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Although the surface energy budget is essential to determine Earth's climate, site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations. This has led to the development of a growing number of surface radiation products, mainly including remotely sensed data, model reanalysis data, and simulations using General Circulation Models (GCMs). This collection of papers introduces new techniques, including the use of machine learning methods for radiation estimation, and evaluates and compares various radiation products, as well as their spatio-temporal variations. These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations. However, remotely sensed radiation products perform relatively better than others. Despite this, there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates. Even though the major radiative parameters including downward shortwave radiation, net longwave radiation, and albedo, from most products show insignificant long-term variation trends on a global scale, only specific regions, such as the Yunnan-Kweichow Plateau (YKP) and regions with permafrost (i.e. Qinghai-Tibet Plateau and Arctic) and glaciers (i.e. Altai Mountains) exhibit remarkable trends. [ABSTRACT FROM AUTHOR]

To assess the effect of ocean‐atmosphere coupling in the climate response to forced sea ice loss, the Polar Amplification Model Intercomparison Project protocol includes centennial coupled atmosphere‐ocean general circulation model simulations with imposed sea ice loss. The protocol, which specifies sea ice concentration and thickness distribution targets, does not prescribe a method for achieving them. Although different methods for imposing sea ice loss (or growth) in models have been documented, testing of the method‐dependence of the resulting climate responses has been limited. Achieving the targeted sea ice state has proven to be challenging using the so‐called ghost‐flux nudging method, which induces ice melt from below, as this method does not constrain the partitioning between thickness and concentration. We propose, describe and test a simple method that combines the advantages of direct sea ice nudging and ghost‐flux nudging. The hybrid nudging method better captures the partitioning between thickness and concentration while conserving total water content. We document how this novel sea ice constraining method reaches specific targets, enhances surface turbulent heat flux responses to sea ice loss, and induces tropospheric warming for both polar regions. Plain Language Summary: The Arctic is warming faster than the global average due to several processes that, once combined, lead to so‐called Arctic Amplification. Part of this anomalous polar warming comes from an intense reduction in ice cover allowing heat into the ocean, warming the Arctic ocean near the surface, hence melting more ice. A joint effort by several climate modeling groups called the Polar Amplification Model Intercomparison Project (PAMIP) aims at better understanding Arctic Amplification through a coordinated set of climate simulations. Among this ensemble of simulations is a set of centennial simulations performed with fully coupled state‐of‐the‐art climate models. In these experiments, Arctic (and Antarctic) sea ice are forced to reach specific states in order to better isolate Arctic Amplification and sea ice loss from the rest of anthropogenic global warming. In this paper, we propose a simple technique to nudge sea ice models to specific states such as prescribed by PAMIP. This new method combines advantages from existing techniques to improve the control over the extent and the thickness of the ice. We document how our novel method produces atmospheric warming and circulation responses that are more consistent with previously published results than the existing techniques. Key Points: Already established sea ice nudging method leads to issues in the partitioning between sea ice concentration (SIC) and sea ice thickness (SIT)New hybrid nudging method significantly improves capture of both SIC and SIT targets for Polar Amplification Model Intercomparison Project, in particular for Arctic sea iceHybrid scheme increases Arctic turbulent heat flux and warming [ABSTRACT FROM AUTHOR]

We examine the past and projected changes in Arctic sea ice properties in six climate models participating in the High-Resolution Model Intercomparison Project (HighResMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Within HighResMIP, each of the experiments is run using a reference resolution configuration (consistent with typical CMIP6 runs) and using higher-resolution configurations. The role of horizontal grid resolution in both the atmosphere model component and the ocean model component in reproducing past and future changes in the Arctic sea ice cover is analysed. Model outputs from the coupled historical (hist-1950) and future (highres-future) runs are used to describe the multi-model, multi-resolution representation of the Arctic sea ice and to evaluate the systematic differences (if any) that resolution enhancement causes. Our results indicate that there is not a strong relationship between the representation of sea ice cover and the ocean/atmosphere grids; the impact of horizontal resolution depends rather on the sea ice characteristic examined and the model used. However, the refinement of the ocean grid has a more prominent effect compared to the refinement of the atmospheric one, with eddy-permitting ocean configurations generally providing more realistic representations of sea ice area and sea ice edges. All models project substantial sea ice shrinking: the Arctic loses nearly 95 % of sea ice volume from 1950 to 2050. The model selection based on historical performance potentially improves the accuracy of the model projections and predicts that the Arctic will turn ice-free as early as 2047. Along with the overall sea ice loss, changes in the spatial structure of the total sea ice and its partition in ice classes are noticed: the marginal ice zone (MIZ) will dominate the ice cover by 2050, suggesting a shift to a new sea ice regime much closer to the current Antarctic sea ice conditions. The MIZ-dominated Arctic might drive development and modification of model physics and parameterizations in the new generation of general circulation models (GCMs). [ABSTRACT FROM AUTHOR]

