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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]

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]

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]

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]

The ice‐free Arctic in summer not only symbolizes human‐induced climate change but also highlights the need to critically consider climate change adaptation policies. To constrain projections when the Arctic Ocean will first become ice‐free, studies have typically combined the historical observations of the Arctic sea ice area (SIA) with future emission scenario simulations. However, these studies primarily relied on the historical climatology and trend of the Arctic SIA, without considering regional variations. In this study, we analyze September SIA projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations, while considering Arctic sub‐regions. Additionally, we assess the impact of incorporating sub‐region September SIA when constraining the first year of ice‐free Arctic in September. CMIP6 models generally overestimate the historical September SIA decreasing trend in the central Arctic, whereas they underestimate this trend in the surrounding shelf seas. The central Arctic, where the region expected to retain sea ice for the longest period in the future, holds particular significance for projecting when the Arctic will first become ice‐free. Consequently, when we employ the historical trend of September SIA in the central Arctic as a constraint, observationally‐constrained projections suggest a delay of 12 years (2056) for the first ice‐free September compared to raw/unconstrained projections (2044 in model average) under a high‐emission scenario. These findings underscore the importance of considering model biases in central Arctic SIA when constraining projections of the first year of ice‐free Arctic, which may occur later than previously projected in many studies. Plain Language Summary: The Arctic sea ice area (SIA) has steadily decreased over the historical period, and future projections indicate a continuous decrease throughout the twenty‐first century, which can potentially lead to an ice‐free Arctic in September. However, there are large uncertainties in climate model projections regarding when the first year of ice‐free Arctic in September will emerge, even under specific emissions scenarios. This study employs a statistical analysis that incorporates historically observed information to generate observationally‐constrained future projections. Particularly, we focus on the decline in September central Arctic SIA, a critical region expected to be the last to become ice‐free Arctic in September in the future. The latest climate model simulations mostly overestimate the observed September SIA decrease in the central Arctic during the historical period. Moreover, the climate model simulations exhibit robust statistical relationship between the historical trends in September central Arctic SIA and the projected year of the first ice‐free Arctic in September. Utilizing this relationship combined with the observed information for the historical trend of September central Arctic SIA, our constrained projections suggest as 12 years delay in the first year of ice‐free Arctic in September compared to the original model projections under a high‐emission scenario. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) models generally overestimate the observed September central Arctic sea ice area (SIA) decreaseHistorical central Arctic SIA trend positively correlates with the first year of ice‐free Arctic in September, serving as a constraintConstrained projections suggest 12 years delayed September ice‐free Arctic emergence than raw projections under a high‐emission scenario [ABSTRACT FROM AUTHOR]

Taking advantage of the Polar Amplification Model Intercomparison Project simulations and using a Lagrangian objective feature tracking algorithm, we determine the response of extratropical cyclones to sea‐ice loss and consequent weakening of the equator‐to‐pole near‐surface temperature gradient. The wintertime storm tracks are found to shift equatorward in the North Atlantic and over Europe, and eastward in the North Pacific. In both regions, cyclones become weaker and slower, particularly on the poleward flank of the storm tracks. On average, there are fewer individual cyclones in the extratropics each winter, they last longer, are weaker, and travel more slowly. These changes are greatest over the Arctic, but still statistically significant in midlatitudes despite being small compared to internal variability. Inter‐model spread in cyclone responses are not strongly correlated with that in Arctic warming or Arctic amplification. Little change in summertime cyclones is found. Plain Language Summary: Using identically performed experiments from eight different climate models, we study how low pressure systems, known as cyclones or storms, may change as Arctic sea ice diminishes. An algorithm is used to identify the weather systems and track them across their lifecycle. In the wintertime, the typical path of storms over the Atlantic Ocean shifts southward toward the equator. Over the Pacific, the storm tracks shifts toward North America. Arctic sea‐ice loss causes there to be fewer storms each winter across the northern midlatitudes, and these tend to be weaker, slower moving, but longer lasting. These changes are largest over the Arctic itself but also found over mid‐latitudes. These changes may be important because the number, location, and character of storms control the winter weather over Europe, North America and Asia. In the summertime, we did not find any detectable impact of Arctic sea‐ice loss on storms. Key Points: The Northern Hemisphere winter storm tracks shift south and/or east in response to Arctic sea‐ice lossSea ice loss leads to fewer, slower, longer lasting, and weaker wintertime cyclonesResponses are small compared to internal variability, but larger in winter than summer [ABSTRACT FROM AUTHOR]

Despite multiple advances in the understanding of the water cycle intensification in a warmer climate, climate models still diverge in their hydrological projections. Here we constrain annual runoff projections over individual and aggregated Arctic river basins. For this purpose, we use two ensembles of global climate models and two statistical methods: a regression scheme assuming similar runoff sensitivities at interannual versus climate change timescales, and a Bayesian method where models are used to derive a posterior runoff response conditioned on historical observations. While both techniques are shown to narrow model uncertainties, more or less substantially depending on rivers, the Bayesian method is less sensitive to the choice of the model ensemble and is more skillful when tested with synthetic observations. It has also been applied over the whole Arctic watershed, showing so far a limited narrowing of the inter‐model spread, but its skill will further improve with increasing climate change. Plain Language Summary: Despite considerable progress in understanding the intensification of the water cycle in response to global warming, projections of changes in the various components of the continental water balance remain highly model‐dependent. This is particularly the case for the evolution of runoff, which remains very sensitive to the way atmospheric and continental processes are represented. Here we evaluate and compare two statistical methods to constrain climate projections of annual runoff at high latitudes via available or synthetic observations. Our Bayesian method, based on historical observations, is found to be more robust than a previous method based on observed interannual climate variability. It underlines the importance of having reliable flow reconstructions to constrain runoff projections and of using empirical observational constraints with caution, as they are likely to lead to over‐confident projections. Key Points: Runoff sensitivity to temperature depends on the considered timescaleObservational constraints based on interannual variability can thus lead to overconfident projectionsObservational constraints based on historical trends can be effective if such trends are driven by human emissions of greenhouse gasses [ABSTRACT FROM AUTHOR]

