Calibrated mass loss projections from the Greenland Ice Sheet

Data files are available at: https://arcticdata.io/data/10.18739/A2G737525/ The potential contribution of ice sheets remains the largest source of uncertainty in projecting sea-level due to the limited predictive skill of numerical ice sheet models, yet effective planning for coming sea level rise n...

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Main Authors: Andy Aschwanden, Douglas J Brinkerhoff
Format: Dataset
Language:unknown
Published: Arctic Data Center 2022
Subjects:
Online Access:https://search.dataone.org/view/urn:uuid:6a3d4812-73d2-4c9e-bd73-ed21d9b22fc3
id dataone:urn:uuid:6a3d4812-73d2-4c9e-bd73-ed21d9b22fc3
record_format openpolar
spelling dataone:urn:uuid:6a3d4812-73d2-4c9e-bd73-ed21d9b22fc3 2024-06-03T18:46:51+00:00 Calibrated mass loss projections from the Greenland Ice Sheet Andy Aschwanden Douglas J Brinkerhoff The Greenland Ice Sheet ENVELOPE(-74.0,-10.0,85.0,59.0) BEGINDATE: 2008-01-01T00:00:00Z ENDDATE: 2100-01-01T00:00:00Z 2022-01-01T00:00:00Z https://search.dataone.org/view/urn:uuid:6a3d4812-73d2-4c9e-bd73-ed21d9b22fc3 unknown Arctic Data Center EARTH SCIENCE > CRYOSPHERE > GLACIERS/ICE SHEETS > GLACIER MASS BALANCE/ICE SHEET MASS BALANCE EARTH SCIENCE SERVICES > MODELS > CRYOSPHERE MODELS Dataset 2022 dataone:urn:node:ARCTIC 2024-06-03T18:18:20Z Data files are available at: https://arcticdata.io/data/10.18739/A2G737525/ The potential contribution of ice sheets remains the largest source of uncertainty in projecting sea-level due to the limited predictive skill of numerical ice sheet models, yet effective planning for coming sea level rise necessitates that predictions are credible and accompanied by a defensible assessment of uncertainty. Characterization of the likelihood of upper-end contributions are particularly important for developing adaptation strategies. While the use of large ensembles of simulations allows these kinds probabilistic assessments, there is no guarantee that simulations are aligned with observations. Here, we show that calibrating an ensemble of model simulations on observations reduces uncertainties in projecting 21st century mass loss from the Greenland Ice Sheet relative to a plausible a priori distribution of model configurations. We find that jointly conditioning on surface speeds and cumulative mass loss reduces the projected 2100 median contribution and 5--95th percentile by 16-30% and 38-56, respectively, compared to the un-calibrated ensemble, resulting in calibrated sea-level contributions ranging from 4 to 30 centimeters at the year 2100. This data set contains several products: - Surrogate model training data. ~1000 surface speed realizations in netCDF format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io) - Trained emulators. 50 trained emulators in HDF5 format, prepared with PyTorch (www.pytorch.org) - Posterior parameter distributions. 50 posterior distributions in CSV format. - Time series of projected mass change. Time series of projected mass change from 2008 until 2100 in CSV format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io), for both the ensemble using the Prior and the Posterior (calibrated) parameter distribution. 500 realizations for each Representative Concentration Pathway (RCP) scenario 2.6, 4.5, and 8.5. Dataset glacier Greenland Ice Sheet Arctic Data Center (via DataONE) Greenland ENVELOPE(-74.0,-10.0,85.0,59.0)
institution Open Polar
collection Arctic Data Center (via DataONE)
op_collection_id dataone:urn:node:ARCTIC
language unknown
topic EARTH SCIENCE > CRYOSPHERE > GLACIERS/ICE SHEETS > GLACIER MASS BALANCE/ICE SHEET MASS BALANCE
EARTH SCIENCE SERVICES > MODELS > CRYOSPHERE MODELS
spellingShingle EARTH SCIENCE > CRYOSPHERE > GLACIERS/ICE SHEETS > GLACIER MASS BALANCE/ICE SHEET MASS BALANCE
EARTH SCIENCE SERVICES > MODELS > CRYOSPHERE MODELS
Andy Aschwanden
Douglas J Brinkerhoff
Calibrated mass loss projections from the Greenland Ice Sheet
topic_facet EARTH SCIENCE > CRYOSPHERE > GLACIERS/ICE SHEETS > GLACIER MASS BALANCE/ICE SHEET MASS BALANCE
EARTH SCIENCE SERVICES > MODELS > CRYOSPHERE MODELS
description Data files are available at: https://arcticdata.io/data/10.18739/A2G737525/ The potential contribution of ice sheets remains the largest source of uncertainty in projecting sea-level due to the limited predictive skill of numerical ice sheet models, yet effective planning for coming sea level rise necessitates that predictions are credible and accompanied by a defensible assessment of uncertainty. Characterization of the likelihood of upper-end contributions are particularly important for developing adaptation strategies. While the use of large ensembles of simulations allows these kinds probabilistic assessments, there is no guarantee that simulations are aligned with observations. Here, we show that calibrating an ensemble of model simulations on observations reduces uncertainties in projecting 21st century mass loss from the Greenland Ice Sheet relative to a plausible a priori distribution of model configurations. We find that jointly conditioning on surface speeds and cumulative mass loss reduces the projected 2100 median contribution and 5--95th percentile by 16-30% and 38-56, respectively, compared to the un-calibrated ensemble, resulting in calibrated sea-level contributions ranging from 4 to 30 centimeters at the year 2100. This data set contains several products: - Surrogate model training data. ~1000 surface speed realizations in netCDF format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io) - Trained emulators. 50 trained emulators in HDF5 format, prepared with PyTorch (www.pytorch.org) - Posterior parameter distributions. 50 posterior distributions in CSV format. - Time series of projected mass change. Time series of projected mass change from 2008 until 2100 in CSV format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io), for both the ensemble using the Prior and the Posterior (calibrated) parameter distribution. 500 realizations for each Representative Concentration Pathway (RCP) scenario 2.6, 4.5, and 8.5.
format Dataset
author Andy Aschwanden
Douglas J Brinkerhoff
author_facet Andy Aschwanden
Douglas J Brinkerhoff
author_sort Andy Aschwanden
title Calibrated mass loss projections from the Greenland Ice Sheet
title_short Calibrated mass loss projections from the Greenland Ice Sheet
title_full Calibrated mass loss projections from the Greenland Ice Sheet
title_fullStr Calibrated mass loss projections from the Greenland Ice Sheet
title_full_unstemmed Calibrated mass loss projections from the Greenland Ice Sheet
title_sort calibrated mass loss projections from the greenland ice sheet
publisher Arctic Data Center
publishDate 2022
url https://search.dataone.org/view/urn:uuid:6a3d4812-73d2-4c9e-bd73-ed21d9b22fc3
op_coverage The Greenland Ice Sheet
ENVELOPE(-74.0,-10.0,85.0,59.0)
BEGINDATE: 2008-01-01T00:00:00Z ENDDATE: 2100-01-01T00:00:00Z
long_lat ENVELOPE(-74.0,-10.0,85.0,59.0)
geographic Greenland
geographic_facet Greenland
genre glacier
Greenland
Ice Sheet
genre_facet glacier
Greenland
Ice Sheet
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