2014: Internal variability in projections of twenty-first century Arctic sea ice loss: Role of the large-scale atmospheric circulation
Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate SystemModel, version 3 (CCSM3) ensemble for the period 2000–61. Eachmember is subject to an identical greenhouse ga...
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| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.698.8768 http://www.cgd.ucar.edu/staff/cdeser/docs/wettstein.IV_Arctic_seaice.jclim14.pdf |
| Summary: | Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate SystemModel, version 3 (CCSM3) ensemble for the period 2000–61. Eachmember is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component’s initial condition. SeptemberArctic sea ice extent trends during 2020–59 range from22.03 106 to25.73 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from27.03 103 to2193 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside theArctic, an atmospheric Rossby wave train over the Pacific sector is associatedwith internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice–atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric tele-connection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings. 1. |
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