Previous studies have found inconsistent responses of the North Atlantic jet to Arctic sea‐ice loss. The response of wintertime atmospheric circulation and surface climate over the North Atlantic‐European region to future Arctic sea‐ice loss under 2°C global warming is analyzed, using model output from the Polar Amplification Model Intercomparison Project. The models agree that the North Atlantic jet shifts slightly southward in response to sea‐ice loss, but they disagree on the sign of the jet speed response. The jet response induces a dipole anomaly of precipitation and storm track activity over the North Atlantic‐European region. The changes in jet latitude and speed induce distinct regional surface climate responses, and together they strongly shape the North Atlantic‐European response to future Arctic sea‐ice loss. Constraining the North Atlantic jet response is important for reducing uncertainty in the North Atlantic‐European precipitation response to future Arctic sea‐ice loss. Plain Language Summary: Variations in the North Atlantic jet affect temperature, precipitation, and storminess in Europe. It is not well understood how the jet and European climate will respond to future sea‐ice loss in the Arctic under human‐caused increases in global temperature. We study how atmospheric circulation and surface climate in winter over the North Atlantic‐European region would respond to future Arctic sea‐ice loss under 2°C global warming, using an unprecedented number of coordinated simulations from different climate models. In many models the North Atlantic jet stream in winter responds by shifting southward, but the response of its speed is less clear. These jet stream changes are found to strongly shape the responses in precipitation and storm track activity. More precise estimates of the jet stream response are key to reducing uncertainty in the North Atlantic‐European precipitation response to future Arctic sea‐ice loss. Key Points: Projected Arctic sea‐ice loss causes a robust equatorward shift of the North Atlantic jet across models, but no robust change in jet speedChanges in jet position and speed strongly shape the European winter surface climate response to projected Arctic sea‐ice lossConstraining the jet response is important for reducing uncertainty in the European precipitation response to future Arctic sea‐ice loss [ABSTRACT FROM AUTHOR]

There are established El Niño‐Southern Oscillation (ENSO) and Quasi‐biennial Oscillation (QBO) teleconnections to the extratropics that alter the strength of the polar vortex. Here, we demonstrate non‐linearity in the stratospheric polar vortex response to ENSO and QBO combined, in climate model simulations and observational data. We show that the combined effect of El Niño and QBO easterly phase (QBO‐E) on the polar vortex is greater than the linear combination of the individual responses to El Niño and QBO‐E separately. Observations support the model results, despite a relatively small sample of observed cases. We propose that non‐linearity in the teleconnection to ENSO and QBO is caused by the combined effects of an increase in wave driving from El Niño and more poleward propagation of wave activity during QBO‐E. Our results have implications for extreme winters when both El Niño and QBO‐E are active. Plain Language Summary: The El Niño‐Southern Oscillation (ENSO) describes cycles of warmer and cooler sea surface temperatures in the tropical Pacific Ocean. The Quasi‐biennial Oscillation (QBO) is a cycle of alternating easterly and westerly winds in the tropical stratosphere, around 15–40 km above ground. Although these climate cycles occur in the tropics, their impacts are felt far outside the tropics. Both ENSO and QBO are known to affect the stratospheric polar vortex, a band of strong winds that encircle the Arctic in winter. Here, we use climate model experiments and observations to quantify the effects of ENSO and QBO on the polar vortex, when these factors occur alone and in combination. Warm ENSO conditions, known as El Niño, and the easterly QBO phase both cause the polar vortex to weaken. We show that the polar vortex weakens more when El Niño and QBO easterly occur together than would be expected by the sum of their individual effects. Our results are relevant to seasonal forecasting of winter weather over Europe and North America, as a weakened polar vortex is often followed by abnormally cold weather. Our results imply that cold winters are more likely when both El Niño and easterly QBO conditions occur together, such as in 2009/10. Key Points: Stronger weakening of the stratospheric polar vortex is found during Quasi‐biennial Oscillation‐easterly phase and El Nino combinedNon‐linear polar vortex weakening is found in both model and observationsThe polar vortex non‐linearity impacts surface pressure and temperature, which has implications for seasonal forecasting [ABSTRACT FROM AUTHOR]

A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea‐surface salinity and sea‐level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea‐ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere‐ocean‐ice interaction occurs. One‐year‐long sea‐ice forecasts initialized on 1 January, 1 April, 1 July and 1 October from 2003 to 2019 are described. To correct systematic forecast errors, sea‐ice concentration from 2011 to 2019 is calibrated by trend‐adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea‐ice edge raw forecast skill is within the range of operational global subseasonal‐to‐seasonal forecast systems, outperforming a climatological benchmark for about 2 weeks in the Arctic and about 3 weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea‐ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations. Plain Language Summary: Ocean data sparseness and systematic model errors pose problems for the initialization of coupled seasonal forecasts, especially in polar regions. Our global forecast system follows a seamless approach with refined ocean resolution in the Arctic. The new version presented here features higher computational efficiency and utilizes more ocean and sea‐ice observations. Ice‐edge forecasts outperform a climatological benchmark for about 1 month, comparable to established systems. Key Points: We describe an upgrade of the AWI Coupled Prediction System with new ocean and atmosphere models and more observations assimilatedIndependent evaluations show advances in the new version on the analysis of the sea‐ice and ocean states against the old oneCalibrated sea‐ice edge forecasts outperform a climatological benchmark for around 1 month in both hemispheres [ABSTRACT FROM AUTHOR]

Employing a model democracy in which each model is equally weighted may lead to a poor estimation of the true uncertainty in climate projections from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The improvement and increase in number of CMIP6 models compared with previous phases of CMIP indicate that both model skill and independence need to be considered to provide convincing projections. In this study, we use a weighting scheme, which weights both the skill and independence of multi‐model simulations, to efficiently constrain the large uncertainty in the projection of the timing of an ice‐free Arctic. The uncertainty‐constrained projections of CMIP6 show that the multi‐model spread of the projected first year of an ice‐free Arctic can be reduced by about 29 years under the Shared Socioeconomic Pathway (SSP)3–7.0 scenario (a regional rivalry scenario), indicating a faster tendency to an ice‐free Arctic summer than projections based solely on model democracy. A fossil‐fuel‐based development scenario (i.e., SSP5‐8.5) leads to an ice‐free Arctic before the 2070s (ranging from 2038 to 2071), while an ice‐free Arctic occurs slightly later (by ∼10 years) under the SSP2‐4.5 scenario (i.e., intermediate scenario) and the SSP3‐7.0 scenario, but is inevitable this century. The sustainable development scenario (i.e., SSP1‐2.6) is likely to prevent the occurrence of an ice‐free Arctic. Internal variability strongly affects the projection estimated by the equally weighted ensemble; however, it has a negligible impact on the results obtained by the weighting scheme, thereby indicating that the results of this study are robust and convincing. Plain Language Summary: Climate models are a key tool used by scientists to estimate climate changes in the future. However, the outputs of such models carry inherent uncertainties that can affect the confidence in projections. These uncertainties may not be appropriately estimated by simple and traditional methods that do not adequately consider the model skill or independence (i.e., similarity to other models). In this study, we found that the uncertainties in Arctic sea‐ice projections can be largely reduced if the outputs of climate models are weighted according to their performance and independence. By doing so, we can increase the level of confidence in estimating the year when the Arctic is likely to see its first ice‐free summer (sea‐ice extent below 1 × 106 km2). It is indicated that a sustainable development scenario may avoid an ice‐free Arctic. In contrast, a fossil‐fuel‐based development scenario will lead to an ice‐free Arctic before the 2070s (ranging from 2038 to 2071); and an ice‐free Arctic will occur slightly later (by ∼10 years) under an intermediate or regional rivalry scenario, but is still inevitable this century. Key Points: The performance and interdependence of climate models may lead to a poor estimation of the true uncertainty in climate projectionsA weighting scheme considering both model skill and independence can reduce the spread in the first year of an ice‐free Arctic by ∼29 yearsThe weighted spread indicates that the first year of an ice‐free Arctic is likely to occur during 2040–2072 under the SSP3‐7.0 scenario [ABSTRACT FROM AUTHOR]

