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The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

Science, engineering, and society increasingly require integrative thinking about emerging problems in complex systems, a notion referred to as convergence science. Due to the concurrent pressures of two main stressors—rapid climate change and industrialization, Arctic research demands such a paradigm of scientific inquiry. This perspective represents a synthesis of a vision for its application in Arctic system studies, developed by a group of disciplinary experts consisting of social and earth system scientists, ecologists, and engineers. Our objective is to demonstrate how convergence research questions can be developed via a holistic view of system interactions that are then parsed into material links and concrete inquiries of disciplinary and interdisciplinary nature. We illustrate the application of the convergence science paradigm to several forms of Arctic stressors using the Yamal Peninsula of the Russian Arctic as a representative natural laboratory with a biogeographic gradient from the forest‐tundra ecotone to the high Arctic. Plain Language Summary: This paper represents a synthesis of conceptual analyses, case study analyses, and practical thoughts on the application of convergence science in Arctic change studies. During a virtual workshop in 2020, a diverse, multi‐national team of authors consisting of social scientists, engineers, earth system scientists, and ecologists came together to formulate broad, scientifically, and societally important questions on how the Arctic system in the Yamal Peninsula of Western Siberia responds to pressures of rapidly changing climate and increasing industrialization. The team "engineered" a novel approach for expert (representing a disciplinary domain) and non‐expert (representatives of other disciplines) communication and at the workshop conclusion developed several convergence science questions of high appeal. Three of such questions are presented in this manuscript to illustrate how the search and identification of appropriate mechanistic linkages are critical to the development of system‐level understanding of stressor impact propagation. The need to understand underlying disciplinary and cross‐disciplinary mechanisms connecting Arctic system elements is viewed to be an inherent part of the convergence science approach. Through pursuit of such understanding, the approach naturally leads to other novel emerging questions, thereby stimulating further application of the process of integrative thinking. Key Points: Arctic research demands convergence science as essential method to understand impacts from novel stressorsAn integrative approach is developed by a diverse team to formulate questions that cannot be fully addressed within disciplinary studiesA convergence science analysis is illustrated for three questions applicable to Yamal, Russian Arctic, a microcosm of the changing Arctic [ABSTRACT FROM AUTHOR]

Deep and abyssal layer decadal temperature trends from the mid‐1980s to the mid‐2010s are mapped globally using Deep Argo and historical ship‐based Conductivity‐Temperature‐Depth (CTD) instrument data. Abyssal warming trends are widespread, with the strongest warming observed around Antarctic Bottom Water (AABW) formation regions. The warming strength follows deep western boundary currents transporting abyssal waters north and decreases with distance from Antarctica. Abyssal cooling trends are found in the North Atlantic and eastern South Atlantic, regions primarily ventilated by North Atlantic Deep Water (NADW). Deep warming trends are prominent in the Southern Ocean south of about 50°S, the Greenland‐Iceland‐Norwegian (GIN) Seas and the western subpolar North Atlantic, with cooling in the eastern subpolar North Atlantic and the subtropical and tropical western North Atlantic. Globally integrated decadal heat content trends of 21.6 (±6.5) TW in the deep and 12.9 (±1.8) TW in the abyssal layer are more certain than previous estimates. Plain Language Summary: Even the deepest waters in the ocean, which sink to the abyss around Antarctica after being cooled and made saltier by heat exchange with the atmosphere and sea ice formation, have been shown to be warming around much of the globe in recent decades. The net warming rate below 2000‐m depth accounts for about 10% of total ocean heat uptake, but uncertainties in prior estimates have been about half the size of the signal owing to sparse sampling in the deep ocean. However, new observations from Deep Argo floats, capable of profiling to the ocean floor in most locations, have improved that situation in some regions. Here we analyze these new observations together with historical observations collected from ships since the 1970s to map decadal ocean temperature trends around the globe. As a result, we use more historical data than previous estimates. We refine the local patterns of warming and cooling in the waters deeper than 2,000 m. We confirm the amplitude of the net warming below 2,000 m estimated in previous studies, and extend the time covered by those estimates. The increased data coverage substantially reduces the uncertainty of the net warming estimate. Key Points: Analysis of Deep Argo float and historical ship‐based CTD data reveal global patterns in deep and abyssal layer decadal temperature trendsHigh resolution maps reveal spreading of abyssal warming from Antarctica and cooling from the North Atlantic on sub‐basin spatial scalesGlobally integrated decadal heat content trends are 21.6 (±6.5) TW in the deep and 12.9 (±1.8) TW in the abyssal layer [ABSTRACT FROM AUTHOR]

