Topic 5.2: Seasonal Forecasts

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1 Topic 5.2: Seasonal Forecasts Contributions from: Maritza Ballester, Eric Blake, Suzanna Camargo, Joanne Camp, Johnny Chan, Phil Klotzbach, Yuriy Kuleshov, Mark Saunders, O. P. Singh, Gabriel Vecchi and Ruifen Zhan

2 INDEX o Tropical storm interannual variability N. Atlantic N. Pacific N. Indian Ocean S. Hemisphere o Seasonal Prediction o Recommendations

3 North Atlantic key areas of research Main sources of predictability: o El-Niño Southern Oscillation (ENSO) o Atlantic Meridional Mode (AMM) o North Atlantic Oscillation (NAO)

4 El-Niño Southern Oscillation Tracks of Major hurricanes La Niña El Nino Klotzbach 2011

5 El-Niño Southern Oscillation- Regional impacts Impact on Hurricane Us landfall Impact on Caribbean TC activity Smith et al, 2007 Tropical storms Hurricanes Major Hurricanes Klotzbach 2011

6 Different Types of El-Niño events El-Niño Modoki Influence on North Atlantic TCs? (Kim et al, 2009)

7 Different Types of El-Niño events Climate model simulations Observations Wang et al., 2014

8 Impact of Atlantic Meridional Mode Kossin and Vimont, 2007 Kossin & Vimont (2007) have shown strong correlations between hurricane activity & AMM SST is not the only factor! Sea-level pressure (and wind), SST and wind shear all act in tandem

9 Impact of concurrent phases of the AMM and ENSO Deviation in ACE Deviation in GPI Patricola et al. 2014

10 Disruption of ENSO teleconnections by the MJO? Hoell et al., 2014

11 Relative SST (North Atlantic SST tropical SST) Cat. 4-5 Hurricane tracks Vecchi et al. 2008

12 Impact of the North Atlantic Oscillation Kossin et al., 2010

13 North Atlantic Oscillation Prediction Correlation: 0.62 Scaife et al. 2014

14 North Pacific - Key areas of research o El-Niño Southern Oscillation (ENSO) o East Indian SSTs o North-East Pacific North Atlantic inverse relationship

15 Impact of ENSO TC track density El-Niño La Niña Camargo et al., 2008

16 El-Niño Modoki Influence on North Pacific TCs? Kim et al., 2011

17 Impact of East Indian Ocean SSTA Zhan et al, 2010

18 North Indian ocean Key areas of research o El-Niño Southern Oscillation (ENSO) o Monsoon onset time o Indian Ocean Dipole

19 Impact of ENSO and IOD North Indian Ocean (OND ) NTC ACE NTI ENSO IOD Bay of Bengal NTC ACE NTI ENSO IOD Arabian Sea NTC ACE NTI ENSO IOD Ng and Chan, 2012

20 South Indian Ocean and Pacific o El-Niño Southern Oscillation (ENSO) o Indian ocean Dipole

21 Impact of ENSO Cluster analysis TC modulation by ENSO dependent on the presence of IOD+ or IOD- (Liu and Chan, 2012) particularly over the western Australian Basin Ramsay et al., 2012

22 SEASONAL FORECASTING

23 Barnston et al., 2012 ENSO Prediction Real-time ENSO prediction

24 Tropical Cyclone seasonal Forecasts 1. Statistical methods 2. Statistical/dynamical methods Use dynamical models to predict the predictors (SSTs, ENSO, vertical wind shear ). Use statistical regression to predict the number of TCs. 3. Dynamical methods Track the tropical cyclones produced explicitly by the model Compute model tropical storm statistics

25 Tropical Cyclone seasonal Forecasts Statistical models Colorado State University (North Atlantic) Tropical Storm Risk (North Atlantic, Northwest Pacific, Australia) Cuban Institute of meteorology (North Atlantic) Statistical/dynamical models NOAA (North Atlantic, Northeast Pacific) Bureau of meteorology (Australia, South Pacific) Shanghai Typhoon Institute (West Pacific) GFDL (North Atlantic) Dynamical models ECMWF (All basins) UK Met Office (North Atlantic) Florida State University (North Atlantic) Huang and Chan (Number of TCs + landfall prediction over West Pacific)

26 Seasonal forecasts of Tropical storms: Examples CSU ECMWF NOAA GFDL

27 WMO Website A recommendation from IWTC-VI

28 Examples of verification Cuban Institute of meteorology TSR GFDL Correlation ECMWF

29 Comparison of ACE skill scores over the North Atlantic Courtesy P. Klotzbach

30 Forecast skill scores Highest skill over North Pacific and North Atlantic Lowest skill over North and South Indian Ocean UK Met Office

31 Use of a regional model (Huang and Chan 2013) o Predicts the number of TCs over the western North Pacific as well as the number of TCs making landfall in various parts of East Asia o Uses a regional climate model (RegCM3) o Outperforms CFS.V2 (Huang and Chan 2013)

32 Resolution of global circulation models is increasing ECMWF seasonal forecasting systems Atmospheric horizontal resolution (km) This increase of resolution should lead to more realistic representation of tropical storms.

33 High-resolution seasonal forecasts Project Athena, Kinter et al km 10 km 40 km Distributions of (left) 10-m tangential wind (m s 1 ) and (right) total column liquid water and ice (TCLWI; kg m 2 ) for the most intense TCs at the peak of their intensity from (a),(d) the NICAM simulation; (b),(e) the IFS 10-km simulation (T2047); and (c),(f) the IFS 39-km simulation (T159

34 Towards more detailed seasonal forecasts The prediction of TS/Hurricane number /ACE over a whole is not always the most useful information (e.g. 1995, 2010)

35 A seasonal forecast bust: 2013 hurricane season Predictions of tropical activity in the 2013 season TSR Source Date Named storms Hurricanes Average ( ) Record high activity Record low activity Major hurricanes December 5, TSR April 5, WSI/TWC April 8, CSU April 10, NCSU April 15, UKMO May 15, * 9* N/A NOAA May 23, FSU COAPS May 30, N/A CSU June 3, TSR June 4, TSR July 5, CSU August 2, August 8, NOAA Actual activity Significant outbreaks of dust from the Sahara as well as increased frequency of midlatitude wave-breaking Very strong wind shear over Carribbeans Fogarty and Klotzbach (2014)

36 Possible recommendations (1) Move towards more regional information and risk of landfall prediction over basins where seasonal forecasts are skilful like N. ATL or NW Pacific Provide better estimate of model uncertainty Move towards more probabilistic forecasts and verification Better understanding of forecast busts (e.g. Atlantic 2013)

37 Possible recommendations (2) Promote the development of multi-model seasonal forecasts. Provide the users with the skill level when issuing the forecasts Research: Limit and sources of predictability- How far can we predict TC seasonal activity Integrate intra-seasonal and seasonal forecasts (dynamical forecasts)

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