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Abstract Summer Arctic sea ice is declining rapidly but with superimposed variability on multiple time scales that introduces large uncertainties in projections of future sea ice loss. To better understand what drives at least part of this variability, we show how a simple linear model can link dominant modes of climate variability to low-frequency regional Arctic sea ice concentration (SIC) anomalies. Focusing on September, we find skillful projections from global climate models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) at lead times of 4–20 years, with up to 60% of observed low-frequency variability explained at a 5-yr lead time. The dominant driver of low-frequency SIC variability is the interdecadal Pacific oscillation (IPO) which is positively correlated with SIC anomalies in all regions up to a lead time of 15 years but with large uncertainty between GCMs and internal variability realization. The Niño-3.4 index and Atlantic multidecadal oscillation have better agreement between GCMs of being positively and negatively related, respectively, with low-frequency SIC anomalies for at least 10-yr lead times. The large variations between GCMs and between members within large ensembles indicate the diverse simulation of teleconnections between the tropics and Arctic sea ice and the dependence on the initial climate state. Further, the influence of the Niño-3.4 index was found to be sensitive to the background climate. Our results suggest that, based on the 2022 phases of dominant climate variability modes, enhanced loss of sea ice area across the Arctic is likely during the next decade. Significance StatementThe purpose of this study is to better understand the drivers of low-frequency variability of Arctic sea ice. Teasing out the complicated relationships within the climate system takes a large number of examples. Here, we use 42 of the latest generation of global climate models to construct a simple linear model based on dominant named climate features to predict regional low-frequency sea ice anomalies at a lead time of 2–20 years. In 2022, these modes of variability happen to be in the phases most conducive to low Arctic sea ice concentration anomalies. Given the context of the longer-term trend of sea ice loss due to global warming, our results suggest accelerated Arctic sea ice loss in the next decade. 

Abstract. The end-of-summer Arctic Ocean is projected to face at least one occurrence of practically ice-free conditions (sea ice extent <1×106 km2) by the middle of the century under all Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. Climate models indicate that this transition toward a practically ice-free Arctic Ocean in late summer will be punctuated by rapid ice loss events (RILEs), i.e., year-to-year reductions in total sea ice extent that occur at a much faster rate than expected from the forced contribution. The extreme sea ice loss associated with RILEs in climate models exceeds any observed rates of sea ice loss since the start of the satellite era, including the highest observed rate of -0.28×106 km2 yr−1 during 2001–2008. As such, there could be a much faster transition toward practically ice-free conditions than expected based on a linear trend of past observations. However, RILEs are not well understood, and it is currently impossible to predict their occurrence a season to several years ahead. It is therefore essential to improve our understanding of these events. This study presents the first comprehensive analysis of RILEs in a diverse set of 26 CMIP6 models, including five large ensembles, following both low- and high-warming scenarios over the period from 1970 to 2100. Our analysis shows that RILEs are expected to occur year-round, but the timing and duration of these events are found to be season-dependent, with less frequent but longer-lived RILEs in winter and spring and more frequent but shorter-lived RILEs in summer and fall under a high-emission scenario. In addition, we find that the warming scenario has a greater influence on RILE characteristics in the winter–spring season than in the summer–fall season. Our results also emphasize that model uncertainty is larger regarding the probability and characteristics of RILEs for winter–spring events compared to summer–fall ones. Finally, while the initial sea ice extent at which RILEs are triggered depends on whether they occur in September or March, the initial sea ice volume is similar for both months, which emphasizes the critical role of sea ice thickness as a preconditioning factor for RILEs. Based on CMIP6 models, there is an approximately 60 % chance that at least one summer RILE will start in September before 2030. This study of RILEs is particularly opportune as CMIP6 models suggest that, following a period of relative stability in Arctic sea ice, the probability of a rapid sea ice reduction will increase. Given the relatively stable conditions observed between 2015 and 2024, the current summer Arctic sea ice state may have an increased probability of being on the verge of a rapid sea ice loss event. 

Abstract The North Water Polynya (NOW) is one of the most productive biological regions in the Arctic with high importance to Inuit and Greenlandic communities. To provide insights into the potential changes of this region as global temperatures rise, we investigated the sea ice and physical and biological oceanic responses of the NOW to low (2°C) and high (>3.5°C) levels of warming using the Community Earth System Model, version 1. As global temperatures increase, sea ice production decreases, spring open water area increases, and summer open water areas in the NOW region connect with open water in central Baffin Bay earlier in the melt season. These sea ice changes contribute to increased stratification, which in turn leads to increased concentrations of nutrient-rich West Greenland Irminger Waters at depth while decreasing surface nutrient concentrations. At low warming levels in the eastern NOW region, warmer water temperatures increase phytoplankton growth rates despite the decrease in surface nutrients, leading to an increase in peak primary production relative to the historical period. In contrast, for high warming in both the eastern and western NOW regions, biological primary production decreases, despite the warmer water temperatures, because increased stratification and decreased surface nutrient concentrations limit phytoplankton production. For all assessed warming levels, changing phytoplankton community composition drives a loss of ecosystem productivity at higher trophic levels. Internal variability plays a negligible role in driving these future sea ice and ocean changes, highlighting the importance of limiting further global temperature increases to avoid large changes to the NOW ecosystem. Significance StatementThe North Water Polynya (NOW) is one of the most productive biological regions in the Arctic with high importance to Inuit and Greenlandic communities. In this paper, we explore how sea ice and physical and biological ocean conditions will change under low (2°C) and high (>3.5°C) levels of global warming. 

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. 

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: &lt;model_name&gt;_resampled_0&lt;month&gt;_individual.nc, &lt;observational_dataset&gt;_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: &lt;model_name&gt;_resampled_0&lt;month&gt;_individual.nc, &lt;observational_dataset&gt;_resampled_individual_1979_2020_03_09.nc - Figure 9: Sigma_mem_SIA.nc, Sigma_LE_SIA.nc