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

Stable water isotopes are used to infer changes in the hydrological cycle for different climate periods and various climatic archives. Following previous developments of δ18 O in the coupled climate model of intermediate complexity, iLOVECLIM, we present here the implementation of the 1 H 2 H 16 O and 1 H 2 17 O water isotopes in the different components of this model and calculate the associated secondary markers deuterium excess (d-excess) and oxygen-17 excess (17 O-excess) in the atmosphere and ocean. So far, the latter has only been modelled by the atmospheric model LMDZ4. Results of a 5000-year equilibrium simulation under preindustrial conditions are analysed and compared to observations and several isotope-enabled models for the atmosphere and ocean components. In the atmospheric component, the model correctly reproduces the first-order global distribution of the δ2 H and d-excess as observed in the data (R=0.56 for δ2 H and 0.36 for d-excess), even if local differences are observed. The model–data correlation is within the range of other water-isotope-enabled general circulation models. The main isotopic effects and the latitudinal gradient are properly modelled, similarly to previous water-isotope-enabled general circulation model simulations, despite a simplified atmospheric component in iLOVECLIM. One exception is observed in Antarctica where the model does not correctly estimate the water isotope composition, a consequence of the non-conservative behaviour of the advection scheme at a very low moisture content. The modelled 17 O-excess presents a too-important dispersion of the values in comparison to the observations and is not correctly reproduced in the model, mainly because of the complex processes involved in the 17 O-excess isotopic value. For the ocean, the model simulates an adequate isotopic ratio in comparison to the observations, except for local areas such as the surface of the Arabian Sea, a part of the Arctic and the western equatorial Indian Ocean. Data–model evaluation also presents a good match for the δ2 H over the entire water column in the Atlantic Ocean, reflecting the influence of the different water masses. [ABSTRACT FROM AUTHOR]

One of the most significant climate change impacts on arctic urban landscapes is the warming and degradation of permafrost, which negatively affects the structural integrity of infrastructure. We estimate potential changes in stability of Russian urban infrastructure built on permafrost in response to the projected climatic changes provided by six preselected General Circulation Models (GCMs) participated in the most recent Climate Model Inter-comparison Project (CMIP5). The analysis was conducted for the entire extent of the Russian permafrost-affected area. According to our analysis a significant (at least 25%) climate-induced reduction in the urban infrastructure stability throughout the Russian permafrost region should be expected by the mid-21st century. However, the high uncertainty, resulting from the GCM-produced climate projections, prohibits definitive conclusion about the rate and magnitude of potential climate impacts on permafrost infrastructure. Results presented in this paper can serve as guidelines for developing adequate adaptation and mitigation strategy for Russian northern cities. [ABSTRACT FROM AUTHOR]

The impact of melt ponds on sea ice albedo has been observed and documented. In general circulation models, ponds are now accounted for through indirect diagnostic treatments ("implicit" schemes) or prognostic melt-pond parameterizations ("explicit" schemes). However, there has been a lack of studies showing the impacts of these schemes on simulated Arctic climate. We focus here on rectifying this using the general circulation model HadGEM3, one of the few models with a detailed explicit pond scheme. We identify the impact of melt ponds on the sea ice and climate, and associated ice–ocean–atmosphere interactions. We run a set of constant forcing simulations for three different periods and show, for the first time, that using mechanistically different pond schemes can lead to very significantly different sea ice and climate states. Under near-future conditions, an implicit scheme never yields an ice-free summer Arctic, while an explicit scheme yields an ice-free Arctic in 35% of years and raises autumn Arctic air temperatures by 5° to 8°C. We find that impacts on climate and sea ice depend on the ice state: under near-future and last-interglacial conditions, the thin sea ice is very sensitive to pond formation and parameterization, whereas during the preindustrial period the thicker sea ice is less sensitive to the pond scheme choice. Both of these two commonly used parameterizations of sea ice albedo yield similar results under preindustrial conditions but in warmer climates lead to very different Arctic sea ice and ocean and atmospheric temperatures. Thus, changes to physical parameterizations in the sea ice model can have large impacts on simulated sea ice, ocean, and atmosphere. Significance Statement: This study investigates the impacts of melt ponds on Arctic sea ice under different climate conditions, using the HadGEM3-GC3.1-LL general circulation model (GCM). Additionally, we study the impact of changing the type of pond scheme used. We find that changing the pond scheme causes large differences to how a GCM simulates Arctic sea ice, the ocean, and the atmosphere, for both near-future and warmer paleoclimate conditions. These large differences have not been found previously, because this is one of the first GCM studies of this type. Our results demonstrate the importance of melt ponds, and their wider impacts on ocean and atmosphere. Furthermore, they suggest that better evaluation of the representation of sea ice processes is vital for the robust projection of future climate change. [ABSTRACT FROM AUTHOR]