Both historical observations and recent modeling studies reveal a faster warming in the Arctic compared to the Antarctic. To understand the role of the Antarctic Circumpolar Circulation (ACC) in this warming asymmetry, we simulate the climate mean state and climate response to doubled CO2 under different climate mean ACC states by closing or opening the Drake Passage (DP) with the Community Earth System Model. From closed to open DP, a stronger climate mean ACC leads to a stronger climate mean Atlantic Meridional Overturning Circulation (AMOC), as well as a colder Antarctic but a warmer Arctic in the climate mean state. The less climate mean sea ice coverage in a warmer Arctic implies less extensive sea ice melting under global warming. This causes a reduced asymmetry in warming between the two poles in response to the doubled CO2. Plain Language Summary: To understand the role of the Antarctic Circumpolar Circulation (ACC) in the asymmetric Arctic and Antarctic warming, we conduct two numerical model experiments. We consider two different climate states by closing or opening the Drake Passage (DP). The former yields a weak ACC, while the latter results in a strong ACC. A stronger climate mean ACC leads to a colder Antarctic but a warmer Arctic, due primarily to a concurrent stronger Atlantic Meridional Overturning Circulation (AMOC). The diminished coverage of sea ice in the warmer Arctic signifies a reduction in sea ice melting under global warming. Consequently, this leads to a reduction in the asymmetry in warming between the two poles in response to doubled CO2. These findings highlight a significant impact of the ACC on the global climate mean state and polar warming asymmetry, and shed light on the uncertainties in the coordinated interaction between major ocean circulation systems (e.g., AMOC and ACC) and on those in inter‐hemisphere interactions. Key Points: A stronger climate mean Antarctic Circumpolar Circulation (ACC) is accompanied by a stronger mean Atlantic Meridional Oceanic Circulation (AMOC)A stronger climate mean AMOC results in a warmer Arctic with less climate mean sea ice coverageThe less asymmetric polar warming in the presence of stronger climate mean ACC and AMOC is largely due to less sea ice melting in Arctic [ABSTRACT FROM AUTHOR]

A novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project. Plain Language Summary: The projected loss of Arctic sea‐ice under 2°C global warming will cause large warming in the Arctic region and climate and weather anomalies outside the Arctic. The warming in the Arctic will mean warmer airmasses coming from the Arctic and also more moisture from the open Arctic Ocean. Furthermore, it will also change atmospheric circulation. These effects together will determine the impacts of Arctic warming. In this study, we introduce a novel sub‐sampling method to isolate atmospheric circulation change in response to the Arctic warming. The method involves selecting members of simulations from the experiment with future Arctic sea‐ice conditions, the average of which is equal to the average of the members of simulations in the experiment with present‐day Arctic sea‐ice conditions. We found that atmospheric circulation change in European regions is relatively weak so that warming effects will dominate the climate and weather response there. On the other hand, atmospheric circulation change will dominate the climate and weather response in East Eurasia. We also found that stronger atmospheric circulation changes will generally increase the response to the Arctic warming. We suggest caution when assessing whether different responses in different models can be interpreted as true differences in model physics. Key Points: A novel sub‐sampling method is introduced to isolate the role of dynamics in the response to projected Arctic sea‐ice lossA dynamical Siberian High response dominates the temperature response over East Eurasia while that of the North Atlantic Oscillation is weakInter‐model differences in Polar Amplification Model Intercomparison Project likely contain a large fraction of internal variability due to the unconstrained dynamic effects [ABSTRACT FROM AUTHOR]

