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(visiting BMRC/CAWCR, Melbourne, Australia, 8/07-7/08)

Some tests of our understanding of what controls tropical cyclone statistics Adam Sobel Columbia University New York, NY USA. (visiting BMRC/CAWCR, Melbourne, Australia, 8/07-7/08). 11/22/07. Collaborators. Suzana Camargo, Lamont-Doherty Earth Observatory, Columbia

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(visiting BMRC/CAWCR, Melbourne, Australia, 8/07-7/08)

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  1. Some tests of our understanding of what controls tropical cyclone statistics Adam Sobel Columbia UniversityNew York, NY USA (visiting BMRC/CAWCR, Melbourne, Australia, 8/07-7/08) 11/22/07

  2. Collaborators • Suzana Camargo, Lamont-Doherty Earth Observatory, Columbia • Allison Wing, Cornell (undergraduate!) • Kerry Emanuel, MIT • Tony Barnston, IRI, Columbia Wing, Sobel, and Camargo 2007: The relationship between the potential and actual intensities of tropical cyclones on interannual time scales. Geophysical Research Letters, 34, L08810, doi:10.1029/2006GL028581. Camargo, Emanuel, and Sobel 2007: Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. Journal of Climate, 20, 4819-4834. Camargo, Sobel, Barnston, and Emanuel 2007: Tropical cyclone genesis potential index in climate models. Tellus, 59A, 428-443.

  3. Big Questions • What controls the statistics of tropical cyclones - their numbers and intensities in a given basin in a given year? • How will these TC statistics change as climate changes? • We examine interannual variability of TC statistics in the historical record and compare it to the interannual variability of theoretically and empirically derived indices of the environment for TC genesis and intensification.

  4. Potential Intensity is a theoretical upper bound to TC intensity (Emanuel 1988, Holland 1997)

  5. In this study we use the Emanuel PI, which is (Bister and Emanuel 1998, 2002) Vm is the maximum gradient wind speed, Ts and T0 surface and outflow temperatures, Ck and CD surface bulk exchange coefficients, µe* is surface saturation µe, subscript m means evaluated at radius of maximum wind. This is equivalent to We use the latter to compute PI. It depends on the whole environmental temperature profile, since CAPE does.

  6. A storm is equally likely to reach any intensity between a lower bound and its potential intensity (Emanuel 2000) Hurricanes and tropical storms separately, cases limited by declining PI removed Cumulative distribution functions of observed TC intensity, normalized by potential Intensity. Derivative of CDF, the PDF, = constant for linear CDF) All storms, but using “modified maximum intensity” (max, or Max reached before V>PI)

  7. Assuming the observed uniform PDF is universal gives a prediction of average intensity changes under climate change • the prediction is that a given percentage increase in PI leads to an equal percentage increase in actual intensity (if no fixed threshold is applied, e.g. TS, category 1 etc. - in that case the predicted intensity change is < PI change) • We refer to this prediction as “PI Theory” although it is partly empirical, since based on the observed uniform PDF • Do the year-to-year fluctuations follow the theory? • Advantage of looking at interannual variability is that the potential time-dependent biases in the data sets won’t project much on it - e.g., observing practices don’t fluctuate with ENSO

  8. Procedure • Best track data for track & intensity (V), north Atlantic & western north Pacific • Compute PI according to Bister & Emanuel (2002), along tracks, using monthly mean NCEP/NCAR reanalysis • Find points ofmaximum intensity, save V & P at those points; for some calculations, exclude those for which V/P>1 (about 20%), with P the PI • Average together the points from each year to make annual time series; normalize each series by its mean, detrend • Compare the two time series: PI theory predicts the regression slope as P/(Vmin+P), with Vmin the lower bound (e.g., TS or Hurricane intensity) • Two time series should have a high correlation if PI theory is to be useful for understanding interannual TC variability

  9. Mean PI and Vmax on actual tracks are correlated, and vary more than MDR PI does Solid: PI Pluses: Vmax Dot-dash: PI averaged over MDR PI and Vmax are computed at points of maximum Vmax for each storm, then averaged over all storms in a given year reaching at least hurricane intensity.

  10. Correlations for hurricanes only; theoretical slopes are within 95% confidence limits Corr = 0.52 Slope=0.49 Theor. Slope=0.65 Corr = 0.45 Slope=0.64 Theor. Slope=0.66

  11. Summary of statistics in all cases where theoretical slope is shown, it is within 95% confidence limits of the obs “TS” means tropical storms included; “tracks” means Vmax and PI computed on tracks; “MDR” means PI averaged over “main development region”

  12. Conclusions • We use interannual variability to test the prediction that PI controls the average maximum intensities of actual storms • Interannual correlations between PI and max intensity are significant, but not large (~0.4-0.5) • The regression slope between annual mean PI and intensity is consistent with theory, within uncertainties • Much of the PI variation from year to year along actual tracks is controlled by track changes. Storms move to regions of different PI, rather than PI changing at fixed location.