Recent decades have seen significant developments in climate prediction capabilities at seasonal-tointerannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initialvalue predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions.We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño-Southern Oscillation. [ABSTRACT FROM AUTHOR]

The paper presents a case study of the regional (≈150 km) morphological and optical properties of a relatively thin, 70–90 cm modal thickness, first-year Arctic sea ice pack in an advanced stage of melt. The study combines in situ broadband albedo measurements representative of the four main surface types (bare ice, dark melt ponds, bright melt ponds and open water) and images acquired by a helicopter-borne camera system during ice-survey flights. The data were collected during the 8-day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic, north of Svalbard at 82.3°N, from 26 July to 3 August 2012. A set of>10 000 classified images covering about 28 km2 revealed a homogeneous melt across the study area with melt-pond coverage of ≈0.29 and open-water fraction of ≈0.11. A decrease in pond fractions observed in the 30 km marginal ice zone (MIZ) occurred in parallel with an increase in open-water coverage. The moving block bootstrap technique applied to sequences of classified sea-ice images and albedo of the four surface types yielded a regional albedo estimate of 0.37 (0.35; 0.40) and regional sea-ice albedo of 0.44 (0.42; 0.46). Random sampling from the set of classified images allowed assessment of the aggregate scale of at least 0.7 km2 for the study area. For the current setup configuration it implies a minimum set of 300 images to process in order to gain adequate statistics on the state of the ice cover. Variance analysis also emphasized the importance of longer series of in situ albedo measurements conducted for each surface type when performing regional upscaling. The uncertainty in the mean estimates of surface type albedo from in situ measurements contributed up to 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea-ice cover. [ABSTRACT FROM AUTHOR]

The performance of general circulation models (GCMs) varies across regions and periods. When projecting into the future, it is therefore not obvious whether to reject or to prefer a certain GCM. Combining the outputs of several GCMs may enhance results. This paper presents a method to combine multimodel GCM projections by means of a Bayesian model combination (BMC). Here the influence of each GCM is weighted according to its performance in a training period, with regard to observations, as outcome BMC predictive distributions for yet unobserved observations are obtained. Technically, GCM outputs and observations are assumed to vary randomly around common means, which are interpreted as the actual target values under consideration. Posterior parameter distributions of the authors'' Bayesian hierarchical model are obtained by a Markov chain Monte Carlo (MCMC) method. Advantageously, all parameters--such as bias and precision of the GCM models--are estimated together. Potential time dependence is accounted for by integrating a Kalman filter. The significance of trend slopes of the common means is evaluated by analyzing the posterior distribution of the parameters. The method is applied to assess the evolution of ice accumulation over the oceanic Arctic region in cold seasons. The observed ice index is created out of NCEP reanalysis data. Outputs of seven GCMs are combined by using the training period 1962--99 and prediction periods 2046--65 and 2082--99 with Special Report on Emissions Scenarios (SRES) A2 and B1. A continuing decrease of ice accumulation is visible for the A2 scenario, whereas the index stabilizes for the B1 scenario in the second prediction period. [ABSTRACT FROM AUTHOR]

The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatiotemporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1 ∘ × 1 ∘ CF dataset in the Arctic during 2000–2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10 %–20 % after fusing, with particularly noticeable improvements in the vicinity of Greenland. Compared with ground-based observations, R2 increased by about 0.20–0.48, and the root mean square error (RMSE) and bias reductions averaged about 6.09 % and 4.04 % for land regions, respectively; these metrics for ocean regions were about 0.05–0.31, 2.85 %, and 3.15 %, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77 % and 4.31 %, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2 , 4.46 % for RMSE, and 3.92 % for bias. The results of the comparison with ERA5 and the Meteorological Research Institute – Atmospheric General Circulation model version 3.2S (MRI-AGCM3-2-S) climate model suggest an obvious improvement in the consistency between the satellite-observed CF and the reanalysis and model data after fusion. This serves as a promising indication that the fused CF results hold the potential to deliver reliable satellite observations for modeling and reanalysis data. Moreover, the fused product effectively supplements the temporal gaps of Advanced Very High Resolution Radiometer (AVHRR)-based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and it extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5 ∘ N. A continuous monthly 1 ∘ CF product covering the entire Arctic during 2000–2020 was generated and is freely available to the public at 10.5281/zenodo.7624605 (Liu and He, 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the Earth's energy imbalance. [ABSTRACT FROM AUTHOR]