Multidecadal variability of Atlantic Meridional Overturning Circulation (AMOC) has been reconstructed by various proxies, simulated in climate models, and linked to multidecadal Arctic salinity variability. Here we construct a simple conceptual model to understand the two‐way interactions of the Arctic with multidecadal AMOC variability through a delayed oscillator mechanism. We revise Stommel's Two‐Box Model by including an advective time delay for the Arctic density/salinity anomalies to reach the subpolar North Atlantic and a coupled negative feedback between the AMOC and the freshwater flux entering the Arctic through atmosphere and/or sea ice responses. Self‐sustained multidecadal AMOC oscillations exist in the revised Stommel's Two‐Box model if the oceanic advective time delay is longer than the oscillation threshold, and the periods of the AMOC delayed oscillator depend crucially on this advective time delay. The coupled freshwater feedback provides additional delayed negative feedback and reduces the advective time delay threshold required for the oscillations. Plain Language Summary: Stommel's Two‐Box Model provides a pioneering and powerful theoretical framework to study the mechanisms of steady Atlantic Meridional Overturning Circulation (AMOC) states and abrupt AMOC changes. However, Stommel's Two‐Box Model itself does not include a self‐sustained multidecadal AMOC oscillation solution. In our study, we revise Stommel's Two‐Box model by considering the delayed oceanic advective time lag for the Arctic density/salinity anomalies to reach the subpolar North Atlantic and the dependence of the freshwater flux entering the Arctic on the AMOC strength. The revised Stommel's Two‐Box Model is able to obtain the AMOC delayed oscillator at multidecadal timescales and suggests the important role of the Arctic salinity anomalies and associated delayed negative feedback in the multidecadal AMOC variability. The regimes and multidecadal periods of the AMOC delayed oscillator depend crucially on the advective time scale of the Arctic signal reaching the subpolar North Atlantic. The AMOC‐related coupled freshwater feedback provides additional delayed negative feedback and reduces the threshold of the advective time delay needed for the multidecadal AMOC oscillations. Key Points: Multidecadal Atlantic Meridional Overturning Circulation (AMOC) variability exists in revised Stommel's Two‐Box Model with advective time delay and coupled freshwater feedback includedRegimes/periods of the AMOC delayed oscillator depend on the advective time lag of Arctic salinity signal and freshwater feedback strengthThe model suggests an important role of the Arctic salinity and delayed negative freshwater feedback in the multidecadal AMOC variability [ABSTRACT FROM AUTHOR]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [ABSTRACT FROM AUTHOR]

The thick multiyear sea ice that once covered large parts of the Arctic is increasingly being replaced by thinner and weaker first‐year ice, making it more vulnerable to breakup by winds. We use the neXtSIM sea ice model to investigate the driving mechanisms behind a large breakup event in the Beaufort Sea during winter 2013. Our simulations are the first to successfully reproduce the timing, location, and propagation of sea ice leads associated with wind‐driven breakup and highlight the importance of accuracy of the atmospheric forcing, sea ice rheology, and changes in sea ice thickness. We found that the breakup resulted in enhanced export of multiyear ice from the Beaufort Sea. Overall, this leads to a relatively thinner and weaker simulated ice cover that potentially preconditions earlier breakup in spring and accelerates sea ice loss. Finally, our simulations indicate that large breakup events could become more frequent as Arctic sea ice continues to thin. Plain Language Summary: The loss of thick multiyear sea ice in the Arctic leads to weaker sea ice that is more easily broken up by strong winds. As a consequence, extreme sea ice breakup events may become more frequent, even during the middle of winter when the sea ice cover is frozen solid. This can lead to an earlier onset of the melt season and potentially accelerate Arctic sea ice loss. Such extreme breakup events are generally not captured by climate models, potentially limiting our confidence in projections of Arctic sea ice. We investigated the driving forces behind sea ice breakup events during winter and how they change in a future climate. Our sea ice model is the first to reproduce such breakup events and reveals that the combination of strong winds and thin sea ice is a key factor for these breakups. We found that winter breakups have a large effect on local heat and moisture transfer and cause enhanced sea ice production, but also increase the overall movement of the sea ice cover, making it more vulnerable. Finally, we show that if the Arctic sea ice continues to thin, these extreme breakup events could become even more frequent. Key Points: An extreme winter breakup event in the Beaufort Sea is accurately simulated with the neXtSIM sea ice modelHigh‐resolution and skillful atmospheric forcing, as well as appropriate sea ice rheology, are necessary to simulate such breakupsIn the model, thinning Beaufort Sea ice is more vulnerable to breakup, which may accelerate loss of multiyear ice [ABSTRACT FROM AUTHOR]