We investigate the role of ocean heat transport (OHT) in driving the decadal variability of the Arctic climate by analyzing the pre‐industrial control simulation of a high‐resolution climate model. While the OHT variability at 65°N is greater in the Atlantic, we find that the decadal variability of Arctic‐wide surface temperature and sea ice area is much better correlated with Bering Strait OHT than Atlantic OHT. In particular, decadal Bering Strait OHT variability causes significant changes in local sea ice cover and air‐sea heat fluxes, which are amplified by shortwave feedbacks. These heat flux anomalies are regionally balanced by longwave radiation at the top of the atmosphere, without compensation by atmospheric heat transport (Bjerknes compensation). The sensitivity of the Arctic to changes in OHT may thus rely on an accurate representation of the heat transport through the Bering Strait, which is difficult to resolve in coarse‐resolution ocean models. Plain Language Summary: We studied how ocean heat transport (OHT) affects decade‐timescale variability in the Arctic climate using a high‐resolution climate model. Specifically, we compared the impacts of heat entering the Arctic Ocean through the Nordic Seas from the Atlantic and through the Bering Strait from the Pacific. Though more heat is transported from the Atlantic, Arctic surface temperature and sea ice respond more strongly to changes in OHT through the Bering Strait. Unlike Atlantic OHT, changes in Bering Strait OHT impact local air temperatures directly, without compensating changes in atmospheric heat transport. Proper representation of the Arctic's sensitivity to future increased OHT may thus rely on correctly representing OHT through Bering Strait, which is challenging in coarse‐resolution ocean models. Key Points: Ocean heat transport variability through the Bering Strait has an outsized effect on Arctic sea ice cover and surface temperatureAtlantic ocean heat transport anomalies into the Arctic are compensated by atmospheric heat transport anomalies on decadal timescales [ABSTRACT FROM AUTHOR]

Surface, aircraft, and satellite measurements indicate pervasive early cold season (Augut–September) CO2 emissions across Arctic regions, consistent with increased ecosystem metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink‐to‐source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. Our simulations demonstrate the potential benefits of increased CO2 sampling for detecting emissions during the early cold season. Plain Language Summary: The Arctic is warming rapidly, leading to an acceleration of carbon exchange across tundra and boreal ecosystems. These changing flows of carbon can be difficult to detect using traditional observing systems. We propose that measurement systems which observe Arctic ecosystems continuously from a quasi‐GEO orbit can more accurately detect these important changes, such that unexpected and dangerous feedbacks to climate warming can be monitored and accounted for. Key Points: The Arctic Fourier Transform Spectrometer Investigation (AURORA) mission concept addresses GHG sampling limitations in the ArcticAURORA uses a highly elliptical orbit (HEO) to increase repeat frequency, and a panchromatic iFTS for SWIR O2 and CO2 bands (0.7–2.5 μm)Simulation experiments show potential benefits of increased sampling frequency for grid scale detection of early cold season CO2 emissions [ABSTRACT FROM AUTHOR]

Predation is an important ecological process that can significantly impact the maintenance of ecosystem services. In arctic environments, the relative ecological importance of predation is thought to be increasing due to climate change, partly because of increased productivity with rising temperatures. Therefore, understanding predator–prey interactions in arctic ecosystems is vital for the sustainable management of these northern regions. Network theory provides a framework for quantifying the structures of ecological interactions. In this study, we use dietary observations on mammalian and avian predators in a high arctic region, including isolated peninsulas on Ellesmere Island and north Greenland, to construct bipartite trophic networks. We quantify the complexity, specialization, and nested as well as modular structures of these networks and also determine if these properties varied among the peninsulas. Mammal prey remains were the dominant diet item for all predators, but there was spatial variation in diet composition among peninsulas. The predator–prey networks were less complex, had more specialized interactions, and were more nested and more modular than random expectations. However, the networks displayed only moderate levels of modularity. Predator species had less specialized interactions with prey than prey had with predators. All network properties differed among the peninsulas, which highlights that ecosystems often show complex responses to environmental characteristics. We suggest that gaining knowledge about spatial variation in the characteristics of predator–prey interactions can enhance our ability to manage ecosystems exposed to environmental perturbations, particularly in high arctic environments subject to rapid environmental change. [ABSTRACT FROM AUTHOR]

Aim: The rapidly changing Arctic is ideal for investigating uncertainties in climate projections. Despite the challenges of collecting data in this region, an unprecedented large‐scale survey of shorebirds has been conducted over the last 30 years. Our study aimed to (1) develop probabilistic estimates for the change in suitable habitat for 10 Arctic shorebird species in Canada by 2075 and (2) assess the contribution of modelling decisions to the uncertainty in these estimates. Location: Arctic Canada. Methods: To evaluate uncertainty, we considered six classes of modelling decisions, yielding 216 unique projections for each species. We tested three decisions that are less commonly explored − the pool of candidate variables, a method for selecting variables, and the maximum distance of tree line dispersal, as well as the modelling algorithm, carbon emissions scenario, and global circulation model. We used a bootstrapping approach, creating a probability distribution for the proportional change in suitable habitat for each species. Results: Our findings indicated a substantial risk for 8/10 species to lose over half of their suitable breeding habitat, but this projection is much less certain than has been described previously. While much uncertainty is unexplained, we were surprised that the largest source of uncertainty among our modelling decisions was from our choice of methods for variable selection, that the other modelling decisions were relatively small sources of uncertainty, overshadowing other modelling decisions. Main Conclusions: While most scenarios predict a northward shift and significant habitat loss for Arctic‐breeding shorebirds, the Arctic Archipelago of Canada will remain an important refuge because in many other Arctic regions, there is no land farther north for these species to shift into. A comprehensive understanding of uncertainty is important for deciding if future projections can or should be used when planning climate‐resilient protected area networks. [ABSTRACT FROM AUTHOR]

Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms. Plain Language Summary: Being able to correctly simulate the amount of ice and liquid in clouds is essential for accurate predictions of the cloud radiative forcing in the climatologically sensitive polar regions. A number of collisional processes between ice and liquid particles in clouds, known as secondary ice production, can significantly enhance the ice crystal number concentrations contained in them. This enhancement is often accompanied by a decrease in the cloud liquid water content, resulting in less opaque clouds to incoming solar radiation, which, in turn, can cause a cloud‐induced warming at the surface. Currently most global climate models are missing the description of the most important secondary ice production processes, which can lead to a biased radiative impact of clouds at the surface. To address this, we propose using a machine learning algorithm trained on high‐resolution model outputs to include the effect of ice multiplication in large‐scale climate models. The machine learning framework effectively captures the physical processes underlying secondary ice production in stratiform clouds using only a few inputs readily available in model frameworks. This approach has the potential to improve model predictions bringing them closer to the observed cloud phase partitioning. Key Points: A random‐forest parameterization for secondary ice production is developed using outputs from a 10‐km horizontal grid spacing simulationCloud phase partitioning agrees within a factor of 3, with radiative biases below 3 Wm−2 compared to the detailed microphysics simulationThe scheme can be adjusted to coarser resolutions typical of climate models without losing computational efficiency and numerical stability [ABSTRACT FROM AUTHOR]

The widespread High Arctic Large Igneous Province (HALIP) exhibits prolonged melting over more than 50 Myr, an observation that is difficult to reconcile with the classic view that large igneous provinces (LIPs) originate from melting in plume heads. Hence, the suggested plume‐related origin and classification of HALIP as a LIP have been questioned. Here, we use numerical models that include melting and melt migration to investigate a rising plume interacting with lithosphere of variable thickness, that is, a basin‐to‐craton setting applicable to the Arctic. Models reveal that melt migration introduces significant spatial and temporal variations in melt volumes and pulses of melt production, including protracted melting for at least about 30–40 Myr, because of the dynamic feedback between migrating melt and local lithosphere thinning. For HALIP, plume material deflected from underneath the Greenland craton can re‐activate melting zones below the previously plume‐influenced Sverdrup Basin after a melt‐free period of about 10–15 Myr, even though the plume is already ∼500 km away. Hence, actively melting zones do not necessarily represent the location of the deeper plume stem at a given time, especially for secondary pulses. Additional processes such as (minor) plume flux variations or local lithospheric extension may alter the timing and volume of HALIP pulses, but are to first order not required to reproduce the long‐lived and multi‐pulse magmatism of HALIP. Since melting zones are always plume‐fed, we would expect HALIP magmatism to exhibit plume‐related trace element signatures throughout time, potentially shifting from mostly tholeiitic toward more alkalic compositions. Plain Language Summary: Typically, the arrival of a large mantle upwelling ("mantle plume") is expected to cause catastrophic large‐scale volcanism that lasts a few million years. However, a massive past volcanic event now distributed onshore and offshore across the Arctic (the High Arctic Large Igneous Province—HALIP) defies this definition. This wide‐spread magmatism exhibits dates spanning more than 50 Myr, with several pulses of activity. Based on this prolonged magmatism, it has been questioned whether all of it can be attributed to a mantle plume, despite the geochemistry of basalts indicating a plume source. Here, we show that a plume can cause prolonged and multi‐pulse magmatism if it interacts with an increase in lithosphere thickness. Once the plume moves below the thicker lithosphere, hot plume material is channeled along the base of the lithosphere toward the adjacent thinner part, where it can reactivate previous melting regions. At this time, the active plume can be about 500 km away from the melting region, hence plume‐related melt cannot be used as a proxy for the plume position at the given time. Based on the models, we suggest that the prolonged HALIP magmatism was caused by a plume interacting with the edge of a craton. Key Points: Mantle plumes interacting with changes in lithosphere thickness at craton edges can cause prolonged melting with pulses in the same regionRejuvenated melting happens underneath previously melt‐affected thinned lithosphere several hundred km downstream of the plume stemThe timing and duration of rejuvenated melting in models correspond to and therefore may explain observations of magmatic pulses from High Arctic Large Igneous Province [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Numerical sea ice models rely on conceptual simplifications of the sea ice and snow conditions observed in the Arctic and Southern Ocean. In particular, we cannot account for the variations in sea ice thickness and snow depth at the meter scale because we have limited computing capabilities and lack a detailed understanding of the processes defining how the sea ice system evolves. Furthermore, when designing sea ice models, we assume that the heat can flow only vertically, while in reality, this exchange is also horizontal and depends on the local topography. Thanks to observations collected during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate expedition, we can better quantify model errors when using thermodynamic approximations. Our findings suggest that slightly more heat flows through the sea ice system than we can simulate with a simplified sea ice model and that the surface temperature of the model is too warm for thin ice and snow conditions. Learning the nature of these errors is useful because we could formulate corrections for our models, investigate the occurrence of climate feedback mechanisms, and possibly provide more reliable predictions about the current state of the sea ice and its future evolution, which is currently heavily impacted by global warming. Key Points: Multidisciplinary Drifting Observatory for the Study of Arctic Climate observations enable improved thermodynamic process understanding and representation in sea ice modelsResolved meter‐scale ice and snow thickness heterogeneity and horizontal heat conduction enhance conductive heat flux by up to 10% in modelsA positive surface temperature model bias up to 5 K suggests a misrepresentation of the surface energy balance over thin ice [ABSTRACT FROM AUTHOR]