Rapid Arctic warming and sea-ice loss have intensified the Arctic hydrological cycle, increasing local evaporation and precipitation. Stable water isotopes as environmental tracers can provide useful insights into the Arctic hydrological cycle. However, the paucity of isotopic observations in the Arctic has limited our understanding of the hydrological changes. Here, we use an isotopeenabled atmospheric general circulation model (IsoGSM) combined with the Global Network of Isotopes in Precipitation (GNIP) observations to investigate the relationship between sea-ice changes and Arctic precipitation δ18O (δ18Op) to reveal the relative influence of local air temperature and evaporation on Arctic summer and winter δ18Op. We find that the Arctic δ18Op is negatively correlated with sea-ice concentration but positively with air temperature. Sea-ice loss leads to enriched Arctic δ18Op through enhanced local evaporation and warming, but the relative importance of these processes varies between seasons. During summer, both local evaporation and warming contribute equally to δ18Op changes. In contrast, winter δ18O is predominantly driven by air temperature. This work improves our understanding of how Arctic precipitation isotopes respond to sea-ice changes and has implications for the Arctic hydrological cycle and palaeotemperature reconstructions. [ABSTRACT FROM AUTHOR]

Our limited understanding of millennial‐scale variability in the context of the last glacial period can be explained by the lack of a reliable modeling framework to study abrupt climate changes under realistic glacial backgrounds. In this article, we describe a new set of long‐run Last Glacial Maximum experiments where such climate shifts were triggered by different snapshots of ice‐sheet meltwater derived from the early stages of the last deglaciation. Depending on the location and the magnitude of the forcing, we observe three distinct dynamical regimes and highlight a subtle window of opportunity where the climate can sustain oscillations between cold and warm modes. We identify the Eurasian Arctic and Nordic Seas regions as being most sensitive to meltwater discharge in the context of switching to a cold mode, compared to freshwater fluxes from the Laurentide ice sheets. These cold climates follow a consistent pattern in temperature, sea ice, and convection, and are largely independent from freshwater release as a result of effective AMOC collapse. Warm modes, on the other hand, show more complexity in their response to the regional pattern of the meltwater input, and within them, we observe significant differences linked to the reorganization of deep water formation sites and the subpolar gyre. Broadly, the main characteristics of the oscillations, obtained under full‐glacial conditions with ice‐sheet reconstruction derived meltwater patterns, share similar characteristics with δ18O records of the last glacial period, although our experiment design prevents detailed conclusions from being drawn on whether these represent actual Dansgaard‐Oeschger events. Plain Language Summary: During the last glacial period (115,000–12,000 years before present), the baseline cold climate was continuously disturbed by intense and abrupt climate changes. They completely modified the climate for a few thousand years or so, resulting, for instance, in massive temperature shifts and complete reorganizations of ocean circulation. These abrupt changes have been observed in climate records from the Northern Hemisphere and also can be traced in records from the Southern Hemisphere. Yet, we still do not know what triggers these changes, and often cannot simulate them at the right time under known environmental conditions. In the context of the Last Glacial Maximum, a cold period 21,000 years ago with extensive ice over the Northern Hemisphere, this article analyses a new set of climate model simulations that test the effects of freshwater melting from the ice sheets at different periods of the early deglaciation (∼21,000 to 18,000 years before present). Under some conditions, the resulting experiments displayed an Atlantic Ocean that oscillates between strong and collapsed basin‐wide circulation, causing approximately 10°C of temperature change over Greenland; a behavior that resembles observed abrupt climate changes. Key Points: New set of long‐run Last Glacial Maximum general circulation model experiments showing millennial‐scale variabilityDetailed description of the impact of ice sheet reconstruction‐derived meltwater distributions on abrupt climate changesIntroduction for future studies of the underlying physical processes of abrupt climate changes [ABSTRACT FROM AUTHOR]

Skillful sea ice drift forecasts are crucial for scientific mission planning and marine safety. Wind is the dominant driver of ice motion variability, but more slowly varying components of the climate system, in particular ice thickness and ocean currents, bear the potential to render ice drift more predictable than the wind. In this study, we provide the first assessment of Arctic sea ice drift predictability in four coupled general circulation models (GCMs), using a suite of "perfect-model" ensemble simulations. We find the position vector from Lagrangian trajectories of virtual buoys to remain predictable for at least a 90 (45) d lead time for initializations in January (July), reaching about 80 % of the position uncertainty of a climatological reference forecast. In contrast, the uncertainty in Eulerian drift vector predictions reaches the level of the climatological uncertainty within 4 weeks. Spatial patterns of uncertainty, varying with season and across models, develop in all investigated GCMs. For two models providing near-surface wind data (AWI-CM1 and HadGEM1.2), we find spatial patterns and large fractions of the variance to be explained by wind vector uncertainty. The latter implies that sea ice drift is only marginally more predictable than wind. Nevertheless, particularly one of the four models (GFDL-CM3) shows a significant correlation of up to -0.85 between initial ice thickness and target position uncertainty in large parts of the Arctic. Our results provide a first assessment of the inherent predictability of ice motion in coupled climate models; they can be used to put current real-world forecast skill into perspective and highlight the model diversity of sea ice drift predictability. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is known as one of the most sensitive regions to climate change, and it has experienced accelerated warming in recent decades. However, to what degree the TP warming amplification relates to remote forcing such as sea ice loss in the Arctic sea ice remains unclear. Here, we found that the decline of sea ice concentration over the Barents-Kara Sea (BKS) could account for 18–32% of the winter warming over the TP by comparing observational data and ensemble experiments from an atmospheric general circulation model. The reduced BKS sea ice and resultant upward turbulent heat fluxes can intensify a Rossby wave train propagating equatorward to the TP. As a result, the enhanced southwesterlies towards the TP strengthen the warm advection over most parts of the TP and lead to TP warming. In addition, an atmospheric teleconnection between the Arctic and the TP also exists in the interannual variability. That is, a tripole mode in air temperature, with warm centers in the Arctic and TP but a cold center in the mid-high latitudes of the Eurasian continent in between. Our results imply that the BKS sea ice loss could intensify such a tripole mode and thus enhancing the winter TP warming. [ABSTRACT FROM AUTHOR]

