IRI Experience in Forecasting and Applications for the Mercosur Countries

Mason & Goddard, 2001, Bull.Amer.Meteor.Soc. Purely Empirical (observational) approach

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 helpful 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

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

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 3/6 Mo. lead Persisted SST Ensembles 3 Mo. lead IRI DYNAMICAL CLIMATE FORECAST SYSTEM POST PROCESSING MULTIMODEL ENSEMBLING PERSISTED GLOBAL SST ANOMALY 2-tiered OCEAN ATMOSPHERE 30

(1) NCEP CFS (2) LDEO (3) CPC Constr Analog IRI CCACPTEC CCA damped persistence damped persistence 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)

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

| || ||| ||||.| || | | || | | |. | | | | | | | | | | Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | The tercile-based 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:

JUL | Aug-Sep-Oct* Sep-Oct-Nov Oct-Nov-Dec Nov-Dec-Jan Monthly issued probability forecasts of seasonal global precipitation and temperature *probabilities of extreme (low and high) 15% issued also Four lead times - example:

OND 1997 OND 1998 very good skill good skill

OND 1999 OND 2000 somewhat negative skill fairly good skill

OND 2001 OND 2002 Neutral (zero) skill good skill

OND 2003 OND 2004 negative skill neutral (zero) skill

Historical skill using actually observed SST Ensemble mean (10 members)

Historical skill using actually observed SST Ensemble mean (24 members)

OND

JFM

AMJ

JAS

OND Precip South America Europe North America Tropical Americas Indonesia Australia/ Indonesia

Some Themes in Customer Demands: 1.Higher skills in existing products: -Tighter range of possibilities 2. More usable and easily understood product 3. Expansion of repertoire of products beyond seasonally averaged climate

Specifically, users consistently request: More concrete forecast format Direct use of physical units (inches, degr C) Best guess quantity, with comparison to average More flexibility in probabilistic forecast format More temporal detail than 3 month averages Outlooks for single months, even at long lead Rainy season (or monsoon) onset time Weather features within season (e.g. dry spells) Chance of extreme event (storm, flood, drought) Climate change nowcast (the new “normal”)

Almost all users want reliable probabilities (probabilities that would match observed frequencies of occurrence over a large sample of identical forecasts). Even if reliable, users for some applications are not impressed with slight changes from the climatological precipitation probabilities. They are interested in forecasts having probabilities that deviate strongly from the neutral, climatological probabilities.

OND 1997

Ranked Probability Skill Score (RPSS) 3 prob prob RPS fcst =  (Fcst icat – Obs icat ) 2 icat =1 icat ranges from 1 (below normal) to 3 (above normal) icat is cumulative, from 1 to icat RPSS = 1 - (RPS fcst / RPS clim ) RPS clim is RPS of forecasts of climatology (33%,33%,33%)

Real-time Forecast Skill (from Goddard et al. 2003, BAMS, p1761)

Real-time Forecast Skill (from Goddard et al. 2003, BAMS, p1761)

Real-time Forecast Skill (from Goddard et al. 2003, BAMS, p1761

Reliability Diagram from Barnston et al. 2003, BAMS, p1783

Reliability Diagram longer “AMIP” period from Goddard et al (EGS-AGU-EUG)

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 Current Multi-model Climate Forecasts