The Arctic is notable as a region where the greatest rate of increase in precipitation associated with global warming is anticipated. The Arctic precipitation simulated by the Coupled Model Intercomparison Project Phase 6 models showed a strong increasing trend since the 1980s. We found that the forcing factor of the trend is a combination of the continued strengthening of greenhouse gas forcing and the leveling off of aerosol forcing dominated in earlier periods. From an energetic perspective, we found that the increased atmospheric radiative cooling and reduced sensible heat transport from lower latitudes contributed equally to the recent increase in Arctic precipitation. The combination of these two energetic factors suggests a doubling of the Arctic amplification factor for precipitation relative to that for temperature. Future Arctic precipitation will change in proportion to the temperature change, and the fractional contributions of the energetic factors will remain stable across various scenarios. Plain Language Summary: The Arctic region is inherently a low‐precipitation area. However, because of global warming, precipitation is expected to increase substantially in the Arctic region compared with the global average when viewed as a percentage change from the original precipitation. This severely affects climate change in the Arctic environment. The latest climate model simulations show that there has been a rapid increase in precipitation in the Arctic region in recent decades. The driving factors behind the rapid increase are the effects of the accelerating growth of greenhouse gas concentrations, which were previously suppressed by the increasing anthropogenic aerosol emissions before the 1980s. Based on the heat budget of the atmosphere, we identified important factors contributing to these precipitation changes. These include enhanced radiative cooling (responding locally to increased air temperature) and reduced heat transport from lower latitudes due to greater temperature increases at higher latitudes. Future precipitation will change in proportion to the temperature change while maintaining consistent fractional contributions across different scenarios. Key Points: Trends in Arctic precipitation in the recent and future decades are examined from multimodel simulationsThe recent rapid increase is driven by accelerating greenhouse gas concentrations and plateauing growth in anthropogenic aerosol emissionsIncreased radiative cooling and reduced poleward sensible heat transport equally contributed to the Arctic precipitation changes [ABSTRACT FROM AUTHOR]