Ice covers in high latitudes play important role in the global atmospheric circulation and abnormal temperature distribution. The different inter-annual variabilities of the Arctic and Antarctic ice covers have been revealed, but their respective climate effects are not clear. The Liang-Kleeman information flow method is used to reveal the causal relationships from the sea ice to the surface air temperature. The results point out that the Arctic and Antarctic sea ice both have significant impacts on the global air temperature. Especially in East Asia and North America, the inter-annual variation of ice cover in the Antarctic has an even stronger impact than that of the Arctic. This causality is further proved by the General Atmospheric Circulation Model (CAM4.0). In the numerical experiments, the ice covers in Arctic and Antarctic are changed individually or simultaneously as the forcing fields, and then the corresponding climate effects are analyzed. The results show that the Arctic and Antarctic ice cover variations can change the intensity of atmospheric baroclinic disturbance in mid-high latitudes of individual hemisphere, generating wave energy transmission across the equator through the wave-train-window over the Eastern Equatorial Pacific, and eventually causing air temperature anomalies in another hemisphere. Furthermore, the Antarctic ice covers are closer to the mid-high latitude atmospheric jets in the southern hemisphere. Therefore, compared to the Arctic ice cover, the inter-annual changes of Antarctic ice cover lead to a larger atmospheric wave-activity flux response, quickly (less than two months) spreading to the northern hemisphere, and causing more significant air temperature anomalies over the East Asia and North America. However, the influence of the inter-annual variability of Arctic ice cover on the climate in the southern hemisphere requires a lag time of at least four months, and the intensity is relatively weak. [ABSTRACT FROM AUTHOR]

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Previous modelling and observational studies have shown discrepancies in the interannual relationship of winter surface air temperature (SAT) between Arctic and East Asia, stimulating the debate about whether Arctic change can influence midlatitude climate. This study uses two sets of coordinated experiments (EXP1 and EXP2) from six different atmospheric general circulation models. Both EXP1 and EXP2 consist of 130 ensemble members, each of which in EXP1 (EXP2) was forced by the same observed daily varying sea ice and daily varying (daily climatological) sea surface temperature (SST) for 1982–2014 but with different atmospheric initial conditions. Large spread exists among ensemble members in simulating the Arctic–East Asian SAT relationship. Only a fraction of ensemble members can reproduce the observed deep Arctic warming–cold continent pattern which extends from surface to upper troposphere, implying the important role of atmospheric internal variability. The mechanisms of deep Arctic warming and shallow Arctic warming are further distinguished. Arctic warming aloft is caused primarily by poleward moisture transport, which in conjunction with the surface warming coupled with sea ice melting constitutes the surface-amplified deep Arctic warming throughout the troposphere. These processes associated with the deep Arctic warming may be related to the forcing of remote SST when there is favorable atmospheric circulation such as Rossby wave train propagating from the North Atlantic into the Arctic. [ABSTRACT FROM AUTHOR]

