J. Climate, submitted December 2015.

Auto-regressive modeling for tropical cyclone intensity climatology.

Chia-Ying Lee
International Research Institute for Climate and Society, Columbia University, Palisades, NY

Michael K. Tippett
Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY

Adam H. Sobel
Department of Applied Physics and Applied Mathematics, and Lamont-Doherty Earth Observatory, Columbia University, New York, NY

Suzana J. Camargo
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY.


An autoregressive model is developed for simulating the climatological distribution of global tropical cyclone (TC) intensity. The model consists of two components: a regression-based deterministic component which advances the TC intensity in time and depends on the storm state and surrounding largescale environment, and a stochastic forcing. Potential intensity, deep-layer mean vertical shear, and mid-level relative humidity are the environmental variables included in the deterministic component. Given a storm track and its environment, the model is initialized and then iterated along the track. The model performance is evaluated by its ability to represent the observed global and basin distributions of TC intensity and as well as lifetime maximum intensity (LMI). The deterministic model alone captures the spatial features of the climatological TC intensity distribution, but with intensities that are no stronger than 100 kt. Addition of white (uncorrelated in time) stochastic forcing reduces this bias, but autocorrelated stochastic forcing is necessary to achieve realistic intensities. Adding a nonlinear dependence on potential intensity to the deterministic component further improves the simulated intensification rates and frequency of major storms.