143 Projected Storm Track Changes in CMIP5 Model Simulations

Monday, 11 January 2016
Albert M. W. Yau, Stony Brook University, Stony Brook, NY; and K. Paul, J. Dennis, and E. K. M. Chang

Cyclones are responsible for much of the high impact weather in the mid-latitudes. The collections of individual cyclone tracks passing through a region are called “storm tracks”. One way to quantify storm track activity is to compute time filtered mean sea level pressure (MSLP) variance keeping only the synoptic time scale. Another way is to use objective cyclone tracking program to locate cyclone tracks and compile track statistics. We have developed a parallel and object-oriented cyclone tracker in the Python programming language. The cyclones are located by local minima and tracked by nearest neighborhood method.

The Coupled Model Intercomparison Project Phase 5 (CMIP5) archive includes 28 models with 6-hourly outputs for the historical and RCP8.5 scenarios covering 1950 to 2100. In this study, we compute storm track activity as the seasonal MSLP variance for all 28 models and explore projected changes by CMIP5 models. In the Northern hemisphere winter, models do not show consistent trends in the MSLP variance, however most models project a decrease over North America, in particular during the second half of the 21st century. In the Southern hemisphere, most models show intensification and poleward shift of storm tracks in both summer and winter, corresponding to similar changes in the jet stream. There is a delay in the change during the first half of the 21st century in summer, consistent with the onset of ozone recovery.

Currently we are verifying the quality of the tracks generated by the Python cyclone tracker. We will test and compare detected cyclone tracks from different tracking strategies, for example by removing a large scale background field before tracking. Preliminary results suggest that strong cyclone counts are more consistent with tracks from other cyclone trackers. We are compiling cyclone track statistics for all CMIP5 model experiments using the Python cyclone tracker. We will present results comparing trends in cyclone track statistics to those computed based on MSLP variance statistics described above.

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