Tropospheric reactive bromine (Bry) influences the oxidation capacity of the atmosphere by acting as a sink for ozone and nitrogen oxides. Aerosol acidity plays a crucial role in Bry abundances through acid‐catalyzed debromination from sea‐salt‐aerosol, the largest global source. Bromine concentrations in a Russian Arctic ice‐core, Akademii Nauk, show a 3.5‐fold increase from pre‐industrial (PI) to the 1970s (peak acidity, PA), and decreased by half to 1999 (present day, PD). Ice‐core acidity mirrors this trend, showing robust correlation with bromine, especially after 1940 (r = 0.9). Model simulations considering anthropogenic emission changes alone show that atmospheric acidity is the main driver of Bry changes, consistent with the observed relationship between acidity and bromine. The influence of atmospheric acidity on Bry should be considered in interpretation of ice‐core bromine trends. Plain Language Summary: Reactive bromine in the air impacts major oxidants in our atmosphere, which remove pollutants and greenhouse gases and has changed over time in the Russian Arctic. Ice‐core bromine and acidity show a significant increase from pre‐industrial to the 1970s followed by a decrease. Our study suggests that human activities caused changes in bromine through the emissions of acidic gases from fossil fuel combustion. Considering relationships between atmospheric acidity and bromine is crucial to interpreting bromine variations in ice cores. Key Points: Bromine (Br) concentrations increased 3.5‐fold from pre‐industrial to 1975 and declined 50% by 1999 in a Russian Arctic ice‐coreA robust correlation between ice‐core Br and acidity highlights acidity's key role in influencing the atmospheric Br budgetModel shows acid‐catalyzed sea‐salt debromination is the largest source of reactive Br and drives ice‐core Br trends [ABSTRACT FROM AUTHOR]

Sea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. Plain Language Summary: Sea ice topography estimates are important for improving ocean‐climate models, understanding sea ice evolution, and planning ice travel. Estimates can be obtained from altimetry missions, but these data are spatially and/or temporally limited compared to recent synthetic aperture radar missions such as the C‐band RADARSAT Constellation Mission (RCM). By comparing RCM backscatter with measurements of height and roughness obtained using Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 in the Canadian Arctic, we demonstrate the potential for using RCM to estimate sea ice topography. We show there is a notable relationship between sea ice roughness and backscatter on first‐year ice in late winter, particularly in the horizontal‐transmit/vertical‐receive channel, and a notable relationship between sea ice height and backscatter on multi‐year ice. We also observe that the relationship between the RCM signal and roughness varies with incidence angle. Based on this analysis we develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with ICESat‐2 data, we find the model performs effectively at estimating key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. These findings will facilitate the use of RCM to validate geophysical models, map rough areas of ice, assess ice evolution and develop new topographic models. Key Points: Sea ice roughness measured by Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 and horizontal‐transmit/vertical‐receive RADARSAT Constellation Mission (RCM) backscatter exhibit a notable relationship on late‐winter first‐year iceSea ice height, as measured by ICESat‐2, and RCM backscatter exhibit a notable relationship on late‐winter multi‐year iceWe develop/validate a model, based on comparison with ICESat‐2 data, for estimating sea ice roughness using RCM backscatter in the Arctic [ABSTRACT FROM AUTHOR]

In a rapidly changing Arctic, multiple lines of evidence suggest that bowhead whale migration is changing. To explore these changes further, we used passive acoustic data to examine bowhead whale presence in the western Beaufort Sea (12 years) and Chukchi Plateau (11 years) spanning 2008 to 2022. Departure from the western Beaufort Sea shifted 45 days later over the 12‐year period. Summer presence increased at both sites, suggesting feeding areas within the Chukchi Sea are becoming more favorable. Likewise, findings from the Bering Strait suggest that some whales are remaining north of the Bering Strait for the winter instead of in the Bering Sea. These Pacific Arctic‐wide changes to migration have occurred over only one decade. Questions remain about prey availability in the Chukchi Sea, implications of migratory changes, such as a northward shift in the core overwintering area, and impact to communities south of the Bering Strait. Plain Language Summary: Bowhead whales are an important top predator in the Pacific Arctic. With increasing temperatures and decreasing sea ice, changes are occurring in the migration patterns of this "ecosystem sentinel" throughout the Pacific Arctic. We analyzed passive acoustic data from the western Beaufort Sea and Chukchi Plateau from 2008 to 2022. Whales are delaying their fall departure from the western Beaufort Sea and spending more time in the western Beaufort Sea and Chukchi Plateau, which, along with increased habitat use on the Chukchi Shelf, suggests that conditions there are becoming more favorable for feeding. Combined with previous data from the Bering Strait, which showed that some whales are spending winter in the southern Chukchi Sea rather than in the northern Bering Sea, these findings suggests that changes are occurring over a short period of time throughout the Pacific Arctic. A northward shift could put bowhead whales in the direct path of ships, especially along the western side of the Chukchi Sea, and impact whaling communities south of the Bering Strait. Key Points: Acoustic data reinforce known delay in bowhead fall departure from the western Beaufort Sea and overwintering in the southern Chukchi SeaIncreased presence in the western Beaufort Sea and Chukchi Plateau suggests these regions are becoming more favorable for bowhead feedingWhales are adapting to a rapidly changing Arctic, but changes and emerging threats may impact them and communities south of Bering Strait [ABSTRACT FROM AUTHOR]

