The firn meltwater retention model intercomparison project (RetMIP): model outputs
This dataset comprises the outputs from nine firn models at four sites of the Greenland ice sheet produced for the firn meltwater Retention Intercomparison Project (RetMIP). The nine models were forced by the same weather-station-derived mass and energy fluxes. The simulated firn density, temperatur...
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GEUS Dataverse
2020
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Online Access: | https://dx.doi.org/10.22008/fk2/cvpujl https://dataverse01.geus.dk/citation?persistentId=doi:10.22008/FK2/CVPUJL |
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openpolar |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
description |
This dataset comprises the outputs from nine firn models at four sites of the Greenland ice sheet produced for the firn meltwater Retention Intercomparison Project (RetMIP). The nine models were forced by the same weather-station-derived mass and energy fluxes. The simulated firn density, temperature, liquid water content, refreezing rates and ice fraction are interpolated on the same grid from the surface down to 20 m deep with 10 cm vertical resolution. For each three-hourly time step, the total firn air and water content, total refreezing and total runoff simulated in the entire firn column are also calculated for each model. For one of the sites, Dye-2, two simulations are provided: one spanning over 17 years and one restricted to the 2016 melt season using a more recent weather station for the forcing. The forcing data for these model runs are available here: Vandecrux, Baptiste, 2020, "The firn meltwater retention model intercomparison project (RetMIP): forcing data", https://doi.org/10.22008/FK2/GZ3CSN, GEUS Dataverse, V1 |
format |
Dataset |
author |
Vandecrux, Baptiste Langen, Peter L. Munneke, Peter Kuipers Simonsen, Sebastian Verjans, Vincent Stevens, C. Max Marchenko, Sergey Pelt, Ward Van Meyer, Colin |
spellingShingle |
Vandecrux, Baptiste Langen, Peter L. Munneke, Peter Kuipers Simonsen, Sebastian Verjans, Vincent Stevens, C. Max Marchenko, Sergey Pelt, Ward Van Meyer, Colin The firn meltwater retention model intercomparison project (RetMIP): model outputs |
author_facet |
Vandecrux, Baptiste Langen, Peter L. Munneke, Peter Kuipers Simonsen, Sebastian Verjans, Vincent Stevens, C. Max Marchenko, Sergey Pelt, Ward Van Meyer, Colin |
author_sort |
Vandecrux, Baptiste |
title |
The firn meltwater retention model intercomparison project (RetMIP): model outputs |
title_short |
The firn meltwater retention model intercomparison project (RetMIP): model outputs |
title_full |
The firn meltwater retention model intercomparison project (RetMIP): model outputs |
title_fullStr |
The firn meltwater retention model intercomparison project (RetMIP): model outputs |
title_full_unstemmed |
The firn meltwater retention model intercomparison project (RetMIP): model outputs |
title_sort |
firn meltwater retention model intercomparison project (retmip): model outputs |
publisher |
GEUS Dataverse |
publishDate |
2020 |
url |
https://dx.doi.org/10.22008/fk2/cvpujl https://dataverse01.geus.dk/citation?persistentId=doi:10.22008/FK2/CVPUJL |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_relation |
