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Abstract
Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.
Abstract
Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.
Abstract
The simulation of Arctic sea ice and surface winds changes significantly when Community Climate System Model version 3 (CCSM3) resolution is increased from T42 (∼2.8°) to T85 (∼1.4°). At T42 resolution, Arctic sea ice is too thick off the Siberian coast and too thin along the Canadian coast. Both of these biases are reduced at T85 resolution. The most prominent surface wind difference is the erroneous North Polar summer anticyclone, present at T42 but absent at T85.
An offline sea ice model is used to study the effect of the surface winds on sea ice thickness. In this model, the surface wind stress is prescribed alternately from reanalysis and the T42 and T85 simulations. In the offline model, CCSM3 surface wind biases have a dramatic effect on sea ice distribution: with reanalysis surface winds annual-mean ice thickness is greatest along the Canadian coast, but with CCSM3 winds thickness is greater on the Siberian side. A significant difference between the two CCSM3-forced offline simulations is the thickness of the ice along the Canadian archipelago, where the T85 winds produce thicker ice than their T42 counterparts. Seasonal forcing experiments, with CCSM3 winds during spring and summer and reanalysis winds in fall and winter, relate the Canadian thickness difference to spring and summer surface wind differences. These experiments also show that the ice buildup on the Siberian coast at both resolutions is related to the fall and winter surface winds.
The Arctic atmospheric circulation is examined further through comparisons of the winter sea level pressure (SLP) and eddy geopotential height. At both resolutions the simulated Beaufort high is quite weak, weaker at higher resolution. Eddy heights show that the wintertime Beaufort high in reanalysis has a barotropic vertical structure. In contrast, high CCSM3 SLP in Arctic winter is found in association with cold lower-tropospheric temperatures and a baroclinic vertical structure.
In reanalysis, the summertime Arctic surface circulation is dominated by a polar cyclone, which is accompanied by surface inflow and a deep Ferrel cell north of the traditional polar cell. The Arctic Ferrel cell is accompanied by a northward flux of zonal momentum and a polar lobe of the zonal-mean jet. These features do not appear in the CCSM3 simulations at either resolution.
Abstract
The simulation of Arctic sea ice and surface winds changes significantly when Community Climate System Model version 3 (CCSM3) resolution is increased from T42 (∼2.8°) to T85 (∼1.4°). At T42 resolution, Arctic sea ice is too thick off the Siberian coast and too thin along the Canadian coast. Both of these biases are reduced at T85 resolution. The most prominent surface wind difference is the erroneous North Polar summer anticyclone, present at T42 but absent at T85.
An offline sea ice model is used to study the effect of the surface winds on sea ice thickness. In this model, the surface wind stress is prescribed alternately from reanalysis and the T42 and T85 simulations. In the offline model, CCSM3 surface wind biases have a dramatic effect on sea ice distribution: with reanalysis surface winds annual-mean ice thickness is greatest along the Canadian coast, but with CCSM3 winds thickness is greater on the Siberian side. A significant difference between the two CCSM3-forced offline simulations is the thickness of the ice along the Canadian archipelago, where the T85 winds produce thicker ice than their T42 counterparts. Seasonal forcing experiments, with CCSM3 winds during spring and summer and reanalysis winds in fall and winter, relate the Canadian thickness difference to spring and summer surface wind differences. These experiments also show that the ice buildup on the Siberian coast at both resolutions is related to the fall and winter surface winds.
The Arctic atmospheric circulation is examined further through comparisons of the winter sea level pressure (SLP) and eddy geopotential height. At both resolutions the simulated Beaufort high is quite weak, weaker at higher resolution. Eddy heights show that the wintertime Beaufort high in reanalysis has a barotropic vertical structure. In contrast, high CCSM3 SLP in Arctic winter is found in association with cold lower-tropospheric temperatures and a baroclinic vertical structure.
