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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]

A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial systemis responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992--2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University--National Center for Atmospheric Research MesoscaleModel (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinderall-sky temperatures (APP); 4) National Centers for Environmental Prediction--National Center for Atmospheric Research (NCEP--NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effecton the output from hydrologic models, and several different datasets should beused for a robust hydrologic assessment. [ABSTRACT FROM AUTHOR]

This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of CESM-LE climate model simulations': the stationary case with pre-industrial radiative forcing and the climate change case with measured and RCP8.5 radiative forcing. Stationary control simulation 1800-year long time-series were used for stationary teleconnection and 40-member ensemble from the period 1920-2100 for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2-meter level, sea-level pressure, sea ice concentration, precipitations, geopotential height and 10-meter level wind speed. The Arctic was divided into seven areas. the Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local factors are stronger. The majority of teleconnections in winter and spring can be explained by climate indexes NAO and AO. During ongoing climate change, the teleconnection patterns did not show remarkable developments by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. To estimate different Arctic regions' collective statistical connections with the Baltic Sea region, we calculated the correlation between the parameter and its Ridge regression estimation. Seasonal coefficient of determination, R2, were highest for winter: for temperature R2 = 0.64, for surface pressure R2 = 0.44 and for precipitation R2 = 0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker with the highest R2 = 0.09 for temperature in the spring. Hence, the forecasting capacity of Arctic climate data for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnections pattern between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century. [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]

This paper addresses the specter of a September ice-free Arctic in the 21st century using newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that large spread in the projected timing of the September ice-free Arctic in 30 CMIP5 models is associated at least as much with different atmospheric model components as with initial conditions. Here we reduce the spread in the timing of an ice-free state using two different approaches for the 30 CMIP5 models: (i) model selection based on the ability to reproduce the observed sea ice climatology and variability since 1979 and (ii) constrained estimation based on the strong and persistent relationship between present and future sea ice conditions. Results from the two approaches show good agreement. Under a high-emission scenario both approaches project that September ice extent will drop to ~ 1.7 million km2 in the mid 2040s and reach the ice-free state (defined as 1 million km2) in 2054-2058. Under a medium-mitigation scenario, both approaches project a decrease to ~1.7 million km2 in the early 2060s, followed by a leveling off in the ice extent. [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]

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]

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]

Manifestations of global warming in the Arctic include amplifications of temperature increases and a general increase in precipitation. Although topography complicates the pattern of these changes in regions such as Alaska, the amplified warming and general increase in precipitation are already apparent in observational data. Changes in snow cover are complicated by the opposing effects of warming and increased precipitation. In this study, high-resolution (0.25°) outputs from simulations by the Community Atmosphere Model, version 5, were analyzed for changes in snow under stabilized global warming scenarios of 1.5 °C, 2.0 °C and 3.0 °C. Future changes in snowfall are characterized by a north–south gradient over Alaska and an east–west gradient over Eurasia. Increased snowfall is projected for northern Alaska, northern Canada and Siberia, while milder regions such as southern Alaska and Europe receive less snow in a warmer climate. Overall, the results indicate that the majority of the land area poleward of 55°N will experience a reduction in snow. The approximate threshold of global warming for a statistically significant increase in temperature over 50% of the pan-Arctic land area is 1.5 °C. The corresponding threshold for precipitation is approximately 2.0 °C. The global warming threshold for the loss of high-elevation snow in Alaska is approximately 2.0 °C. The results imply that limiting global warming to the Paris Agreement target is necessary to prevent significant changes in winter climates in Alaska and the Arctic. [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]

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 Arctic is rapidly changing, with increasingly dramatic sea ice loss and surface warming in recent decades. Shortwave radiation plays a key role in Arctic warming during summer months, and absorbed shortwave radiation has been increasing largely because of greater sea ice loss. Clouds can influence this ice-albedo feedback by modulating the amount of shortwave radiation incident on the Arctic Ocean. In turn, clouds impact the amount of time that must elapse before forced trends in Arctic shortwave absorption emerge from internal variability. This study determines whether the forced climate response of absorbed shortwave radiation in the Arctic has emerged in the modern satellite record and global climate models. From 18 years of satellite observations from CERES-EBAF, we find that recent declines in sea ice are large enough to produce a statistically significant trend (1.7 x 106 PJ or 3.9% per decade) in observed clear-sky absorbed shortwave radiation. However, clouds preclude any forced trends in all-sky absorption from emerging within the existing satellite record. Across 18 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), the predicted time to emergence of absorbed shortwave radiation trends varies from 8 to 39 and from 8 to 35 years for all-sky and clear-sky conditions, respectively, across two future scenarios. Furthermore, most models fail to reproduce the observed cloud delaying effect because of differences in internal variability. Contrary to observations, one-third of models suggest that clouds may reduce the time to emergence of absorbed shortwave trends relative to clear skies, an artifact that may be the result of inaccurate representations of cloud feedbacks. [ABSTRACT FROM AUTHOR]

