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Climate simulation uncertainties arise from internal variability, model structure, and external forcings. Model intercomparisons (such as the Coupled Model Intercomparison Project; CMIP) and single-model large ensembles have provided insight into uncertainty sources. Under the Community Earth System Model (CESM) project, large ensembles have been performed for CESM2 (a CMIP6-era model) and CESM1 (a CMIP5-era model). We refer to these as CESM2-LE and CESM1-LE. The external forcing used in these simulations has changed to be consistent with their CMIP generation. As a result, differences between CESM2-LE and CESM1-LE ensemble means arise from changes in both model structure and forcing. Here we present new ensemble simulations which allow us to separate the influences of these model structural and forcing differences. Our new CESM2 simulations are run with CMIP5 forcings equivalent to those used in the CESM1-LE. We find a strong influence of historical forcing uncertainty due to aerosol effects on simulated climate. For the historical period, forcing drives reduced global warming and ocean heat uptake in CESM2-LE relative to CESM1-LE that is counteracted by the influence of model structure. The influence of the model structure and forcing vary across the globe, and the Arctic exhibits a distinct signal that contrasts with the global mean. For the 21st century, the importance of scenario forcing differences (SSP3–7.0 for CESM2-LE and RCP8.5 for CESM1-LE) is evident. The new simulations presented here allow us to diagnose the influence of model structure on 21st century change, despite large scenario forcing differences, revealing that differences in the meridional distribution of warming are caused by model structure. Feedback analysis reveals that clouds and their impact on shortwave radiation explain many of these structural differences between CESM2 and CESM1. In the Arctic, albedo changes control transient climate evolution differences due to structural differences between CESM2 and CESM1. [ABSTRACT FROM AUTHOR]

. Climate simulation uncertainties arise from internal variability, model structure, and external forcings. Model intercomparisons (such as the Coupled Model Intercomparison Project; CMIP) and single-model large ensembles have provided insight on uncertainty sources. Under the Community Earth System Model (CESM) project, large ensembles have been performed for CESM2 (a CMIP6-era model) and CESM1 (a CMIP5-era model). We refer to these as CESM2-LE and CESM1-LE. The external forcing used in these simulations changed to be consistent with their CMIP generation. As a result, differences between CESM2-LE and CESM1-LE ensemble means arise from changes in both model structure and forcing. Here we present new ensemble simulations which allow us to separate the influences of these model structural and forcing differences. Our new CESM2 simulations are run with CMIP5 forcings equivalent to those used in the CESM1-LE. We find a strong influence of historical forcing uncertainty due to aerosol effects on simulated climate. For the historical period, forcing drives reduced global warming and ocean heat uptake in CESM2-LE relative to CESM1-LE that is counteracted by the influence of model structure. The influence of model structure and forcing vary across the globe and the Arctic exhibits a distinct signal that contrasts with the global mean. For the 21st century, the importance of scenario forcing differences (SSP3-7.0 for CESM2-LE and RCP8.5 for CESM1-LE) is evident. The new simulations presented here allow us to diagnose the influence of model structure on 21st century change despite large scenario forcing differences, revealing that differences in the meridional distribution of warming are caused by model structure. Feedback analysis reveals that clouds and their impact on shortwave radiation explain many of these structural differences between CESM2 and CESM1. In the Arctic, albedo changes control transient climate evolution differences due to structural differences between CESM2 and CESM1. [ABSTRACT FROM AUTHOR]

We provide a high-level review of sea ice models used for climate studies and of the recent advances made with these models to understand sea ice predictability. Models currently in use for the Coupled Model Intercomparison Project and new developments coming online that will enable enhanced predictions are discussed. Previous work indicates that seasonal sea ice can be predicted based on mechanisms associated with long-lived ice thickness or ocean heat anomalies. On longer timescales, internal climate variability is an important source of uncertainty, although anthropogenic forcing is sizable, and studies suggest that anthropogenic signals have already emerged from internal climate noise. Using new analysis from the Multi-Model Large Ensemble, we show that while models differ in the magnitude and timing of predictable signals, many ice predictability characteristics are robust across multiple models. This includes the reemergence of predictable seasonal signals in ice area and the sizable uncertainty in predictions of ice-free Arctic timing associated with internal variability. [ABSTRACT FROM AUTHOR]

In the high-latitude Arctic, wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. While the climate warms, wintertime Arctic surface heat fluxes remain dominated by the insulating effects of snow and sea ice covering the ocean until the sea ice thins enough or sea ice concentrations decrease enough to allow for direct ocean–atmosphere heat fluxes. The Community Earth System Model version 1 Large Ensemble (CESM1-LE) simulates increases in wintertime conductive heat fluxes in the ice-covered Arctic Ocean by ∼ 7–11 W m -2 by the mid-21st century, thereby driving an increased warming of the atmosphere. These increased fluxes are due to both thinning sea ice and decreasing snow on sea ice. The simulations analyzed here use a sub-grid-scale ice thickness distribution. Surface heat flux estimates calculated using grid-cell mean values of sea ice thicknesses underestimate mean heat fluxes by ∼16 %–35 % and overestimate changes in conductive heat fluxes by up to ∼36 % in the wintertime Arctic basin even when sea ice concentrations remain above 95 %. These results highlight how wintertime conductive heat fluxes will increase in a warming world even during times when sea ice concentrations remain high and that snow and the distribution of snow significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic. [ABSTRACT FROM AUTHOR]

We assess the influence of snow on sea ice in experiments using the Community Earth System Model version 2 for a preindustrial and a 2xCO2 climate state. In the preindustrial climate, we find that increasing simulated snow accumulation on sea ice results in thicker sea ice and a cooler climate in both hemispheres. The sea ice mass budget response differs fundamentally between the two hemispheres. In the Arctic, increasing snow results in a decrease in both congelation sea ice growth and surface sea ice melt due to the snow's impact on conductive heat transfer and albedo, respectively. These factors dominate in regions of perennial ice but have a smaller influence in seasonal ice areas. Overall, the mass budget changes lead to a reduced amplitude in the annual cycle of ice thickness. In the Antarctic, with increasing snow, ice growth increases due to snow–ice formation and is balanced by larger basal ice melt, which primarily occurs in regions of seasonal ice. In a warmer 2xCO2 climate, the Arctic sea ice sensitivity to snow depth is small and reduced relative to that of the preindustrial climate. In contrast, in the Antarctic, the sensitivity to snow on sea ice in the 2xCO2 climate is qualitatively similar to the sensitivity in the preindustrial climate. These results underscore the importance of accurately representing snow accumulation on sea ice in coupled Earth system models due to its impact on a number of competing processes and feedbacks that affect the melt and growth of sea ice. [ABSTRACT FROM AUTHOR]