The global warming potential of Ozone‐depleting substances (ODS) has long been known to be thousands of times larger than the one of CO2, but their climate impacts as greenhouse gases, i.e. unmediated ozone depletion, has received relatively little attention. Focusing on the period 1955–2005, we here present results from offline radiative forcing (RF) calculations from a global chemistry climate model. Using realistic distributions of ODS and consistent stratospheric ozone, we show that ODS dominate the adjusted stratospheric warming of the lower stratosphere, where CO2 has little radiative impact. We also show that the global mean RF of stratospheric ozone only cancels a fraction of the RF of ODS, leaving an important ODS contribution to anthropogenic forcing. Finally we show that the RF of ODS opposes Arctic amplification, its equator‐to‐pole gradient being larger than the one of CO2. Plain Language Summary: In the decades following World War II, organic compounds of chlorine and bromine, widely used in refrigerators and spray cans, entered the atmosphere and led to the formation of the ozone hole. However, these compounds, are also powerful greenhouse gases and are important for global warming. Here we study their warming effects in the stratosphere and at the surface. We find that, over the period 1955–2005, they were the dominant atmospheric gas warming the tropical lower stratosphere, where CO2 is largely inactive; that their surface warming effect (their radiative forcing) amounted to approximately one third of the CO2 value, even after accounting for reductions due to the ozone depletion they induce; and, finally that, even more than CO2, their radiative effects warmed the equatorial more the polar regions and, as such, cannot be the reason why the Arctic is warming more than the rest of the planet. Key Points: The radiative effect of Ozone‐depleting substances on lower stratospheric temperatures is the largest of all well‐mixed greenhouse gasesTheir radiative forcing is only partially (∼25%) canceled by the stratospheric ozone depletion they induceTheir radiative forcing is weaker at the pole than at the equator opposing Arctic amplification even more than CO2 forcing [ABSTRACT FROM AUTHOR]

Arctic sea ice has declined strikingly over the past 40 years under climate change. The physical processes driving these changes and the relative roles of external forcing versus natural variability remain under debate, partly due to lack of confidence in the simulation of Arctic sea ice by coupled climate models. Here, we nudge a widely‐used CMIP5‐class model (CESM1) toward observed winds above the boundary layer. These simulations reproduce observed Arctic trend patterns and Arctic‐average interannual fluctuations in near‐surface temperature. We find that atmospheric circulation plays a crucial role in September Arctic sea ice loss and sea ice interannual variability. Trends in the observed atmospheric circulation that are not part of the modeled forced response cause approximately 20%–25% of the modeled September sea ice decline, which is in close agreement with the observed trend. However, our simulations do not capture sea ice loss in other seasons, pointing to important model biases in winter and spring. Plain Language Summary: Arctic sea ice has declined strikingly over the past 40 years. This reduction has occurred due to both external forcing (including anthropogenic greenhouse gas emissions and aerosols) and natural climate variability, but the precise contribution of each remains uncertain. Here, we nudge a global coupled climate model toward observed winds in the high latitudes. We find that the model reproduces many features of Arctic climate change and summer Arctic sea ice loss, although it does not capture observed sea ice trends in other seasons. These experiments reveal the impact of observed atmospheric circulation variability on Arctic sea ice and climate relative to the model's response to external forcing. Our results may help to better understand and predict changes in the warming Arctic. Key Points: We nudge winds in a fully coupled climate model to observed anomalies over the satellite periodCirculation trends that are not part of the modeled forced response result in an enhanced September sea ice decline close to observationsWinter and spring sea ice trend biases remain after nudging winds, suggesting ice‐ocean biases in the model [ABSTRACT FROM AUTHOR]

Whether sea‐ice loss or lapse‐rate feedback dominates the Arctic amplification (AA) remains an open question. Analysis of data sets based upon observations reveals a 1.11 K per decade surface warming trend in the Arctic (70°–90°N) during 1979–2020 cold season (October–February) that is five times higher than the corresponding global mean. Based on surface energy budget analysis, we show that the largest contribution (∼82%) to this cold season warming trend is attributed to changes in clear‐sky downward longwave radiation. In contrast to that in Arctic summer and over tropics, a reduction in lower‐tropospheric inversions plays a unique role in explaining the reduction of the downward longwave radiation associated with atmospheric nonuniform temperature and corresponding moisture changes. Our analyses also suggest that Arctic lower‐tropospheric stability should be considered in conjunction with sea‐ice decline during the preceding warm season to explain AA. Plain Language Summary: Observations and climate models have consistently shown a stronger surface warming in the Arctic than the global mean, a.k.a. Arctic amplification (AA), which has a strong asymmetry between the cold season and warm season. Previous studies suggested that key contributors to AA are the positive surface‐albedo feedback and lapse‐rate feedback. However, the lapse‐rate feedback itself depends on temperature profiles and sea‐ice loss. Whether sea‐ice loss or lapse‐rate feedback dominates AA remains an open question. Here, by analyzing the latest generation of observationally‐based reanalysis data (1979–2020), we present a unique role of lower‐tropospheric temperature inversion changes in representing the contribution of vertically inhomogeneous atmospheric temperature and associated moisture changes to clear‐sky downward longwave radiation during the cold season. This unique role is not found either in the tropics or during Arctic summertime. We further link the inversion during the cold season to sea‐ice loss during the preceding warm season. These results reinforce previous findings not only that lapse‐rate feedback and sea‐ice loss play a key role in AA but also that lapse‐rate feedback in the cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season. Key Points: An increase in clear‐sky downward longwave radiation (rldsclr) explains 82% of the recent cold season Arctic surface warming trendReduced low inversion largely explains the Arctic rldsclr reduction due to vertical nonuniform temperature and related moisture changesLapse‐rate feedback in the Arctic cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season [ABSTRACT FROM AUTHOR]

Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L‐moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L‐variation, and of L‐skewness and L‐kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value (GEV) distribution coupled with a novel application of the Anderson‐Darling Goodness‐of‐fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within ±10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3‐GC31‐MM model performing well in all three scales when compared to the ground‐based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi‐arid regions. Plain Language Summary: Annual maxima of daily precipitation are widely used to design critical infrastructures such as dams and stormwater networks. Climate change is expected to increase the frequency and intensity of extreme events. Climate model projections offer a glimpse into the future and can help assess potential changes and impacts. It is reasonable to assume that climate models simulating accurately the historical climate may also simulate well the future. Here, we assessed the latest generation of climate models, that is the CMIP6 models, to reproduce the historical annual maximum daily precipitation. We compared simulations and observations of extreme precipitation using advanced summary statistics, novel probability similarity measures, and robust statistical tests. The results indicate that models, in general, reproduce well the behavior of annual maximum precipitation, but biases exist especially in the simultaneous behavior of mean and variance. Shortcomings of CMIP6 models are highlighted in the Arctic, Tropics, arid, and semi‐arid regions. Key Points: Assessment of CMIP6 biases in precipitation extremes using L‐moments, novel probability similarity measures and statistical testsCMIP6 models reproduce fairly well observed annual maximum precipitation but biases exist in the joint behavior of mean and variabilityShortcomings of CMIP6 models are highlighted in the Arctic, Tropics, arid, and semi‐arid regions [ABSTRACT FROM AUTHOR]