In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice‐ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023, https://doi.org/10.1029/2023ms003757) for the purpose of predicting sea ice concentration (SIC) data assimilation (DA) increments. An initial implementation of the CNN shows systematic improvements in SIC biases relative to the free‐running model, however large summertime errors remain. We show that these residual errors can be significantly improved with a novel sea ice data augmentation approach. This approach applies sequential CNN and DA corrections to a new simulation over the training period, which then provides a new training data set to refine the weights of the initial network. We propose that this machine‐learned correction scheme could be utilized for generating improved initial conditions, and also for real‐time sea ice bias correction within seasonal‐to‐subseasonal sea ice forecasts. Plain Language Summary: Climate models contain errors which often lead to predictions which are consistently out of agreement with what we observe in reality. In some cases we know the origin of these errors, for example, predicting too much sea ice as a result of consistently cool ocean temperatures. In reality, however, there are typically numerous model errors interacting across the atmosphere, ocean and sea ice, and to manually parse through large volumes of climate model data in an attempt to isolate these errors in time and space is highly impractical. Machine learning on the other hand is a framework which is well‐suited to this task. In this work we take a machine learning model which, at any given moment, ingests information about a climate model's atmosphere, ocean and sea ice conditions, and predicts how much error there is in the climate model's representation of sea ice, without seeing any actual sea ice observations. We use this to adjust the sea ice conditions in one particular climate model as it is running forward in time making predictions, and we find that this significantly reduces the model's sea ice errors globally. Key Points: We use a convolutional neural network (CNN) to perform online sea ice bias correction within global ice‐ocean simulationsThe CNN systematically reduces the free‐running model bias in both the Arctic and AntarcticThe online performance can be improved by combining CNN and data assimilation corrections in order to iteratively augment the training data [ABSTRACT FROM AUTHOR]

Algal bioindicators, such as diatoms, often show subdued responses to eutrophication in Arctic lakes because climate‐related changes (e.g., ice cover) tend to be the overriding factors influencing assemblage composition. Here, we examined how sub‐Arctic ponds historically receiving high nutrient inputs from nesting seabirds have responded to recent climate change. We present diatom data obtained from 12 sediment cores in seaduck‐affected ponds located on islands through Hudson Strait, Canada. All study cores show consistently elevated values of sedimentary ẟ15N, an established proxy for tracking marine‐derived nutrients, indicating seabirds have been present on these islands for at least the duration of the sediment records (~100 to 400 years). We document diverse epiphytic diatom assemblages to the base of all sediment cores, which is in marked contrast to seabird‐free Arctic ponds—these oligotrophic sites typically record epilithic diatom flora prior to recent warming. Diatoms are likely responding indirectly to seabird nutrients via habitat as nutrients promote the growth of mosses supporting epiphytic diatom communities. This masks the typical diatom response to increased warming in the Arctic, which also results in habitat changes and the growth of mosses around the pond edges. Changes in sedimentary chlorophyll a were not consistently synchronous with large changes in ẟ15N values, suggesting that primary production in ponds is not responding linearly to changes in seabird‐derived nitrogen. Across all ponds, we recorded shifts in diatom epiphytic assemblages (e.g., increases in % relative abundance of many Nitzschia species) that often align with increases in chlorophyll a. The changes in diatoms and chlorophyll a, although variable, are most likely driven by climate change as they are generally consistent with longer ice‐free conditions and growing seasons. Together, our results show that to effectively use diatoms in animal population reconstructions across the sub‐Arctic and Arctic, a strong understanding of eutrophication and climate change, based on supplementary proxies, is also required. [ABSTRACT FROM AUTHOR]