The sea ice component of the Community Earth System Model version 2 (CESM2) contains new "mushy‐layer" physics that simulates prognostic salinity in the sea ice, with consequent modifications to sea ice thermodynamics and the treatment of melt ponds. The changes to the sea ice model and their influence on coupled model simulations are described here. Two simulations were performed to assess the changes in the vertical thermodynamics formulation with prognostic salinity compared to a constant salinity profile. Inclusion of the mushy layer thermodynamics of Turner et al. (2013, https://doi.org/10.1002/jgrc.20171) in a fully coupled Earth system model produces thicker and more extensive sea ice in the Arctic, with relatively unchanged sea ice in the Antarctic compared to simulations using a constant salinity profile. While this is consistent with the findings of uncoupled ice‐ocean model studies, the role of the frazil and congelation growth is more important in fully coupled simulations. Melt pond drainage is also an important contribution to simulated ice thickness differences as also found in the uncoupled simulations of Turner and Hunke (2015; https://doi.org/10.1002/2014JC010358). However, it is an interaction of the ponds and the snow fraction that impacts the surface albedo and hence the top melt. The changes in the thermodynamics and resulting ice state modify the ice‐ocean‐atmosphere fluxes with impacts on the atmosphere and ocean states, particularly temperature. Plain Language Summary: We investigate the role of a new approach for sea ice thermodynamics in the Community Earth System Model, based on mushy‐layer theory. The new approach produces thicker sea ice in the Arctic with subsequent impacts on the atmosphere and ocean. Key Points: The choice of sea ice thermodynamics impacts the sea ice mean stateThe choice of sea ice thermodynamics has a modest impact on the coupled system [ABSTRACT FROM AUTHOR]

We use a large ensemble set of simulations and initialized model forecasts to assess changes in the initial-value seasonal predictability of summer Arctic sea ice area from the late-twentieth to the mid-twenty-first century. Ice thickness is an important seasonal predictor of September ice area because early summer thickness anomalies affect how much melt out occurs. We find that the role of this predictor changes in a warming climate, leading to decadal changes in September ice area predictability. In January-initialized prediction experiments, initialization errors grow over time leading to forecast errors in ice thickness at the beginning of the melt season. The magnitude of this ice thickness forecast error growth for regions important to summer melt out decreases in a warming climate, contributing to enhanced predictability. On the other hand, the influence of early summer thickness anomalies on summer melt out and resulting September ice area increases as the climate warms. Given this, for the same magnitude ice thickness forecast error in early summer, a larger September ice area anomaly results in the warming climate, contributing to reduced predictability. The net result of these competing factors is that a sweet spot for predictability exists when the ice thickness forecast error growth is modest and the influence of these errors on melt out is modest. This occurs at about 2010 in our simulations. The predictability of summer ice area is lower for earlier decades, because of higher ice thickness forecast error growth, and for later decades because of a stronger influence of ice thickness forecast errors on summer melt out. [ABSTRACT FROM AUTHOR]

Previous studies have shown that sea ice extent in the Southern Ocean is influenced by the intensity and location of the Amundsen Sea Low (ASL), through their effect on the meridional winds. However, the inhomogeneous nature of the influence of the ASL on sea ice as well as its influence during critical periods of the sea ice annual cycle is not clear. In this study, we do a spatio-temporal analysis of links between the ASL and the sea ice during the advance and retreat periods of the ice over the period 1979-2013 focusing on the role of the meridional and zonal winds. We use the ERA-Interim monthly-averaged 500 mb geopotential height and 10 m wind data along with monthly Passive Microwave Sea Ice Concentrations (SIC) to examine the seasonal and interannual relationships between the ASL and SIC in the Ross-Amundsen sea ice sector. To characterize the state of the ASL we use indices that describe its location and its intensity. We show that the ASL has preferred locations and intensities during ice advance and retreat seasons. The strength and direction of the influence of the ASL are not spatially homogeneous and can change from advance to retreat season and there are strong significant relationships between the characteristics of the ASL and SIC, within and across seasons and interannually. [ABSTRACT FROM AUTHOR]

The dramatic warming of the Arctic over the last three decades has reduced both the thickness and extent of sea ice, opening opportunities for business in diverse sectors and increasing human exposure to meteorological hazards in the Arctic. It has been suggested that these changes in environmental conditions have led to an increase in extreme cyclones in the region, therefore increasing this hazard. In this study, we investigate the response of Arctic synoptic scale cyclones to climate change in a large initial value ensemble of future climate projections with the CESM1-CAM5 climate model (CESM-LE). We find that the response of Arctic cyclones in these simulations varies with season, with significant reductions in cyclone dynamic intensity across the Arctic basin in winter, but with contrasting increases in summer intensity within the region known as the Arctic Ocean cyclone maximum. There is also a significant reduction in winter cyclogenesis events within the Greenland-Iceland-Norwegian sea region. We conclude that these differences in the response of cyclone intensity and cyclogenesis, with season, appear to be closely linked to changes in surface temperature gradients in the high latitudes, with Arctic poleward temperature gradients increasing in summer, but decreasing in winter. [ABSTRACT FROM AUTHOR]

It has been suggested that recent regional trends in Antarctic sea ice might have been caused by the formation of the ozone hole in the late twentieth century. Here we explore this by examining two ensembles of a climate model over the ozone hole formation period (1955-2005). One ensemble includes all known historical forcings; the other is identical except for ozone levels, which are fixed at 1955 levels. We demonstrate that the model is able to capture, on interannual and decadal timescales, the observed statistical relationship between summer Amundsen Sea Low strength (when ozone loss causes a robust deepening) and fall sea ice concentrations (when observed trends are largest). In spite of this, the modeled regional trends caused by ozone depletion are found to be almost exactly opposite to the observed ones. We deduce that the regional character of observed sea ice trends is likely not caused by ozone depletion. [ABSTRACT FROM AUTHOR]

The article offers information on the Sea Ice Summer Camp based over the former Naval Arctic Research Laboratory (NARL) that held on May 25, 2017 to June 1, 2017. Topics discussed include prevalence of different activities in a camp such as sea ice observations along with classrooms for making ice models; assistance of the camp in analysis of approaches regarding climatology of polar regions; and administration of discussions regarding environmental changes.

In the original publication, Figs. 4 and 5 of the original article have the units of meridional temperature gradient, dSAT/dy, incorrectly stated as K/km, rather than K/100 km. In addition, the values of area mean dSAT/dy in subfigures 5d, e and f are incorrect. The original article was corrected. [ABSTRACT FROM AUTHOR]

This study uses coupled climate model experiments to identify the influence of atmospheric physics [Community Atmosphere Model, versions 4 and 5 (CAM4; CAM5)] and ocean model complexity (slab ocean, full-depth ocean) on the equilibrium Arctic climate response to an instantaneous CO2 doubling. In slab ocean model (SOM) experiments using CAM4 and CAM5, local radiative feedbacks, not atmospheric heat flux convergence, are the dominant control on the Arctic surface response to increased greenhouse gas forcing. Equilibrium Arctic surface air temperature warming and amplification are greater in the CAM5 SOM experiment than in the equivalent CAM4 SOM experiment. Larger 2 × CO2 radiative forcing, more positive Arctic surface albedo feedbacks, and less negative Arctic shortwave cloud feedbacks all contribute to greater Arctic surface warming and sea ice loss in CAM5 as compared to CAM4. When CAM4 is coupled to an active full-depth ocean model, Arctic Ocean horizontal heat flux convergence increases in response to the instantaneous CO2 doubling. Though this increased ocean northward heat transport slightly enhances Arctic sea ice extent loss, the representation of atmospheric processes (CAM4 versus CAM5) has a larger influence on the equilibrium Arctic surface climate response than the degree of ocean coupling (slab ocean versus full-depth ocean). These findings underscore that local feedbacks can be more important than northward heat transport for explaining the equilibrium Arctic surface climate response and response differences in coupled climate models. That said, the processes explaining the equilibrium climate response differences here may be different than the processes explaining intermodel spread in transient climate projections. [ABSTRACT FROM AUTHOR]

