Development and applications of a new genesis potential index

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Presentation transcript:

Development and applications of a new genesis potential index Suzana J. Camargo, Lamont-Doherty Earth Observatory, Columbia University Michael K. Tippett, International Research Institute for Climate and Society, Columbia University Adam H. Sobel, Department of Applied Physics and Applied Mathematics, Department of Earth and Environmental Sciences, Columbia University Gabriel A. Vecchi and Ming Zhao Geophysics Fluids Dynamics Laboratory, NOAA 29th AMS Conference on Hurricanes and Tropical Meteorology, 10-14 May 2010, Tucson, AZ

Outline Development of an new index Tippett, M.K., S.J. Camargo, and A.H. Sobel, 2010: A Poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis, submitted to J. Climate, April 2010 Application to the GFDL 50km global climate model Work in progress Collaboration with Gabriel Vecchi and and Ming Zhao (GFDL)

Motivation Relationship: tropical cyclone genesis (TCG) and large-scale climate - empirical Incremental improvement on Emanuel and Nolan’s GPI Index development: clear, objective and reproducible Role of potential intensity (PI) as a thermodynamic predictor Choice of relative humidity on a single level as a thermodynamic predictor Systematic biases of GPI (Atlantic Main Development Region) Application of TCGI to GFDL 50km model

Poisson Regression Poisson regression appropriate: model TC counts Poisson distribution: P (N=n) = e- n / n! Poisson linear regression model: = exp (bT x + log cos) Offset term log cos ( is latitude): units number of TCG events per area. Coefficients: maximizing the log-likelihood Variables - similar to Emanuel’s GPI - climatology: Absolute vorticity 850hPa Relative humidity at 600hPa / column relative humidity Vertical shear between 200hPa & 850hPa Relative SST (difference SST and tropical mean SST)

Relative humidity 600hPa

Relative Humidity: Column relative humidity (procedure developed in Bretherton et al. 2004)* W = SSMI column-integrated liquid water (daily average) W* = Daily saturation water vapor path (temperature from reanalysis) from saturation specific humidity at each pressure level H = W/W* ---> monthly means, and monthly climatology Column vs. 600hPa relative humidity: Better fit to observations (ERA+NCEP) Separate fits ERA & NCEP: improvement over NCEP but not ERA. * Thanks to Larissa Back for scripts for calculating column relative humidity

Potential Intensity and relative SST Strong empirical relationship with PI (Vecchi & Soden 2007) Easy to calculate More appropriate than absolute SST: influence of climate variability to genesis Potential Intensity (PI) TC theoretical maximum intensity - not necessarily optimal choice for genesis More complex computation Includes atmospheric data Better fit of Poisson model with relative SST than PI.

Role of vorticity Experiment adding powers and products of variables in the Poisson regression Vorticity: square with negative coefficient: Large absolute vorticity: reduced number TCGs High latitudes: high vorticity and lack of TCGCs

Interpretation of vorticity dependency Clipped vorticity: better performance! Vorticity: necessary for genesis (no genesis very near the equator) Once vorticity ABOVE a threhsold: not a limiting factor for genesis - other variables more critical role (SST, shear). Daily time-scales: vorticity in precursor important. Monthly time-scales: genesis probability does not increase over a vorticity threshold.

TCGI = exp(-11.18 + 1.22 min(||,3.7) + TCG index Best fit TCG index: TCGI = exp(-11.18 + 1.22 min(||,3.7) + 0.10 H +0.52 RSST - 0.12 VS) || = absolute vorticity (850hPa) x 105 H = column relative humidity RSST = relative SST (SST - mean tropical SST) VS = vertical shear (200hPa and 850hPa)

Application to GFDL 50 km global model

Zhao et al., MWR, 2009

Summary New tropical cyclone genesis index developed Dependence on relative SST, column relative humidity explored Vorticity dependency suggests that increasing the value of environmental vorticity over a certain value does not increase the probability of genesis. Application of TCGI to GFDL model shows the validity of using the index in models. Preliminary diagnostics on GFDL model in warm climates show the importance of the SST patterns on TC response. Future work: Quantitative diagnostic using TCGI coefficients Comparison with Emanuel’s new GPI (different humidity variable)