Impact of Daily Arctic Sea Ice Variability in CAM3.0 during Fall and Winter

• Main Authors: Dammann, Dyre O., Bhatt, Uma S., Langen, Peter L., Krieger, Jeremy R., Zhang, Xiangdong
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2013
• Subjects: arctic; sea; ice; climate; Arctic Ocean; Sea ice
• Online Access: http://hdl.handle.net/11122/1877

Climate projections suggest that an ice-free summer Arctic Ocean is possible within several decades and with this comes the prospect of increased ship traffic and safety concerns. The daily sea ice concentration tendency in five Coupled Model Intercomparison Project phase 5 (CMIP5) simulations is compared with observations to reveal that many models underestimate this quantity that describes high-frequency ice movements, particularly in the marginal ice zone. To investigate whether high-frequency ice variability impacts the atmosphere, the Community Atmosphere Model, version 3.0 (CAM3.0), is forced by sea ice with and without daily fluctuations. Two 100-member ensemble experiments with daily varying (DAILY) and smoothly varying (SMTH) sea ice are conducted, along with a climatological control, for an anoma- lously low ice period (August 2006–November 2007). Results are presented for three periods: September 2006, October 2006, and December–February (DJF) 2006/07. The atmospheric response differs between DAILY and SMTH. In September, sea ice differences lead to an anomalous high and weaker storm activity over northern Europe. During October, the ice expands equatorward faster in DAILY than SMTH in the Siberian seas and leads to a local response of near-surface cooling. In DJF, there is a 1.5-hPa positive sea level pressure anomaly over North America, leading to anomalous northerly flow and anomalously cool continental U.S. temperatures. While the atmospheric responses are modest, the differences arising from high temporal frequency ice variability cannot be ignored. Increasing the accuracy of coupled model sea ice variations on short time scales is needed to improve short-term coupled model forecasts.