Abstract Internal variability is the dominant cause of projection uncertainty of Arctic sea ice in the short and medium term. However, it is difficult to determine the realism of simulated internal variability in climate models, as observations only provide one possible realization while climate models can provide numerous different realizations. To enable a robust assessment of simulated internal variability of Arctic sea ice, we use a resampling technique to build synthetic ensembles for both observations and climate models, focusing on interannual variability, which is the dominant time scale of Arctic sea ice internal variability. We assess the realism of the interannual variability of Arctic sea ice cover as simulated by six models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) that provide large ensembles compared to four observational datasets. We augment the standard definition of model and observational consistency by representing the full distribution of resamplings, analogous to the distribution of variability that could have randomly occurred. We find that modeled interannual variability typically lies within observational uncertainty. The three models with the smallest mean state biases are the only ones consistent in the pan-Arctic for all months, but no model is consistent for all regions and seasons. Hence, choosing the right model for a given task as well as using internal variability as an additional metric to assess sea ice simulations is important. The fact that CMIP5 large ensembles broadly simulate interannual variability consistent within observational uncertainty gives confidence in the internal projection uncertainty for Arctic sea ice based on these models. Significance Statement The purpose of this study is to evaluate the historical simulated internal variability of Arctic sea ice in climate models. Determining model realism is important to have confidence in the projected sea ice evolution from these models, but so far only mean state and trends are commonly assessed metrics. Here we assess internal variability with a focus on the interannual variability, which is the dominant time scale for internal variability. We find that, in general, models agree well with observations, but as no model is within observational uncertainty for all months and locations, choosing the right model for a given task is crucial. Further refinement of internal variability realism assessments will require reduced observational uncertainty.
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Interannual variability of Arctic sea ice concentration and area for six climate model large ensembles and four observational datasets, 1979-2020
This dataset includes statistically resampled monthly time series data of Arctic sea ice area and gridded data for March and September for sea ice concentration for a selection of large ensemble climate models and observational datasets. Arctic sea ice concentrations and areas are resampled from all available members of six coupled climate models from the Coupled Model Intercomparison Project 5 (CMIP5). These six models are: The second generation Canadian Earth System Model (CanESM2), The Community Earth System Mode version 1 (CESM1), The Commonwealth Scientific and Industrial Research Organisation Global Climate Model Mark 3.6 (CSIRO MK3.6), The Geophysical Fluid Dynamics Laboratory Coupled Climate Model version 3 (GFDL CM3), Geophysical Fluid Dynamics Laboratory Earth System Model version 2 with Modular Ocean Model version 4.1 (GFDL ESM2M), Max Planck Institute Earth System Model version 1 (MPI ESM1). The Four observational datasets are The Hadley Centre Sea Ice and Sea Surface Temperature data set version 1 (HadISST1), The National Oceanic and Atmospheric Administration and National Snow and Ice Data Center Climate Data Record Version 4 (CDR), The The National Aeronautics and Space Administration Team Algorithm (NT), and the The National Aeronautics and Space Administration Bootstrap Team Algorithm (BT). The sea ice area data is resampled 10,000 times and then the standard deviation of those resamplings is calculated, which can be considered analagous to interannual variability of sea ice area (SIA). The standard deviation (sigma) and mean (mu) of these data represent the variability and typical values respectively of interannual variability found in each ensemble member or observational dataset. Sea ice concentration is resampled 1000 times with the same standard deviation and mean metrics for sea ice concentration. This dataset was created to evaluate climate model projections of Arctic sea ice interannual variability and is used in the article Wyburn-Powell, Jahn, England (2022), Modeled Interannual Variability of Arctic Sea Ice Cover is Within Observational Uncertainty, Journal of Climate, https://doi.org/10.1175/JCLI-D-21-0958.1. This work was conducted at the University of Colorado Boulder from 2020-2022. The figures from the Journal of Climate article can be reproduced from the following datasets. The code used to create the datasets can be located at https://www.doi.org/10.5281/zenodo.6687725. - Figure 1: Sigma_obs_SIA.nc - Figure 2: Sigma_obs_SIA.nc, Mu_obs_SIA.nc, Sigma_mem_SIA.nc, Mu_mem_SIA.nc - Figure 3: Sigma_mem_varying_time_periods_1965_2066_03.nc, Sigma_LE_varying_time_periods_1965_2066_03.nc, Sigma_LE_varying_time_periods_1970_2040_09.nc, Sigma_obs_varying_time_periods_1953_2020.nc - Figure 4: Sigma_obs_SIA.nc, Sigma_mem_SIA.nc - Figure 5: Sigma_obs_SIA.nc - Figure 6: <model_name>_resampled_0<month>_individual.nc, <observational_dataset>_resampled_individual_1979_2020_03_09.nc - Figure 7: Sigma_obs_SIA.nc, Mu_obs_SIA.nc, Sigma_mem_SIA.nc, Mu_mem_SIA.nc - Figure 8: <model_name>_resampled_0<month>_individual.nc, <observational_dataset>_resampled_individual_1979_2020_03_09.nc - Figure 9: Sigma_mem_SIA.nc, Sigma_LE_SIA.nc
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- Award ID(s):
- 1847398
- PAR ID:
- 10596599
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Sea Ice Concentration Coupled Climate Models Satellite Observations CMIP5 Large Ensemble
- 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 The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models’ sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance StatementThe credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved.more » « less
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