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- Author or Editor: Cecilia Bitz x
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Abstract
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
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
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
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.
