D., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., Andersen, S. B., Colgan, W., Karlsson, N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., Nielsen, S., Pedersen, A. Ø., Shields, C. L., Solgaard, A. M., and Box, J. E.: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth Syst. Sci. Data, 13, 3819–3845, https://doi.org/10.5194/essd-13-3819-2021, 2021. - Include a reference to the data itself (see citation and DOI at the top of this page). If the data are crucial to the main conclusions of a manuscript or presentation of any kind, please contact a relevant member of the PROMICE team at GEUS and include them in the manuscript author list.

GC-Net Level 1 automated weather station data In Memory of Dr. Konrad (Koni) Steffen Author: B. Vandecrux Contact: bav@geus.dk Last update: 2023-09-01 Citation Steffen, K.; Vandecrux, B.; Houtz, D.; Abdalati, W.; Bayou, N.; Box, J.; Colgan, L.; Espona Pernas, L.; Griessinger, N.; Haas-Artho, D.; Heilig, A.; Hubert, A.; Iosifescu Enescu, I.; Johnson-Amin, N.; Karlsson, N. B.; Kurup Buchholz, R.; McGrath, D.; Cullen, N.J.; Naderpour, R.; Molotch, N.P.; Pederson, A. Ø.; Perren, B.; Philipps, T.; Plattner, G.K.; Proksch, M.; Revheim, M. K.; Særrelse, M.; Schneebli, M.; Sampson, K.; Starkweather, S.; Steffen, S.; Stroeve, J.; Watler, B.; Winton, Ø. A.; Zwally, J.; Ahlstrøm, A., 2023, "GC-Net Level 1 automated weather station data", https://doi.org/10.22008/FK2/VVXGUT, GEUS Dataverse, V3 as described and processed by: Vandecrux, B., Box, J. E., Ahlstrøm, A. P., Andersen, S. B., Bayou, N., Colgan, W. T., Cullen, N. J., Fausto, R. S., Haas-Artho, D., Heilig, A., Houtz, D. A., How, P., Iosifescu Enescu, I., Karlsson, N. B., Kurup Buchholz, R., Mankoff, K. D., McGrath, D., Molotch, N. P., Perren, B., Revheim, M. K., Rutishauser, A., Sampson, K., Schneebeli, M., Starkweather, S., Steffen, S., Weber, J., Wright, P. J., Zwally, H. J., and Steffen, K.: The historical Greenland Climate Network (GC-Net) curated and augmented Level 1 dataset, Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, 2023. Description The Greenland Climate Network (GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen on the Greenland Ice Sheet (GrIS). This first station, "Swiss Camp" or the "ETH-CU" camp, was initiated in 1990 by A. Ohmura et al. (1991, 1992) with K. Steffen taking over the site from 1995 and expending the network from that year to 31 stations at 30 sites in Greenland (Steffen et al., 1996, 2001). The GC-Net was supported by multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado. Provided in this dataset are the 25 two-level stations from 24 sites on the Greenland ice sheet and 3 experimental stations in Antarctica. The remaining 6 Greenland stations have a different design and will be added once quality checked. Although the GC-Net AWS transmitted their data near-real time through satellite communication, the present dataset was made from uncorrupted datalogger files, retrieved every 1-2 years during maintenance. Full dataset description publication will be forthcoming. The Geological Survey of Denmark and Greenland (GEUS) has undertaken the continuation of multiple GC-Net sites through the Programme for Monitoring of the Greenland Ice Sheet (PROMICE.dk). The level 1 data is provided in the newly described csv-compatible NEAD format, which is a csv file with added metadata header. The format is documented at https://doi.org/10.16904/envidat.187 and a python package is available to read and write NEAD files: https://github.com/GEUS-Glaciology-and-Climate/pyNEAD . The GC-Net stations measure: - Air temperature from four sensors at two heights above the surface - Relative humidity at two heights above the surface - Wind speed and direction at two heights above the surface - Air pressure - Surface height from two sonic sounders - Incoming and outgoing shortwave radiation - Net radiation (long- and short-wave)* - Firn or ice temperatures at 10 levels below the surface In the L1 dataset, these measurements are cleaned from sensor, station or logger malfunctions, adjusted and/or filtered when and where possible. Additionally, the L1 dataset contains the following derived variables: - Surface height (corrected from the shifts in sonic sounder height) - Instrument heights (derived from sonic sounder height and station geometry) - Inter- or extrapolated temperature, relative humidity and wind speed at respectively 2, 2, and 10 m above the surface - Estimated depth of the subsurface temperature measurements (adjusted for snow accumulation, ice ablation and instrument replacement) - Interpolated firn or ice temperature at 10 m below the surface - Calculated solar an azimuth angles - Sensible and latent heat fluxes calculated after Steffen and Demaria (1996) Important links: - The level 1 processing scripts and discussion page for Q&A and issue reporting (under "issues" tab) is available at: https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing - The level 0 data (from which the L1 data was built from) is available at: https://www.doi.org/10.16904/envidat.1. - The compilation of handheld GPS coordinates for each site and for multiple years is available here: Vandecrux, B. and Box, J.E.: GC-Net AWS observed and estimated positions (Version v1) [Data set]. Zenodo....