In reanalysis, the summertime Arctic surface circulation is dominated by a polar cyclone, which is accompanied by surface inflow and a deep Ferrel cell north of the traditional polar cell. The Arctic Ferrel cell is accompanied by a northward flux of zonal momentum and a polar lobe of the zonal-mean jet. These features do not appear in the CCSM3 simulations at either resolution.
Abstract
This study explores the role of sea ice freshwater and salt fluxes in modulating twenty-first-century surface warming in the Southern Ocean via analysis of sensitivity experiments in the Community Climate System Model, version 3 (CCSM3). In particular, the role of a change in these fluxes in causing surface cooling, expanding sea ice, and increasing deep oceanic storage of heat in the Southern Ocean is investigated. The results indicate that in response to the doubling of CO2 concentrations in the atmosphere in CCSM3, net freshwater input from sea ice to the ocean increases south of 58°S (owing to less growth) and decreases from 48° to 58°S (owing to less melt). The freshwater source from changing precipitation in the model is considerably less than from sea ice south of 58°S, but it serves to compensate for the reduction in sea ice melt near the ice edge, leaving almost no net freshwater flux change between about 48° and 58°S. As a result, freshwater input principally from sea ice reduces ocean convection, which in turns reduces the entrainment of heat into the mixed layer and reduces the upward heat transport along isopycnals below about 1000 m. The reduced upward heat transport (from all sources) causes deep-ocean heating south of 60°S and below 500-m depth, with a corresponding surface cooling in large parts of the Southern Ocean in the model. These results indicate that changing sea ice freshwater and salt fluxes are a major component of the twenty-first-century delay in surface warming of the Southern Ocean and weak reduction in Antarctic sea ice in model projections.
Abstract
This study explores the role of sea ice freshwater and salt fluxes in modulating twenty-first-century surface warming in the Southern Ocean via analysis of sensitivity experiments in the Community Climate System Model, version 3 (CCSM3). In particular, the role of a change in these fluxes in causing surface cooling, expanding sea ice, and increasing deep oceanic storage of heat in the Southern Ocean is investigated. The results indicate that in response to the doubling of CO2 concentrations in the atmosphere in CCSM3, net freshwater input from sea ice to the ocean increases south of 58°S (owing to less growth) and decreases from 48° to 58°S (owing to less melt). The freshwater source from changing precipitation in the model is considerably less than from sea ice south of 58°S, but it serves to compensate for the reduction in sea ice melt near the ice edge, leaving almost no net freshwater flux change between about 48° and 58°S. As a result, freshwater input principally from sea ice reduces ocean convection, which in turns reduces the entrainment of heat into the mixed layer and reduces the upward heat transport along isopycnals below about 1000 m. The reduced upward heat transport (from all sources) causes deep-ocean heating south of 60°S and below 500-m depth, with a corresponding surface cooling in large parts of the Southern Ocean in the model. These results indicate that changing sea ice freshwater and salt fluxes are a major component of the twenty-first-century delay in surface warming of the Southern Ocean and weak reduction in Antarctic sea ice in model projections.
Abstract
El Niño–Southern Oscillation (ENSO) and its teleconnections form the leading mode of interannual variability in the global climate system, yet the small sample size of ENSO events during which we have reliable Arctic observations makes constraining its influence on Arctic sea ice challenging. We compare the influence of ENSO on Arctic sea ice in six models from the Multi-Model Large Ensemble Archive with that in observations. Each model simulates reduced Arctic sea ice area and volume in the seasons following an El Niño relative to a La Niña. The patterns of sea ice concentration and thickness responses to ENSO are spatially heterogeneous, with regions of increased and decreased sea ice. The small sample size of ENSO events in observations is shown to preclude a statistically significant sea ice response from being identified. While models agree with one another on many aspects of the sea ice response to ENSO, some features are model dependent. For example, the CESM1-LE alone displays a delayed melting response in summer, driven by reduced surface albedo and increased shortwave absorption. A positive Arctic Oscillation and a deepened Aleutian low are common responses to ENSO across models and observations. These patterns of atmospheric variability are quantitatively shown to be key in linking ENSO to Arctic sea ice in most models, acting primarily through sea ice dynamics to generate anomalous sea ice thickness and concentration patterns.