A preindustrial control run and an ensemble of twentieth-century integrations of the Community Climate System Model, version 4 (CCSM4), are evaluated for Antarctic sea ice climatology, modes of variability, trends, and covariance with related physical variables such as surface temperature and sea level pressure. Compared to observations, the mean ice cover is too extensive in all months. This is in part related to excessively strong westerly winds over ~50°-60°S, which drive a large equatorward meridional ice transport and enhanced ice growth near the continent and also connected with a cold bias in the Southern Ocean. In spite of these biases in the climatology, the model's sea ice variability compares well to observations. The leading mode of austral winter sea ice concentration exhibits a dipole structure with anomalies of opposite sign in the Atlantic and Pacific sectors. Both the El Niño-Southern Oscillation and the southern annular mode (SAM) project onto this mode. In twentieth-century integrations, Antarctic sea ice area exhibits significant decreasing annual trends in all six ensemble members from 1950 to 2005, in apparent contrast to observations that suggest a modest ice area increase since 1979. Two ensemble members show insignificant changes when restricted to 1979-2005. The ensemble mean shows a significant increase in the austral summer SAM index over 1960-2005 and 1979-2005 that compares well with the observed SAM trend. However, Antarctic warming and sea ice loss in the model are closely connected to each other and not to the trend in the SAM. [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]

The Community Climate System Model, version 4 has revisions across all components. For sea ice, the most notable improvements are the incorporation of a new shortwave radiative transfer scheme and the capabilities that this enables. This scheme uses inherent optical properties to define scattering and absorption characteristics of snow, ice, and included shortwave absorbers and explicitly allows for melt ponds and aerosols. The deposition and cycling of aerosols in sea ice is now included, and a new parameterization derives ponded water from the surface meltwater flux. Taken together, this provides a more sophisticated, accurate, and complete treatment of sea ice radiative transfer. In preindustrial CO2 simulations, the radiative impact of ponds and aerosols on Arctic sea ice is 1.1 W m−2 annually, with aerosols accounting for up to 8 W m−2 of enhanced June shortwave absorption in the Barents and Kara Seas and with ponds accounting for over 10 W m−2 in shelf regions in July. In double CO2 (2XCO2) simulations with the same aerosol deposition, ponds have a larger effect, whereas aerosol effects are reduced, thereby modifying the surface albedo feedback. Although the direct forcing is modest, because aerosols and ponds influence the albedo, the response is amplified. In simulations with no ponds or aerosols in sea ice, the Arctic ice is over 1 m thicker and retains more summer ice cover. Diagnosis of a twentieth-century simulation indicates an increased radiative forcing from aerosols and melt ponds, which could play a role in twentieth-century Arctic sea ice reductions. In contrast, ponds and aerosol deposition have little effect on Antarctic sea ice for all climates considered. [ABSTRACT FROM AUTHOR]

Arctic sea ice mass budgets for the twentieth century and projected changes through the twenty-first century are assessed from 14 coupled global climate models. Large inter-model scatter in contemporary mass budgets is strongly related to variations in absorbed solar radiation, due in large part to differences in the surface albedo simulation. Over the twenty-first century, all models simulate a decrease in ice volume resulting from increased annual net melt (melt minus growth), partially compensated by reduced transport to lower latitudes. Despite this general agreement, the models vary considerably regarding the magnitude of ice volume loss and the relative roles of changing melt and growth in driving it. Projected changes in sea ice mass budgets depend in part on the initial (mid twentieth century) ice conditions; models with thicker initial ice generally exhibit larger volume losses. Pointing to the importance of evolving surface albedo and cloud properties, inter-model scatter in changing net ice melt is significantly related to changes in downwelling longwave and absorbed shortwave radiation. These factors, along with the simulated mean and spatial distribution of ice thickness, contribute to a large inter-model scatter in the projected onset of seasonally ice-free conditions. [ABSTRACT FROM AUTHOR]

Linear trends in arctic sea-ice extent over the period 1979 to 2006 are negative in every month. This ice loss is best viewed as a combination of strong natural variability in the coupled ice-ocean-atmosphere system and a growing radiative forcing associated with rising concentrations of atmospheric greenhouse gases, the latter supported by evidence of qualitative consistency between observed trends and those simulated by climate models over the same period. Although the large scatter between individual modal simulations leads to much uncertainty as to when a seasonally ice-free Arctic Ocean might be realized, this transition to a new arctic state may be rapid once the ice thins to a more vulnerable state. Loss of the ice cover is expected to affect the Arctic's freshwater system and surface energy budget and could be manifested in middle latitudes as altered patterns of atmospheric circulation and precipitation. [ABSTRACT FROM AUTHOR]

The Arctic Ocean freshwater budgets in climate model integrations of the twentieth and twenty-first century are examined. An ensemble of six members of the Community Climate System Model version 3 (CCSM3) is used for the analysis, allowing the anthropogenically forced trends over the integration length to be assessed. Mechanisms driving trends in the budgets are diagnosed, and the implications of changes in the Arctic–North Atlantic exchange on the Labrador Sea and Greenland–Iceland–Norwegian (GIN) Seas properties are discussed. Over the twentieth and the twenty-first centuries, the Arctic freshens as a result of increased river runoff, net precipitation, and decreased ice growth. For many of the budget terms, the maximum 50-yr trends in the time series occur from approximately 1975 to 2025, suggesting that we are currently in the midst of large Arctic change. The total freshwater exchange between the Arctic and North Atlantic increases over the twentieth and twenty-first centuries with decreases in ice export more than compensated for by an increase in the liquid freshwater export. Changes in both the liquid and solid (ice) Fram Strait freshwater fluxes are transported southward by the East Greenland Current and partially removed from the GIN Seas. Nevertheless, reductions in GIN sea ice melt do result from the reduced Fram Strait transport and account for the largest term in the changing ocean surface freshwater fluxes in this region. This counteracts the increased ocean stability due to the warming climate and helps to maintain GIN sea deep-water formation. [ABSTRACT FROM AUTHOR]

