10

9

10

10

10

24

10

24

10

FORECAST SST

TROP. PACIFIC (multi-models, dynamical and statistical)

TROP. ATL, INDIAN (statistical)

EXTRATROPICAL (damped persistence)

GLOBAL ATMOSPHERIC

MODELS

ECPC(Scripps)

ECHAM4.5(MPI)

CCM3.x(NCAR)

NCEP(MRF9)

NSIPP(NASA)

COLA2.x

ForecastSST

Ensembles3/6 Mo. lead

PersistedSST

Ensembles3 Mo. lead

IRI DYNAMICAL CLIMATE FORECAST SYSTEM

POSTPROCESSING

MULTIMODELENSEMBLING

PERSISTED

GLOBAL

SST

ANOMALY

2-tiered OCEAN ATMOSPHERE

Empirical toolsalso used

Mason & Goddard, 2001, Bull.Amer.Meteor.Soc.

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

200

| || ||| ||||.| || | | || | | | . | | | | | | | | | |

Rainfall Amount (mm)

Below| Near | Below| Near | Above Below| Near |

The tercile category system:Below, near, and above normal

(30 years of historical data for a particular location & season)

Forecasts of the climate

Data:

33% 33% 33%Probability:

OCT | Nov-Dec-Jan* Dec-Jan-Feb

Jan-Feb-Mar Feb-Mar-Apr

Monthly issued probability forecasts of seasonal global precipitation and temperature

*probabilities of extreme (low and high) 15% issued also

Four lead times - example:

 

Atmospheric General Circulation Models Used in the IRI's Seasonal Forecasts, for Superensembles

 Name Where Model Was Developed Where Model Is Run

NCEP MRF-9 NCEP, Washington, DC QDNR, Queensland, Australia

ECHAM 4.5 MPI, Hamburg, Germany IRI, Palisades, New York

NSIPP NASA/GSFC, Greenbelt, MD NASA/GSFC, Greenbelt, MD

COLA COLA, Calverton, MD COLA, Calverton, MD

ECPC SIO, La Jolla, CA SIO, La Jolla, CA

CCM3.x NCAR, Boulder, CO NCAR, Boulder, CO

(forthcoming) GFDL, Princeton, NJ GFDL or IRI

Sources of the Global Sea Surface Temperature Forecasts 

Tropical Pacific Tropical Atlantic Indian Ocean Extratropical Oceans

NCEP Coupled CPTEC Statistical IRI Statistical Damped PersistenceLDEO Coupled Constr Analogue 

Ranked Probability Skill Score (RPSS)

3

RPSfcst = (Fcsticat – Obsicat)2

icat=1

icat ranges from 1 (below normal) to 3 (above normal)

RPSS = 1 - (RPSfcst / RPSclim)

Skill of Model Hindcasts Using Observed SST

Skill of Model Hindcasts Using Observed SST

RPSS Skill of Individual Model Simulations: JAS 1950-97Precipitation

Real-timeForecast

Skill

Real-timeForecast

Skill

1998-2001 Reliability Diagram

Reliability Diagramlonger “AMIP”

period

from Goddardet al. 2003

(EGS-AGU-EUG)

Next:Bayesian weighting methodfor multi-model ensembling

IRI uses two multi-model ensembling methods:

Bayesian method

Canonical variate method

Both methods analyze historical model performance in responding to observed

SST.

JAS 2003: Six GCM Precip.Forecasts

Likelihood

L(w) E(Qk*t

)t1

N

k

* represents the category that was observed at time t

"a multi-year product of the probabilities that were hindcast for the category that was observed..."

• Dirichlet distribution is appropriate for a multinomial process (i.e. terciles)– Combination of a and b is also Dirichlet with parameter

a+b

Rajagopalan et al., 2002:. Mon. Wea. Rev.

Ways to compute weights

Effective sample sizesEffective sample sizes

E[Qkt ] wcn

13 wmm mk

m

wcn wmm

Individual GCM + Climatology:1

f (Q t | Pt(y)) D(wcnPt(x) wmmPt(y))

Multiple GCMs + Climatology:

2

E[Qkt ] w j m j Pktj (y)

j1

Jw j m jj1

J

Combine the individual weights from several models using a Two-Stage scheme:

3

E[Qkt ] wcn

13 wmm mk

m

wcn wmm(a) For each model in turn:

E[Qkt ] wcn

13 wpmp

1

wjj

wj

mjkt

mj

j1

J

wcn wpmp

mp m jj1

J

(b) For the pooled ensemble thus created:

w j mj

mpwpFinal weights:

Model Weights – “Two-Stage”JAS

Model Weights – Two-Stage, Cross-Validated (XV)JAS

Climatological Weights JAS

Combination Forecasts of Jul-Sep PrecipitationOne-Stage Two-Stage

Two-Stage, xv, Spatial Smoothing

Two-Stage Cross-Validated

JAS 2003

Canonical variate method:

A kind of discriminant analysis.

Input: ensemble mean, ensemble spread,ensemble skewness

Algorithm finds linear combinations ofpredictors leading to each catergorical

(tercile) result. Differences amongthe sets of predictor weights are

maximized.

Multi-model ensemblingof dynamical predictions appearsto be slightly superior to currently

used statistical tools at NOAA/NCEP/CPC

temperature

precipitation

Most important current needsin IRI forecast system:

Improvement of SST prediction in 2-tiered system1) Tropical Pacific: better model consolidation, with

development of multiple evolving scenarios2) Outside tropical Pacific:“Smarter” persistence scenario,

or multivariate statistical model (e.g. CCA)

Use of 1-tiered climate model for some regionsSlab ocean model in locations where 2-tiered system fails

(Indian Ocean, far west Pacific and tropical Atlantic oceans)