Abstract
El Niño–Southern Oscillation (ENSO) and its teleconnections form the leading mode of interannual variability in the global climate system, yet the small sample size of ENSO events during which we have reliable Arctic observations makes constraining its influence on Arctic sea ice challenging. We compare the influence of ENSO on Arctic sea ice in six models from the Multi-Model Large Ensemble Archive with that in observations. Each model simulates reduced Arctic sea ice area and volume in the seasons following an El Niño relative to a La Niña. The patterns of sea ice concentration and thickness responses to ENSO are spatially heterogeneous, with regions of increased and decreased sea ice. The small sample size of ENSO events in observations is shown to preclude a statistically significant sea ice response from being identified. While models agree with one another on many aspects of the sea ice response to ENSO, some features are model dependent. For example, the CESM1-LE alone displays a delayed melting response in summer, driven by reduced surface albedo and increased shortwave absorption. A positive Arctic Oscillation and a deepened Aleutian low are common responses to ENSO across models and observations. These patterns of atmospheric variability are quantitatively shown to be key in linking ENSO to Arctic sea ice in most models, acting primarily through sea ice dynamics to generate anomalous sea ice thickness and concentration patterns.
Abstract
The Northern Hemisphere sea ice cover has diminished rapidly in recent years and is projected to continue to diminish in the future. The year-to-year retreat of Northern Hemisphere sea ice extent is faster in summer than winter, which has been identified as one of the most striking features of satellite observations as well as of state-of-the-art climate model projections. This is typically understood to imply that the sea ice cover is most sensitive to climate forcing in summertime, and previous studies have explained this by calling on factors such as the surface albedo feedback. In the Southern Hemisphere, however, it is the wintertime sea ice extent that retreats fastest in climate model projections. Here, it is shown that the interhemispheric differences in the model projections can be attributed to differences in coastline geometry, which constrain where sea ice can occur. After accounting for coastline geometry, it is found that the sea ice changes simulated in both hemispheres in most climate models are consistent with sea ice retreat being fastest in winter in the absence of landmasses. These results demonstrate that, despite the widely differing rates of ice retreat among climate model projections, the seasonal structure of the sea ice retreat is robust among the models and is uniform in both hemispheres.
Abstract
The Northern Hemisphere sea ice cover has diminished rapidly in recent years and is projected to continue to diminish in the future. The year-to-year retreat of Northern Hemisphere sea ice extent is faster in summer than winter, which has been identified as one of the most striking features of satellite observations as well as of state-of-the-art climate model projections. This is typically understood to imply that the sea ice cover is most sensitive to climate forcing in summertime, and previous studies have explained this by calling on factors such as the surface albedo feedback. In the Southern Hemisphere, however, it is the wintertime sea ice extent that retreats fastest in climate model projections. Here, it is shown that the interhemispheric differences in the model projections can be attributed to differences in coastline geometry, which constrain where sea ice can occur. After accounting for coastline geometry, it is found that the sea ice changes simulated in both hemispheres in most climate models are consistent with sea ice retreat being fastest in winter in the absence of landmasses. These results demonstrate that, despite the widely differing rates of ice retreat among climate model projections, the seasonal structure of the sea ice retreat is robust among the models and is uniform in both hemispheres.
