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Biomass burning can affect climate via the emission of aerosols and their subsequent impact on radiation, cloud microphysics, and surface and atmospheric albedo. Biomass burning emissions (BBEs) over the boreal region have strongly increased during the last decade and are expected to continue increasing as the climate warms. Climate models simulate aerosol processes, yet historical and future Coupled Model Intercomparison Project (CMIP) simulations have no active fire component, and BBEs are prescribed as external forcings. Here, we show that CMIP6 used future boreal BBEs scenarios with unrealistic near-zero trends that have a large impact on climate trends. By running sensitivity experiments with ramped up boreal emissions based on observed trends, we find that increasing boreal BBEs reduces global warming by 12% and Arctic warming by 38%, reducing the loss of sea ice. Tropical precipitation shifts southward as a result of the hemispheric difference in boreal aerosol forcing and subsequent temperature response. These changes stem from the impact of aerosols on clouds, increasing cloud droplet number concentration, cloud optical depth, and low cloud cover, ultimately reducing surface shortwave flux over northern latitudes. Our results highlight the importance of realistic boreal BBEs in climate model simulations and the need for improved understanding of boreal emission trends and aerosol–climate interactions. 

Abstract. The ocean and sea ice are central to Earth's climate system, influencing global heat and carbon cycles, weather patterns, and sea level rise. Recent decades have seen rapid advances in Earth System Models (ESMs), but limitations remain in simulating and comparing key oceanic and cryospheric processes across models. A recurring challenge in model intercomparison efforts like the Coupled Model Intercomparison Project (CMIP) is determining the output variables that best represent essential mechanisms while remaining manageable in volume and complexity. Here we present the CMIP7 ocean and sea ice data request, developed through an international, community-based process to prioritize variables for model output. We identify seven opportunities – science-based use cases spanning ocean and cryosphere drivers and responses, paleoclimate, polar amplification, extremes, wind waves, and rapid model evaluation – to guide variable selection and temporal resolution. To address these opportunities, we request new high-frequency and depth-integrated variables, support improved diagnostics of ocean heat uptake, sea ice processes, and model-observation comparison, and build on lessons from CMIP6. Our approach enables targeted, efficient, and transparent data curation to support a wide range of users, from model developers to policymakers. This effort reflects a growing need for more sophisticated, integrative model outputs that address pressing climate questions, including regional extremes and tipping points, while laying the groundwork for future modeling developments. 

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 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)"]} 

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

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 under the high emissions scenario, a behavior that had not been previously seen in CESM simulations. Differences in climate sensitivity and higher levels of atmospheric CO2 by 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. In recent decades, Arctic sea ice has shifted toward ayounger, thinner, seasonal ice regime. Studying and understanding this“new” Arctic will be the focus of a year-long ship campaign beginning inautumn 2019. Lagrangian tracking of sea ice floes in the Community EarthSystem Model Large Ensemble (CESM-LE) during representative “perennial”and “seasonal” time periods allows for understanding of the conditionsthat a floe could experience throughout the calendar year. These modeltracks, put into context a single year of observations, provide guidance onhow observations can optimally shape model development, and how climatemodels could be used in future campaign planning. The modeled floe tracksshow a range of possible trajectories, though a Transpolar Drift trajectoryis most likely. There is also a small but emerging possibility of high-risktracks, including possible melt of the floe before the end of a calendaryear. We find that a Lagrangian approach is essential in order to correctlycompare the seasonal cycle of sea ice conditions between point-basedobservations and a model. Because of high variability in the melt season seaice conditions, we recommend in situ sampling over a large range of ice conditionsfor a more complete understanding of how ice type and surface conditionsaffect the observed processes. We find that sea ice predictability emergesrapidly during the autumn freeze-up and anticipate that process-basedobservations during this period may help elucidate the processes leading tothis change in predictability.