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This dataset contains data used in the paper: Revisiting the Last Ice Area Projections from a High-Resolution Global Earth System Model - Fol et al (2025). Results are organized in excel files or numpy arrays with the dataset name, variable and ensemble member (for simulations) in the name of the file. See below for more information on what variables are included in the files and their structure. CESM_HR : CESM_HR_SIAFluxes - per ensemble member: Timeseries of monthly SIA flux per gate. CESM_HR_fluxesCAADiv.npy, _fluxesQEIDiv.npy, _fuxesQEIDivMeltSeason.npy : Timeseries of annual divergence over the Queen Elizabeth Islands and the Southern Canadian Arctic Archipelago derived from monthly SIA fluxes at the entry and exit gates. CESM_HR_ThicknessDistribution.xlsx : Thickness distribution for the LIA-N, QEI, and CAA-S computed from the simulated thickness distribution (aicen001, aicen002, aicen003, aicen004, aicen005). CESM_HR_tendencies - per ensemble member- per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESH_HR_sitPanArctic - per ensemble member: Timeseries of pan-Arctic mean may sea ice thickness. CESM_HR_sieSept and CESM_HR_sieMarch - per ensemble member- per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March or September sea ice extent, sea ice area. CESM_HR_sic - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of mean sea ice concentration for grid cells having more than 15% of SIC (no open water). CESM_HR_meltSeason - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0. CESM_HR_mean_mayThickness.npy and CESM_HR_meanseptconc.npy: Results for map of the mean september sea ice concentration and may ice thickness for 1981-2000, 2001-2020, 2021-2040 and 2041-2060. CESM_LR : CESM_LR_sieSept - per region (LIA-N, QEI, CAA-S) and pan-Arctic: Timeseries of September sea ice extent and sea ice area. CESM_LR_sitPanArctic.xlsx: Timeseries of pan-Arctic mean May sea ice thickness. CESM_LR_tendencies - per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESM_LR_meltSeason - per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0. CESM2_LE: CESM2_LE_CAA_sept, _LIAN_sept, _QEI_sept, panArctic: Mean September sea ice extent and sea ice area per region. There is one excel tab per ensemble member in each file. CESM2_LE_pan_Arctic_hi_may: Pan-Arctic mean May sea ice thickness. There is one excel tab per ensemble member in each file. PIOMAS: PIOMAS_panArctic_hi.xlsx: Timeseries of mean may sea ice thickness. PIOMAS_mean_1981_2000_mean_mayThickness.npy : Results for map of the mean May ice thickness for 1981-2000 and 2001-2020. Observations: CIS_marchSept_1982_1990_sie - per region (QEI and CAA-S) : Timeseries of March and September mean sea ice extent and area. CIS ice charts do not fully cover the LIA-N. NSDICCDR_1979_2023_sia - per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March and September mean sea ice extent and area. NSDICCDR_1981_2000_mean_septConc.npy: Results for map of the mean September sea ice concentration for 1981-2000 and 2001-2020. NSDICCDR_1981_2000_LIAN_sic.xlsx: Timeseries of monthly mean sea ice concentration in the LIA-N. NSDICCDR_1981_2000_QEI_sic.xlsx: Timeseries of monthly mean sea ice concentration in the QEI. These results are derived from the following datasets: Ensemble members 1 and 3 of simulations from the high-resolution Community Earth System Model version 1.3 (CESM1.3-HR) produced for the International Laboratory for High-Resolution Earth System Prediction (iHESP) by the Qingdao National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the U.S. National Center for Atmospheric Research (NCAR). The lower resolution simulation is also used (CESM1.3-LR). (Chang et al., 2020; Zhang et al., 2020). The 100-ensemble members Community Earth System Model (version 2) Large Ensemble (CESM2-LE) (Danabasoglu et al., 2020). Satellite-derived monthly mean SIA fluxes through entry and exit gates of the CAA and Nares Strait (Howell et a., 2019; 2021; 2023; 2024; Smedsrud et al., 2017; Kwok, 2006). The National Snow and Ice Data Center (NSIDC) Climate Data Record (CDR) (version 4) sea ice concentration, stored on a 25 x 25 km polar stereographic grid centered on the North Pole from 1979 to 2023 (Meier et al., 2021). The gridded version of the regional Canadian Ice Service (CIS) Digital Archive ice charts from the Eastern and Western Arctic regions (Tivy et al., 2011). The Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) assimilated sea ice concentration and derived ice thickness distribution estimates in the Arctic from 1978 to 2022 (Zhang et al., 2000).
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Defining the “Ice Shed” of the Arctic Ocean's Last Ice Area and Its Future Evolution
Abstract Sea ice will persist longer in the Last Ice Area (LIA), north of Canada and Greenland, than elsewhere in the Arctic. We combine earth system model ensembles with a sea‐ice tracking utility (SITU) to explore sources of sea ice (the “ice shed”) to the LIA under two scenarios: continued high warming (HW) rates and low warming (LW) rates (mean global warming below ca. 2°C) through the 21st century. Until mid‐century, the two scenarios yield similar results: the primary ice source shifts from the Russian continental shelves to the central Arctic, mobility increases, and mean ice age in the LIA drops from about 7 years to less than one. After about 2050, sea ice stabilizes in the LW scenario, but continues to decline in the HW scenario until LIA sea ice is nearly entirely seasonal and locally formed. Sea ice pathways through the ice shed determine LIA ice conditions and transport of material, including biota, sediments, and pollutants (spilled oil and industrial or agricultural contaminants have been identified as potential hazards). This study demonstrates that global warming has a dramatic impact on the sources, pathways and ages of ice entering the LIA. Therefore, we suggest that maintaining ice quality and preserving ice‐obligate ecologies in the LIA, including the Tuvaijuittuq Marine Protected Area north of Nunavut, Canada, will require international governance. The SITU system used in this study is publicly available as an online utility to support researchers, policy analysts, and educators interested in past and future sea ice sources and trajectories.
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- Award ID(s):
- 1928235
- PAR ID:
- 10449034
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth's Future
- Volume:
- 9
- Issue:
- 9
- ISSN:
- 2328-4277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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