Tropical Cyclone Climate in the Asia- Pacific Region and the Indian Oceans
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1 Tropical Cyclone Climate in the Asia- Pacific Region and the Indian Oceans Johnny Chan Guy Carpenter Asia-Pacific Climate Impact Centre School of Energy and Environment City University of Hong Kong
2 Annual No. of TS & TY in the western North Pacific El Niño La Niña 1
3 SST (5-30 o N, o E) 2
4 Annual No. of TS & TY in the western North Pacific El Niño La Niña 3
5 Anomaly of TC No. in the North Indian Ocean AS BB NIO 4
6 SST (0-15 o N, o E) 5
7 Anomaly of TC No. in the North Indian Ocean AS BB NIO 6
8 Annual No. of TCs in the Australian region 7
9 SST (0-30 o S, o E) 8
10 Annual No. of TCs in the Australian region 9
11 Annual No. of TS & TY in the western North Pacific El Niño La Niña 10
12 Emanuel s (2005) Nature paper PDI: (max wind) 3 W. North Pacific 11
13 Correlation between SST (May-Nov) and % of Typhoons
14 May-Nov SSTA; High vs. Low ACE ( 0.5σ ) High ACE Low ACE 13
15 May-Nov 850U; High vs. Low ACE ( 0.5σ ) High ACE Low ACE 14
16 May-Nov 200U; High vs. Low ACE ( 0.5σ ) High ACE Low ACE 15
17 ACE vs.. VORT, SHEAR and MSE Science, 311, 1713b, Tellus 2007 ACE VORT EOF1 SHEAR EOF1 MSE EOF
18 Correlations with MPI 21-year running correlations between MPI & NCat45 Ocean Basin Period Correlation (best track) Atlantic Correlation (Kossin et al. 2007) Western North Pacific Eastern North Pacific South Indian Ocean South Pacific
19 year running correlations with NCat45 - WNP 21-year running correlations with WNP NCat MPI CAPE T0 SST
20 standardized anomalies Standardized anomalies standardized anomalies Standardized anomalies standardized anomalies Standardized anomalies Standardized anomalies Variations of Landfall in Each Area at Various Oscillation Periods STC original 2-8 yr 8-16 yr yr 4 3 South Asia standardized anomalies Year MTC original 2-8 yr 8-16 yr yr 4 3 East China Japan/Korea Year standardized anomalies standardized anomalies
21 No. of Landfalling TCs in the Philippines
22 No. of Landfalling TCs in South China
23 No. of Landfalling TCs in East China Year 22
24 Correlation between SST and BB TCs BB local SST VS BB ACE (OND)
25 Correlation between SST and BB TCs after removal of ENSO
26 EOF1 (12.5%) El Niño La Niña 25
27 Typical Examples for EOF1 El Niño La Niña 26
28 EOF2 (10 %) El Niño La Niña 27
29 Dipole Mode Index (Sep-Nov) vs PC2 r =
30 Typical Tracks in IOD+ and IOD- years IOD+ IOD- 29
31 EOF3 (9.1 %) El Niño La Niña 30
32 No. of Southern Hemisphere TCs vs PC3 r =
33 Typical track patterns in high and low activity years 32
34 Nov-Apr 850-hPa wind anomalies EOF1+ vs EOF1-1982/83, 1986/87, 1991/92, 1997/98, 2004/05 El Niño 1984/85, 1995/96, 1999/2000 La Niña 33
35 IOD+ 1987/88, 1994/95, 2002/03, 2006/ /99, 2005/06 Sep-Nov SST anomalies EOF2+ vs EOF2- IOD- 34
36 1987/88, 1994/95, 2002/03, 2006/07 IOD+ 1998/99, 2005/06 Sep-Nov 850-hPa wind anomalies EOF2+ vs EOF2- IOD- 35
37 1987/88, 1994/95, 2002/03, 2006/07 IOD+ 1998/99, 2005/06 Nov-Apr 850-hPa wind anomalies EOF2+ vs EOF2- IOD- 36
38 Summary Variations of TC activity on climate (interannual or interdecadal) time scales in the western North Pacific, Indian Oceans and the Southern Hemisphere cannot be explained by local SST variations, which suggests that global warming cannot be attributed to the observed TC variability. Dynamic factors (horizontal and vertical shear), which can be forced by remote SST variations such as ENSO or IOD, are mainly responsible for the observed TC variability in these regions. Models must be demonstrated to be capable of simulating variations in these dynamic factors before their predictions can be used to project future TC activity in these regions. 37
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