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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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