The modeling of paleoclimate, using physically based tools, is increasingly seen as a strong out-of-sample test of the models that are used for the projection of future climate changes. New to the Coupled Model Intercomparison Project (CMIP6) is the Tier 1 Last Interglacial experiment for 127 000 years ago (lig127k), designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models as for the future and following a common experimental protocol. Here we present a first analysis of a multi-model ensemble of 17 climate models, all of which have completed the CMIP6 DECK (Diagnostic, Evaluation and Characterization of Klima) experiments. The equilibrium climate sensitivity (ECS) of these models varies from 1.8 to 5.6 ∘ C. The seasonal character of the insolation anomalies results in strong summer warming over the Northern Hemisphere continents in the lig127k ensemble as compared to the CMIP6 piControl and much-reduced minimum sea ice in the Arctic. The multi-model results indicate enhanced summer monsoonal precipitation in the Northern Hemisphere and reductions in the Southern Hemisphere. These responses are greater in the lig127k than the CMIP6 midHolocene simulations as expected from the larger insolation anomalies at 127 than 6 ka. New synthesis for surface temperature and precipitation, targeted for 127 ka, have been developed for comparison to the multi-model ensemble. The lig127k model ensemble and data reconstructions are in good agreement for summer temperature anomalies over Canada, Scandinavia, and the North Atlantic and for precipitation over the Northern Hemisphere continents. The model–data comparisons and mismatches point to further study of the sensitivity of the simulations to uncertainties in the boundary conditions and of the uncertainties and sparse coverage in current proxy reconstructions. The CMIP6–Paleoclimate Modeling Intercomparison Project (PMIP4) lig127k simulations, in combination with the proxy record, improve our confidence in future projections of monsoons, surface temperature, and Arctic sea ice, thus providing a key target for model evaluation and optimization. [ABSTRACT FROM AUTHOR]

Climate models predict that by 2050 the Arctic Ocean will be sea ice free each summer. Removing this barrier between the Atlantic and the Pacific will modify a wide range of ecological processes, including bird migration. Using published information, we identified 29 arctic-breeding seabird species, which currently migrate in the North Atlantic and could shift to a transarctic migration towards the North Pacific. We also identified 24 arctic-breeding seabird species which may shift from a migratory strategy to high-arctic year-round residency. To illustrate the biogeographical consequences of such drastic migratory shifts, we performed an in-depth study of little auks (Alle alle), the most numerous artic seabird. Coupling species distribution models and climatic models, we assessed the adequacy of future wintering and breeding areas for transarctic migrants and high-arctic year-round residents. Further, we used a mechanistic bioenergetics model (Niche Mapper), to compare the energetic costs of current little auk migration in the North Atlantic with potential transarctic and high-arctic residency strategies. Surprisingly, our results indicate that transarctic little auk migration, from the North Atlantic towards the North Pacific, may only be half as costly, energetically, than high-arctic residency or migration to the North Atlantic. Our study illustrates how global warming may radically modify the biogeography of migratory species, and provides a general methodological framework linking migratory energetics and spatial ecology. [ABSTRACT FROM AUTHOR]

The Arctic frontal zone (AFZ) is a narrow band of strong horizontal temperature gradients that develops along the Arctic Ocean coastline each summer in response to differential heating of the atmosphere over adjacent land and ocean surfaces. Past research has linked baroclinicity within the AFZ to summer Arctic cyclone development, especially by intensifying storms that migrate northward from the Eurasian continent. This study uses the Community Earth System Model Large Ensemble in conjunction with an advanced cyclone detection and tracking algorithm to assess how the AFZ, summer Arctic cyclone activity, and the relationship between them respond to warming under the representative concentration pathway 8.5 (RCP8.5) scenario. Under this strong warming scenario, the AFZ remains a significant cyclone intensifier. Changes to the AFZ are largely restricted to June, when earlier snowmelt leads to strengthening of land-ocean temperature contrasts. This strengthening is accompanied by enhanced cyclogenesis along the east Siberian coast, but no change is observed for overall cyclone frequency over the Arctic Ocean. However, simultaneous changes to subpolar storm tracks impact Arctic cyclone activity in all summer months, sometimes in opposition to the impact of the AFZ. In June, the storms migrating poleward to the Arctic Ocean become weaker under RCP8.5, leading to lower Arctic cyclone intensity. In July and August, the poleward shift of the North Pacific storm track enhances cyclone activity in the Beaufort and Chukchi Seas. [ABSTRACT FROM AUTHOR]

