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Cluster Analysis of Tropical Cyclone Tracks and ENSO

Cluster Analysis of Tropical Cyclone Tracks and ENSO. Suzana J. Camargo, Andrew W. Robertson, International Research Institute for Climate Prediction, Columbia Earth Institute, Palisades, NY Scott J. Gaffney and Padhraic Smyth Department of Information and Computer Science,

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Cluster Analysis of Tropical Cyclone Tracks and ENSO

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  1. Cluster Analysis of Tropical Cyclone Tracks and ENSO Suzana J. Camargo, Andrew W. Robertson, International Research Institute for Climate Prediction, Columbia Earth Institute, Palisades, NY Scott J. Gaffney and Padhraic Smyth Department of Information and Computer Science, University of California, Irvine, CA

  2. Outline • Introduction • Clustering Technique • Previous works on cluster analysis and tropical cyclones • Western North Pacific Results • Mean Regression Trajectories • Tracks • Properties of main clusters • ENSO Relationship: tracks, tracks density, NTC, ACE • Composites: SST, SLP, winds, wind shear • North Atlantic Results • Mean Regression Trajectories and Tracks • ENSO relationship • Atlantic multi-decadal signal • Eastern North Pacific Results • Mean Regression Trajectories and Tracks • ENSO relationship • Summary

  3. Introduction • Identify different track types, their seasonality and relation with large-scale circulation and ENSO. • Importance: different track types have higher incidence on some years and make landfall in different regions. • New clustering technique used. • Best track datasets: • Western North Pacific – JTWC 1950-2002. • North Atlantic – NHC 1851-2003. • Eastern North Pacific – NHC 1949-2003. • Only tropical cyclones (TCs) with tropical storm or hurricane (typhoon) intensity (no tropical depressions).

  4. Clustering Technique • Developed by S.J. Gaffney and P. Smyth: - S.J. Gaffney (2004), Ph.D. thesis, University of California, Irvine. • Mixture of polynomial regression models (curves) to fit the geographical “shape” of the trajectories. • Extension of the standard multivariate finite mixture model to allow quadratic functions. • Enable highly non-Gaussian density functions to be expressed as a mixture of a few PDFs. • Fitting by maximizing the likelihood of the parameters. • Rigorous probabilistic context for clustering • Accommodate easily tropical cyclone tracks of different lengths.

  5. Previous works on Cluster Analysis and Tropical Cyclones • Western North Pacific: • P.A. Harr and R.L. Elsberry, Mon. Wea. Rev. 123, 1225-1246 (1985). • J.B. Elsner and K.B. Liu, Climate Research25, 43-54 (2003); • North Atlantic: • J.B. Elsner, Bull. Amer. Meteor. Soc. 84, 353-356 (2003); J.B. Elsner et al., J. Climate13, 2293-2305 (2000). • Eastern North Pacific (TC precursors): • J.B. Mozer and J.A. Zehnder, J. Geophys. Res. – Atmos.99, 8085-8093 (1994).

  6. Western North Pacific Tropical Cyclones Cluster AnalysisResults

  7. Mean Regression Trajectories • Appropriate number of clusters appears to be seven. • Quantitative (out of sample likelihood) and subjective analysis. • Two main trajectory-types: “straight-movers” and “recurvers”. • Additional clusters: detailed differences in shape among these types.

  8. TRACKS TROPICAL CYCLONES Western North Pacific 1983-2002 TRACKS MEAN REGRESSION TRAJECTORIES

  9. Number of TCs per Cluster

  10. TRACK DENSITY Cluster A FIRST POSITION DENSITY NTC ANNUAL CYCLE Landfall 63% Regression Trajectory • 67% reach typhoon intensity

  11. TRACK DENSITY Cluster B FIRST POSITION DENSITY NTC ANNUAL CYCLE Landfall 61% Regression Trajectory 50% only reach TS intensity.

  12. TRACK DENSITY Cluster C FIRST POSITION DENSITY NTC ANNUAL CYCLE Landfall 7% Regression Trajectory • 70% reach typhoon intensity

  13. ENSO Relationship NTC- Number of Tropical Cyclones ACE – Accumulated Cyclone Energy Total NTC per year is not significantly correlated with ENSO (e.g. Wang & Chan, 2002). Total ACE has a well known relationship with ENSO (Camargo & Sobel, 2004).

  14. Tracks El Niño years Tracks La Niña years Cluster A Cluster E Cluster G

  15. Track Density per year: Difference El Niño and La Niña years Full basin Cluster A Cluster G Cluster E

  16. Mean NTC and ACE per cluster and ENSO A E G A G E

  17. SST Anomalies Composites TCs first positions SST and TC data for SST composites:11/81 – 12/02 Regression trajectory

  18. Sea Level Pressure Anomalies Composites NCEP Reanalysis and TC data for composites: 1950-2002

  19. Anomalous Low Level Wind Composites

  20. Wind Shear Composites Magnitude of the total wind shear between 200hPa and 850hPa

  21. North Atlantic Tropical Cyclones Cluster AnalysisResults

  22. Tracks and Regression Trajectories Mean Regression Trajectory TRACKS Tracks Atlantic named Tropical Cyclones 1970-2003.

  23. Number of TCs per cluster

  24. ENSO Relationship NTC correlations ACE correlations

  25. Tracks El Niño years Tracks La Niña years Cluster 1 Cluster 2 Cluster 3 Named Tropical Cyclones in warm/cold ENSO years 1950-2003

  26. Named Tropical Cyclones: 1950-2003

  27. SST Anomalies Composites Main Development Region SST and TC data for SST composites: 11/81 – 12/2003 TCs First Positions

  28. Wind shear composites NCEP Reanalysis and TC data for composites: 1950-2003. Magnitude of the total wind shear between 200hPa and 850hPa.

  29. Atlantic Multi-Decadal Signal • S.B. Goldenberg, C.W. Landsea, A.M. Mesta-Nuñez and W.M. Gray, Science 293, • 474-478 (2001).

  30. Number of Major Hurricanes per cluster

  31. SST anomalies composite SST composites: 11/1981-12/2003

  32. Eastern North PacificTropical CyclonesCluster Analysis Results

  33. Mean Regression Trajectories and Tracks

  34. ENSO Relationship

  35. Tracks El Niño years Tracks La Niña years Cluster 1 Cluster 2 Cluster 3

  36. Summary • New clustering technique applied to Northern Hemisphere TC tracks. • Clusters with different properties: genesis and track regions, intensity, timing. • In all basins clusters strongly related to ENSO are identified. • Composites of large scale fields with different characteristics for each cluster identify the factors influencing the formation and movement of TCs in each cluster.

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