Freshwater, in the form of glacial runoff, is hypothesized to play a critical role in centennial- to millennial-scale climate variability, such as the Younger Dryas and Dansgaard–Oeschger events, but this relationship is not straightforward. Large-scale glacial runoff events, such as Meltwater Pulse 1a (MWP1a), are not always temporally proximal to subsequent large-scale cooling. Moreover, the typical design of hosing experiments that support this relationship tends to artificially amplify the climate response. This study explores the impact that limitations in the representation of runoff in conventional "hosing" simulations has on our understanding of this relationship by examining where coastally released freshwater is transported when it reaches the ocean. We particularly focus on the impact of (1) the injection of freshwater directly over sites of deep-water formation (DWF) rather than at runoff locations (i.e. hosing), (2) excessive freshwater injection volumes (often by a factor of 5), and (3) the use of present-day (rather than palaeo) ocean gateways. We track the routing of glaciologically constrained freshwater volumes from four different inferred injection locations in a suite of eddy-permitting glacial ocean simulations using the Massachusetts Institute of Technology General Circulation Model (MITgcm) under both open and closed Bering Strait conditions. Restricting freshwater forcing values to realistic ranges results in less spreading of freshwater across the North Atlantic and indicates that the freshwater anomalies over DWF sites depend strongly on the geographical location of meltwater input. In particular, freshwater released into the Gulf of Mexico generates a very weak freshwater signal over DWF regions as a result of entrainment by the turbulent Gulf Stream. In contrast, freshwater released into the Arctic with an open Bering Strait or from the Eurasian ice sheet is found to generate the largest salinity anomalies over DWF regions in the North Atlantic and GIN (Greenland–Iceland–Norwegian) seas region respectively. Experiments show that when the Bering Strait is open, the Mackenzie River source exhibits more than twice as much freshening of the North Atlantic deep-water formation regions as when the Bering Strait is closed. Our results illustrate that applying freshwater hosing directly into the North Atlantic with even "realistic" freshwater amounts still overestimates the amount of terrestrial runoff reaching DWF regions. Given the simulated salinity anomaly distributions and the lack of reconstructed impact on deep-water formation during the Bølling–Allerød, our results support that the majority of the North American contribution to MWP1a was not routed through the Mackenzie River. [ABSTRACT FROM AUTHOR]

When simulating past warm climates, such as the early Cretaceous and Paleogene periods, general circulation models (GCMs) underestimate the magnitude of warming in the Arctic. Additionally, model intercomparisons show a large spread in the magnitude of Arctic warming for these warmer-than-modern climates. Several mechanisms have been proposed to explain these disagreements, including the unrealistic representation of polar clouds or underestimated poleward heat transport in the models. This study provides an intercomparison of Arctic cloud and atmospheric heat transport (AHT) responses to strong imposed polar-amplified surface ocean warming across four atmosphere-only GCMs. All models simulate an increase in high clouds throughout the year; the resulting reduction in longwave radiation loss to space acts to support the imposed Arctic warming. The response of low- and midlevel clouds varies considerably across the models, with models responding differently to surface warming and sea ice removal. The AHT is consistently weaker in the imposed warming experiments due to a large reduction in dry static energy transport that offsets a smaller increase in latent heat transport, thereby opposing the imposed surface warming. Our idealized polar amplification experiments require very large increases in implied ocean heat transport (OHT) to maintain steady state. Increased CO2 or tropical temperatures that likely characterized past warm climates reduce the need for such large OHT increases. [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]

Sea levels of different atmosphere–ocean general circulation models (AOGCMs) respond to climate change forcing in different ways, representing a crucial uncertainty in climate change research. We isolate the role of the ocean dynamics in setting the spatial pattern of dynamic sea-level (ζ) change by forcing several AOGCMs with prescribed identical heat, momentum (wind) and freshwater flux perturbations. This method produces a ζ projection spread comparable in magnitude to the spread that results from greenhouse gas forcing, indicating that the differences in ocean model formulation are the cause, rather than diversity in surface flux change. The heat flux change drives most of the global pattern of ζ change, while the momentum and water flux changes cause locally confined features. North Atlantic heat uptake causes large temperature and salinity driven density changes, altering local ocean transport and ζ. The spread between AOGCMs here is caused largely by differences in their regional transport adjustment, which redistributes heat that was already in the ocean prior to perturbation. The geographic details of the ζ change in the North Atlantic are diverse across models, but the underlying dynamic change is similar. In contrast, the heat absorbed by the Southern Ocean does not strongly alter the vertically coherent circulation. The Arctic ζ change is dissimilar across models, owing to differences in passive heat uptake and circulation change. Only the Arctic is strongly affected by nonlinear interactions between the three air-sea flux changes, and these are model specific. [ABSTRACT FROM AUTHOR]

In this work, we use Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations from 10 Earth system models (ESMs) and general circulation models (GCMs) to study the fast climate responses on pre-industrial climate, due to present-day aerosols. All models carried out two sets of simulations: a control experiment with all forcings set to the year 1850 and a perturbation experiment with all forcings identical to the control, except for aerosols with precursor emissions set to the year 2014. In response to the pattern of all aerosols effective radiative forcing (ERF), the fast temperature responses are characterized by cooling over the continental areas, especially in the Northern Hemisphere, with the largest cooling over East Asia and India, sulfate being the dominant aerosol surface temperature driver for present-day emissions. In the Arctic there is a warming signal for winter in the ensemble mean of fast temperature responses, but the model-to-model variability is large, and it is presumably linked to aerosol-induced circulation changes. The largest fast precipitation responses are seen in the tropical belt regions, generally characterized by a reduction over continental regions and presumably a southward shift of the tropical rain belt. This is a characteristic and robust feature among most models in this study, associated with weakening of the monsoon systems around the globe (Asia, Africa and America) in response to hemispherically asymmetric cooling from a Northern Hemisphere aerosol perturbation, forcing possibly the Intertropical Convergence Zone (ITCZ) and tropical precipitation to shift away from the cooled hemisphere despite that aerosols' effects on temperature and precipitation are only partly realized in these simulations as the sea surface temperatures are kept fixed. An interesting feature in aerosol-induced circulation changes is a characteristic dipole pattern with intensification of the Icelandic Low and an anticyclonic anomaly over southeastern Europe, inducing warm air advection towards the northern polar latitudes in winter. [ABSTRACT FROM AUTHOR]

