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Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness. [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]

The loss of Arctic sea ice is already having profound environmental, societal, and ecological impacts locally. A highly uncertain area of scientific research, however, is whether such Arctic change has a tangible effect on weather and climate at lower latitudes. There is emerging evidence that the geographical location of sea ice loss is critically important in determining the large-scale atmospheric circulation response and associated midlatitude impacts. However, such regional dependencies have not been explored in a thorough and systematic manner. To make progress on this issue, this study analyzes ensemble simulations with an atmospheric general circulation model prescribed with sea ice loss separately in nine regions of the Arctic, to elucidate the distinct responses to regional sea ice loss. The results suggest that in some regions, sea ice loss triggers large-scale dynamical responses, whereas in other regions sea ice loss induces only local thermodynamical changes. Sea ice loss in the Barents-Kara Seas is unique in driving a weakening of the stratospheric polar vortex, followed in time by a tropospheric circulation response that resembles the North Atlantic Oscillation. For October-March, the largest spatial-scale responses are driven by sea ice loss in the Barents-Kara Seas and the Sea of Okhotsk; however, different regions assume greater importance in other seasons. The atmosphere responds very differently to regional sea ice losses than to pan-Arctic sea ice loss, and the response to pan-Arctic sea ice loss cannot be obtained by the linear addition of the responses to regional sea ice losses. The results imply that diversity in past studies of the simulated response to Arctic sea ice loss can be partly explained by the different spatial patterns of sea ice loss imposed. [ABSTRACT FROM AUTHOR]

Arctic temperatures have risen dramatically relative to those of lower latitudes in recent decades, with a common supposition being that sea ice declines are primarily responsible for amplified Arctic tropospheric warming. This conjecture is central to a hypothesis in which Arctic sea ice loss forms the beginning link of a causal chain that includes weaker westerlies in midlatitudes, more persistent and amplified midlatitude waves, and more extreme weather. Through model experimentation, the first step in this chain is examined by quantifying contributions of various physical factors to October-December (OND) mean Arctic tropospheric warming since 1979. The results indicate that the main factors responsible for Arctic tropospheric warming are recent decadal fluctuations and long-term changes in sea surface temperatures (SSTs), both located outside the Arctic. Arctic sea ice decline is the largest contributor to near-surface Arctic temperature increases, but it accounts for only about 20% of the magnitude of 1000-500-hPa warming. These findings thus disconfirm the hypothesis that deep tropospheric warming in the Arctic during OND has resulted substantially from sea ice loss. Contributions of the same factors to recent midlatitude climate trends are then examined. It is found that pronounced circulation changes over the North Atlantic and North Pacific result mainly from recent decadal ocean fluctuations and internal atmospheric variability, while the effects of sea ice declines are very small. Therefore, a hypothesized causal chain of hemisphere-wide connections originating from Arctic sea ice loss is not supported. [ABSTRACT FROM AUTHOR]

It is one of the most robust projected responses of climate models to the increase of atmospheric CO2 concentration that the Arctic experiences a rapid warming with a magnitude larger than the rest of the world. While many processes are proposed as important, the relative contribution of individual processes to the Arctic warming is not often investigated systematically. Feedbacks are quantified in two different versions of an atmosphere-ocean GCM under idealized transient experiments based on an energy balance analysis that extends from the surface to the top of the atmosphere. The emphasis is placed on the largest warming from late autumn to early winter (October-December) and the difference from other seasons. It is confirmed that dominating processes vary with season. In autumn, the largest contribution to the Arctic surface warming is made by a reduction of ocean heat storage and cloud radiative feedback. In the annual mean, on the other hand, it is the albedo feedback that contributes the most, with increasing ocean heat uptake to the deeper layers working as a negative feedback. While the qualitative results are robust between the two models, they differ quantitatively, indicating the need for further constraint on each process. Ocean heat uptake, lower tropospheric stability, and low-level cloud response probably require special attention. [ABSTRACT FROM AUTHOR]

The authors summarize the twenty-first-century Arctic climate simulated by NCAR's Community Climate System Model, version 4 (CCSM4). Under a strong radiative forcing scenario, the model simulates a much warmer, wetter, cloudier, and stormier Arctic climate with considerably less sea ice and a fresher Arctic Ocean. The high correlation among the variables composing these changes-temperature, precipitation, cloudiness, sea level pressure (SLP), and ice concentration-suggests that their close coupling collectively represents a fingerprint of Arctic climate change. Although the projected changes in CCSM4 are generally consistent with those in other GCMs, several noteworthy features are identified. Despite more global warming in CCSM4, Arctic changes are generally less than under comparable greenhouse forcing in CCSM3, as represented by Arctic amplification (16%% weaker) and the date of a seasonally ice-free Arctic Ocean (20 years later). Autumn is the season of the most pronounced Arctic climate change among all the primary variables. The changes are very similar across the five ensemble members, although SLP displays the largest internal variability. The SLP response exhibits a significant trend toward stronger extreme Arctic cyclones, implying greater wave activity that would promote coastal erosion. Based on a commonly used definition of the Arctic (the area encompassing the 10°C July air temperature isotherm), the region shrinks by about 40%% during the twenty-first century, in conjunction with a nearly 10-K warming trend poleward of 70°N. Despite this pronounced long-term warming, CCSM4 simulates a hiatus in the secular Arctic climate trends during a decade-long stretch in the 2040s and to a lesser extent in the 2090s. These pauses occur despite averaging over five ensemble members and are remarkable because they happen under the most extreme greenhouse-forcing scenario and in the most climatically sensitive region of the world. [ABSTRACT FROM AUTHOR]

