365 Effects of Ocean Mesoscale Eddies on Atmospheric Circulation and Predictability

Monday, 8 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Yinglai Jia, Ocean Univ. of China, Qingdao, China; and P. Chang, I. Szunyogh, and R. Saravanan

Recent studies have demonstrated that mesoscale SST anomalies along major oceanic fronts, such as the Kuroshio Extension and Gulf Stream, can have a significant influence on not only local air-sea coupling, but also downstream winter storm track and rainfall. However, a fundamental understanding of the mechanism linking mesoscale SST forcing to storm track changes is still lacking. We attempt to further explore this issue by conducting large ensembles of subseasonal-style forecast experiments using a high-resolution (0.25°x0.25°) AGCM, i.e., NCAR CAM5 model, coupled to a slab ocean model (hereafter CAM5-SOM). Two ensembles of runs were carried out, each consisting of thirty 30-day forecasts for December 2007. The two ensembles differ in their treatment of SST. For control runs (CTRL), high-resolution (0.25°x0.25°) observed daily SST was used to both derive the Q-flux for CAM5-SOM and initialize the forecasts, whereas for filtered runs (FLTR), a 5°x5° 2D boxcar filter was applied to the high-resolution SST before it was used for Q-flux calculation and forecast initialization. As a result, the FLTR ensemble significantly weakens the influence of mesoscale SSTs on the atmosphere while retaining the large-scale SST influence. Comparisons between CTRL and FLTR reveal a robust difference in both the mean state and inter-ensemble deviation of extratropical atmosphere circulations, indicating a marked influence of mesoscale SSTs on the atmosphere. In the North Pacific sector, filtering the mesoscale SST results in an equivalent barotropic circulation anomaly in the eastern North Pacific, indicative of a change in the Pacific storm track, which is consistent with previous modeling studies (e.g. Ma et al. 2015). We discuss the implications of these modeling results for subseasonal-to-seasonal predictability.
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