Understanding the recent evolution of methane emissions in the Arctic is necessary to interpret the global methane cycle. Emissions are affected by significant uncertainties and are sensitive to climate change, leading to potential feedbacks. A polar version of the CHIMERE chemistry-transport model is used to simulate the evolution of tropospheric methane in the Arctic during 2012, including all known regional anthropogenic and natural sources, in particular freshwater emissions which are often overlooked in methane modelling. CHIMERE simulations are compared to atmospheric continuous observations at six measurement sites in the Arctic region. In winter, the Arctic is dominated by anthropogenic emissions; emissions from continental seepages and oceans, including from the East Siberian Arctic Shelf, can contribute significantly in more limited areas. In summer, emissions from wetland and freshwater sources dominate across the whole region. The model is able to reproduce the seasonality and synoptic variations of methane measured at the different sites. We find that all methane sources significantly affect the measurements at all stations at least at the synoptic scale, except for biomass burning. In particular, freshwater systems play a decisive part in summer, representing on average between 11 and 26 % of the simulated Arctic methane signal at the sites. This indicates the relevance of continuous observations to gain a mechanistic understanding of Arctic methane sources. Sensitivity tests reveal that the choice of the land-surface model used to prescribe wetland emissions can be critical in correctly representing methane mixing ratios. The closest agreement with the observations is reached when using the two wetland models which have emissions peaking in August–September, while all others reach their maximum in June–July. Such phasing provides an interesting constraint on wetland models which still have large uncertainties at present. Also testing different freshwater emission inventories leads to large differences in modelled methane. Attempts to include methane sinks (OH oxidation and soil uptake) reduced the model bias relative to observed atmospheric methane. The study illustrates how multiple sources, having different spatiotemporal dynamics and magnitudes, jointly influence the overall Arctic methane budget, and highlights ways towards further improved assessments. [ABSTRACT FROM AUTHOR]

The wintertime Arctic temperature (T; surface-400 hPa) decreased from 1979 to 1997 and increased rapidly from 1998 to 2012, in contrast to the global mean surface air temperature. Here aspects of circulation variability that are associated with these temperature changes are examined using the NCEP-NCAR reanalysis and ERA-Interim products. It is found that the Nordic-Siberia seesaw of meridional winds near 70°N is associated with two-thirds of the variance of the Arctic winter mean T, possibly contributing to the cooling and warming trends. It is suggested here that the seesaw accounts for much of the difference in Arctic amplification between observations and climate models. Growth of sea ice in winter is hindered by southerly winds over the Nordic region (0°-60°E). Through modulation of the wind seesaw, the eastern Atlantic (EA) pattern is found to be significantly associated with Arctic and East Asia winter climate variations. In one phase of the EA pattern, a midlatitude North Atlantic ridge anomaly is associated with a poleward shift of the mean storm track, a weakened eddy-driven jet over Eurasia, and above-normal sea level pressure (SLP) over Siberia, most significantly in the region to the northwest of Lake Baikal. The EA pattern is associated with two-thirds of the variance of winter-average SLP over Siberia. [ABSTRACT FROM AUTHOR]