The sea ice simulation of the Community Climate System Model version 3 (CCSM3) T42-gx1 and T85-gx1 control simulations is presented and the influence of the parameterized sea ice thickness distribution (ITD) on polar climate conditions is examined. This includes an analysis of the change in mean climate conditions and simulated sea ice feedbacks when an ITD is included. It is found that including a representation of the subgrid-scale ITD results in larger ice growth rates and thicker sea ice. These larger growth rates represent a higher heat loss from the ocean ice column to the atmosphere, resulting in warmer surface conditions. Ocean circulation, most notably in the Southern Hemisphere, is also modified by the ITD because of the influence of enhanced high-latitude ice formation on the ocean buoyancy flux and resulting deep water formation. Changes in atmospheric circulation also result, again most notably in the Southern Hemisphere. There are indications that the ITD also modifies simulated sea ice–related feedbacks. In regions of similar ice thickness, the surface albedo changes at 2XCO2 conditions are larger when an ITD is included, suggesting an enhanced surface albedo feedback. The presence of an ITD also modifies the ice thickness–ice strength relationship and the ice thickness–ice growth rate relationship, both of which represent negative feedbacks on ice thickness. The net influence of the ITD on polar climate sensitivity and variability results from the interaction of these and other complex feedback processes. [ABSTRACT FROM AUTHOR]

The ability of five, global coupled climate models to simulate important atmospheric circulation characteristics in the Southern Hemisphere for the period 1960–1999 is assessed. The circulation features examined are the Southern Hemisphere annular mode (SAM), the semi-annual oscillation (SAO) and the quasi-stationary zonal wave 3 (ZW3). The models assessed are the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3), the Commonwealth Scientific and Industrial Research Organisation Mark 3, the Geophysical Fluid Dynamics Laboratory Model, the Goddard Institute for Space Studies Model ER (GISS-ER) and the UK Meteorological Office Hadley Center Coupled Model Version 3. The simulations were compared to the NCAR–NCEP reanalyses. The models simulate a SAO which differs spatially from the observed over the Pacific and Indian oceans. The amplitudes are too high over the southern ocean and too low over the midlatitudes. These differences are attributed to a circumpolar trough which is too deep and extends too far north, and to the inability of the models to simulate the middle to high latitude temperature gradient. The SAM is well-represented spatially by most models but there are important differences which may influence the flow over the Pacific and in the region extending from the Ross to Weddell Seas. The observed trend towards positive polarity in the SAM is apparent in the ensemble averages of the GISS-ER and CCSM3 simulations, suggesting that the trend is due to external forcing by changes in the concentration of ozone and greenhouse gases. ZW3 is well-represented by the models but the observed trend towards positive phases of ZW3 is not apparent in the simulations suggesting that the observed trend may be due to natural variability, not external forcing. [ABSTRACT FROM AUTHOR]

We examine the representation of the mean state and interannual variability of Antarctic sea ice in six simulations of the twentieth century from coupled models participating in the Intergovernmental Panel on Climate Change fourth assessment report. The simulations exhibit a largely seasonal southern hemisphere ice cover, as observed. There is a considerable scatter in the monthly simulated climatological ice extent among different models, but no consistent bias when compared to observations. The scatter in maximum winter ice extent among different models is correlated to the strength of the climatological zonal winds suggesting that wind forced ice transport is responsible for much of this scatter. Observations show that the leading mode of southern hemisphere ice variability exhibits a dipole structure with anomalies of one sign in the Atlantic sector associated with anomalies of the opposite sign in the Pacific sector. The observed ice anomalies also exhibit eastward propagation with the Antarctic circumpolar current, as part of the documented Antarctic circumpolar wave phenomenon. Many of the models do simulate dipole-like behavior in sea ice anomalies as the leading mode of ice variability, but there is a large discrepancy in the eastward propagation of these anomalies among the different models. Consistent with observations, the simulated Antarctic dipole-like variations in the ice cover are led by sea-level pressure anomalies in the Amundsen/ Bellingshausen Sea. These are associated, to different degrees in different models, with both the southern annular mode and the El Nino-Southern Oscillation (ENSO). There are indications that the magnitude of the influence of ENSO on the southern hemisphere ice cover is related to the strength of ENSO events simulated by the different models. [ABSTRACT FROM AUTHOR]

Several mechanisms have been proposed to explain natural climate variability in the Arctic. These include processes related to the influence of the North Atlantic Oscillation/Arctic Oscillation (NAO/AO), anticyclonic/cyclonic regimes, changes in the oceanic and atmospheric North Atlantic–Arctic exchange, and changes in the Atlantic meridional overturning circulation. After a brief critical review, the influence and interrelation of the above processes in a long climate integration of the Community Climate System Model, version 2 (CCSM2) are examined. The analysis is based on the time series of surface air temperature integrated northward of 70°N, which serves as a useful proxy for general Arctic climate conditions. This gives a large-scale view of the evolution of Arctic climate. It is found that changes in oceanic exchange and heat transport in the Barents Sea dominate in forcing the Arctic surface air temperature variability in CCSM2. Changes in atmospheric circulation are consistent with a wind forcing of this variability, while changes in the deep overturning circulation in the Atlantic are more weakly related in CCSM2. Over some time periods, the NAO/AO is significantly related to these changes in Arctic climate conditions. However, this is not robust over longer time scales. [ABSTRACT FROM AUTHOR]

The mechanisms forcing variability in Southern Ocean sea ice and sea surface temperature from 600 years of a control climate coupled model integration are discussed. As in the observations, the leading mode of simulated variability exhibits a dipole pattern with positive anomalies in the Pacific sector associated with negative anomalies in the Atlantic. It is found that in the Pacific ocean circulation changes associated with variable wind forcing modify the ocean heat flux convergence and sea ice transport, resulting in sea surface temperature and sea ice anomalies. The Pacific ice and ocean anomalies persist over a number of years due to reductions in ocean shortwave absorption reinforcing the initial anomalies. In the Atlantic sector, no single process dominates in forcing the anomalies. Instead there are contributions from changing ocean and sea ice circulation and surface heat fluxes. While the absorbed solar radiation in the Atlantic is modified by the changing surface albedo, the anomalies are much shorter-lived than in the Pacific because the ocean circulation transports them northward, removing them from ice formation regions. Sea ice and ocean anomalies associated with the El Niño–Southern Oscillation and the Southern Annular Mode both exhibit a dipole pattern and contribute to the leading mode of ice and ocean variability. [ABSTRACT FROM AUTHOR]

Recent observations suggest that large and widespread changes are occurring in the Arctic climate system. Many of these are associated with the North Atlantic Oscillation (NAO) or the closely related Arctic Oscillation (AO). Here, the Arctic climate and its response to the NAO–AO is examined in a control simulation of the newly released Community Climate System Model, version 2 (CCSM2). Variability in the atmosphere and sea ice systems are considered and the physical mechanisms that drive the variations are discussed. It is found that the model reasonably simulates the spatial structure and variance of the sea level pressure, surface air temperature, and precipitation associated with the NAO–AO. The sea ice response to the NAO–AO also compares well to observations. However, it varies over the length of the time series, which is related to variations in the spatial structure of the sea level pressure anomalies associated with the NAO–AO over time. The model results suggest that these variations, which are similar to changes that occur over the observed record, are common and part of the natural variability of the system. However, the magnitude of the observed trends over the last 40 yr in the NAO–AO index are never realized in the model simulations, suggesting that these trends may be associated with changes in anthropogenic forcing, which the simulation examined here does not include. [ABSTRACT FROM AUTHOR]