The pypromice toolbox is for retrieving, processing and handling PROMICE (Programme for Monitoring of the Greenland Ice Sheet) and GC-Net (Greenland Climate Network) automated weather station (AWS) data. The content here reflects pypromice releases for processing and handling the AWS data products. See the pypromice Github repo for more recent developments and full documentation.

GC-NET automated weather station (AWS) data v01 from the Greenland Ice Sheet. Data are collected from the two-boom mast design located in the accumulation area, with variables as listed in the AWS_variables.csv file provided. See pypromice for how we process the data product. See variables.csv for the most up-to-date description of the the data variables.

The GlacioBasis Zackenberg glaciological monitoring programme, is a subprogram of the Greenland Ecosystem Monitoring (GEM, g-e-m.dk) at Zackenberg Research station, NE Greenland. The data presented here is from a transect of three Automatic Ablation and Weather Stations (AAWSs) located on the A. P. Olsen ice cap (referred to here as APO or the Ice Cap), located in the hydrologocal catchment of Zackenberg River. The first two AAWSs of the APO transect were installed in April 2008 in the ablation zone, and the third AAWS was installed in August 2009 in the accumulation zone at the Ice Cap summit. These AAWSs have been running with alternating instrumentation until April 2022. In spring 2022 installation of new standardized AAWSs was initiated, these stations are similar to the PROMICE and GC-Net stations (Fausto and others, 2021). With the new standardized setup, the data from the APO transect will be handled as a PROMICE and GC-Net dataset and data processing will be done using the python package pypromice described in How and others (2023). The continuation of this dataset from the A. P. Olsen transect can be found as part of the PROMICE and GC-Net automated weather station data in Greenland: https://doi.org/10.22008/FK2/IW73UU as well as in the GEM database: https://data.g-e-m.dk/ which is updated once a year in spring. The variables published here are: Ice ablation, air temperature, relative humidity, air pressure, wind speed, incoming and outgoing shortwave and longwave radiation as well as AAWS tilt, snow depth and the derived variables cloud cover fraction, surface temperature and albedo. References: Fausto, R. S., As, D. V., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., Andersen, S. B., Colgan, W., Karlsson, N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., Nielsen, S., Pedersen, A., Shields, C. L., Solgaard, A. M., and Box, J. E.: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth System Science Data, 13, 3819–3845, https://doi.org/10.5194/essd-13-3819-2021, 2021. How, P. R., Wright, P. J., Mankoff, K. D., Vandecrux, B., Fausto, R. S., and Ahlstrøm, A. P.: pypromice: A Python package for processing automated weather station data, Journal of Open Source Software, 8, 5298, https://doi.org/10.21105/joss.05298, 2023.

This dataset contains the forcing provided to all models for the firn meltwater Retention Intercomparison Project (RetMIP). The forcing files consist of 1) three-hourly time series of snow surface temperature, net accumulation (positive for snowfall and vapor deposition and negative for sublimation) and melt and 2) initial profiles for firn density, temperature and liquid water content. These fields are available at four sites located in different climate zones of the Greenland ice sheet: Summit, Dye-2, KAN_U and the firn aquifer site (FA). At the Dye-2 sites, two forcing data files are available: one covering 1998-2015 and another one derived from more recent instruments over the 2016 melt season. At Summit and Dye-2 (for 1998-2015), we use data from the GC-Net automatic weather stations (AWS), at Dye-2 for 2016 we use the AWS from Samimi et al. (2020), at KAN_U data from the PROMICE AWS, and at FA data from the AWS maintained by IMAU at Utrecht University. Surface temperature and vapour fluxes were calculated at hourly resolution using the surface energy budget closure from van As (2005), as implemented in Vandecrux et al. (2018). The energy budget used measurements of air temperature, humidity, pressure and net shortwave radiation. Downward longwave radiations was measured at Dye-2 (for 2016), KAN_U and FA but were extracted from the closest cell in HIRHAM regional climate model at Dye-2 (1998-2015) and Summit. Sensible and latent heat fluxes were calculated using the bulk approach and upward longwave radiation was solved iteratively at each time step. Snowfall was derived from surface height observations available at each station, first with an a-priori surface snow density of 315 kg m-3 (Fausto et al., 2016), then adjusted to multiple snowpit observations available at each site. Gap-free meteorological fields were obtained using the approach from Vandecrux et al. (2018) which adjusts the output from HIRHAM climate model to match at best the observations and use these adjusted values to fill the gaps in the instrumental records. More information about the RetMIP protocol and on the recommended boundary conditions can be found in Vandecrux et al. (2020) and on the RetMIP webpage. The model outputs for the RetMIP are available here: Vandecrux, Baptiste; Peter L. Langen; Peter Kuipers Munneke; Sebastian Simonsen; Vincent Verjans; C. Max Stevens; Sergey Marchenko; Ward van Pelt; Colin Meyer, 2020, "The firn meltwater retention model intercomparison project (RetMIP): model outputs", https://doi.org/10.22008/FK2/CVPUJL, GEUS Dataverse