Matt Huddleston With thanks to: Frederic Vitart (ECMWF), Ruth McDonald

© Crown copyright 2006 Page 1

Matt Huddleston

With thanks to: Frederic Vitart (ECMWF), Ruth McDonald

& Met Office Seasonal forecasting team

14th March 2007

Dynamically-Based Seasonal forecasts of Atlantic Tropical-storm Activity


Seasonal forecasts of tropical storms

Recent hurricane years in the Tropical Atlantic

Forecasting technology

Forecast skill

Future work



Atlantic Hurricanes

2004 Four hurricanes struck FloridaUnprecedented 10 tropical cyclones struck Japan

2005 Record hurricane activity (28) in the AtlanticFour category 5 hurricanes (Emily, Katrina, Rita, Wilma) Activity in other regions was either quiet or normal

2006 Nothing out of the ordinary – 9 storms

• Atlantic has been active for last decade, but no change in other regions

• Natural variations in activity are likely to mask any clear climate change link for the foreseeable future

• Observational studies suggest no change in frequency, but an increase in intensity in recent decades

• Historical database is not considered robust enough to use for detailed climate studies

c/o & thanks to NASA (also previous animation)


2005 tropical cyclone activity

0

50

100

150

200

250

300

Per

cen

tag

e o

f n

orm

al

NorthAtlantic

North-West

Pacific

North-EastPacific

NorthIndian

South-West

Indian

Australian Globe

Tropical Storms

Hurricanes/Typhoons


Seasonal Forecasting technology


Current seasonal tropical storm forecasting: statistical

• E.g. Gray/Klotzbach – June to November Atlantic season forecasts• Use a range of statistical predictors for each forecast• Wide range of predictions made: numbers of storms, strength, landfall etc

Taken from Klotzbach P.J. and Gray W.M.,Extended Range Forecast of Atlantic Seasonal HurricaneActivity and U.S. Landfall Strike Probability

© Crown copyright 2006 Page 8The Daily Telegraph – 7th October 2006

Amaranth Advisors, the US-based hedge fund that lost about $6bn (£3.2bn) betting on gas prices, has sought help as it liquidates its remaining assets. BBC News 2nd Oct 2006

Impact of long-range forecasts


Seasonal forecasting using climate models:Multi-model seasonal forecasting

Sea Surface Temperature

Recent global atmospheric wind, rain, solar heating etc

Global Coupled Climate modelEnsembles

Ocean observationsIncluding ARGO floats

EUROSIP


Model Tropical Storms

Out flow

In flow

Average observed typhoon from Gray (1979)

Low level cyclonic

Single tropical storm from HadAM3 N144 Model

Low level

Model winds speeds are too low, max is too far out

Top row: Gray (1979), Bottom row: McDonald et al. (2005) Climate Dynamics

Anticyclonic flow too far from centre

Upper level

Too strong, too low and confined near centre

Upper level anticyclonic

Tangential Winds Radial Winds

Summary: Despite their low horizontal resolution climate models are able to simulate some of the features of tropical cyclones


Examples of model cyclone tracks

Tropical Storms for 15 years – AGCM + observed SST

Tracks look sensible, despite low resolution (~100Km / N144 ) and poor simulation of individual cyclones


Tropical storm genesis in N144 HadAM3

Units = TS per grid box per 10 years

Observations:- NHC best track data

Model:- 1980s

Only one TS observed

Too many Too few


Forecast skill


Impact of El Niño/wind shear in the Atlantic

1997:

7 tropical storms

3 hurricanes

1 major hurricane

Strong El Niño prevented hurricane development due to high wind shear

1995:

19 tropical storms

11 hurricanes

5 major hurricanes


Key to forecasting climate – sea surface temperature

NINO 3.4 SSTs

Atlantic SSTs

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

26

27

28

29

30

SS

T(K

)

Ens. Mean NCEP analysis 2 Standard Deviations

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

27

28

SS

T(K

)

Ens. Mean NCEP analysis 2 Standard Deviations

Pacific (NINO3.4) SSTs

Forecast of August to October SSTs from 1st June Correlation of forecast ensemble mean and observed SSTs

0.87 in Pacific

0.81 in Atlantic

Persistence is 0.47 in Pacific 0.73 in Atlantic


EUROSIP – June forecast for July-November

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

123456789

1011121314151617181920212223242526

Num

ber

of T

SRMS Error= 3.07( 4.56)Correlation=0.78( 1.00)

FORECAST Observations 2 Standard Deviations (95% population)


June forecast of number tropical Atlantic storms Jun-Nov

EUROSIP TropicalStorm Risk

Colorado State Uni.

Correlation 0.78 (0.002) 0.53 (0.055) 0.39 (0.26)

RMS error 3.1 4.98 4.84

Met Office + ECMWF

TSR CSU

Correlation 0.80 (0.0002) 0.62 (0.004) 0.53 (0.015)

RMS error 3.34 4.39 4.42

1993-2006

1987-2006


Recent predictions

Model 1 Model 2 Model 3 EUROSIP NOAA TSR CSU OBS

2005 14.3 15 19.4 16.2 12-15 13.8 15 27

2006 10.5 10.7 15.3 12.1 13-16 15.9 17 9

• 2005 was an extremely active year

• 2006 was an (just-below) average


Climate forecasts & risk mitigation

Chances of an extreme Atlantic tropical storm season in 2005:

Above 20 storms was forecast as twice as likely 1st June forecast: 37% chance of being above 20 storms where 1993-2004 average chance

was 18%

Above 27 storms was forecast as 3 times as likely 1st June forecast: 13% chance of being above 27 storms where 1993-2004 average chance

was 4.2%


DEMETER Probabilistic verification 1959-2001

Forecasts that differ from climatology frequency for above or below normal activity are reliable


Future potential

Increasing model resolution

Land fall predictions Better representation of hurricane intensity

Statistical techniques could be used now:

Calibrate inter-annual variability of ensemble Calibrate land-falling hurricanes

Assessment in more locations and lead times

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Matt Huddleston With thanks to: Frederic Vitart (ECMWF), Ruth McDonald

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