The simulated influence of Arctic sea ice on the variability of the North Atlantic climate is discussed in the context of a global coupled ice-ocean-atmosphere model. This coupled system incorporates a general circulation ocean model, an atmospheric energy moisture balance model, and a dynamic-thermodynamic sea ice model. Under steady seasonal forcing, an equilibrium solution is obtained with very little variability. To induce variability in the model, daily varying stochastic anomalies are applied to the wind forcing of the Northern Hemisphere sea ice cover. These stochastic anomalies have observed spatial patterns but are random in time. Model simulations are run for 1000 yr from an equilibrium state and the variability in the system is analyzed. The sensitivity of the system to the ice-ocean coupling of both heat and freshwater is also examined. Under the stochastic forcing conditions, the thermohaline circulation (THC) responds with variability that is approximately 10% of the mean. This variability has enhanced spectral power at interdecadal timescales that is concentrated at approximately 20 yr. It is forced by fluctuations in the export of ice from the Arctic into the North Atlantic. Substantial changes in sea surface temperature and salinity are related to changes in the overturning circulation and the sea ice coverage in the northern North Atlantic. Additionally, the THC variability influences the Arctic Basin through heat transport under the ice pack. Results from sensitivity studies suggest that the freshwater exchange from the variable ice cover is the dominant process for forcing variability in the overturning. The simulated Arctic ice export appears to provide stochastic forcing to the northern North Atlantic that excites a damped oscillatory ocean-only mode. The insulating capacity of the variable sea ice has a negligible effect on the THC. Ice-ocean thermal coupling acts to damp THC variability, causing an approximately 25% reduction in the... [ABSTRACT FROM AUTHOR]

The importance of the Arctic region for global climate change has recently been highlighted in the results from general circulation model simulations under increasing atmospheric CO2 scenarios. The warming that is predicted by these studies is most pronounced in the polar regions, indicating that it may be the first place in which the effects of global climate change will be detected. However, the natural variability that is present in the Arctic climate system is largely unknown and is likely to obscure the detection of anthropogenically forced changes. Additionally, there is little information on the internal processes of the Arctic ice pack, which are important for determining the variability of the ice cover. In an effort to address these issues, the variability of the Arctic ice volume is examined using a single column sea ice--ocean mixed layer model. The model contains an ice thickness distribution and the parameterization of export and ridging due to ice divergence and shear. Variability in the ice cover is forced by applying stochastic perturbations to the air temperature and ice divergence forcing fields. Several sensitivity tests are performed in order to assess the role of different physical processes in determining the variability of the perennial Arctic ice pack. It is found that the surface albedo and ice--ocean feedback mechanisms act to enhance the variability of the ice volume and are particularly important for the simulated response of the sea ice to fluctuations in air temperature, accounting for approximately 62% and 25% of the ice volume variance, respectively. The details of the ice thickness distribution also significantly affect the simulated variability. In particular, the ridging process acts to decrease the simulated variability of the ice pack. It reduces the variance of the ice volume by 50% when air temperature stochastic forcing is applied. [ABSTRACT FROM AUTHOR]

The article discusses the mechanisms underlying decadal variability in Arctic sea ice remain actively debated. Here, we show that variability in boreal biomass burning (BB) emissions strongly influences simulated Arctic sea ice on multidecadal time scales. In particular, we find that a strong acceleration in sea ice decline in the early 21st century in the Community Earth System Model version 2 is related to increased variability in prescribed BB emissions in the sixth phase.

This study isolates the influence of sea ice mean state on pre‐industrial climate and transient 1850–2100 climate change within a fully coupled global model: The Community Earth System Model version 2 (CESM2). The CESM2 sea ice model physics is modified to increase surface albedo, reduce surface sea ice melt, and increase Arctic sea ice thickness and late summer cover. Importantly, increased Arctic sea ice in the modified model reduces a present‐day late‐summer ice cover bias. Of interest to coupled model development, this bias reduction is realized without degrading the global simulation including top‐of‐atmosphere energy imbalance, surface temperature, surface precipitation, and major modes of climate variability. The influence of these sea ice physics changes on transient 1850–2100 climate change is compared within a large initial condition ensemble framework. Despite similar global warming, the modified model with thicker Arctic sea ice than CESM2 has a delayed and more realistic transition to a seasonally ice free Arctic Ocean. Differences in transient climate change between the modified model and CESM2 are challenging to detect due to large internally generated climate variability. In particular, two common sea ice benchmarks—sea ice sensitivity and sea ice trends—are of limited value for comparing models with similar global warming. More broadly, these results show the importance of a reasonable Arctic sea ice mean state when simulating the transition to an ice‐free Arctic Ocean in a warming world. Additionally, this work highlights the importance of large initial condition ensembles for credible model‐to‐model and observation‐model comparisons. Plain Language Summary: Satellite observations available from 1979 to present show dramatic Arctic sea ice loss. As a result, projecting when the Arctic Ocean may become ice free and the resulting impacts is of broad interest to those living in the Arctic and beyond. Climate models are the main tool for making such future projections. Yet, projecting sea ice loss is hard because it is affected by multiple factors that are often impossible to disentangle including physical processes, unpredictable climate variability, and differences in climate drivers. Unique to this work, we analyze the influence of the sea ice surface melt while also controlling for all other confounding factors such as the amount of global warming and unpredictable climate variability. Our work demonstrates that under similar global warming, surface melt affects the timing of an ice‐free Arctic Ocean. Specifically, simulations with less surface melt and more sea ice transition to an ice‐free Arctic Ocean later. From the perspective of model development and transient climate change, we also found sea ice amounts and the timing toward an ice‐free Arctic have negligible influence on warming, precipitation, and sea level pressure outside of the polar regions. Key Points: Decreasing surface melt decreases late‐summer Arctic sea ice cover biases and delays transition to an ice‐free Arctic OceanInternal variability limits value of sea ice trends and sea ice sensitivity as metrics to constrain model performance under similar global warmingIncreasing sea ice thickness and area has negligible impacts on non‐polar climate and climate change [ABSTRACT FROM AUTHOR]

We explore current variability and future projections of winter Arctic sea ice thickness and growth using data from climate models and satellite observations. Winter ice thickness in the Community Earth System Model's Large Ensemble compares well against thickness estimates from the Pan‐Arctic Ice Ocean Modeling and Assimilation System and CryoSat‐2, despite some significant regional differences—for example, a high thickness bias in Community Earth System Model's Large Ensemble in the western Arctic. Differences across the available CryoSat‐2 thickness products hinder more robust validation efforts. We assess the importance of the negative conductive feedback of sea ice growth (thinner ice grows faster) by regressing October atmosphere/ice/ocean conditions against winter ice growth. Our regressions demonstrate the importance of a strong negative conductive feedback process in our current climate, which increases winter growth for thinner initial ice, but indicate that later in the 21st century this negative feedback is overwhelmed by variations in the fall atmosphere/ocean state. Plain language summary: In this study we explore the thickness and growth of Arctic sea ice through winter using data from climate models and satellite observations. Winter Arctic sea ice thickness in a widely used set of climate model simulations compares well against thickness estimates produced from a climate model constrained by observations and sea ice thickness estimates derived from satellite observations, although important regional differences are found. Our analysis suggests an increase in the amount of Arctic sea ice grown in winter through the coming decades, partly due to the fact thinner ice grows faster than thicker, more insulated, ice. As the Arctic warms rapidly, the strong atmosphere and ocean forcing dominates over this feedback and is projected to promote declines in sea ice growth. Key Points: Winter Arctic sea ice thickness and growth variability is explored using data from climate models and satellite observationsWe project an increase in winter Arctic sea ice growth over the coming decades, due in‐part to the presence of a negative conductive feedback associated with thinner iceThe importance of the negative feedback reduces through the end of the 21st century, as the Arctic warms significantly and promotes delays and overall declines in Arctic sea ice growth [ABSTRACT FROM AUTHOR]