State‐of‐the‐art climate models simulate a large spread in the projected decline of Arctic sea‐ice area (SIA) over the 21st century. Here we diagnose causes of this intermodel spread using a simple model that approximates future SIA based on present SIA and the sensitivity of SIA to Arctic temperatures. This model accounts for 70%–95% of the intermodel variance, with the majority of the spread arising from present‐day biases. The remaining spread arises from intermodel differences in Arctic warming, with some contribution from differences in the local sea‐ice sensitivity. Using observations to constrain the projections moves the probability of an ice‐free Arctic forward by 10–35 years when compared to unconstrained projections. Under a high‐emissions scenario, an ice‐free Arctic will likely (>66% probability) occur between 2036 and 2056 in September and between 2050 and 2068 from July to October. Under a medium‐emissions scenario, the "likely" date occurs between 2040 and 2062 in September and much later in the 21st century from July to October. Plain Language Summary: Arctic sea ice coverage has declined substantially over the past few decades and is projected to continue to decline over the next century. These projections, however, are marred by large uncertainties which arise primarily due to differences between climate models. In this study, we use a simple model that emulates the future evolution of Arctic sea ice as simulated by climate models to explain where this uncertainty comes from. We show that biases in simulating present‐day Arctic sea ice contribute most of the uncertainty, with differences in the simulated amount of Arctic warming from climate models contributing much of the rest. We use observations to constrain our simple model and show that under a high emissions scenario it is likely the Arctic will be free of sea ice in September sometime between 2036 and 2056 and from July to October sometime between 2050 and 2068. We also show that the emissions pathway impacts the length of ice free summers in the Arctic, indicating a low‐emissions pathway will reduce the likelihood of seeing ice‐free Arctic summers. Key Points: A model relating future sea‐ice area (SIA) to present SIA and local sea‐ice sensitivity is used to explain the intermodel spread in Arctic SIA projectionsBiases in simulating present‐day SIA contribute most to the spread, with model differences in Arctic warming contributing the restUnder a high‐emissions scenario, the Arctic will likely be ice‐free in September between 2036 and 2056 and from July to October between 2050 and 2068 [ABSTRACT FROM AUTHOR]

We apply the linear response function to investigate the most excitable mode of the September Arctic sea ice in the Community Earth System Model. We find that this sea ice mode preferentially takes place over the Pacific side of the Arctic and its remote forcing corresponds to a dipole pattern of precipitation anomaly in the tropics with an increase of precipitation over the western and central tropical Pacific ocean while a decrease over the Maritime Continent. The tropical precipitation anomaly likely drives a Rossby wave train propagating toward the higher latitudes and leads to a ridge anomaly over the Pacific side of the Arctic, resulting in poleward atmospheric heat transport, enhanced downward longwave radiation and thus melting of the sea ice. In addition, a good agreement is found with the leading tropical‐Arctic teleconnection mode in a pre‐industrial simulation, supporting the usefulness and robustness of the linear response function method. Plain Language Summary: The minimum state of Arctic sea ice in September has experienced a drastic decline in the past two decades and is projected to continue to decline in the future warming climate. It is of both scientific and societal importance to understand both the year‐to‐year variability and anthropogenic change of the Arctic sea ice and their underlying physical mechanisms. This study, via a novel dynamical approach rather than a statistical one, reveals the optimal forcing pattern that excites the largest sea ice response in September. Our results will contribute to an improved understanding of both the natural variability and anthropogenic change of the September Arctic sea ice in climate model simulations. Key Points: Linear response function reveals the most excitable mode of September Arctic sea ice, which is maximized over the Pacific side of the ArcticPrecipitation anomaly over the western and central tropical Pacific ocean and its resulting teleconnection is likely the causeGood agreement is found with statistical leading mode of a pre‐industrial run, indicating the robustness of linear response function results [ABSTRACT FROM AUTHOR]

The climate response to volcanic eruptions in the twentieth century is difficult to separate from the influence of anthropogenic greenhouse gas forcing and internal variability. Here, we make use of recently available large ensembles of Earth‐system model simulations to better understand the forced climate response to contemporary volcanic eruptions. While the Pinatubo eruption in June 1991 resulted in approximately symmetric forcing between the Northern and Southern Hemispheres, the ensemble‐mean simulated temperature and sea ice responses it produces are asymmetric. The strongest cooling and sea ice expansion occur in the Arctic, while the responses in the Antarctic are weak. The damped high‐latitude Southern Hemisphere response to volcanic forcing is analogous to the fast response to increased greenhouse gas concentrations, despite the differing physical nature of the forcing. We find that Arctic cooling in response to a Pinatubo‐scale eruption may not occur due to the high internal variability in that region Plain Language Summary: Volcanic eruptions can have a large short‐term influence on global climate due to the injection of reflective particles into the stratosphere that cool the planet. However, the impact of twentieth century volcanic eruptions on climate has been difficult to distinguish from the impact of other factors such as anthropogenic greenhouse gas forcing and natural variability. Averaging the response over many climate model simulations, each with different natural variations but the same climate, suppresses the influence of natural variability and allows the true response to become more evident. The eruption of Pinatubo in June 1991 spread reflective particles into the stratosphere roughly evenly between the Northern and Southern Hemispheres. However, the changes in temperature and sea ice it produced were quite different between the two hemispheres. The area of Arctic sea ice grew, and the region cooled after the eruption, while the Antarctic showed little response. Previously, this result had only been shown in single climate models with idealized conditions where all forcing except the volcanoes was held fixed. Our results show that this response is a robust feature of the climate response to volcanic eruptions, and is not strongly dependent on the particular climate model used. Key Points: Analysis of model large ensembles shows stronger cooling in the Arctic than in the Antarctic in response to symmetric volcanic forcingArctic sea ice area and volume increase for several years after the eruption while Antarctic sea ice area and volume show little responseThe damped Southern Ocean cooling is analogous to the weak warming in the short‐term response to greenhouse gas forcing in that region [ABSTRACT FROM AUTHOR]

