### Overview This dataset contains simulated significant wave height data generated from the WaveWatch III model run from 2020 up to 2070. It was produced to predict future environmental hazards threatening maritime navigation within the Arctic. Four unique simulations were produced using different Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models' wind and sea ice projections along the shared socioeconomic pathways 5-8.5 (SSP5-8.5) future emissions scenario. The climate models used include: CNRM-CM6-1-HR, EC-Earth3, MPI-ESM1-2-HR, and MRI-ESM2-0. For each climate model, data is organized into yearly files written to NetCDF format. The data is contained on a spatially-varying unstructured triangular mesh which spans from 50° North (N) to 89.9°N and 180° West (W) to 180° East (E). The 'hs' variable presents the significant wave height (highest one thirds of wave heights) to occur for each node during the simulation in 6 hour intervals. ### Access Data files can be accessed via: [https://arcticdata.io/data/10.18739/A2ST7DZ74/](https://arcticdata.io/data/10.18739/A2ST7DZ74/)
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The High-resolution (6 km) Ice-Ocean Modeling and Assimilation System (HIOMAS), 2010-2069
The High-resolution (6 kilometer (km)) Ice-Ocean Modeling and Assimilation System (HIOMAS) is used to simulate the evolution of sea ice for the Arctic Ocean and adjacent areas, including the Barents Sea, Norwegian Sea, Greenland Sea, Baffin Bay, and waters along Northwest Passage, over the period 2010 to 2069. The hindcast and future forcing over the period is from one of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models, the CNRM-CM6-1-HR global climate model (GCM) run at the National Center for Meteorological Research, Météo-France and CNRS Laboratory (CNRM). Monthly mean sea ice thickness (meters (m)) is provided over 2010 to 2069 in NETCDF file format, with model grid information such as latitudes and longitudes of model grid cells included. I have archived future projection of monthly mean sea ice thickness files over 2010 to 2069 in https://pscfiles.apl.uw.edu/zhang/HIOMAS_6km/. The files are in netcdf format, which are created by running HIOMAS using the future projection forcing of the CNRM-CM6-1-HR GCM run conducted at the National Center for Meteorological Research, Météo-France and CNRS Laboratory (CNRM). Martin interpolated the forcing onto the new, expanded HIOMAS grid. There is a readme.txt file.
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- Award ID(s):
- 1927785
- PAR ID:
- 10639409
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Arctic sea ice thickness ice growth dynamic open water creation
- Format(s):
- Medium: X Other: text/xml
- Sponsoring Org:
- National Science Foundation
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This dataset includes annual, gridded Arctic sea ice seasonal transition metrics (dates and periods) for fifteen Coupled Model Intercomparison Project version 6 (CMIP6) models and the Community Earth System Model version 1.1 (CESM1.1) Large Ensemble (CESM LE) (Kay, et al., 2015). Seasonal transition dates include melt onset, opening, break-up, freeze onset, freeze-up and closing. Seasonal transition periods include the melt period, the seasonal loss-of-ice period, the freeze period, the seasonal gain-of-ice period, the melt season, the open water period and the outer ice-free period. Data are provided for one ensemble member of the following models: Australian Community Climate and Earth System Simulator CM2 (ACCESS-CM2), Beijing Climate Center Climate System Model 2 MR (BCC-CSM2-MR), Beijing Climate Center Earth System Model 1 (BCC-ESM1), Community Earth System Model 2 (CESM2), Community Earth System Model 2 FV2 (CESM2-FV2), Community Earth System Model 2 Whole Atmosphere Community Climate Model (CESM2-WACCM), Community Earth System Model 2 Whole Atmosphere Community Climate Model FV2 (CESM2-WACCM-FV2), Centre National de Recherches Météorologiques ESM 2-1 (CNRM-ESM2-1), Centre National de Recherches Météorologiques CM 6-1 (CNRM-CM6-1), EC-Earth3, Meteorological Research Institute Earth System Model 2-0 (MRI-ESM2-0), Norwegian Earth System Model 2 LM (NorESM2-LM) and Norwegian Earth System Model 2 MM (NorESM2-MM). Data are provided for 40 members of the Community Earth System Model Large Ensemble (CESM LE), 35 members of Canadian Earth System Model 5 (CanESM5) and 30 members of Institut Pierre Simon Laplace CM6A LR (IPSL-CM6A-LR). The data is stored in netcdf format, and includes metadata in the netcdf files. The raw CMIP6 and CESM LE model output that these transition metrics are calculated from are publicly available at https://esgf-node.llnl.gov/projects/cmip6/ and https://www.earthsystemgrid.org/ respectively. This dataset was created to evaluate climate model projections of Arctic sea ice using seasonal transition metrics in the context of both observations and internal variability. It is used in the article Smith, Jahn, Wang (2020), Seasonal transition dates can reveal biases in Arctic sea ice simulations, The Cryosphere, in press. The discussion paper with a link to the final paper can be found at https://doi.org/10.5194/tc-2020-81. This work was conducted at the University of Colorado Boulder from 2019-2020.more » « less
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Abstract Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.more » « less
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