Gan Zhang

(Princeton University)

Reconciling Predictability and Uncertainties in Seasonal Predictions and Future Projections of Tropical Cyclone Activity.

When Nov 20, 2019
from 03:30 pm to 04:30 pm
Where 112 Walker Building, John J. Cahir Auditorium
Contact Name Kevin Bowley
Contact email
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Gan Zhang Princeton

Reconciling Predictability and Uncertainties in Seasonal Predictions and Future Projections of Tropical Cyclone Activity 

Gan Zhang 

Improvements of dynamic models have transformed long-range predictions of tropical cyclone (TC) activity, and these improvements also open up new paths for exploring the predictability and uncertainties for such predictions.

The first part of this talk focuses on the future projection of TC activity on the regional scale. The regional projection is currently considered highly uncertain because of uncertainties related to the large-scale environmental changes. Here we focus on some future environmental changes that are relatively robustly simulated by CMIP5 models but received little attention among TC researchers. Such environmental changes include a weakening of extratropical eddy activity and a poleward shift of midlatitude westerlies. Using idealized regional simulations, we show that a dramatic suppression of extratropical weather variability may profoundly affect TC activity in the subtropics, including the storm frequency and their movement. Using more realistic global large-ensemble simulations, we show that potential future changes in extratropical circulation with global warming could affect TC propagation. The findings may have important implications for populated coastal regions outside the tropics, where TC-related risks are relatively low in the current climate.

The second part of this talk investigates the seasonal predictability of TC activity and pathways to improve dynamic prediction systems. Using the ensemble hindcasts by GFDL’s FLOR prediction system, we show that TC activity in coastal regions and/or at higher latitudes is sensitive to uncertainties of initial conditions. Our analysis also suggests that the seasonal predictability of regional and basin-wide TC activity might be higher than the skill that has been realized by pre-existing FLOR prediction systems. Using idealized prediction experiments, we show that the gap might be narrowed by reducing ocean biases or improving the initialization of land-atmosphere components. With improved simulations of the large-scale atmospheric environment in the tropics and/or the extratropics, the skill gains of seasonal TC prediction are statistically significant. The findings suggest new research opportunities and will help with the design of next-generation prediction systems of TC activity.