Given uncertainty in the processes involved in polar amplification, elucidating the role of poleward heat and moisture transport is crucial. The Polar Amplification Model Intercomparison Project (PAMIP) permits robust separation of the effects of sea ice loss from sea surface warming under climate change. We utilize a moist isentropic circulation framework that accounts for moisture transport, condensation, and eddy transport, in order to analyze the circulation connecting the mid‐latitudes and the Arctic. In PAMIP's atmospheric general circulation model experiments, prescribed sea ice loss reduces poleward heat transport (PHT) by warming the returning moist isentropic circulation at high latitudes, while prescribed warming of the ocean surface increases PHT by strengthening the moist isentropic circulation. Inter‐model spread of PHT into the Arctic reflects the tug‐of‐war between sea‐ice and surface‐warming effects. Plain Language Summary: A major conundrum in current climate science is to understand what Arctic changes imply for the climate and environment in mid‐latitude regions. The Polar Amplification Model Intercomparison Project (PAMIP) designed a set of climate model experiments to specifically answer this question in a carefully designed, idealized framework. PAMIP's approach is to separate historic and projected climate change into parts associated with Arctic sea ice loss and ocean surface warming and investigate how these two contributions can influence the atmosphere. To isolate these effects, only models can be used, because, in reality, sea ice loss and ocean surface warming are strongly linked together. This letter focuses on what the PAMIP experiments imply for the transport and redistribution of heat and moisture in the atmosphere. In PAMIP, we learn that Arctic sea ice loss causes the atmosphere to reduce the transport of dry and cold air away from the Arctic while ocean warming causes more transport of moist warm air toward the Arctic. These two effects are in a tug‐of‐war, suggesting that climate change can cause a mix of competing impacts on the global energy transport from the tropics to the Arctic. Key Points: Sea ice loss exports static energy from the Arctic by warming the equatorward branch of the global mass circulationSea surface warming imports static energy into the Arctic by increasing the mid‐latitude mass transport, overwhelming the sea ice effectThere is more spread amongst models for the effect of sea‐surface warming than for sea ice loss [ABSTRACT FROM AUTHOR]

The IPSL‐CM6‐LR atmosphere‐ocean coupled model exhibits a pronounced multicentennial variability of the Atlantic meridional overturning circulation (AMOC), driven by delayed freshwater accumulation and release in the Arctic. The AMOC fluctuations are preceded by salinity‐driven density anomalies in the main deep convection sites in the Labrador and Greenland seas. During a strong AMOC, a combination of reduced sea ice volume and anomalous currents reduces the freshwater export from the Arctic and leads instead to a slow accumulation of freshwater in the central Arctic. Simultaneously, the saltier Atlantic inflow through the Barents Sea results in a positive salinity anomaly in the Eastern Arctic subsurface. When the surface Central Arctic freshwater pool finally reaches the Lincoln Sea, the oceanic currents around Greenland reorganize, leading to the export of the anomalous Arctic freshwater to the North Atlantic, enhancing the stratification in deep convection sites. The AMOC then decreases, positive salinity anomalies appear in the Central Arctic, and the variability switches to the opposite phase. These AMOC and sea ice fluctuations have broader climate impacts, with a strong AMOC leading to a mean warming of about 0.4°C north of 20°N, reaching up to 1°C in the Arctic lower troposphere during winter. In all seasons, a northward displacement of the intertropical convergence zone is also simulated. Plain Language Summary: The North Atlantic Ocean is known to have large climate fluctuations emerging from the different components of the climate system and their interactions. These fluctuations play a crucial role in the North American and European climate or the Arctic sea ice. A proper understanding of such internal variations is key to attribute the observed climate changes to anthropogenic activities or to assess the skill of decadal forecast systems. However, investigations of the long‐term basin‐scale variations are restrained by the limited instrumental observations. Therefore, an atmosphere‐ocean general circulation model is used here to explore the low‐frequency variability. This model simulates a large variability in the North Atlantic with a period between 1 century and 1 millennium. We found that this variability owes its existence to the freshwater exchanges between the North Atlantic and Arctic. Such North Atlantic variability has important impacts, as typical positive anomalies of the Atlantic oceanic northward heat transport reduce the sea ice, warm up the whole Northern Hemisphere by 0.4°C, and shift northward the intertropical convergence zone. Key Points: A centennial to multicentennial variability emerges from the North Atlantic and Arctic Ocean in the IPSL‐CM6A‐LR modelAMOC oscillations are driven by freshwater build‐up and release in the ArcticThe interplay between sea ice and oceanic freshwater export modulates the salinity anomalies in the Arctic [ABSTRACT FROM AUTHOR]

Most climate models neglect cloud longwave (LW) scattering because scattering is considered negligible compared to strong LW absorption by clouds and greenhouse gases. While this rationale is valid for simulating extrapolar regions, it is questionable for the polar regions, where the atmosphere is dry and hence has weak absorption, and ice clouds that have strong scattering capability frequently occur. Using the slab‐ocean Community Earth System Model, we show that ice cloud LW scattering can warm winter surface air temperature by 0.8–1.8 K in the Arctic and 1.3–1.9 K in the Antarctic, while this warming becomes much weaker in polar summer. Such scattering effect cannot be correctly assessed when sea surface temperature and sea ice are prescribed as this effect is manifested through a surface‐atmosphere coupling. Cloud LW scattering is a necessity for the correct simulation of polar climate and surface radiation budget, especially in the winter. Plain Language Summary: Cloud longwave scattering has never been deemed as a necessity in climate models. Out of all climate models in the IPCC fifth and sixth assessments, only three modeling centers have longwave scattering included in their models. Our study explained why the traditional wisdom of neglecting longwave scattering breaks down for the simulation of high‐latitude climate in the fully coupled models. We showed the critical importance of atmosphere‐surface radiative coupling for correctly assessing the role of cloud longwave scattering in the model simulation of climate mean state as well as climate changes, an issue overlooked by all previous studies. We argued that the cloud longwave scattering is a necessity in climate models, not an option. Key Points: Cloud longwave scattering is more important in the polar regions than the extrapolar regionsBy surface‐atmosphere radiative coupling, cloud longwave scattering can warm the polar surface, more in the winter than in the summerCloud longwave scattering is a necessity instead of an option for correctly simulating polar climate and surface energy budget [ABSTRACT FROM AUTHOR]

