On The Seasonal Predictability of East Asian Rainfall and Rapidly Intensifying North Atlantic Tropical Cyclones - Ng, Ching Ho Justin (Princeton, NJ : Princeton University, 2019)
The provision of accurate weather and climate predictions at timescales of days to decades has been a major research goal in atmospheric and oceanic sciences. This dissertation explores the issue of 'seasonal predictability', the potential of the climate system being predicted a season in advance. The studies in this dissertation examine the seasonal predictability of two aspects of the climate system: (1) rainfall, including its extremes; and (2) tropical cyclones (TCs), particularly those that undergo rapid intensification (RI). The first study examines the response of rainfall in East Asia to the El Nino-Southern Oscillation (ENSO) phenomenon, and demonstrates an asymmetric response of rainfall to ENSO along the southeastern coast of China during boreal fall/winter. Anomalous rainfall is observed during both El Nino and La Nina compared to the ENSO-Neutral phase. We argue that precipitation anomalies during El Nino arise from anomalous onshore moisture fluxes, while those during La Nina are driven by the persistence of terrestrial moisture anomalies from earlier excess rainfall in this region, highlighting the role of land-atmosphere interactions in maintaining ENSO-climate teleconnections. The second study explores the observational connections between the large-scale environment and the seasonal statistics of rapidly intensifying North Atlantic TCs. For TCs in the Central/Eastern tropical North Atlantic, the interannual variability of their probability to experience RI is influenced by the seasonal large-scale environment, but not for TCs over the Gulf of Mexico and Western Caribbean Seas. We suggest that this differentiated response is due to the former region exhibiting negatively correlated seasonal anomalies of vertical wind shear and potential intensity. This motivates a subsequent chapter, which examines the physical mechanisms behind the negative correlation, and applies the findings to global TC basins. The final chapter extends the environmental controls on RI to numerical models, and explores: (1) the simulation of the seasonal large-scale environment in climate models, as an indirect means of RI seasonal predictability; (2) the role of large-scale environmental biases in TC intensity biases in weather forecast models. Assessment of RI predictability through weather and climate models will contribute to the long-term research effort in TC modeling and prediction.