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IRI Climate Forecasting System

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Presentation on theme: "IRI Climate Forecasting System"— Presentation transcript:

1 IRI Climate Forecasting System
Regional Projects, through regional COFs Sectoral workshops (agric, hydrol, health, coastal management Visitors to/from regions

2 Mason & Goddard, 2001, Bull.Amer.Meteor.Soc.
Purely Empirical (observational) approach: El Nino Probabilistic Composite Mason & Goddard, 2001, Bull.Amer.Meteor.Soc.

3 Correlation Skill for NINO3 forecasts
Correlation Skill for NINO3 Forecasts Made by an Intermediate Coupled Prediction Model low medium good

4 Models say that neutral ENSO
conditions are most likely for the rest of 2008 and into 2009.

5 IRI forecasters use the ENSO forecast models, and
recent observations of the ENSO system, and assign probabilities to the forecasts for ENSO conditions.

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7 Last week’s SST anomaly
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8 IRI’s Forecast System IRI is presently (in 2008) 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.

9 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 3 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 3/6 Mo. lead 24 model weighting 24 30 12 30 30

10 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) Average of predictions of CFS (NCEP), LDEO5 (Lamont) and Constructed Analog (statistical; NCEP/CPC) 2) same as 1), plus an uncertainty pattern 3) same as 1), minus an uncertaintypattern TROP. ATL, and INDIAN oceans Same as Pacific, without LDEO5 and with statistical (CCA) forecast for Indian Oc. EXTRATROPICAL damped persistence

11 Method of Forming 3 SST Predictions for Climate Predictions
damped persistence 0.25 NCEP CFS CPC CA IRI CCA 3 scenarios NCEP CFS Model LDEO Model CPC Constructed Analog (CA): (1)mean, (2)mean+, (3) mean- NCEP CFS CPC CA 3 scenarios damped persistence For each ocean basin, the 3 SST scenarios are (1) mean of the models used for that basin, (2) mean+p and (3) mean-p p is uncertainty factor from 1st EOF of model historical error

12 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)

13 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 Persistence LDEO 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, Australia ECHAM 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 CCM NCAR, Boulder, CO IRI, Palisades, New York GFDL GFDL, Princeton, NJ GFDL, Princeton, NJ

14 IRI’s monthly issued probability forecasts of
seasonal global precipitation and temperature We issue forecasts at four lead times. For example: NOV | Dec-Jan-Feb Jan-Feb-Mar Feb-Mar-Apr Mar-Apr-May Forecast models are run 7 months into future. Observed data are available through the end of the previous month (end of October in example above). Probabilities are given for the three tercile-based categories of the climatological distribution.

15 Forecasts of the climate
The tercile category system: Below, near, and above normal Probability: 33% % % Below| Near | Below| Near | Above  Data: | || ||| ||||.| || | | || | | | . | | | | | | | | | | Rainfall Amount (tenths of inch) (30 years of historical data for a particular location & season) (Presently, we use )

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21 Probability of above normal, near normal, and below normal rainfall, based on observations during

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23 Above-Normal Below-Normal (3-model) JAS Precipitation, 30S-30N
Favorable results of application of Bayesian consolidation are evidenced in an analysis of reliability (the correspondence between forecast probability and relative observed frequency of occurrence). Simple pooling (assignment of equal weights to all AGCMs) gives more reliability than that of individual AGCMs, but the Bayesian method results in still much more reliability. Note that flattish lines show model overconfidence; 45º line shows perfect reliability. Above-Normal Below-Normal Bayesian Pooled Observed relative Freq. Observed relative Freq. Individual AGCM Forecast probability Forecast probability (3-model) JAS Precipitation, 30S-30N See also Barnston et al. (2003), Bull. Amer. Meteor. Soc., , Fig 7 (page 1793). from Goddard et al. 2003 EGS-AGU-EGU Joint Assembly, Nice, France, 7-11 April

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

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

26 Indonesia OND Precip 1950- 2000 North America Europe Uses actually
Australia/ Indonesia OND Precip 1950- 2000 Uses actually observed SSTs Europe North America Tropical Americas South America

27 Ranked Probability Skill Score (RPSS)
prob prob RPSfcst =  (Fcsticat – Obsicat)2 icat=1 icat ranges from 1 (below normal) to 3 (above normal) icat is cumulative, from 1 to icat RPSS = 1 - (RPSfcst / RPSclim) RPSclim is RPS of forecasts of climatology (33%,33%,33%)

28 Observation “Probability” (N occurred)
Cumulative Shown in Table Probability forecast (B,N,A): XXXX XXXX RPS(fcst) = ( )2 + (.60 – 1.00)2 + ( )2 = = .20 : RPS(clim) = ( )2 + (.667 – 1.00)2 = = .2222 RPSS = 1 – (.20 / .222) = .10 Observation “Probability” (N occurred)

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

30 Sahel Precipitation 11 - 19N, 19W – 29E RPSS Nino3.4 SST / 10
Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

31 Sahel Precipitation (only MJJ,JJA,JAS,ASO) 11 - 19N, 19W – 29E RPSS
Nino3.4 SST / 10 Nino34 Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

32 OND 1997 to MJJ 2008: first lead time Frequency of issuance

33 Jun-Jul-Aug 2008 precipitation anomaly

34 IRI’s Africa forecast from May 2008

35 Sahel to Red Sea Precipitation 11 - 19N, 19W – 46E
Nino3.4 SST / 10 Nino34 Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

36 Sahel to Red Sea Precipitation (MJJ,JJA,JAS,ASO) 11 - 19N, 19W – 46E
Nino3.4 SST / 10 Nino34 Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

37 Greater Horn of Africa Precipitation 9N – 9S, 31 – 51E
Nino3.4 SST / 10 Nino34 Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

38 Greater Horn of Africa Precipitation (JJA,JAS,ASO,SON,OND,NDJ,DJF)
9N – 9S, 31 – 51E Nino3.4 SST / 10 Nino34 Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

39 Southern Africa Precipitation 1 – 34S, 9 – 54E
Nino3.4 SST / 10 Nino34 Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

40 Southern Africa Precipitation 1 – 34S, 9 – 54E (OND,NDJ,DJF,JFM)
Nino3.4 SST / 10 Nino34 Lead 1 Lead 2 Lead 3 Lead 4 RPSS | | | | | | | | | | | | | | | | | | | | | | 2008

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43 25N – 25S Nino34 Lead 1 Lead 2 Lead 3 Lead 4 Nino3.4 SST / 10
| | | | | | | | | | | | | | | | | | | | | | 2008

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

45 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

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