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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. 

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

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. 

Abstract Sea ice will persist longer in the Last Ice Area (LIA), north of Canada and Greenland, than elsewhere in the Arctic. We combine earth system model ensembles with a sea‐ice tracking utility (SITU) to explore sources of sea ice (the “ice shed”) to the LIA under two scenarios: continued high warming (HW) rates and low warming (LW) rates (mean global warming below ca. 2°C) through the 21st century. Until mid‐century, the two scenarios yield similar results: the primary ice source shifts from the Russian continental shelves to the central Arctic, mobility increases, and mean ice age in the LIA drops from about 7 years to less than one. After about 2050, sea ice stabilizes in the LW scenario, but continues to decline in the HW scenario until LIA sea ice is nearly entirely seasonal and locally formed. Sea ice pathways through the ice shed determine LIA ice conditions and transport of material, including biota, sediments, and pollutants (spilled oil and industrial or agricultural contaminants have been identified as potential hazards). This study demonstrates that global warming has a dramatic impact on the sources, pathways and ages of ice entering the LIA. Therefore, we suggest that maintaining ice quality and preserving ice‐obligate ecologies in the LIA, including the Tuvaijuittuq Marine Protected Area north of Nunavut, Canada, will require international governance. The SITU system used in this study is publicly available as an online utility to support researchers, policy analysts, and educators interested in past and future sea ice sources and trajectories. 

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.