Recent studies have shown a large spread in the transport of atmospheric tracers into the Arctic among a suite of chemistry climate models and have suggested that this is related to the spread in the meridional extent of the Hadley Cell (HC). Here we examine the HC‐transport relationship using an idealized model, where we vary the mean circulation and isolate its impact on transport to the Arctic. It is shown that the poleward transport depends on the relative position between the northern edge of the HC and the tracer source, with maximum transport occurring when the HC edge lies near the middle of the source region. Such dependence highlights the critical role of near‐surface transport by the Eulerian mean circulation rather than eddy mixing in the free troposphere and suggests that variations in the HC edge and the tracer source region are both important for modeling Arctic composition. Plain Language Summary: Long‐range transport plays a crucial role in determining the distribution of pollutants in the Arctic, as many pollutants have their sources in northern middle latitudes. Recent studies show large differences in transport into the Arctic among models, and it has been suggested that this is related to differences in the northern edge of the Hadley Cell (HC) in the models. We revisit this topic using an idealized model in which the extent of the HC can be varied in a controlled manner. We show that the relative position between the tracer source and the northern edge of the HC plays an important role in determining how rapidly air is transported into the Arctic. The most rapid transport occurs when the HC edge lies near the middle of the tracer source region. This results suggest that variations in the HC edge and the tracer source region are both important for modeling Arctic composition. Key Points: Transport of tracers from the northern midlatitude surface into the Arctic is sensitive to changes in the northern edge of the Hadley CellThe relative position between a tracer source and the Hadley Cell edge plays a major role in the transport into the ArcticDifferences in the poleward transport are mainly due to differences in the near‐surface mean flow rather than mixing in the free troposphere [ABSTRACT FROM AUTHOR]

Using a statistical method, we examined the time of emergence (ToE), which was defined as the year when the global mean surface temperature (GMST) trend exceeds the trend explained by internal variability, using 33 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models for 1861–2100. We found that the GMST trend explained by internal variability showed minimal changes even though the GMST trend rapidly increased under the Representative Climate Pathway 8.5 scenario. However, there was large intermodel diversity for the ToEs in CMIP5 climate models. The intensity of Arctic amplification was closely associated with intermodel diversity of the ToEs. A significant difference in the amount of Arctic sea ice extent during the reference period (1861‐1910) played a role in determining whether there was slow or fast simulation of the ToE in CMIP5 climate models. We argue that a slow or fast ToE is influenced by how the climate models initially simulate the sea ice extent during the reference period. Plain Language Summary: In our society, it is quite important to know the time of emergence (ToE), which is typically defined as the notion that a new climate state can be said to have emerged when it deviates significantly from a prior reference period, to correctly build up an adaptation and mitigation strategy in a changing climate. Although we did not use a typical definition of ToE, we found that the ToEs simulated in climate models are quite diverse. We found the intensity of Arctic amplification was closely associated with intermodel diversity of the ToEs. Our further analysis indicated that the amount of sea ice extent in the Arctic during the reference period may influence the ToEs in climate model. Therefore, it is quite important to correctly simulate the Arctic sea ice extent to correctly project the ToEs in a changing climate. Key Points: We define the time of emergence as the year when the GMST trend exceeds the trend explained by IV in climate modelsThe diversity of GMST in CMIP5 climate models after the mid‐21st century could be mainly attributed to the model's characteristicsThe sea ice extent during the reference period plays a key role in determining the ToE in CMIP5 climate models [ABSTRACT FROM AUTHOR]

Sea ice formation processes occur on subgrid scales, and the detailed physics describing the processes are therefore not generally represented in climate models. One likely consequence of this is the premature closing of areas of open water in model simulations, which may result in a misrepresentation of heat and gas exchange between the ocean and atmosphere. This work demonstrates the implementation of a more realistic model of sea ice formation, introducing grease ice as a wind and oceanic stress‐dependent intermediary state between water and new sea ice. We use the fully coupled land‐atmosphere‐ocean‐sea ice model, HadGEM3‐GC3.1 and perform a three‐member ensemble with the new grease ice scheme from 1964 to 2013. Comparing our sea ice results with the existing ensemble without grease ice formation shows an increase in sea ice thickness and volume in the Arctic. In the Antarctic, including grease ice processes results in large local changes to both simulated sea ice concentration and thickness, but no change to the total area or volume. Plain Language Summary: The way that new sea ice forms in most climate models may result in new sea ice forming more quickly than it does in reality, prematurely closing areas of open water that are important to heat and gas exchange between the ocean and atmosphere, and impacting the albedo, and therefore the radiation budget, of the planet. In this work, we implement a more realistic representation of how new sea ice forms in a fully coupled climate model, and demonstrate the effect using an ensemble of historical climate simulations. Key Points: A more detailed representation of sea ice formation was implemented in a coupled climate model ensembleIncluding grease ice processes resulted in increased Arctic sea ice thickness and volumeIncluding grease ice processes resulted in large local changes to Antarctic winter sea ice concentration and thickness [ABSTRACT FROM AUTHOR]

The wintertime midlatitude atmospheric circulation is evaluated in CMIP6 models. The biases have been reduced since CMIP5 although the low‐level flow is still too zonal. CMIP5 and CMIP6 projections of 850 hPa zonal wind are then analyzed and are consistent under the RCP8.5 and the SSP5–8.5 scenarios, respectively. A poleward shift is identified in the Pacific, while a tripole structure is found in the North Atlantic: The zonal wind strengthens over Western Europe and decreases north and south. A multiple linear regression allows us to quantify the contribution of different drivers to the intermodel spread in zonal wind projections. It supports the importance of projected tropical warming and changes in the stratospheric vortex but also suggests a contribution of the asymmetry in the projected surface warming of the equatorial Pacific and of the present‐day biases in the eddy‐driven jet position. The North Atlantic warming hole plays a weaker role. Plain Language Summary: The projection of the midlatitude atmospheric circulation is highly uncertain. Climate models exhibit a significant ensemble dispersion although some robust signals seem to emerge at the end of the 21st century: a poleward shift of the 850 hPa zonal wind in the Pacific and a strengthening over Northern Europe. This response is consistent between the former and current‐generation global climate models. Several drivers have been proposed to explain this response. Here, it is confirmed that the amplified warming of the tropical high troposphere and variation of the stratospheric vortex play a more important role than the amplified warming over the Arctic. In addition, it is suggested that the West‐East temperature gradient in the tropical Pacific and the biases of the jet position have a significant contribution, as opposed to the minimum warming in the North Atlantic. Key Points: Ensemble mean biases and projections of 850 hPa zonal wind are consistent between CMIP5 and CMIP6 modelsIntermodel spread remains high but can be related to a limited number of remote tropical and extratropical driversModel biases in the jet stream position also contribute significantly to the spread [ABSTRACT FROM AUTHOR]