Many general circulation models (GCMs) have difficulty simulating Arctic clouds and climate, causing substantial inter-model spread. To address this issue, two Atmospheric Model Intercomparison Project (AMIP) simulations from the Community Atmosphere Model version 5 (CAM5) and Seoul National University (SNU) Atmosphere Model version 0 (SAM0) with a unified convection scheme (UNICON) are employed to identify an effective mechanism for improving Arctic cloud and climate simulations. Over the Arctic, SAM0 produced a larger cloud fraction and cloud liquid mass than CAM5, reducing the negative Arctic cloud biases in CAM5. The analysis of cloud water condensation rates indicates that this improvement is associated with an enhanced net condensation rate of water vapor into the liquid condensate of Arctic low-level clouds, which in turn is driven by enhanced poleward transports of heat and moisture by the mean meridional circulation and transient eddies. The reduced Arctic cloud biases lead to improved simulations of surface radiation fluxes and near-surface air temperature over the Arctic throughout the year. The association between the enhanced poleward transports of heat and moisture and increase in liquid clouds over the Arctic is also evident not only in both models, but also in the multi-model analysis. Our study demonstrates that enhanced poleward heat and moisture transport in a model can improve simulations of Arctic clouds and climate. [ABSTRACT FROM AUTHOR]

Over the last 40 years observations show a teleconnection between summertime Pacific Ocean sea surface temperatures and September Arctic sea ice extent. However, the short satellite observation record has made it difficult to further examine this relationship. Here, we use 30 fully coupled general circulation models (GCMs) participating in Phase 5 of the Coupled Model Intercomparison Project to assess the ability of GCMs to simulate this teleconnection and analyze its stationarity over longer timescales. GCMs can temporarily simulate the teleconnection in continuous 40‐year segments but not over longer, centennial timescales. Each GCM exhibits considerable teleconnection variability on multidecadal timescales. Further analysis shows that the teleconnection depends on an equally nonstationary atmospheric bridge from the subequatorial Pacific Ocean to the upper Arctic troposphere. These findings indicate that the modulation of Arctic sea ice loss by subequatorial Pacific Ocean variability is not fixed in time, undermining the assumption of teleconnection stationarity as defined by the satellite record. Plain Language Summary: Understanding the processes leading to Arctic sea ice change remains a central goal in climate science. These changes affect not only weather and climate but also local ecosystems, indigenous populations, and socioeconomic activities in the region. Recent studies have shown that during the summer months, the Pacific Ocean influences Arctic sea ice. Such a relationship suggests that this region of the Pacific Ocean may be a key source of predictability for Arctic sea ice, especially for the summer minimum. However, our understanding of this relationship is derived from a short observational record, which makes it difficult to study how this relationship evolves over time. To overcome this limitation, we use long simulations from 30 different global climate models. We show that models are able to simulate this relationship, but the relationship changes considerably over time. This suggests that the observed link between the Pacific Ocean and Arctic sea ice may change in the coming decades; therefore, caution should be applied when forecasting or reconstructing Arctic sea ice and assuming that this relationship is constant in time. Key Points: An observed teleconnection between Pacific Ocean SSTs and Arctic sea ice extent is analyzed in 30 fully coupled GCMs participating in CMIP5Summer SST anomalies in the Pacific Ocean modulate September Arctic sea ice loss through changes in upper Arctic air conditionsThis teleconnection is found to be nonstationary on multidecadal timescales both in the GCMs able to simulate it and in observations [ABSTRACT FROM AUTHOR]

We investigate the behavior of clouds during rapid sea ice loss events (RILEs) in the Arctic, as simulated by multiple ensemble projections of the 21st century in the Community Climate System Model (CCSM3). Trends in cloud properties and sea ice coverage during RILEs are compared with their secular trends between 2000 and 2049 during summer, autumn, and winter. The results suggest that clouds promote abrupt Arctic climate change during RILEs through increased (decreased) cloudiness in autumn (summer) relative to the changes over the first half of the 21st century. The trends in cloud characteristics (cloud amount, water content, and radiative forcing) during RILEs are most strongly and consistently an amplifying effect during autumn, the season in which RILEs account for the majority of the secular trends. The total cloud trends in every season are primarily due to low clouds, which show a more robust response than middle and high clouds across RILEs. Lead-lag correlations of monthly sea ice concentration and cloud cover during autumn reveal that the relationship between less ice and more clouds is enhanced during RILEs, but there is no evidence that either variable is leading the other. Given that Arctic cloud projections in CCSM3 are similar to those from other state-of-the-art GCMs and that observations show increased autumn cloudiness associated with the extreme 2007 and 2008 sea ice minima, this study suggests that the rapidly declining Arctic sea ice will be accentuated by changes in polar clouds. [ABSTRACT FROM AUTHOR]