Abstract
The sensitivity of global climate with respect to forcing is generally described in terms of the global climate feedback—the global radiative response per degree of global annual mean surface temperature change. While the global climate feedback is often assumed to be constant, its value—diagnosed from global climate models—shows substantial time variation under transient warming. Here a reformulation of the global climate feedback in terms of its contributions from regional climate feedbacks is proposed, providing a clear physical insight into this behavior. Using (i) a state-of-the-art global climate model and (ii) a low-order energy balance model, it is shown that the global climate feedback is fundamentally linked to the geographic pattern of regional climate feedbacks and the geographic pattern of surface warming at any given time. Time variation of the global climate feedback arises naturally when the pattern of surface warming evolves, actuating feedbacks of different strengths in different regions. This result has substantial implications for the ability to constrain future climate changes from observations of past and present climate states. The regional climate feedbacks formulation also reveals fundamental biases in a widely used method for diagnosing climate sensitivity, feedbacks, and radiative forcing—the regression of the global top-of-atmosphere radiation flux on global surface temperature. Further, it suggests a clear mechanism for the “efficacies” of both ocean heat uptake and radiative forcing.
Abstract
The sensitivity of global climate with respect to forcing is generally described in terms of the global climate feedback—the global radiative response per degree of global annual mean surface temperature change. While the global climate feedback is often assumed to be constant, its value—diagnosed from global climate models—shows substantial time variation under transient warming. Here a reformulation of the global climate feedback in terms of its contributions from regional climate feedbacks is proposed, providing a clear physical insight into this behavior. Using (i) a state-of-the-art global climate model and (ii) a low-order energy balance model, it is shown that the global climate feedback is fundamentally linked to the geographic pattern of regional climate feedbacks and the geographic pattern of surface warming at any given time. Time variation of the global climate feedback arises naturally when the pattern of surface warming evolves, actuating feedbacks of different strengths in different regions. This result has substantial implications for the ability to constrain future climate changes from observations of past and present climate states. The regional climate feedbacks formulation also reveals fundamental biases in a widely used method for diagnosing climate sensitivity, feedbacks, and radiative forcing—the regression of the global top-of-atmosphere radiation flux on global surface temperature. Further, it suggests a clear mechanism for the “efficacies” of both ocean heat uptake and radiative forcing.
Abstract
Stratospheric sulfate aerosol injection has been proposed to counteract anthropogenic greenhouse gas warming and prevent regional climate emergencies. Global warming is projected to be largest in the polar regions, where consequences to climate change could be emergent, but where the climate response to global warming is also most uncertain. The Community Climate System Model, version 3, is used to evaluate simulations with enhanced CO2 and prescribed stratospheric sulfate to investigate the effects on regional climate. To further explore the sensitivity of these regions to ocean dynamics, a suite of simulations with and without ocean dynamics is run.
The authors find that, when global average warming is roughly canceled by aerosols, temperature changes in the polar regions are still 20%–50% of the changes in a warmed world. Atmospheric circulation anomalies are also not canceled, which affects the regional climate response. It is also found that agreement between simulations with and without ocean dynamics is poorest in the high latitudes. The polar climate is determined by processes that are highly parameterized in climate models. Thus, one should expect that the projected climate response to geoengineering will be at least as uncertain in these regions as it is to increasing greenhouse gases. In the context of climate emergencies, such as melting arctic sea ice and polar ice sheets and a food crisis due to a heated tropics, the authors find that, while it may be possible to avoid tropical climate crises, preventing polar climate emergencies is not certain. A coordinated effort across modeling centers is required to generate a more robust depiction of a geoengineered climate.
Abstract
Stratospheric sulfate aerosol injection has been proposed to counteract anthropogenic greenhouse gas warming and prevent regional climate emergencies. Global warming is projected to be largest in the polar regions, where consequences to climate change could be emergent, but where the climate response to global warming is also most uncertain. The Community Climate System Model, version 3, is used to evaluate simulations with enhanced CO2 and prescribed stratospheric sulfate to investigate the effects on regional climate. To further explore the sensitivity of these regions to ocean dynamics, a suite of simulations with and without ocean dynamics is run.
