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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. 

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 

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020), a year-long drift with the Arctic sea ice, has provided the scientific community with an unprecedented, multidisciplinary dataset from the Eurasian Arctic Ocean, covering high atmosphere to deep ocean across all seasons. However, the heterogeneity of data and the superposition of spatial and temporal variability, intrinsic to a drift campaign, complicate the interpretation of observations. In this study, we have compiled a quality-controlled physical hydrographic dataset with best spatio-temporal coverage and derived core parameters, including the mixed layer depth, heat fluxes over key layers, and friction velocity. We provide a comprehensive and accessible overview of the ocean conditions encountered along the MOSAiC drift, discuss their interdisciplinary implications, and compare common ocean climatologies to these new data. Our results indicate that, for the most part, ocean variability was dominated by regional rather than seasonal signals, carrying potentially strong implications for ocean biogeochemistry, ecology, sea ice, and even atmospheric conditions. Near-surface ocean properties were strongly influenced by the relative position of sampling, within or outside the river-water influenced Transpolar Drift, and seasonal warming and meltwater input. Ventilation down to the Atlantic Water layer in the Nansen Basin allowed for a stronger connectivity between subsurface heat and the sea ice and surface ocean via elevated upward heat fluxes. The Yermak Plateau and Fram Strait regions were characterized by heterogeneous water mass distributions, energetic ocean currents, and stronger lateral gradients in surface water properties in frontal regions. Together with the presented results and core parameters, we offer context for interdisciplinary research, fostering an improved understanding of the complex, coupled Arctic System. 

Atlantic predatory nekton resides further north than expected, adding a trophic level to the central Arctic ecosystem model. 

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future. 

The international and interdisciplinary sea-ice drift expedition “The Multidisciplinary drifting Observatory for the Study of Arctic Climate” (MOSAiC) was conducted from October 2019 to September 2020. The aim of MOSAiC was to study the interconnected physical, chemical, and biological characteristics and processes from the atmosphere to the deep sea of the central Arctic system. The ecosystem team addressed current knowledge gaps and explored unknown biological properties over a complete seasonal cycle focusing on three major research areas: biodiversity, biogeochemical cycles, and linkages to the environment. In addition to the measurements of core properties along a complete seasonal cycle, dedicated projects covered specific processes and habitats, or organisms on higher taxonomic or temporal resolution in specific time windows. A wide range of sampling instruments and approaches, including sea-ice coring, lead sampling with pumps, rosette-based water sampling, plankton nets, remotely operated vehicles, and acoustic buoys, was applied to address the science objectives. Further, a broad range of process-related measurements to address, for example, productivity patterns, seasonal migrations, and diversity shifts, were made both in situ and onboard RV Polarstern. This article provides a detailed overview of the sampling approaches used to address the three main science objectives. It highlights the core sampling program and provides examples of habitat- or process-specific sampling. The initial results presented include high biological activities in wintertime and the discovery of biological hotspots in underexplored habitats. The unique interconnectivity of the coordinated sampling efforts also revealed insights into cross-disciplinary interactions like the impact of biota on Arctic cloud formation. This overview further presents both lessons learned from conducting such a demanding field campaign and an outlook on spin-off projects to be conducted over the next years. 

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean. 

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.