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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. 

Included are 100 years of monthly mean ocean model output from CESM1.2 integrations for the Eocene carried out by Adam Aleksinski and Matthew Huber, with critical assistance from Alexandra Jahn, and with assistance and support from Jiang Zhu (NCAR). These simulations were carried out at NCAR. These simulations incorporate results using the standard Eocene Deepmip 1 boundary conditions (Lunt et al, 2017), including the boundary condition datasets (Herold et al., 2014), and were branched off originally from simulations carried out at NCAR by Jiang Zhu (Zhu et al., 2020). The three simulations included here incorporate neodymium in them for the first time and span a range of CO2 and gateway configurations that make it appropriate for the Middle Eocene to late Eocene. The continuation (“SF_SU_55Ma_init-hycont”) experiment run continued from the end of Zhu et al. (2020)’s 3x preindustrial pCO2 (854.1 ppm) experiment, which used DeepMIP compliant geography and bathymetry for simulating the early Eocene. This simulation was run for a total of 4,800 years. Two runs each branched from this SF_SU_55Ma_init-hycont after 1,200 years of runtime, and each ran for 3,600 years after that point. In the Open Drake Passage experimental run (SF_SU_55Ma_open-hycont), the atmospheric pCO2 concentration from the continuation simulation was retained, and the bathymetry of the Drake Passage and Tasman Seaway were both lowered to a depth of 1973 mbsl. In the Halved pCO2 experiment SF_SU_55Ma_cool), the Herold et al original bathymetry was retained, but atmospheric pCO2 was reduced by a factor of half, to 427.05 ppm. The model output is global in extent and is netcdf format, which has been tarred and gzipped, and follows standard conventions for ocean GCMs. The data are on an irregular 'POP' grid. All the necessary information to read and process these data are included in the netcdf metadata. 

Abstract Since the 1980s, observations show the Arctic surface has warmed four times more than the global mean. Over the Arctic Ocean, this recent large warming is connected to sea ice loss. While earth system models are useful tools for prediction, exact replication of observed Arctic warming and sea ice loss is not expected in freely-evolving models because of internal climate variability. Previous studies have shown that historical hindcasts with model winds nudged to reanalysis can reproduce recent Arctic warming and sea ice loss. However, the influence of observed winds on these recent Arctic changes in absence of anthropogenic forcing has not been assessed. Here, we show that nudging to recent (1980–2023) observed winds alone in a pre-industrial model experiment does not reproduce the magnitude of observed warming and sea ice extent loss. This means that the large-scale winds are not the primary driver of recently observed large Arctic trends. Yet, the winds do partially reproduce the interannual, seasonal, and spatial variability, especially in spring. We also show that in a pre-industrial climate simulation, these results are largely independent of mean state sea ice thickness. In short, the observed winds drive part of the Arctic temperature and sea ice variability but not long-term trends. 

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. 

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 &gt; 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 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. 

Abundant proxy records suggest a profound reorganization of the Atlantic Meridional Overturning Circulation (AMOC) during the Last Glacial Maximum (LGM, ~21,000 y ago), with the North Atlantic Deep Water (NADW) shoaling significantly relative to the present-day (PD) and forming Glacial North Atlantic Intermediate Water (GNAIW). However, almost all previous observational and modeling studies have focused on the zonal mean two-dimensional AMOC feature, while recent progress in the understanding of modern AMOC reveals a more complicated three-dimensional structure, with NADW penetrating from the subpolar North Atlantic to lower latitude through different pathways. Here, combining²³¹Pa/²³⁰Th reconstructions and model simulations, we uncover a significant change in the three-dimensional structure of the glacial AMOC. Specifically, the mid-latitude eastern pathway (EP), located east of the Mid-Atlantic Ridge and transporting about half of the PD NADW from the subpolar gyre to the subtropical gyre, experienced substantial intensification during the LGM. A greater portion of the GNAIW was transported in the eastern basin during the LGM compared to NADW at the PD, resulting in opposite²³¹Pa/²³⁰Th changes between eastern and western basins during the LGM. Furthermore, in contrast to the wind-steering mechanism of EP at PD, the intensified LGM EP was caused primarily by the rim current forced by the basin-scale open-ocean convection over the subpolar North Atlantic. Our results underscore the importance of accounting for three-dimensional oceanographic changes to achieve more accurate reconstructions of past AMOC. 

{"Abstract":["The original "CESM2 Tuned Sea Ice Albedo" experiments were run to investigate the impact of sea ice snow albedo tuning on Arctic mean state and global responses. These simulations were run by David Bailey, and the results were written up in Kay et al. 2022 [https://doi.org/10.1029/2021MS002679]. These were configured the same as the standard CMIP6 runs, but with adjusted r_snw=1.5 and dt_mlt=1.0 in the sea ice model (cice) namelist. (For local disk access, these data are all in the main directory and are not separated by case name.)Original experimentsControl run: b.e21.B1850.f09_g17.CMIP6-piControl.001_branch2 * Data are generally available years 811-1350 * These runs are fully coupled * Monthly Ocean, Sea Ice, and Atmosphere variables are availableHistorical runs: b.e21.BHIST.f09_g17.CMIP6-historical.01? * There are four ensemble members * Ensemble number 012 started in 1850 and ran to 2015 * Ensemble numbers 013, 014, 015 start in 1920, branched from ensemble 012 with a pertlim, and go to 2015 * These runs are fully coupled * Monthly Ocean, Sea Ice, and Atmosphere variables are availableFuture runs: b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.? * The first three SSP runs (012,013,014) were configured the same as the CMIP6 scenario runs, but there was a bug in the future forcing * New runs were then done with the updated forcing (112, 113, 114, 115, 116) * The runs use standard CMIP6 SSP370 standard forcing, start in 2015 and go through 2100 * These runs are fully coupled * Monthly Ocean, Sea Ice, and Atmosphere variables are availableThere are also some experiments that are not fully coupled experiments: * e.e21.E1850.f09_g17.CMIP6-piControl.* * d.e21.f09_g17.{001,002,003,004,005}Additional ExperimentsThe additional CESM2 Tuned Sea Ice Albedo experiments were run to extend the dataset and include more frequent (daily) output as well as some modifications to the external forcing. This was done as part of a NSF Navigating the New Arctic (NNA) project (NSF #1928119): NNA Track 1: Collaborative Research: Maritime transportation in a changing Arctic: Navigating climate and sea ice uncertainties. These simulations were run by Alice DuVivier, and the data will be used in various studies. (For local disk access, these data are all in the main directory and are separated by case name.)Historical runs: b.e21.BHISTsmbb.f09_g17.CMIP6-historical.1? * There are five ensemble members: 118, 119, 120, 121, 122 * These ensembles start in 1990 and run through 2015 * These use smoothed biomass burning forcing (smbb), which uses the smoothed biomass burning forcing starting the 1990s. Information about the smoothed biomass burning impacts can be found in DeRepentigny et al. 2022 [https://doi.org/10.1126/sciadv.abo2405] * These runs are fully coupled * Daily and Monthly Ocean, Sea Ice, and Atmosphere variables are available * Restart files are also available upon request to co-authorsFuture runs: b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.2? * There are five ensemble members: 218, 219, 220, 221, 222 * The runs use standard CMIP6 SSP370 standard forcing, start in 2015 and go through 2100. They are continued from the SMBB historical runs (above) * These runs are fully coupled * Daily and Monthly Ocean, Sea Ice, and Atmosphere variables are available * Restart files are also available upon request to co-authors * These runs were done on the Derecho supercomputer (instead of Cheyenne, which was used for all the above experiments)"]}