The authors find that, when global average warming is roughly canceled by aerosols, temperature changes in the polar regions are still 20%–50% of the changes in a warmed world. Atmospheric circulation anomalies are also not canceled, which affects the regional climate response. It is also found that agreement between simulations with and without ocean dynamics is poorest in the high latitudes. The polar climate is determined by processes that are highly parameterized in climate models. Thus, one should expect that the projected climate response to geoengineering will be at least as uncertain in these regions as it is to increasing greenhouse gases. In the context of climate emergencies, such as melting arctic sea ice and polar ice sheets and a food crisis due to a heated tropics, the authors find that, while it may be possible to avoid tropical climate crises, preventing polar climate emergencies is not certain. A coordinated effort across modeling centers is required to generate a more robust depiction of a geoengineered climate.
Abstract
A simple model for studying the Dansgaard–Oeschger (D-O) cycles of the last glacial period is presented, based on the T. Dokken et al. hypothesis for D-O cycles. The model is a column model representing the Nordic seas and is composed of ocean boxes stacked below a one-layer sea ice model with an energy-balance atmosphere; no changes in the large-scale ocean overturning circulation are invoked. Parameterizations are included for latent heat polynyas and sea ice export from the column. The resulting heuristic model was found to cycle between stadial and interstadial states at times scales similar to those seen in the proxy observational data, with the presence or absence of perennial sea ice in the Nordic seas being the defining characteristic for each of these states. The major discrepancy between the modeled oscillations and the proxy record is in the length of the interstadial phase, which is shorter than that observed. The modeled oscillations were found to be robust to parameter changes, including those related to the ocean heat flux convergence (OHFC) into the column. Production of polynya ice was found to be an essential ingredient for such sustained oscillatory behavior. A simple parameterization of natural variability in the OHFC enhances the robustness of the modeled oscillations. The authors conclude by discussing the implications of such a hypothesis for the state of the Nordic seas today and its state during the Last Glacial Maximum and contrasting the model to other hypotheses that invoke large-scale changes in the Atlantic meridional overturning circulation for explaining millennial-scale variability in the climate system. An extensive time-scale analysis will be presented in the future.
Abstract
A simple model for studying the Dansgaard–Oeschger (D-O) cycles of the last glacial period is presented, based on the T. Dokken et al. hypothesis for D-O cycles. The model is a column model representing the Nordic seas and is composed of ocean boxes stacked below a one-layer sea ice model with an energy-balance atmosphere; no changes in the large-scale ocean overturning circulation are invoked. Parameterizations are included for latent heat polynyas and sea ice export from the column. The resulting heuristic model was found to cycle between stadial and interstadial states at times scales similar to those seen in the proxy observational data, with the presence or absence of perennial sea ice in the Nordic seas being the defining characteristic for each of these states. The major discrepancy between the modeled oscillations and the proxy record is in the length of the interstadial phase, which is shorter than that observed. The modeled oscillations were found to be robust to parameter changes, including those related to the ocean heat flux convergence (OHFC) into the column. Production of polynya ice was found to be an essential ingredient for such sustained oscillatory behavior. A simple parameterization of natural variability in the OHFC enhances the robustness of the modeled oscillations. The authors conclude by discussing the implications of such a hypothesis for the state of the Nordic seas today and its state during the Last Glacial Maximum and contrasting the model to other hypotheses that invoke large-scale changes in the Atlantic meridional overturning circulation for explaining millennial-scale variability in the climate system. An extensive time-scale analysis will be presented in the future.
Abstract
The temporal characteristics of Arctic sea ice extent and area are analyzed in terms of their lagged correlation in observations and a GCM ensemble. Observations and model output generally match, exhibiting a red-noise spectrum, where significant correlation (or memory) is lost within 2–5 months. September sea ice extent is significantly correlated with extent of the previous August and July, and thus these months show a predictive skill of the summer minimum extent. Beyond this initial loss of memory, there is an increase in correlation—a reemergence of memory—that is more ubiquitous in the model than observations. There are two distinct modes of memory reemergence in the model. The first, a summer-to-summer reemergence arises within the model from the persistence of thickness anomalies and their influence on ice area. The second, which is also seen in observations, is associated with anomalies in the growth season that originate in the melt season. This reemergence stems from the several-month persistence of SSTs. In the model memory reemergence is enhanced by the sea ice albedo feedback. The same mechanisms that give rise to reemergence also enhance the 1-month lagged correlation during summer and winter. The study finds the least correlation between successive months when the sea ice is most rapidly advancing or retreating.