The Amundsen Sea Low (ASL) is an important driver of Antarctic sea ice variations largely because of wind‐driven sea ice and ocean transport anomalies. However, the nature of the relationship between the ASL and sea ice is complicated by large seasonality in the ice cover and the ASL location and depth. Here we explore these relationships as a function of region, season, and lag. We find that the ASL can have a markedly different and sometimes opposite sign influence on sea ice in some regions, such as the western Ross Sea, depending on the season. This is in part due to differing influences of ASL‐related meridional and zonal winds for ice transport in different times of year. The sea ice response to ASL variations is often largest at a lag of some months and can persist for up to 8 months. Plain Language Summary: The Amundsen Sea Low (ASL) is a dominant feature of the atmospheric circulation in the Southern Ocean. Variations in this low pressure center influence winds over the Antarctic sea ice that can cause anomalies in sea ice transport and ice concentration. However, because the location of the low and the sea ice cover differ seasonally, the influence of ASL variability on sea ice differs by region and season. Additionally, the sea ice can exhibit a lagged response to the ASL, resulting in sea ice anomalies many months following variations in the ASL. Key Points: The Amundsen Sea Low is an important driver of regional Antarctic sea ice variabilityThe influence of the Amundsen Sea Low on sea ice varies by region, season, and lagThe Amundsen Sea Low can have an opposite sign influence on sea ice in some regions depending on the season [ABSTRACT FROM AUTHOR]

We assess Antarctic sea ice climatology and variability in version 2 of the Community Earth System Model (CESM2) and compare it to that in the older CESM1 and (where appropriate) real‐world observations. In CESM2, Antarctic sea ice is thinner and less extensive than in CESM1, though sea ice area is still approximately 1 million km2 greater in CESM2 than in present‐day observations. Though there is less Antarctic sea ice in CESM2, the annual cycle of ice growth and melt is more vigorous in CESM2 than in CESM1. A new mushy layer thermodynamics formulation implemented in the latest version of the Community Ice Code (CICE) in CESM2 accounts for both greater frazil ice formation in coastal polynyas and more snow‐to‐ice conversion near the edge of the ice pack in the new model. Greater winter ice divergence in CESM2 (relative to CESM1) is due to stronger stationary wave activity and greater wind stress curl over the ice pack. Greater wind stress curl, in turn, drives more warm water upwelling under the ice pack, thinning it and decreasing its extent. Overall, differences between Antarctic sea ice in CESM2 and CESM1 arise due to both differences in their sea ice thermodynamics formulations and differences in their coupled atmosphere‐ocean states. Plain Language Summary: Sea ice is a central part of the Antarctic climate system, and Earth system models are an indispensable tool for studying the climate of the Antarctic. Advances in modeling are essential for understanding and projecting future changes in the region as the globe warms. Here, we describe Antarctic sea ice climatology in the state‐of‐the‐art Community Earth System Model, version 2 (CESM2). CESM2 incorporates several modeling advances which collectively improve representation of Antarctic climate compared to previous model versions. Among these is a "mushy layer" treatment of sea ice, where the ice is modeled as a mixture of solid ice and salty water. Modeling sea ice as a mushy layer changes the way that Antarctic sea ice grows in CESM2, in a manner more closely resembling how Antarctic sea ice has been observed to grow in the real world. Antarctic sea ice area in CESM2 also more closely matches observed sea ice area, due primarily to differences in atmospheric winds and ocean heating. In conjunction with observations and other state‐of‐the‐art global climate models, CESM2 will be an important tool for furthering understanding of Antarctic climate at present and in the future. Key Points: Antarctic sea ice is thinner and less extensive in CESM2 than in CESM1A new mushy layer thermodynamics formulation in CICE5 accelerates ice growth in coastal polynyas and augments snow‐to‐ice conversionGreater surface wind stress curl increases warm water upwelling under the ice pack in CESM2, which thins ice and decreases its extent [ABSTRACT FROM AUTHOR]

In recent decades, Arctic sea ice has shifted toward a younger, thinner, seasonal ice regime. Studying and understanding this "new" Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian tracking of sea ice floes in the Community Earth System Model Large Ensemble (CESM-LE) during representative "perennial" and "seasonal" time periods allows for understanding of the conditions that a floe could experience throughout the calendar year. These model tracks, put into context a single year of observations, provide guidance on how observations can optimally shape model development, and how climate models could be used in future campaign planning. The modeled floe tracks show a range of possible trajectories, though a Transpolar Drift trajectory is most likely. There is also a small but emerging possibility of high-risk tracks, including possible melt of the floe before the end of a calendar year. We find that a Lagrangian approach is essential in order to correctly compare the seasonal cycle of sea ice conditions between point-based observations and a model. Because of high variability in the melt season sea ice conditions, we recommend in situ sampling over a large range of ice conditions for a more complete understanding of how ice type and surface conditions affect the observed processes. We find that sea ice predictability emerges rapidly during the autumn freeze-up and anticipate that process-based observations during this period may help elucidate the processes leading to this change in predictability. [ABSTRACT FROM AUTHOR]

This study investigates winter polynyas in the southern Ross Sea, Antarctica where several polynyas are known to form. Coastal polynyas are areas of lower sea ice concentration and/or thickness along the coast that are otherwise surrounded by more extensive, thicker sea ice pack. Polynyas are also locations where organisms can exploit both the ice substrate and pelagic resources. Using a self organizing map algorithm, we identify polynya events in the Community Earth System Model Version 2 Large Ensemble (CESM2-LE). The neural network algorithm is able to identify polynya events without imposing an ice concentration or thickness threshold, as is often done when identifying polynyas. The CESM2-LE produces a wintertime polynya feature comparable in size and location to the Ross Sea polynya, and during polynya events there are large turbulent heat fluxes and export of sea ice from the Ross Sea. In the CESM2-LE polynya event frequency is projected to decrease sharply in the later twentyfirst century, leading to increasing sea ice concentrations and thicknesses in the region. The drivers of the polynya frequency decline are likely both large scale circulation changes and local atmosphere and ocean feedbacks. If declines in wintertime polynya frequency over the twentyfirst century do occur they may impact Antarctic Bottom Water formation and local net primary productivity. Thus, better understanding potential local and unexpected sea ice changes in the Ross Sea is important for both assessing climate system impacts and ecological impacts on the Ross Sea ecosystem, which is currently protected by an internationally recognized marine protected area. [ABSTRACT FROM AUTHOR]

