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Abstract Projections of a sea ice-free Arctic have so far focused on monthly-mean ice-free conditions. We here provide the first projections of when we could see the first ice-free day in the Arctic Ocean, using daily output from multiple CMIP6 models. We find that there is a large range of the projected first ice-free day, from 3 years compared to a 2023-equivalent model state to no ice-free day before the end of the simulations in 2100, depending on the model and forcing scenario used. Using a storyline approach, we then focus on the nine simulations where the first ice-free day occurs within 3–6 years, i.e. potentially before 2030, to understand what could cause such an unlikely but high-impact transition to the first ice-free day. We find that these early ice-free days all occur during a rapid ice loss event and are associated with strong winter and spring warming. 

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 We provide an assessment of the current and future states of Arctic sea ice simulated by the Community Earth System Model version 2 (CESM2). The CESM2 is the version of the CESM contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We analyze changes in Arctic sea ice cover in two CESM2 configurations with differing atmospheric components: the CESM2(CAM6) and the CESM2(WACCM6). Over the historical period, the CESM2(CAM6) winter ice thickness distribution is biased thin, which leads to lower summer ice area compared to CESM2(WACCM6) and observations. In both CESM2 configurations, the timing of first ice‐free conditions is insensitive to the choice of CMIP6 future emissions scenario. In fact, the probability of an ice‐free Arctic summer remains low only if global warming stays below 1.5°C, which none of the CMIP6 scenarios achieve. By the end of the 21st century, the CESM2 simulates less ocean heat loss during the fall months compared to its previous version, delaying sea ice formation and leading to ice‐free conditions for up to 8 months under the high emissions scenario. As a result, both CESM2 configurations exhibit an accelerated decline in winter and spring ice area, a behavior that had not been previously seen in CESM simulations. Differences in climate sensitivity and higher levels of atmospheric CO₂by 2100 in the CMIP6 high emissions scenario compared to its CMIP5 analog could explain why this winter ice loss was not previously simulated by the CESM. 

Abstract. Climate simulation uncertainties arise from internal variability, model structure, and external forcings. Model intercomparisons (such as the Coupled Model Intercomparison Project; CMIP) and single-model large ensembles have provided insight into uncertainty sources. Under the Community Earth System Model (CESM) project, large ensembles have been performed for CESM2 (a CMIP6-era model) and CESM1 (a CMIP5-era model). We refer to these as CESM2-LE and CESM1-LE. The external forcing used in these simulations has changed to be consistent with their CMIP generation. As a result, differences between CESM2-LE and CESM1-LE ensemble means arise from changes in both model structure and forcing. Here we present new ensemble simulations which allow us to separate the influences of these model structural and forcing differences. Our new CESM2 simulations are run with CMIP5 forcings equivalent to those used in the CESM1-LE. We find a strong influence of historical forcing uncertainty due to aerosol effects on simulated climate. For the historical period, forcing drives reduced global warming and ocean heat uptake in CESM2-LE relative to CESM1-LE that is counteracted by the influence of model structure. The influence of the model structure and forcing vary across the globe, and the Arctic exhibits a distinct signal that contrasts with the global mean. For the 21st century, the importance of scenario forcing differences (SSP3–7.0 for CESM2-LE and RCP8.5 for CESM1-LE) is evident. The new simulations presented here allow us to diagnose the influence of model structure on 21st century change, despite large scenario forcing differences, revealing that differences in the meridional distribution of warming are caused by model structure. Feedback analysis reveals that clouds and their impact on shortwave radiation explain many of these structural differences between CESM2 and CESM1. In the Arctic, albedo changes control transient climate evolution differences due to structural differences between CESM2 and CESM1. 

This dataset contains the daily Arctic sea ice area (SIA) and sea ice extent (SIE) data for all CMIP6 models and the historical period based on the NOAA/NSIDC Climate Data Record (CDR) created for Heuzé and Jahn, The first ice-free day in the Arctic Ocean could occur before 2030, accepted, Nature Communications. This is a derived dataset based on publicly available underlying data: - For the CMIP6 data, the SIA and SIE data included here is based on the daily siconc and siconca CMIP6 model output freely available on the CMIP6 data portals (https://pcmdi.llnl.gov/CMIP6/). These pan-Arctic daily SIA and SIE were calculated north of 30N, on each model's native grid, using each models grid area data (areacello or areacella). SIA was defined as sea ice concentration multiplied by the grid cell area and summed over all grid cells. SIE was defined as the sum of the grid cell area for all grid cells where the sea ice concentration was larger than 0.15. All processed SIA and SIE data is included in this dataset, even if the model was later excluded from the analysis for one reason or another (see Heuzé and Jahn 2024, Methods section). All data included has the same number of days as the underlying model. The historical data spans 1980-2014 and can be found in the CMIP6_historical_data.zip file, and the scenario data spans 2015 to the end of the 21st century simulation, for multiple scenarios (SSPs), and can be found in CMIP6_ssp_data.zip. Files are provided as .zip files to make it easy to download all data at once, as the SIA and SIE data is saved in one file per model and ensemble member, and for the scenario simulations, also per ssp. - For the NOAA/NSIDC Climate Data Record (CDR), the SIA and SIE data included here is based on the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, doi:10.7265/efmz-2t65, Meier et al 2021. The sea ice concentration is multiplied by the grid size of each grid box, for this data, 25x25 kilometers (km) = 625 kilometers squared (km2), and then summed over the full domain. In doing that, we include the interpolated data in the pole hole as included in the sea ice concentration data, but exclude all land/coastal grid points (i.e., values > 2.5 in the underlying data). As the filename indicates, we removed all leap year data from this data (dropped every Feb 29th) so that all years have 365 days. Note that while the file name says this data is for 19790101 to 20231231, it does indeed include 1978 as first year (so 1978-01-01-2023-12-31), with daily data starting on 1978-10-25 (nan before then). We did not change the name of the data file to still allow all archived scripts using this datafile to run. Scripts that work on this data associated with Heuzé and Jahn (2024) can be found at: https://zenodo.org/records/14008665, doi:10.5281/zenodo.14006059 References: Meier, W. N., F. Fetterer, A. K. Windnagel, and S. Stewart. 2021. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. Boulder, Colorado, USA. NSIDC: National Snow and Ice Data Center https://doi.org/10.7265/efmz-2t65 

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. 

The sensitivity of sea ice to fire emissions highlights climate model uncertainty related to the accuracy of prescribed forcings. 

Abstract. Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and must therefore define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator, the Arctic Ocean Observation Operator (ARC3O), to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using Community Earth System Model version 2 (CESM2) ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place.