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ftdatacite:10.22008/fk2/cvpujl 2023-05-15T16:29:04+02:00 The firn meltwater retention model intercomparison project (RetMIP): model outputs Vandecrux, Baptiste Langen, Peter L. Munneke, Peter Kuipers Simonsen, Sebastian Verjans, Vincent Stevens, C. Max Marchenko, Sergey Pelt, Ward Van Meyer, Colin 2020 https://dx.doi.org/10.22008/fk2/cvpujl https://dataverse01.geus.dk/citation?persistentId=doi:10.22008/FK2/CVPUJL unknown GEUS Dataverse https://dx.doi.org/10.22008/fk2/cvpujl/zdz6tb https://dx.doi.org/10.22008/fk2/cvpujl/hrvxzf https://dx.doi.org/10.22008/fk2/cvpujl/7zpppz https://dx.doi.org/10.22008/fk2/cvpujl/0yioje https://dx.doi.org/10.22008/fk2/cvpujl/0b2ucr https://dx.doi.org/10.22008/fk2/cvpujl/n43hal https://dx.doi.org/10.22008/fk2/cvpujl/ikekbl https://dx.doi.org/10.22008/fk2/cvpujl/sugfoj https://dx.doi.org/10.22008/fk2/cvpujl/glbhb1 https://dx.doi.org/10.22008/fk2/cvpujl/h2gune https://dx.doi.org/10.22008/fk2/cvpujl/r63jbe https://dx.doi.org/10.22008/fk2/cvpujl/thpkdj https://dx.doi.org/10.22008/fk2/cvpujl/qywfeh https://dx.doi.org/10.22008/fk2/cvpujl/tgkm8g https://dx.doi.org/10.22008/fk2/cvpujl/b5bolc https://dx.doi.org/10.22008/fk2/cvpujl/v47so9 https://dx.doi.org/10.22008/fk2/cvpujl/f1iekt https://dx.doi.org/10.22008/fk2/cvpujl/ilnfw3 https://dx.doi.org/10.22008/fk2/cvpujl/vf7ski https://dx.doi.org/10.22008/fk2/cvpujl/t7vzhr https://dx.doi.org/10.22008/fk2/cvpujl/d5sfur https://dx.doi.org/10.22008/fk2/cvpujl/qlxfhc https://dx.doi.org/10.22008/fk2/cvpujl/0pbcgo https://dx.doi.org/10.22008/fk2/cvpujl/wgm6bx https://dx.doi.org/10.22008/fk2/cvpujl/1kth3u https://dx.doi.org/10.22008/fk2/cvpujl/nt3wem https://dx.doi.org/10.22008/fk2/cvpujl/utmkjg https://dx.doi.org/10.22008/fk2/cvpujl/aundbi https://dx.doi.org/10.22008/fk2/cvpujl/bfhirp https://dx.doi.org/10.22008/fk2/cvpujl/ow8eic https://dx.doi.org/10.22008/fk2/cvpujl/9zcacc https://dx.doi.org/10.22008/fk2/cvpujl/h3grsw https://dx.doi.org/10.22008/fk2/cvpujl/gderbd https://dx.doi.org/10.22008/fk2/cvpujl/fkkkf3 https://dx.doi.org/10.22008/fk2/cvpujl/w4twd4 https://dx.doi.org/10.22008/fk2/cvpujl/wma9ae https://dx.doi.org/10.22008/fk2/cvpujl/jlnspc https://dx.doi.org/10.22008/fk2/cvpujl/mwkg38 https://dx.doi.org/10.22008/fk2/cvpujl/xfu9kr https://dx.doi.org/10.22008/fk2/cvpujl/7kvcir https://dx.doi.org/10.22008/fk2/cvpujl/otxr9e https://dx.doi.org/10.22008/fk2/cvpujl/mpdagx https://dx.doi.org/10.22008/fk2/cvpujl/uxbtgl https://dx.doi.org/10.22008/fk2/cvpujl/mglp5z https://dx.doi.org/10.22008/fk2/cvpujl/3mblkc https://dx.doi.org/10.22008/fk2/cvpujl/dyv2gy https://dx.doi.org/10.22008/fk2/cvpujl/jpvpm2 https://dx.doi.org/10.22008/fk2/cvpujl/j2mccb https://dx.doi.org/10.22008/fk2/cvpujl/wqjbxi https://dx.doi.org/10.22008/fk2/cvpujl/kg0gkn dataset Dataset 2020 ftdatacite https://doi.org/10.22008/fk2/cvpujl https://doi.org/10.22008/fk2/cvpujl/zdz6tb https://doi.org/10.22008/fk2/cvpujl/hrvxzf https://doi.org/10.22008/fk2/cvpujl/7zpppz https://doi.org/10.22008/fk2/cvpujl/0yioje https://doi.org/10.22008/fk2/cvpujl/0 2021-11-05T12:55:41Z This dataset comprises the outputs from nine firn models at four sites of the Greenland ice sheet produced for the firn meltwater Retention Intercomparison Project (RetMIP). The nine models were forced by the same weather-station-derived mass and energy fluxes. The simulated firn density, temperature, liquid water content, refreezing rates and ice fraction are interpolated on the same grid from the surface down to 20 m deep with 10 cm vertical resolution. For each three-hourly time step, the total firn air and water content, total refreezing and total runoff simulated in the entire firn column are also calculated for each model. For one of the sites, Dye-2, two simulations are provided: one spanning over 17 years and one restricted to the 2016 melt season using a more recent weather station for the forcing. The forcing data for these model runs are available here: Vandecrux, Baptiste, 2020, "The firn meltwater retention model intercomparison project (RetMIP): forcing data", https://doi.org/10.22008/FK2/GZ3CSN, GEUS Dataverse, V1 Dataset Greenland Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) Greenland |