We use a large ensemble of simulations from the Community Earth System Model to quantify simulated changes in the twentieth and twenty-first century Arctic surface shortwave heating associated with changing incoming solar radiation and changing ice conditions. For increases in shortwave absorption associated with albedo reductions, the relative influence of changing sea ice surface properties and changing sea ice areal coverage is assessed. Changes in the surface sea ice properties are associated with an earlier melt season onset, a longer snow-free season and enhanced surface ponding. Because many of these changes occur during peak solar insolation, they have a considerable influence on Arctic surface shortwave heating that is comparable to the influence of ice area loss in the early twenty-first century. As ice area loss continues through the twenty-first century, it overwhelms the influence of changes in the sea ice surface state, and is responsible for a majority of the net shortwave increases by the mid-twenty-first century. A comparison with the Arctic surface albedo and shortwave heating in CMIP5 models indicates a large spread in projected twenty-first century change. This is in part related to different ice loss rates among the models and different representations of the late twentieth century ice albedo and associated sea ice surface state. [ABSTRACT FROM AUTHOR]

The lower trophic levels of marine ecosystems play a critical role in the Earth System mediating fluxes of carbon to the ocean interior. Many of the functional relationships describing biological rate processes, such as primary productivity, in marine ecosystem models are nonlinear functions of environmental state variables. As a result of nonlinearity, rate processes computed from mean fields at coarse resolution will differ from similar computations that incorporate small-scale heterogeneity. Here we examine how subgrid-scale variability in sea ice thickness impacts simulated net primary productivity (NPP) in a [ABSTRACT FROM AUTHOR]

The "surface scattering layer" (SSL) is the highly‐scattering, coarse‐grained ice layer that forms on the surface of melting, drained sea ice during spring and summer. Ice of sufficient thickness with an SSL has an observed persistent broadband albedo of ∼0.65, resulting in a strong influence on the regional solar partitioning. Experiments during the Multidisciplinary drifting Observatory for the Study of the Arctic Climate expedition showed that the SSL re‐forms in approximately 1 day following manual removal. Coincident spectral albedo measurements provide insight into the SSL evolution, where albedo increased on sunny days with higher solar insolation. Comparison with experiments in radiative transfer and global climate models show that the sea ice albedo is greatly impacted by the SSL thickness. The presence of SSL is a significant component of the ice‐albedo feedback, with an albedo impact of the same order as melt ponds. Changes in SSL and implications for Arctic sea ice within a warming climate are uncertain. Plain Language Summary: During the summer melt season, the surface of Arctic sea ice generally transforms into a crumbly, snow‐like layer that is known as the "surface scattering layer" (SSL). As the name implies, it is highly‐scattering, resulting in a high reflectivity where typically around 65% of incoming solar energy is reflected. However, this value has been observed to be lower, particularly when the layer is relatively thin, and there is very little known about what controls its variability. We completed experiments on summer sea ice to observe what happens when the layer is completely removed. We completed similar experiments in models, where we were able to simulate the expected reflectivity over a range of layer thicknesses, and then see what the expected impact on Arctic sea ice would be with complete removal. We find that removal of this layer dramatically reduces sea ice albedo and Arctic sea ice volume. The SSL is an under‐appreciated feature of Arctic sea ice, and more work to understand its variability in a warming Arctic is needed. Key Points: Surface scattering layer (SSL) is a persistent feature of bare, melting sea ice that is central to the sea ice‐albedo feedbackObservations and modeling both show sea ice albedo sensitivity to SSL thicknessModels represent the optical properties of the SSL layer as a function of ice thickness only, but these properties also vary with morphology [ABSTRACT FROM AUTHOR]

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981-2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends. [ABSTRACT FROM AUTHOR]

Hydrologic cycle intensification is an expected manifestation of a warming climate. Although positive trends in several global average quantities have been reported, no previous studies have documented broad intensification across elements of the Arctic freshwater cycle (FWC). In this study, the authors examine the character and quantitative significance of changes in annual precipitation, evapotranspiration, and river discharge across the terrestrial pan-Arctic over the past several decades from observations and a suite of coupled general circulation models (GCMs). Trends in freshwater flux and storage derived from observations across the Arctic Ocean and surrounding seas are also described. With few exceptions, precipitation, evapotranspiration, and river discharge fluxes from observations and the GCMs exhibit positive trends. Significant positive trends above the 90%% confidence level, however, are not present for all of the observations. Greater confidence in the GCM trends arises through lower interannual variability relative to trend magnitude. Put another way, intrinsic variability in the observations tends to limit confidence in trend robustness. Ocean fluxes are less certain, primarily because of the lack of long-term observations. Where available, salinity and volume flux data suggest some decrease in saltwater inflow to the Barents Sea (i.e., a decrease in freshwater outflow) in recent decades. A decline in freshwater storage across the central Arctic Ocean and suggestions that large-scale circulation plays a dominant role in freshwater trends raise questions as to whether Arctic Ocean freshwater flows are intensifying. Although oceanic fluxes of freshwater are highly variable and consistent trends are difficult to verify, the other components of the Arctic FWC do show consistent positive trends over recent decades. The broad-scale increases provide evidence that the Arctic FWC is experiencing intensification. Efforts that aim to develop an adequate observation system are needed to reduce uncertainties and to detect and document ongoing changes in all system components for further evidence of Arctic FWC intensification. [ABSTRACT FROM AUTHOR]

Observations show that increased Arctic cloud cover in the spring is linked with sea ice decline. As the atmosphere and sea ice can influence each other, which one plays the leading role in spring remains unclear. Here we demonstrate, through observational data diagnosis and numerical modeling, that there is active coupling between the atmosphere and sea ice in early spring. Sea ice melting and thus the presence of more open water lead to stronger evaporation and promote cloud formation that increases downward longwave flux, leading to even more ice melt. Spring clouds are a driving force in the disappearance of sea ice and displacing the mechanism of atmosphere‐sea ice coupling from April to June. These results suggest the need to accurately model interactions of Arctic clouds and radiation in Earth System Models in order to improve projections of the future of the Arctic. Plain Language Summary: Arctic summer sea ice has declined by nearly 50%, leading to a larger exposed area of open water that persists longer than before. Clouds have large influences on Arctic sea ice long‐term trends and variability. Atmosphere and sea ice are believed to actively interact with each other in spring. But attributing cause and effect is difficult. Therefore, this study seeks to answer the following question: does the atmosphere primarily drive the sea ice changes or does the sea ice dominate changes in the atmosphere in spring? In this study, we isolated the atmospheric response to Arctic sea ice changes from coupled system through both observations and model simulations. It suggests that this relationship is initiated with active coupling in March. Spring clouds then become a driving force in the disappearance of sea ice from April to June. Overall, identifying the two‐way interactions between Arctic sea ice and atmosphere is a critical step to improve seasonal sea ice forecasts and future sea ice prediction. The sea ice coverage and length of the open water season is important for human activities and wildlife. The long‐term time series will inform future planning of military, civilian, and commercial infrastructure. Key Points: Active coupling is found between the atmosphere and sea ice in early springClouds are one of important drivers for sea ice melting from April to June [ABSTRACT FROM AUTHOR]

