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Abstract
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
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
Arctic sea ice has undergone significant change with large reductions in thickness and areal extent over the historical record. Numerical models project sea ice loss to continue for the foreseeable future, with the possibility of September ice-free conditions later this century. Understanding the mechanisms behind ice loss and its consequences for the larger Arctic and global systems is important if we are to anticipate and plan for the future. Meeting this challenge requires the collective and collaborative insights of scientists investigating the system from numerous perspectives. One impediment to progress has been a disconnect between the observational and modeling research communities. Advancing the science requires enhanced integration between these communities and more collaborative approaches to understanding Arctic sea ice loss. This paper discusses a successful effort to further these aims: a weeklong sea ice summer camp held in Barrow, Alaska (now known as Utqiaġvik), in May 2016. The camp brought together 25 participants who were a heterogeneous mix of observers and modelers from 13 different institutions at career stages from graduate students to senior researchers. The summer camp provided an accelerated program on sea ice observations and models and also fostered future collaborative interdisciplinary activities. A dialogue with Barrow community members was initiated in order to further understand the local consequences of Arctic sea ice loss. The discussion herein describes lessons learned from this activity and paths forward to advance the understanding and prediction of Arctic climate change.
Abstract
Arctic sea ice has undergone significant change with large reductions in thickness and areal extent over the historical record. Numerical models project sea ice loss to continue for the foreseeable future, with the possibility of September ice-free conditions later this century. Understanding the mechanisms behind ice loss and its consequences for the larger Arctic and global systems is important if we are to anticipate and plan for the future. Meeting this challenge requires the collective and collaborative insights of scientists investigating the system from numerous perspectives. One impediment to progress has been a disconnect between the observational and modeling research communities. Advancing the science requires enhanced integration between these communities and more collaborative approaches to understanding Arctic sea ice loss. This paper discusses a successful effort to further these aims: a weeklong sea ice summer camp held in Barrow, Alaska (now known as Utqiaġvik), in May 2016. The camp brought together 25 participants who were a heterogeneous mix of observers and modelers from 13 different institutions at career stages from graduate students to senior researchers. The summer camp provided an accelerated program on sea ice observations and models and also fostered future collaborative interdisciplinary activities. A dialogue with Barrow community members was initiated in order to further understand the local consequences of Arctic sea ice loss. The discussion herein describes lessons learned from this activity and paths forward to advance the understanding and prediction of Arctic climate change.
Abstract
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 THC standard deviation.
Abstract
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 THC standard deviation.