Abstract
The temporal characteristics of Arctic sea ice extent and area are analyzed in terms of their lagged correlation in observations and a GCM ensemble. Observations and model output generally match, exhibiting a red-noise spectrum, where significant correlation (or memory) is lost within 2–5 months. September sea ice extent is significantly correlated with extent of the previous August and July, and thus these months show a predictive skill of the summer minimum extent. Beyond this initial loss of memory, there is an increase in correlation—a reemergence of memory—that is more ubiquitous in the model than observations. There are two distinct modes of memory reemergence in the model. The first, a summer-to-summer reemergence arises within the model from the persistence of thickness anomalies and their influence on ice area. The second, which is also seen in observations, is associated with anomalies in the growth season that originate in the melt season. This reemergence stems from the several-month persistence of SSTs. In the model memory reemergence is enhanced by the sea ice albedo feedback. The same mechanisms that give rise to reemergence also enhance the 1-month lagged correlation during summer and winter. The study finds the least correlation between successive months when the sea ice is most rapidly advancing or retreating.
Abstract
Sea ice predictability is a rapidly growing area of research, with most studies focusing on the Arctic. This study offers new insights by comparing predictability between the Arctic and Antarctic sea ice anomalies, focusing on the effects of regional differences in ice thickness and ocean dynamics. Predictability in simulated regional sea ice area and volume is investigated in long control runs of an Earth system model. Sea ice area predictability in the Arctic agrees with results from other studies, with features of decaying initial persistence and reemergence because of ocean mixed layer processes and memory in thick ice. In pan-Arctic averages, sea ice volume and the area covered by thick ice are the best predictors of September area for lead times greater than 2 months. In the Antarctic, area is generally the best predictor of future area for all times of year. Predictability of area in summer differs between the hemispheres because of unique aspects of the coupling between area and volume. Generally, ice volume only adds to the predictability of summer sea ice area in the Arctic. Predictability patterns vary greatly among different regions of the Arctic but share similar seasonality among regions of the Antarctic. Interactive ocean dynamics influence anomaly reemergence differently in the Antarctic than the Arctic, both for the total and regional area. In the Antarctic, ocean dynamics generally decrease the persistence of area anomalies, reducing predictability. In the Arctic, the presence of ocean dynamics improves ice area predictability, mainly through mixed layer depth variability.
Abstract
Sea ice predictability is a rapidly growing area of research, with most studies focusing on the Arctic. This study offers new insights by comparing predictability between the Arctic and Antarctic sea ice anomalies, focusing on the effects of regional differences in ice thickness and ocean dynamics. Predictability in simulated regional sea ice area and volume is investigated in long control runs of an Earth system model. Sea ice area predictability in the Arctic agrees with results from other studies, with features of decaying initial persistence and reemergence because of ocean mixed layer processes and memory in thick ice. In pan-Arctic averages, sea ice volume and the area covered by thick ice are the best predictors of September area for lead times greater than 2 months. In the Antarctic, area is generally the best predictor of future area for all times of year. Predictability of area in summer differs between the hemispheres because of unique aspects of the coupling between area and volume. Generally, ice volume only adds to the predictability of summer sea ice area in the Arctic. Predictability patterns vary greatly among different regions of the Arctic but share similar seasonality among regions of the Antarctic. Interactive ocean dynamics influence anomaly reemergence differently in the Antarctic than the Arctic, both for the total and regional area. In the Antarctic, ocean dynamics generally decrease the persistence of area anomalies, reducing predictability. In the Arctic, the presence of ocean dynamics improves ice area predictability, mainly through mixed layer depth variability.