• Intrinsic predictability is spatially variable across populations of a species. • Intrinsic predictability can indicate temporal transferability of time series models. • Intrinsic predictability and strength of distance decay is positively associated. Ecological predictions are necessary for testing whether processes hypothesized to regulate species population dynamics are generalizable across time and space. In order to demonstrate generalizability, model predictions should be transferable in one or more dimensions, where transferability is the successful prediction of responses outside of the model data bounds. While much is known as to what makes spatially-oriented models transferable, there is no general consensus as to the spatio-temporal transferability of ecological time series models. Here, we examine whether the intrinsic predictability of a time series, as measured by its complexity, could limit such transferability using an exceptional long-term dataset of Adélie penguin breeding abundance time series collected at 24 colonies around Antarctica. For each colony, we select a suite of environmental variables from the Community Earth System Model, version 2 to predict population growth rates, before assessing how well these environmentally-dependent population models transfer temporally and how reliably temporal signals replicate through space. We show that weighted permutation entropy (WPE), a model-free measure of intrinsic predictability recently introduced to ecology, varies spatially across Adélie penguin populations, perhaps in response to stochastic environmental events. We demonstrate that WPE can strongly limit temporal predictive performance, although this relationship could be weakened if intrinsic predictability is not constant over time. Lastly, we show that WPE can also limit spatial forecast horizon, which we define as the decay in spatial predictive performance with respect to the physical distance between focal colony and predicted colony. Irrespective of intrinsic predictability, spatial forecast horizons for all Adélie penguin breeding colonies included in this study are surprisingly short and our population models often have similar temporal and spatial predictive performance compared to null models based on long-term average growth rates. For cases where time series are complex, as measured by WPE, and the transferability of biologically-motivated mechanistic models are poor, we advise that null models should instead be used for prediction. These models are likely better at capturing more generalizable relationships between average growth rates and long-term environmental conditions. Lastly, we recommend that WPE can provide valuable insights when evaluating model performance, designing sampling or monitoring programs, or assessing the appropriateness of preexisting datasets for making conservation management decisions in response to environmental change. [ABSTRACT FROM AUTHOR]

The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño-Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden-Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings. [ABSTRACT FROM AUTHOR]

Projections of polar bear (Ursus maritimus) sea ice habitat distribution in the - polar basin during the 21st century were developed to understand the consequences of anticipated sea ice reductions on polar bear populations. We used location data from satellite- collared polar bears and environmental data (e.g., bathymetry, distance to coastlines, and sea ice) collected from 1985 to 1995 to build resource selection functions (RSFs). RSFs described habitats that polar bears preferred in summer, autumn, winter, and spring. When applied to independent data from 1996 to 2006, the RSFs consistently identified habitats most frequently used by polar bears. We applied the RSFs to monthly maps of 21st-century sea ice concentration projected by 10 general circulation models (GCM5) used in the Intergovernmental Panel of Climate Change Fourth Assessment Report, under the Al B greenhouse gas forcing scenario. Despite variation in their projections, all GCMs indicated habitat losses in the polar basin during the 21st century. Losses in the highest-valued RSF habitat (optimal habitat) were greatest in the southern seas of the polar basin, especially the Chukchi and Barents seas, and least along the Arctic Ocean shores of Banks Island to northern Greenland. Mean loss of optimal polar bear habitat was greatest during summer; from an observed 1.0 million km2 in 1985-1995 (baseline) to a projected multi-model mean of 0.32 million km2 in 2090-2099 (-68% change). Projected winter losses of polar bear habitat were less: from 1.7 million km2 in 1985-1995 to 1.4 million km2 in 2090-2099 (-17% change). Habitat losses based on GCM multi-model means may be conservative; simulated rates of habitat loss during 1985-2006 from many GCMs were less than the actual observed rates of loss. Although a reduction in the total amount of optimal habitat will likely reduce polar bear populations, exact relationships between habitat losses and population demographics remain unknown. Density and energetic effects may become important as polar bears make long-distance annual migrations from traditional winter ranges to remnant high-latitude summer sea ice. These impacts will likely affect specific sex and age groups differently and may ultimately preclude bears from seasonally returning to their traditional ranges. [ABSTRACT FROM AUTHOR]

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations. [ABSTRACT FROM AUTHOR]

The seasonal and annual climatological behavior of selected components of the hydrological cycle are presented from coupled and uncoupled configurations of the atmospheric component of the Community Climate System Model (CCSM) Community Atmosphere Model version 3 (CAM3). The formulations of processes that play a role in the hydrological cycle are significantly more complex when compared with earlier versions of the atmospheric model. Major features of the simulated hydrological cycle are compared against available observational data, and the strengths and weaknesses are discussed in the context of specified sea surface temperature and fully coupled model simulations. The magnitude of the CAM3 hydrological cycle is weaker than in earlier versions of the model, and is more consistent with observational estimates. Major features of the exchange of water with the surface, and the vertically integrated storage of water in the atmosphere, are generally well captured on seasonal and longer time scales. The water cycle response to ENSO events is also very realistic. The simulation, however, continues to exhibit a number of long-standing biases, such as a tendency to produce double ITCZ-like structures in the deep Tropics, and to overestimate precipitation rates poleward of the extratropical storm tracks. The lower-tropospheric dry bias, associated with the parameterized treatment of convection, also remains a simulation deficiency. Several of these biases are exacerbated when the atmosphere is coupled to fully interactive surface models, although the larger-scale behavior of the hydrological cycle remains nearly identical to simulations with prescribed distributions of sea surface temperature and sea ice. [ABSTRACT FROM AUTHOR]

The trend for cold‐season (November‐December‐January‐February, NDJF) decreases in Arctic sea ice extent from 2000 to 2014 was about a factor of two larger than the 1979–2000 trend, and the warm‐season (June‐July‐August‐September, JJAS) trend was about a factor of three larger. Sensitivity experiments with an atmospheric model show that a negative convective heating anomaly in the tropical Pacific, associated with the negative Interdecadal Pacific Oscillation phase after 2000, produces an atmospheric teleconnection pattern over the Arctic comparable to the observations in NDJF but not JJAS. A positive convective heating anomaly over the tropical Atlantic, associated with warming sea surface temperatures there in the 2000–2014 period, produces a teleconnection pattern over the Arctic comparable to the observations in JJAS but not NDJF. Thus, the observed anomalously strong Arctic surface winds and sea ice drifts after 2000, which produced accelerated decreases in sea ice extent, likely had contributions from decadal‐time scale variability in the tropical Pacific and Atlantic. Plain Language Summary: Why did the trend of Arctic sea ice loss accelerate after about 2000? We show that a combination of decadally varying tropical sea surface temperatures in the Pacific and Atlantic drove seasonally dependent patterns of stronger surface winds and sea ice drifts over the Arctic that produced accelerated decreases of Arctic sea ice concentrations after 2000. Key Points: The trend of Arctic sea ice loss accelerated after about 2000Decadal‐time scale variability of tropical SSTs in the Pacific and Atlantic drove anomalously strong wind patterns over the Arctic after 2000Teleconnections from the tropics contributed to winds and ice drifts and consequent accelerated decreases of Arctic sea ice concentrations [ABSTRACT FROM AUTHOR]