The Arctic Ocean and sea ice form a feedback system that plays an important role in the global climate. The complexity of highly parameterized global circulation (climate) models makes it very difficult to assess feedback processes in climate without the concurrent use of simple models where the physics is understood. We introduce a two-dimensional energy-based regular network model to investigate feedback processes in an Arctic ice-ocean layer. The model includes the nonlinear aspect of the ice-water phase transition, a nonlinear diffusive energy transport within a heterogeneous ice-ocean lattice, and spatiotemporal atmospheric and oceanic forcing at the surfaces. First results for a horizontally homogeneous ice-ocean layer show bistability and related hysteresis between perennial ice and perennial open water for varying atmospheric heat influx. Seasonal ice cover exists as a transient phenomenon. We also find that ocean heat fluxes are more efficient than atmospheric heat fluxes to melt Arctic sea ice. [ABSTRACT FROM AUTHOR]

The authors investigate the response of the Arctic Ocean freshwater budget to changes in the North Atlantic Oscillation (NAO) using a regional-ocean configuration of the Massachusetts Institute of Technology GCM (MITgcm) and carry out several different 10-yr and 30-yr integrations. At 1/6° (∼18 km) resolution the model resolves the major Arctic transport pathways, including Bering Strait and the Canadian Archipelago. Two main calculations are performed by repeating the wind fields of two contrasting NAO years in each run for the extreme negative and positive NAO phases of 1969 and 1989, respectively. These calculations are compared both with a control run and the compiled observationally based freshwater budget estimate of Serreze et al. The results show a clear response in the Arctic freshwater budget to NAO forcing, that is, repeat NAO negative wind forcing results in virtually all freshwater being retained in the Arctic, with the bulk of the freshwater content being pooled in the Beaufort gyre. In contrast, repeat NAO positive forcing accelerates the export of freshwater out of the Arctic to the North Atlantic, primarily via Fram Strait (∼900 km3 yr-1) and the Canadian Archipelago (∼500 km3 yr-1), with a total loss in freshwater storage of ∼13 000 km3 (15%) after 10 yr. The large increase in freshwater export through the Canadian Archipelago highlights the important role that this gateway plays in redistributing the freshwater of the Arctic to subpolar seas, by providing a direct pathway from the Arctic basin to the Labrador Sea, Gulf Stream system, and Atlantic Ocean. The authors discuss the sensitivity of the Arctic Ocean to long-term fixed extreme NAO states and show that the freshwater content of the Arctic is able to be restored to initial values from a depleted freshwater state after ∼20 yr. [ABSTRACT FROM AUTHOR]

The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC AR6, 2021). Even assuming that some of them are sufficiently reliable for scenario forecasts, such a large ECS uncertainty requires a pre-selection of the most reliable models. Herein the performance of 38 CMIP6 models are tested in reproducing the surface temperature changes observed from 1980–1990 to 2011–2021 in three temperature records: ERA5-T2m, ERA5-850mb, and UAH MSU v6.0 Tlt. Alternative temperature records are briefly discussed but found to be not appropriate for the present analysis because they miss data over large regions. Significant issues emerge: (1) most GCMs overestimate the warming observed during the last 40 years; (2) there is great variability among the models in reconstructing the climatic changes observed in the Arctic; (3) the ocean temperature is usually overestimated more than the land one; (4) in the latitude bands 40° N–70° N and 50° S–70° S (which lay at the intersection between the Ferrel and the polar atmospheric cells) the CMIP6 GCMs overestimate the warming; (5) similar discrepancies are present in the east-equatorial pacific region (which regulates the ENSO) and in other regions where cooling trends are observed. Finally, the percentage of the world surface where the (positive or negative) model-data discrepancy exceeds 0.2, 0.5 and 1.0 °C is evaluated. The results indicate that the models with low ECS values (for example, 3 °C or less) perform significantly better than those with larger ECS. Therefore, the low ECS models should be preferred for climate change scenario forecasts while the other models should be dismissed and not used by policymakers. In any case, significant model-data discrepancies are still observed over extended world regions for all models: on average, the GCM predictions disagree from the data by more than 0.2 °C (on a total mean warming of about 0.5 °C from 1980–1990 to 2011–2021) over more than 50% of the global surface. This result suggests that climate change and its natural variability remain poorly modeled by the CMIP6 GCMs. Finally, the ECS uncertainty problem is discussed, and it is argued (also using semi-empirical climate models that implement natural oscillations not predicted by the GCMs) that the real ECS could be between 1 and 2 °C, which implies moderate warming for the next decades. [ABSTRACT FROM AUTHOR]