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

Purely Empirical (observational) approach: El Niño Composite

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 Ni ñ o 3.4

SSTA:

Weekly SST animation: Oct – Dec 2006

Weekly SST anomalies: Oct – Dec 2006

JAN | Feb-Mar-Apr Mar-Apr-May Apr-May-Jun May-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 data are available through the end of the previous month (end of December in example above). Probabilities are given for the three tercile-based categories of the climatological distribution. Now | Forecasts made for:

| || ||| ||||.| || | | || | | |. | | | | | | | | | | 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 ) Forecasts of the climate Data: 33% 33% 33% Probability:

Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | 20% 35% 45% Example of a typical climate forecast for a particular location & season Probability:

Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | 5% 25% 70% Example of a STRONG climate forecast for a particular location & season Probability:

Below Above Historically, the probabilities of above and below are 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 Historical distribution Forecast distribution The probabilistic nature of climate forecasts

Correlation Skill for NINO3 forecasts Northern Spring barrier Skill bonus 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 good 3-7 day weather fair Second week weatherpoor, but not zero 3rd to 4 th week weathervirtually zero 1-month climate (day 1-31) poor to fair 1.5-month climate (day 15-45) poor, but not zero 3-month climate (day 15-99) poor to fair At shorter ranges, forecasts are based on initial conditions 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) Weather forecasts (from initial conditions) Potential sub-seasonal predictability Seasonal forecasts (from SST boundary conditions) Lead time and forecast skill good fair poor zero (from MJO, land surface)

(12) Dynamical

(8)(8)Statistical

Models say that El Nino is very likely to continue until N. Spring 2007 From December 2006

El Nino is likely to continue until MAM 2007 From December 2006

Prediction Systems: statistical vs. dynamical system ADVANTAGES Based on actual, real-world observed data. Knowledge of physical processes not needed. Many climate relationships quasi-linear, quasi-Gaussian Uses proven laws of physics. Quality observational data not required (but needed for val- idation). Can handle cases that 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 in the prediction; lack of drift that can appear in 1-tier system Reasonably effective for regions impacted 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 tier

IRI’s Forecast System IRI is presently (in 2006) using a 2-tiered prediction system to probabilistically predict global temperature and precipitation with respect to terciles of the historical 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 prediction scenarios, and combines the predictions of 7 AGCMs. The merging of 7 predictions into a single one uses two multi-model ensemble systems: Bayesian and canonical variate. These give somewhat differing solutions, and are presently given equal weight.

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 Forecast SST Ensembles 1-4 Mo. lead Persisted SST Ensembles 1 Mo. lead IRI DYNAMICAL CLIMATE FORECAST SYSTEM POST PROCESSING MULTIMODEL ENSEMBLING PERSISTED GLOBAL SST ANOMALY 2-tiered OCEAN ATMOSPHERE 30 model weighting

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 MULTIPLE GLOBAL ATMOSPHERIC MODELS 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 Analog IRI CCACPTEC CCA damped persistence damped persistence Method of Forming 3 SST Predictions for Climate Predictions

Method of Forming 4 th 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 PacificTropical AtlanticIndian OceanExtratropical Oceans NCEP CoupledCPTEC Statistical IRI StatisticalDamped Persistence LDEO Coupled Constr Analogue

Historical skill using observed SST

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IRI Forecast Information Pages on the Web IRI Home Page: ENSO Quick Look: IRI Probabilistic ENSO Forecast: Current Individual Numerical Model Climate Forecasts Individual Numerical Model Hindcast Skill Maps