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Multi-model Ensemble Forecast: El Ni ñ o and Climate Prediction

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

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Multi-model Ensemble Forecast: El Ni ñ o and Climate Prediction

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  1. Multi-model Ensemble Forecast: El Niño and Climate Prediction Shuhua Li International Research Institute for Climate and Society Columbia University IAP visit, JAN 4, 2007

  2. Much of our predictability comes about due to

  3. Purely Empirical (observational) approach: El Niño Composite

  4. 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

  5. Niño Indices: Recent Evolution 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 3.4 Niño 3.4

  6. SSTA: 1982 - 2006

  7. Weekly SST animation: Oct – Dec 2006

  8. Weekly SST anomalies: Oct – Dec 2006

  9. IRI’s monthly issued probability forecasts of seasonal global precipitation and temperature We issue forecasts at four lead times. For example: Now | Forecasts made for: JAN |Feb-Mar-Apr Mar-Apr-May Apr-May-Jun May-Jun-Jul 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.

  10. Forecasts of the climate The tercile category system: Below, near, and above normal Probability: 33% 33% 33% Below| Near | Below| Near | Above Below| Near | | || ||| ||||.| || | | || | | | . | | | | | | | | | | Data: 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm) (30 years of historical data for a particular location & season) (Presently, we use 1970-2000)

  11. Example of a typical climate forecast for a particular location & season Probability: 20% 35% 45% Below| Near | Below| Near | Above Below| Near | 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm)

  12. Example of a STRONGclimate forecast for a particular location & season Probability: 5% 25% 70% Below| Near | Below| Near | Above Below| Near | 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm)

  13. The probabilistic nature of climate forecasts Forecast distribution Historical distribution Above Below 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.

  14. Correlation Skill for NINO3 forecasts Correlation Skill for NINO3 Forecasts Made by a Coupled Prediction Model Skill bonus Northern Spring barrier useless low fair good

  15. Skill of forecasts at different time ranges: 1-2 day weather good 3-7 day weather fair Second week weather poor, but not zero 3rd to 4th week weather virtually 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.

  16. Lead time and forecast skill Weather forecasts (from initial conditions) Forecast Skill Potential sub-seasonal predictability Seasonal forecasts (from SST boundary conditions) good fair poor zero (from MJO, land surface) 10 20 30 60 80 90 Forecast lead time (days)

  17. (12) Dynamical

  18. Statistical (8)

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

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

  21. 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

  22. 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 ------ 2-tier

  23. 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.

  24. IRI DYNAMICAL CLIMATE FORECAST SYSTEM 2-tiered OCEAN ATMOSPHERE GLOBAL ATMOSPHERIC MODELS ECPC(Scripps) ECHAM4.5(MPI) CCM3.6(NCAR) NCEP(MRF9) NSIPP(NASA) COLA2 GFDL PERSISTED GLOBAL SST ANOMALY Persisted SST Ensembles 1 Mo. lead 10 POST PROCESSING MULTIMODEL ENSEMBLING 24 24 10 FORECAST SST TROP. PACIFIC: THREE (multi-models, dynamical and statistical) TROP. ATL, INDIAN (ONE statistical) EXTRATROPICAL (damped persistence) 12 Forecast SST Ensembles 1-4 Mo. lead 24 model weighting 24 30 12 30 30

  25. IRI DYNAMICAL CLIMATE FORECAST SYSTEM 2-tiered OCEAN ATMOSPHERE MULTIPLE GLOBAL ATMOSPHERIC MODELS ECPC(Scripps) ECHAM4.5(MPI) CCM3.6(NCAR) NCEP(MRF9) NSIPP(NASA) COLA2 GFDL PERSISTED GLOBAL SST ANOMALY 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

  26. Method of Forming 3 SST Predictions for Climate Predictions damped persistence 0.25 -------------------------------------------------- (1) NCEP CFS Model (2) LDEO Model (3) CPC Constructed Analog IRI CCA CPTEC CCA -------------------------------------------------- damped persistence

  27. 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)

  28. 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 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, AustraliaECHAM 4.5 MPI, Hamburg, Germany IRI, Palisades, New YorkNSIPP NASA/GSFC, Greenbelt, MD NASA/GSFC, Greenbelt, MDCOLA 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

  29. Historical skill using observed SST

  30. Historical skill using observed SST

  31. A N B

  32. A N B

  33. 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 Forecasts http://iri.columbia.edu/forecast/climate/prec03.html Individual Numerical Model Hindcast Skill Maps http://iri.columbia.edu/forecast/climate/skill/SkillMap.html

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