This study diagnoses the changes in Arctic clouds simulated by the Community Climate System Model version 3 (CCSM3) in a transient 2 × CO2 simulation. Four experiments-one fully coupled and three with prescribed SSTs and/or sea ice cover-are used to identify the mechanisms responsible for the projected cloud changes. The target simulation uses a T42 version of the CCSM3, in which the atmosphere is coupled to a dynamical ocean with mobile sea ice. This simulation is approximated by a T42 atmosphere-only integration using CCSM3's atmospheric component [the Community Atmosphere Model version 3 (CAM3)] forced at its lower boundary with the changes in both SSTs and sea ice concentration from CCSM3's 2 × CO2 run. The authors decompose the combined effect of the higher SSTs and reduced sea ice concentration on the Arctic cloud response in this experiment by running two additional CAM3 simulations: one forced with modern SSTs and the projected sea ice cover changes in CCSM3 and the other forced with modern sea ice coverage and the projected changes in SSTs in CCSM3. The results suggest that future increases in Arctic cloudiness simulated by CCSM3 are mostly attributable to two separate processes. Low cloud gains are primarily initiated locally by enhanced evaporation within the Arctic due to reduced sea ice, whereas cloud increases at middle and high levels are mostly driven remotely via greater meridional moisture transport from lower latitudes in a more humid global atmosphere. The enhanced low cloudiness attributable to sea ice loss causes large increases in cloud radiative forcing during the coldest months and therefore promotes even greater surface warming. Because CCSM3's Arctic cloud response to greenhouse forcing is similar to other GCMs, the driving mechanisms identified here may be applicable to other models and could help to advance our understanding of likely changes in the vertical structure of polar clouds. [ABSTRACT FROM AUTHOR]

Rapid changes to the Arctic hydrological cycle challenge both our process understanding and our ability to find appropriate adaptation strategies. We have investigated the relevance and accuracy development of climate change projections for assessment of water cycle changes in major Arctic drainage basins. Results show relatively good agreement of climate model projections with observed temperature changes, but high model inaccuracy relative to available observation data for precipitation changes. Direct observations further show systematically larger (smaller) runoff than precipitation increases (decreases). This result is partly attributable to uncertainties and systematic bias in precipitation observations, but still indicates that some of the observed increase in Arctic river runoff is due to water storage changes, for example melting permafrost and/or groundwater storage changes, within the drainage basins. Such causes of runoff change affect sea level, in addition to ocean salinity, and inland water resources, ecosystems, and infrastructure. Process-based hydrological modeling and observations, which can resolve changes in evapotranspiration, and groundwater and permafrost storage at and below river basin scales, are needed in order to accurately interpret and translate climate-driven precipitation changes to changes in freshwater cycling and runoff. In contrast to this need, our results show that the density of Arctic runoff monitoring has become increasingly biased and less relevant by decreasing most and being lowest in river basins with the largest expected climatic changes. [ABSTRACT FROM AUTHOR]

The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution. These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline. [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]

Climate change is affecting species' distributions and abundances worldwide. Baseline population estimates, against which future observations may be compared, are necessary if we are to detect ecological change. Arctic sea ice ecosystems are changing rapidly and we lack baseline population estimates for many ice-associated species. Provided we can detect them, changes in Arctic marine ecosystems may be signaled by changes in indicator species such as ringed seals (Pusa hispida). Ringed seal monitoring has provided estimates of survival and fertility rates, but these have not been used for population-level inference. Using matrix population models, we synthesized existing demographic parameters to obtain estimates of historical ringed seal population growth and structure in Amundsen Gulf and Prince Albert Sound, Canada. We then formalized existing hypotheses about the effects of emerging environmental stressors (i.e., earlier spring ice breakup and reduced snow depth) on ringed seal pup survival. Coupling the demographic model to ice and snow forecasts available from the Coupled Model Intercomparison Project resulted in projections of ringed seal population size and structure up to the year 2100. These projections showed median declines in population size ranging from 50% to 99%. Corresponding to these projected declines were substantial changes in population structure, with increasing proportions of ringed seal pups and adults and declining proportions of juveniles. We explored if currently collected, harvest-based data could be used to detect the projected changes in population stage structure. Our model suggests that at a present sample size of 100 seals per year, the projected changes in stage structure would only be reliably detected by mid-century, even for the most extreme climate models. This modeling process revealed inconsistencies in existing estimates of ringed seal demographic rates. Mathematical population models such as these can contribute both to understanding past population trends as well as predicting future ones, both of which are necessary if we are to detect and interpret future observations., (© 2019 by the Ecological Society of America.)