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Abstract Arctic sea ice has undergone non-monotonic changes since the middle of the last century. Here, we investigate the cause of this behavior by isolating and quantifying the effects of anthropogenic aerosols, well-mixed greenhouse gases, and biomass burning on sea ice dynamics through climate model simulations. We find minimal changes in Arctic sea ice from 1956 to 1980, which largely reflect a balance between the warming effect of greenhouse gases and the cooling effect of aerosols. This balance, however, is disrupted in subsequent decades. Both sea ice area and volume exhibit marked declines between 1981 and 2005, owing primarily to intensified warming by greenhouse gases and a shift in aerosols’ role from mitigating to exacerbating sea ice loss. Our sea ice volume budget analysis demonstrates that sea ice changes since 1956 are mostly driven by thermodynamic processes: greenhouse gases significantly promote surface melting, whereas aerosols and biomass burning diminish surface melting by reducing surface shortwave radiation during boreal summer. From 1956–1980 to 1981–2005, the transitional effects of aerosols are associated with increased bottom ice melting and decreased bottom ice formation, which are primarily driven by changes in the Atlantic meridional overturning circulation. 

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 Phytoplankton in the Arctic Ocean and sub‐Arctic seas support a rich marine food web that sustains Indigenous communities as well as some of the world's largest fisheries. As sea ice retreat leads to further expansion of these fisheries, there is growing need for predictions of phytoplankton net primary production (NPP), which will likely allow better management of food resources in the region. Here, we use perfect model simulations of the Community Earth System Model version 2 (CESM2) to quantify short‐term (month to 2 years) predictability of Arctic Ocean NPP. Our results indicate that NPP is potentially predictable during the most productive summer months for at least 2 years, largely due to the highly predictable Arctic shelves where fisheries in the Arctic are projected to expand. Sea surface temperatures, which are an important limitation on phytoplankton growth and also are predictable for multiple years, are the most important physical driver of this predictability. Finally, we find that the predictability of NPP in the 2030s is enhanced relative to the 2010s, indicating that the utility of these predictions may increase in the near future. This work indicates that operational forecasts using Earth system models may provide moderately skillful predictions of NPP in the Arctic, possibly aiding in the management of Arctic marine resources. 

Abstract The primary sources of recent summer Arctic moistening trends in reanalysis are uncertain, hindering attribution of observed Arctic warming due to radiative effects from water vapor changes. Here, we use a combined online numerical water tracer and circulation nudging approach in the Community Earth System Model to track the sources of water vapor beyond its initial sources. Trends in boreal summer large-scale circulation have driven moistening of the Arctic over recent decades, having a large impact on the Arctic radiative budget, accounting for 94% of the strengthening water vapor radiative feedback. We identify two key regions supplying the Arctic water vapor feedback: Northeast North America and western/central Eurasia. In both regions, anticyclonic circulations over the southwest Atlantic and eastern Europe move moisture from the tropical oceans poleward to high latitude land through precipitation in winter and spring. During summer, evapotranspiration over land releases this water vapor, and it is transported by winds into the Arctic. We refer to this sequence of terrestrial moisture storage and release as the land capacitor effect. Thus, the impacts of circulation changes on poleward moisture transport and land-atmosphere interactions over high latitudes represent the underlying mechanisms of the recent moistening and warming in the Arctic. 

Over thousands of years, Indigenous hunters in the Bering and Chukchi seas have adapted to changes in weather, sea ice, and sea state that influence their access to walruses. In recent decades, 10 however, those conditions have been changing at unprecedented rates. Safely adapting to changing conditions will be essential to the well-being of communities. 

Abstract Models struggle to accurately simulate observed sea ice thickness changes, which could be partially due to inadequate representation of thermodynamic processes. We analyzed co‐located winter observations of the Arctic sea ice from the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate for evaluating and improving thermodynamic processes in sea ice models, aiming to enable more accurate predictions of the warming climate system. We model the sea ice and snow heat conduction for observed transects forced by realistic boundary conditions to understand the impact of the non‐resolved meter‐scale snow and sea ice thickness heterogeneity on horizontal heat conduction. Neglecting horizontal processes causes underestimating the conductive heat flux of 10% or more. Furthermore, comparing model results to independent temperature observations reveals a ∼5 K surface temperature overestimation over ice thinner than 1 m, attributed to shortcomings in parameterizing surface turbulent and radiative fluxes rather than the conduction. Assessing the model deficiencies and parameterizing these unresolved processes is required for improved sea ice representation. 

{"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)"]} 

Light transmission through a sea ice cover has strong implications for the heat content of the upper ocean, the magnitude of bottom and lateral ice melt, and primary productivity in the ocean. Light transmittance in the vicinity of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) Central Observatory was estimated by driving a two-stream radiative transfer model with physical property observations. Data include point and transect observations of snow depth, surface scattering layer thickness, ice thickness, and pond depth. The temporal evolution of light transmittance at specific sites and the spatial variability along transect lines were computed. Ponds transmitted 4–6 times as much solar energy per unit area as bare ice. On July 25, ponds covered about 18% of the area and contributed roughly 50% of the sunlight transmitted through the ice cover. Approximating the transmittance along a transect line using average values for the physical properties will always result in lower light transmittance than finding the average light transmittance using the full distribution of points. Transmitted solar energy calculated using the standard five ice thickness categories and three surface types used in the Los Alamos sea ice model CICE, the sea ice component of many weather and climate models, was only about 1 W m−2 less than using all the points along the transect. This minor difference suggests that the important processes and resulting feedbacks relating to solar transmittance can be represented in models that use five or more categories of ice thickness distributions.