We simulated the seasonal temperature evolution in the atmosphere of Antarctica and the Arctic focusing on infrared processes. Contributions by other processes were parametrized and kept fixed throughout the simulations. The model was run for current CO2 and CH4 and for doubled concentrations. For doubling CH4 the warming in Antarctica is restricted to the lowest few hundred meters above the surface while in the Arctic we find a warming in the whole troposphere. We find that the amount of water is the main driver for the differences between both polar regions. When increasing both, CO2 and CH4 from pre‐industrial values to current concentrations, and averaged over the whole troposphere, we find a warming of 0.42 K for the Arctic and a slight cooling of 0.01 K for Antarctica. Our results contribute to the understanding of the lack of warming seen in Antarctica throughout the last decades. Plain Language Summary: In 2015 we have initiated a discussion on a fundamental property of the radiation in the atmosphere over Antarctica: The negative greenhouse effect (Schmithüsen et al., 2015, https://doi.org/10.1002/2015GL066749). A negative greenhouse effect means, the atmosphere emits more radiation to space than it receives from the surface. This results in a cooling somewhere in the Antarctic atmosphere during some months of the year, when increasing CO2. We now simulate how the Antarctic atmospheric temperature responds in all altitude levels to CO2 and CH4 increases, and show this is different from the temperature response in the Arctic. We show for example, that an increase in CH4 cools nearly the whole troposphere, although the response for CH4 is much lower in amplitude than for CO2. We find that the amount of water is the main driver for the differences between both polar regions. Since the amount of water vapor strongly depends on temperature, the colder Antarctic atmosphere responds differently to the Arctic when greenhouse gases increase. Our studies could be one important factor when understanding the lack of warming in Antarctica throughout the last decades. Key Points: We simulate the temperature development in both polar regions in the infrared and find that doubling CO2 and CH4 lead to opposing forcingsThe different amount of water vapor shows to be responsible for the differences in warming/cooling in both polar regionsIn Antarctica doubling CH4 leads to a cooling of almost the whole troposphere, a future increase in H2O could invert this [ABSTRACT FROM AUTHOR]

Passive microwave (PM) observations have been used to monitor ice retreat in the Arctic. However, various PM sea ice concentration (SIC) algorithms are prone to underestimate ice fraction during summer. We evaluated the accuracy of 2002–2019 low SICs in the Central Arctic Ocean of four PM products from the University of Bremen, the National Snow and Ice Data Center (NSIDC), and the Ocean and Sea Ice Satellite Application Facility (OSI SAF), and two reanalysis data sets from the fifth generation of European ReAnalysis (ERA5) and the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2). Three reference fields were used: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) true‐color composites, (b) MODIS sea ice extent, and (c) multi‐product ensemble (MPE‐SIC) comprising the median of collocated SIC estimates. Our results indicate SICs derived from the Advanced Microwave Scanning Radiometer ‐ Earth Observing System (AMSR‐E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) high frequency channels have the best accuracy. Reanalysis SICs indicate almost identical patterns as their remote sensing inputs. The assessment shows that the Bremen (+1.06%) and NSIDC (+0.99%) SICs are higher than the median field, while the OSI‐401 (−6.65%) and OSI‐408 (−4.64%) have negative mean deviations. The mean error of MODIS‐derived SIC (−0.80%) is smaller than PM SICs. These small mean values belie wide distributions of values. The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96. Key Points: We compared Arctic sea ice concentration (SIC) using passive microwave (PM), optical, and reanalysis data sets for the summers of 2002–2019The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96The average differences between multi‐product ensemble (MPE‐SIC) and PM SIC range from −6.65% to +1.09% [ABSTRACT FROM AUTHOR]

Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs. Plain Language Summary: Black carbon (BC) mixing state is an important property for assessing impacts of BC on human health and climate. It describes how BC is distributed among the particle population and within individual particles. Due to computational costs, global climate models (GCMs) consider aerosols as either external mixtures (e.g., bulk models) or fully internal mixtures with uniform composition within a certain size range (e.g., an aerosol bin in sectional models or an aerosol mode in modal models). To improve BC mixing state representation, we coupled a machine learning (ML) model learned from particle‐resolved simulations to the online simulations of the Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4‐ML). We used the mass ratio of coating to BC (RBC) to determine the mass fraction of BC‐free particles and BC‐containing particles, and we adjusted RBC to make the modeled BC mixing state match ML model. The MAM4‐ML model reduces the degree of internal mixture of BC substantially. In addition, MAM4‐ML RBC is reduced by 52%, leading to a 20% reduction in BC activation fraction and a 4% increase in BC burden. This study proposes a method for the application of the ML to improve GCMs. Key Points: Machine learning model trained on particle‐resolved simulations has been coupled to CAM6 MAM4 to improve BC mixing state representationThe BC mixing state index is reduced by 19%, and the mass ratio of coating to BC is reduced by 52%, in better agreement with observationsDue to the drop in hygroscopicity of BC‐containing aerosol (9%), BC activation fraction decreased by 20%, and BC burden increased by 4% [ABSTRACT FROM AUTHOR]