The downward trend in Arctic sea ice extent is one of the most dramatic signals of climate change during recent decades. Comprehensive climate models have struggled to reproduce this trend, typically simulating a slower rate of sea ice retreat than has been observed. However, this bias has been widely noted to have decreased in models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) compared with the previous generation of models (CMIP3). Here simulations are examined from both CMIP3 and CMIP5. It is found that simulated historical sea ice trends are influenced by volcanic forcing, which was included in all of the CMIP5 models but in only about half of the CMIP3 models. The volcanic forcing causes temporary simulated cooling in the 1980s and 1990s, which contributes to raising the simulated 1979-2013 global-mean surface temperature trends to values substantially larger than observed. It is shown that this warming bias is accompanied by an enhanced rate of Arctic sea ice retreat and hence a simulated sea ice trend that is closer to the observed value, which is consistent with previous findings of an approximately linear relationship between sea ice extent and global-mean surface temperature. Both generations of climate models are found to simulate Arctic sea ice that is substantially less sensitive to global warming than has been observed. The results imply that much of the difference in Arctic sea ice trends between CMIP3 and CMIP5 occurred because of the inclusion of volcanic forcing, rather than improved sea ice physics or model resolution. [ABSTRACT FROM AUTHOR]

The authors examine the transition from a seasonally ice-covered Arctic to an Arctic Ocean that is sea ice free all year round under increasing atmospheric CO2 levels. It is shown that in comprehensive climate models, such loss of Arctic winter sea ice area is faster than the preceding loss of summer sea ice area for the same rate of warming. In two of the models, several million square kilometers of winter sea ice are lost within only one decade. It is shown that neither surface albedo nor cloud feedbacks can explain the rapid winter ice loss in the climate model MPI-ESM by suppressing both feedbacks in the model. The authors argue that the large sensitivity of winter sea ice area in the models is caused by the asymmetry between melting and freezing: an ice-free summer requires the complete melt of even the thickest sea ice, which is why the perennial ice coverage decreases only gradually as more and more of the thinner ice melts away. In winter, however, sea ice areal coverage remains high as long as sea ice still forms, and then drops to zero wherever the ocean warms sufficiently to no longer form ice during winter. The loss of basinwide Arctic winter sea ice area, however, is still gradual in most models since the threshold mechanism proposed here is reversible and not associated with the existence of multiple steady states. As this occurs in every model analyzed here and is independent of any specific parameterization, it is likely to be relevant in the real world. [ABSTRACT FROM AUTHOR]

Rapid sea ice loss events (RILEs) in a miniensemble of regional Arctic coupled climate model scenario experiments are analyzed. Mechanisms of sudden ice loss are strongly related to atmospheric circulation conditions and preconditioning by sea ice thinning during the seasons and years before the event. Clustering of events in time suggests a strong control by large-scale atmospheric circulation. Anomalous atmospheric circulation is providing warm air anomalies of up to 5K and is forcing ice flow, affecting winter ice growth. Even without a seasonal preconditioning during winter, ice drop events can be initiated by anomalous inflow of warm air during summer. It is shown that RILEs can be generated based on atmospheric circulation changes as a major driving force without major competing mechanisms, other than occasional longwave effects during spring and summer. Other anomalous seasonal radiative forcing or short-lived forcers (e.g., soot) play minor roles or no role at all in our model. RILEs initiated by ocean forcing do not occur in the model, although cannot be ruled out due to model limitations. Mechanisms found are qualitatively in line with observations of the 2007 RILE. [ABSTRACT FROM AUTHOR]

Understanding Arctic climate change requires knowledge of both the external and the local drivers of Arctic climate as well as local feedbacks within the system. An Arctic feedback mechanism relating changes in sea ice extent to an alteration of the emission of sea salt aerosol and the consequent change in radiative balance is examined. A set of idealized climate model simulations were performed to quantify the radiative effects of changes in sea salt aerosol emissions induced by prescribed changes in sea ice extent. The model was forced using sea ice concentrations consistent with present day conditions and projections of sea ice extent for 2100. Sea salt aerosol emissions increase in response to a decrease in sea ice, the model results showing an annual average increase in number emission over the polar cap (70-90° N) of 86 x 106 m-2 s-1 (mass emission increase of 23 μg m-2 s-1 . This in turn leads to an increase in the natural aerosol optical depth of approximately 23% In response to changes in aerosol optical depth, the natural component of the aerosol direct forcing over the Arctic polar cap is estimated to be between -0.2 and -0.4 W m-2 for the summer months, which results in a negative feedback on the system. The model predicts that the change in first indirect aerosol effect (cloud albedo effect) is approximately a factor of ten greater than the change in direct aerosol forcing although this result is highly uncertain due to the crude representation of Arctic clouds and aerosol-cloud interactions in the model. This study shows that both the natural aerosol direct and first indirect effects are strongly dependent on the surface albedo, highlighting the strong coupling between sea ice, aerosols, Arctic clouds and their radiative effects. [ABSTRACT FROM AUTHOR]