  13. Genesis Potential Index - an empirical indicator of the environment for TC genesis

  14. Genesis Potential Index • Developed by Kerry Emanuel (Emanuel and Nolan 2004). • Refinement of Gray’s tropical cyclone genesis index. Other similar indices by DeMaria et al. (2001), Royer et al. (1998) • Choice of predictors based on environmental factors that are known to be important for cyclogenesis. • No parameters used that are tuned to the present climate. PI replaces Gray’s SST threshold. • Multiple regression techniques were used to obtain an index that can reproduced the observed climatology of tropical cyclogenesis (spatial distribution and seasonality) • No information on interannual variability used in developing the index

  15. Genesis Potential Index GP= |105η|3/2 (H/50)3 (Vpot/70)3 (1+0.1Vshear)-2 η = absolute vorticity at 850hPa (s-1) H = relative humidity at 600hPa (%) Vpot = potential intensity (m/s) Vshear = magnitude of the vertical wind shear between 200 and 850hPa (m/s). K.A. Emanuel and D.S. Nolan, 26th AMS Conf. Hurricanes & Tropical Meteorology, 2004.

  16. Genesis Potential Index Climatology -February

  17. Genesis Potential Climatology - September

  18. Genesis Potential Climatology and Number of Genesis Events - whole hemispheres Southern Hemisphere Northern Hemisphere

  19. Annual climatologies by basin Genesis Potential Number of Tropical Cyclones

  20. Genesis Potential Anomalies & ENSO (August - October) El Niño La Niña

  21. Genesis potential and observed genesis densityEl Niño - Niña (ASO) Genesis Potential Index Anomaly Observed Genesis Density Anomaly

  22. GPI and observed track densityEl Niño - La Niña (ASO) Genesis Potential Index Anomaly Observed Track Density Anomaly

  23. GPI and observed track densityEl Niño - La Niña (JFM) Genesis Potential Index Observed Track Density

  24. How much does each individual factor contribute to the ENSO signal in GPI? • Compute GPI using climatology for 3 out of 4 variables, fourth one varying interannually • E.g.: Vorticity, humidity, potential intensity are assigned the same climatological values each year, vertical wind shear is assigned observed, interannually varying values • Repeat for each of the 4 variables

  25. Genesis Potential – ENSO Difference ASO

  26. Genesis Potential ENSO difference ASOOne variable: observations & 3 other variables: climatology VORTICITY HUMIDITY Potential Intensity Vertical Wind Shear

  27. Genesis Potential ENSO difference JFMOne variable: observations & 3 other variables: climatology Relative Humidity Absolute Vorticity Potential Intensity Vertical Wind Shear Full GPI

  28. Conclusions • The genesis potential index successfully predicts the qualitative features of ENSO variations in TC number in most or all basins • This is a fair test of the index since its derivation used no information about interannual variability • We can decompose the index to get information about the role of different variables • Vertical shear is important to ENSO effect on TCs in the N. Atlantic and NE Pacific, humidity and relative vorticity are important to the genesis location shift in the NW Pacific • SH response is mix of all factors, but shear most resembles the total Papers online at www.columbia.edu/~ahs129/pubs.html

  29. Genesis Potential Intensity in Climate Models Camargo, Sobel, Barnston and Emanuel (2007), Tellus A, 59A, 428-443..

  30. Models’ Genesis Potential Climatologies NSIPP1 ECHAM4.5 NCEP Reanalysis ECHAM5

  31. Models’ annual cycle in GPI Western North Pacific North Atlantic North Indian South Indian

  32. Track Density in Climate Models CCM3.6 Echam3 NSIPP1 Echam4.5 Echam5 Observations

  33. GPI and NTC in Models –Western North Pacific Echam3 CCM3.6 Echam4.5 NSIPP1 Echam5 Obs (NTC) & Rean. (GPI)

  34. GPI and NTC in Models – North Atlantic Echam3 CCM3.6 Echam4.5 NSIPP1 Echam5 Obs (NTC) & Rean. (GPI)

  35. GPI x NTC in Models Western North Pacific and Central North Pacific Central North Pacific

  36. Genesis Potential Index Climatology – Annual Maximum

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