Multi-model Ensemble Forecast:El Niño and Climate Prediction

International Research Institute for Climate and Society

Columbia University

Shuhua Li

IAP visit, JAN 4, 2007

Much of our predictability comes about due to

PurelyEmpirical(observational)approach:El NiñoComposite

Strong trade winds

Westward currents

Cold east, warm west

Convection, rising motion in west

Weak trade winds

Eastward currents

Warm west and east

Enhanced convection,

eastward displacement

The latest weekly SST anomalies are

between +1.2 and 1.3C in all of the

Niño regions, except for the Niño 1+2

region.

Niño Indices: Recent Evolution

Niño 3.4

Niño 3.4

SSTA: 1982 - 2006

Weekly SST animation: Oct – Dec 2006

Weekly SST anomalies: Oct – Dec 2006

JAN | Feb-Mar-Apr Mar-Apr-May

Apr-May-JunMay-Jun-Jul

IRI’s monthly issued probability forecasts of seasonal global precipitation and temperature

We issue forecasts at four lead times. For example:

Forecast models are run 7 months into future. Observed dataare available through the end of the previous month (end ofDecember in example above). Probabilities are given for thethree tercile-based categories of the climatological distribution.

Now | Forecasts made for:

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)(Presently, we use 1970-2000)

Forecasts of the climate

Data:

33% 33% 33%Probability:

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 |

20% 35% 45%

Example of a typical climate forecastfor a particular location & season

Probability:

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 |

5% 25% 70%

Example of a STRONG climate forecastfor a particular location & season

Probability:

Below Above

Historically, the probabilities of above and below are 0.33. Shifting the mean by half a standard-deviation and reducing the variance by 20% (red curve) changes the probability of below to 0.15 and of above to 0.53.

Historical distribution Forecast distribution

The probabilistic nature of climate forecasts

Correlation Skill for NINO3 forecasts

NorthernSpringbarrier

Skillbonus

useless low fair good

Correlation Skill for NINO3 Forecasts Made by a Coupled Prediction Model

Skill of forecasts at different time ranges:

1-2 day weather good3-7 day weather fairSecond week weather poor, but not zero3rd to 4th week weather virtually zero

1-month climate (day 1-31) poor to fair1.5-month climate (day 15-45) poor, but not zero3-month climate (day 15-99) poor to fair

At shorter ranges, forecasts are based on initialconditions and skill deteriorates quickly with time.

Skill gets better at long range for ample time-averaging,due to consistent boundary condition forcing.

Forecast Skill

Forecast lead time (days)

10 20 30 60 80 90

Weather forecasts (from initial conditions)

Potential sub-seasonal predictability

Seasonal forecasts (from SST boundary conditions)

Lead time and forecast skill

good

fair

poorzero

(from MJO, land surface)

(12)Dynamical

(8)Statistical

Models say that El Nino is very likely to continue until N. Spring 2007

From December 2006

El Nino is likely to continueuntil MAM 2007

From December 2006

Prediction Systems:statistical vs. dynamical system

ADVANTAGES

Based on actual, real-worldobserved data. Knowledge ofphysical processes not needed.

Many climate relationshipsquasi-linear, quasi-Gaussian------------------------------------Uses proven laws of physics.Quality observational data not required (but needed for val-idation). Can handle casesthat have never occurred.

DISADVANTAGES

Depends on quality and length of observed data

Does not fully account for climate change, or new climate situations.------------------------------ Some physical laws must be abbreviated or statis- tically estimated, leading to errors and biases.

Computer intensive.

Stati-stical

-------

Dyna-mical

In Dynamical Prediction System:2-tiered vs. 1-tiered forecast system

ADVANTAGES

Two-way air-sea interaction,as in real world (required Where fluxes are as important as large scale ocean dynamics)

--------------------------------------More stable, reliable SST inthe prediction; lack of driftthat can appear in 1-tier system

Reasonably effective for regionsimpacted most directly by ENSO

DISADVANTAGES

Model biases amplify (drift); flux corrections

Computationally expensive------------------------------ Flawed (1-way) physics, especially unacceptable in tropical Atlantic and Indian oceans (monsoon)

1-tier

------

2-tier

IRI’s Forecast System

IRI is presently (in 2006) using a 2-tiered prediction system to probabilistically predict global temperatureand precipitation with respect to terciles of thehistorical climatological distribution.

We are interested in utilizing fully coupled (1-tier) systems also, and are looking into incorporating those.

Within the 2-tiered system IRI uses 4 SST predictionscenarios, and combines the predictions of 7 AGCMs.

The merging of 7 predictions into a single one usestwo multi-model ensemble systems: Bayesian andcanonical variate. These give somewhat differing solutions, and are presently given equal weight.

30

12

30

24

12

24

24

10

24

10

FORECAST SST

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

TROP. ATL, INDIAN (ONE statistical)

EXTRATROPICAL (damped persistence)

GLOBAL ATMOSPHERIC

MODELS

ECPC(Scripps)

ECHAM4.5(MPI)

CCM3.6(NCAR)

NCEP(MRF9)

NSIPP(NASA)

COLA2

GFDL

ForecastSST

Ensembles1-4 Mo. lead

PersistedSST

Ensembles1 Mo. lead

IRI DYNAMICAL CLIMATE FORECAST SYSTEM

POSTPROCESSING

MULTIMODELENSEMBLING

PERSISTED

GLOBAL

SSTANOMALY

2-tiered OCEAN ATMOSPHERE

30

modelweighting

FORECAST SST

TROP. PACIFIC: THREE scenarios: 1) CFS prediction 2) LDEO prediction 3) Constructed Analog prediction TROP. ATL, and INDIAN oceans CCA, or slowly damped persistence EXTRATROPICAL damped persistence

MULTIPLEGLOBAL

ATMOSPHERICMODELS

ECPC(Scripps)

ECHAM4.5(MPI)

CCM3.6(NCAR)

NCEP(MRF9)

NSIPP(NASA)

COLA2

GFDL

IRI DYNAMICAL CLIMATE FORECAST SYSTEM

PERSISTED

GLOBAL

SST

ANOMALY

2-tiered OCEAN ATMOSPHERE

(1) NCEP CFS Model(2) LDEO Model

(3) CPC Constructed AnalogIRI CCA CPTEC

CCA

dampedpersistence

damped persistence

0.25

--------------------------------------------------

--------------------------------------------------

Method of Forming 3 SST Predictions for Climate Predictions

Method of Forming 4th SST Prediction for Climate Predictions

(4)Anomaly

persistence

from most

recently

observed

month

(all oceans)

(only used for the first lead time)

 

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

CCM3.6 NCAR, Boulder, CO IRI, Palisades, New York

GFDL GFDL, Princeton, NJ GFDL, Princeton, NJ

Collaboration on Input to Forecast Production

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 

Historical skill using observed SST

Historical skill using observed SST

ANB

ANB

IRI Forecast Information Pages on the Web

IRI Home Page: http://iri.columbia.edu/

ENSO Quick Look:http://iri.columbia.edu/climate/ENSO/currentinfo/QuickLook.html

IRI Probabilistic ENSO Forecast:http://iri.columbia.edu/climate/ENSO/currentinfo/figure3.html

Current Individual Numerical Model Climate Forecastshttp://iri.columbia.edu/forecast/climate/prec03.html

Individual Numerical Model Hindcast Skill Mapshttp://iri.columbia.edu/forecast/climate/skill/SkillMap.html