multi-model ensemble forecast: el ni ñ o and climate prediction
DESCRIPTION
Multi-model Ensemble Forecast: El Ni ñ o and Climate Prediction. Shuhua Li. International Research Institute for Climate and Society Columbia University. IAP visit, JAN 4, 2007. Much of our predictability comes about due to. Purely Empirical (observational) approach: El Ni ñ o - PowerPoint PPT PresentationTRANSCRIPT
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