We formulate seasonal probabilistic forecasts of Arctic sea ice concentration from a multimodel (MM) ensemble constructed from six state‐of‐the‐art climate models. Trend‐adjusted quantile mapping is applied to postprocess individual model forecasts prior to MM combination, and a comparison is made against two benchmark MM ensembles: one uncorrected and another where individual models are adjusted for mean and trend bias. Focusing on September hindcasts over 2000–2015 initialized monthly from April–August, calibration improves forecast skill for all models tested, but greatest skill is achieved by the calibrated MM ensemble. Compared against a climatology adjusted for trends, skill is seen over most of the Arctic for all MM formulations and at all lead times, with highest skill obtained by the calibrated MM ensemble. Furthermore, despite the overall skill for each MM formulation, we find evidence of the "spring predictability skill barrier" for forecasts initialized before June. Key Points: Methods are explored for improving probabilistic Arctic sea ice forecasts a posteriori in a multimodel settingQuantile mapping based postprocessing of September sea ice forecasts adds skill to all models consideredSkill of these postprocessed multimodel forecasts exceeds that of any single model [ABSTRACT FROM AUTHOR]

Mineral dust plays an important role in the primary formation of ice crystals in mixed‐phase clouds by acting as ice nucleating particles (INPs). It can influence the cloud phase transition and radiative forcing of mixed‐phase clouds, both of which are crucial to global energy budget and climate. In this study, we investigate the dust indirect effects on mixed‐phase clouds through heterogeneous ice nucleation with the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). Dust and INP concentrations simulated from two versions of E3SM with three ice nucleation parameterizations were evaluated against observations in the Northern Hemisphere. Constrained by these observations, E3SM shows that dust INPs induce a global mean net cloud radiative effect of 0.05 to 0.26 W/m2 with the predominant warming appearing in the Northern Hemisphere midlatitudes. However, a cooling effect is found in the Arctic due to reduced longwave cloud forcing. Plain Language Summary: Mineral dust is one of the most important ice nucleating particles in the atmosphere. In other words, it can initiate ice crystal formation in cold clouds. This process has large impacts on cloud liquid and ice content and cloud radiative forcing, which are crucial to global energy budget and climate. In this study, we investigate the dust indirect effects on mixed‐phase clouds through the freezing of cloud water using global climate models. Dust and ice nucleating particle concentrations from model simulations were evaluated against observations focusing on the Northern Hemisphere. Constrained by these observations, the model results indicate that dust induces a global mean net warming cloud effect. The warming is found predominantly in the Northern Hemisphere midlatitudes, whereas a cooling effect is found in the Arctic. Key Points: Dust indirect effects on mixed‐phase clouds by acting as ice nucleating particles are investigated with global climate modelsDust induces a global mean net warming cloud radiative effectThe dust warming effect is located predominantly in the Northern Hemisphere midlatitudes, while a cooling effect is found in the Arctic [ABSTRACT FROM AUTHOR]

Though climate models exhibit broadly similar agreement on key long-term trends, they have significant temporal and spatial differences due to intermodel variability. Such variability should be considered when using climate models to project the future marine Arctic. Here we present multiple scenarios of 21st-century Arctic marine access as driven by sea ice output from 10 CMIP5 models known to represent well the historical trend and climatology of Arctic sea ice. Optimal vessel transits from North America and Europe to the Bering Strait are estimated for two periods representing early-century (2011-2035) and mid-century (2036-2060) conditions under two forcing scenarios ( RCP 4.5/8.5), assuming Polar Class 6 and open-water vessels with medium and no ice-breaking capability, respectively. Results illustrate that projected shipping viability of the Northern Sea Route ( NSR) and Northwest Passage ( NWP) depends critically on model choice. The eastern Arctic will remain the most reliably accessible marine space for trans-Arctic shipping by mid-century, while outcomes for the NWP are particularly model-dependent. Omitting three models ( GFDL-CM3, MIROC-ESM-CHEM, and MPI-ESM-MR), our results would indicate minimal NWP potential even for routes from North America. Furthermore, the relative importance of the NSR will diminish over time as the number of viable central Arctic routes increases gradually toward mid-century. Compared to vessel class, climate forcing plays a minor role. These findings reveal the importance of model choice in devising projections for strategic planning by governments, environmental agencies, and the global maritime industry. [ABSTRACT FROM AUTHOR]

We estimate the uncertainty of satellite‐retrieved Arctic sea‐ice thickness, sea‐ice volume, and their trends stemming from the lack of reliable snow‐thickness observations. To do so, we simulate a Cryosat2‐type ice‐thickness retrieval in an ocean‐model simulation forced by atmospheric reanalysis, pretending that only freeboard is known as model output. We then convert freeboard to sea‐ice thickness using different snow climatologies and compare the resulting sea‐ice thickness retrievals to each other and to the real sea‐ice thickness of the reanalysis‐forced simulation. We find that different snow climatologies cause significant differences in the obtained ice thickness and ice volume. In addition, we show that Arctic ice‐volume trends derived from ice‐thickness retrievals using any snow‐depth climatology are highly unreliable because the estimated trend in ice volume can strongly be influenced by the neglected interannual variability in snow volume. Plain Language Summary: In our study, we show that the lack of snow‐thickness information can cause substantial uncertainties in estimated sea‐ice thickness and pan‐Arctic sea‐ice volume from satellites such as Cryosat2. In particular, even the sign of short‐term trends can be erroneous when changes in snow thickness are ignored. The satellites measure how much the sea ice extends above the ocean surface. Current algorithms interpret any change in this quantity from one year to the next to be entirely caused by a change in the associated sea‐ice thickness. This is because existing algorithms assume that the snow thickness on the sea ice is constant from one year to the next. However, in reality, the snow thickness can vary substantially, which in turn influences how much the ice extends above the ocean surface for a given sea‐ice thickness. Our findings suggest that better estimates and inclusion of realistic snow depths are crucial for reliable ice‐thickness retrievals from satellite altimeter data. A possible step forward is the usage of accumulated snow depth from reanalysis as a source for interannually varying snow depth data. Key Points: Lack of snow‐depth observations causes high uncertainty of satellite retrieved sea‐ice thicknessSatellite‐derived ice‐volume trends are highly unreliable if temporal snow variability is neglectedReanalyzed snow depths can help to estimate and improve accuracy of ice‐thickness retrievals [ABSTRACT FROM AUTHOR]