1. DEPARTAMENTO DE METEOROLOGIA UNIVERSIDADE FEDERAL DE ALAGOAS molion@radar.ufal.br REGIONAL MEETING ON CLIPS AND AGROMETEOROLOGICAL APPLICATIONS FOR THE MERCOSUR COUNTRIES LUIZ CARLOS B. MOLION LONG-TERM CLIMATE PREDICTION AS A MARKETING STRATEGY CAMPINAS, SÃO PAULO, BRAZIL - JULY 13 TO 16 2005

2. CLIMATE MONITORING AND PREDICTION: A KEY FACTOR TO INCREASING PRODUCTION WITH REDUCED COST GLOBALIZATION REQUIRES MARKETING STRATEGIES WORLD’S POPULATION IS INCREASING AND MEETING THE FOOD DEMAND IS A CHALLENGE

3. EXAMPLE 1 : SOYBEAN

5. TOP SOYBEAN PRODUCING COUNTRIES   

6. EXAMPLE 2: SUGAR

7. TOP SUGAR PRODUCING COUNTRIES (x 1.000.000 MTONS) EUROPEAN COMMUNITY............20 EUROPEAN COMMUNITY............20 ÍNDIA..............................................16 ÍNDIA..............................................16 CHINA............................................ 11 CHINA............................................ 11 USA................................................ 8 USA................................................ 8 THAILAND..................................... 7 THAILAND..................................... 7 EASTERN EUROPE...................... 7 EASTERN EUROPE...................... 7 AUSTRALIA................................... 5 AUSTRALIA................................... 5 BRASIL......................................... 28 BRASIL......................................... 28

9. CLIMATE ANOMALIES MONITORING

10. MONTHLY RAINFALL ANOMALIES - 2003 SOURCE:CAMS XIE, CPTEC/INPE

11. PREDICTING CLIMATE VARIABILITY OR CLIMATE EXTREMES IS A CHALLENGE BECAUSE OF ITS STRONG IMPACT ON SOCIETY !

12. METHODS FOR CLIMATE PREDICTION SHORT-RANGE:SEASONAL TO INTERANNUAL SUCCESSFUL EXAMPLE: EL NIÑO 1997-98 SUCCESSFUL EXAMPLE: EL NIÑO 1997-98 SYSTEMATIC APPROACH: USE AGCM/ARCM SYSTEMATIC APPROACH: USE AGCM/ARCM SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEB SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEB

13. FORECAST OF THE EXPERIMENTAL CLIMATE PREDICTION CENTER (ECPC), SAN DIEGO, CA, USA

14. J. ROADS

16. METHODS FOR CLIMATE PREDICTION SHORT-RANGE:SEASONAL TO INTERANNUAL SUCCESSFUL EXAMPLE: EL NIÑO 1997-98 SUCCESSFUL EXAMPLE: EL NIÑO 1997-98 SYSTEMATIC APPROACH: USE AGCM/ARCM SYSTEMATIC APPROACH: USE AGCM/ARCM SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEB SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEB POOLED MULTI - MODEL ENSEMBLES: IRI GENERATES PROBABILITIES DISTRIBUTION FORECASTS IMPROVED FORECASTS ! POOLED MULTI - MODEL ENSEMBLES: IRI GENERATES PROBABILITIES DISTRIBUTION FORECASTS IMPROVED FORECASTS !

17. FORECAST OF THE INTERNATIONAL RESEARCH INSTITUTE FOR CLIMATE PREDICTION (IRI), NEW YORK, USA

18.    85%  T. BARNSTON

19. METHODS FOR CLIMATE PREDICTION SHORT-RANGE:SEASONAL TO INTERANNUAL SUCCESSFUL EXAMPLE: EL NIÑO 1997-98 SUCCESSFUL EXAMPLE: EL NIÑO 1997-98 SYSTEMATIC APPROACH: USE AGCM/ARCM SYSTEMATIC APPROACH: USE AGCM/ARCM SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEB SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEB POOLED MULTI - MODEL ENSEMBLES: IRI GENERATES PROBABILITIES DISTRIBUTION FORECASTS IMPROVED FORECASTS ! POOLED MULTI - MODEL ENSEMBLES: IRI GENERATES PROBABILITIES DISTRIBUTION FORECASTS IMPROVED FORECASTS ! “SIGNS” OF NATURE:FARMERS ALMANACK ALLIGATOR, DUCK, JOÃO-DE-BARRO (“OVENBIRD”) “SIGNS” OF NATURE:FARMERS ALMANACK ALLIGATOR, DUCK, JOÃO-DE-BARRO (“OVENBIRD”)

20. LONG-RANGE:DECADAL TO INTERDECADAL PURE STATISTICAL / STOCHASTIC DO NOT TAKE IN ACCOUNT CLIMATE DYNAMICS. RELY ON “STATIONARY SIGNAL” (CYCLES). PURE STATISTICAL / STOCHASTIC DO NOT TAKE IN ACCOUNT CLIMATE DYNAMICS. RELY ON “STATIONARY SIGNAL” (CYCLES). USE OF “SIMILARITY” BETWEEN “CLIMATE STATES OR REGIMES” COMBINED WITH STATISTICAL / STOCHASTIC AND DIAGNOSTICS STUDIES. EXAMPLE : PDO USE OF “SIMILARITY” BETWEEN “CLIMATE STATES OR REGIMES” COMBINED WITH STATISTICAL / STOCHASTIC AND DIAGNOSTICS STUDIES. EXAMPLE : PDO METHODS FOR CLIMATE PREDICTION

21. PACIFIC DECADAL OSCILLATION

22.   WARM PHASECOLD PHASE   DATA SOURCE: NOAA CIRES / CDC

23. SST PDO: WARM PHASE MINUS COLD PHASE >1.0°C  < - 0.4°C

24.  1947-1976   1977-1998   1925-1946  WARMWARM COLD PACIFIC DECADAL OSCILLATION

25. WORLD CLIMATE

26. GLOBAL MEAN TEMPERATURE ANOMALIES AND PDO PHASES --------------------------------------------------------------------- ------------------------------------------ --------------------------------- WARM -------------------------- COLD -------------------------- WARM COINCIDENCE.....???? LITTLE ICE AGE SOURCE: CRU / EAU /UK

27.  1947-1976   1977-1998    1925-1946  WARMWARM COLDCOLD PACIFIC DECADAL OSCILLATION

28. --------------------------------------------------------------------- -------------------------- - 0,14°C  1947-1976  COLD GLOBAL MEAN TEMPERATURE ANOMALIES AND PDO PHASES

29. 1976   1998 COLD WARMYEARS STANDARD DEVIATIONS MULTIVARIATE ENSO INDEX (MEI)

30. 1976 (MEI) MULTIVARIATE ENSO INDEX STANDARD DEVIATIONS YEARS

31. SOUTH AMERICA CLIMATE IMPACTS

32. SLP 1948/76 – 1948/98 SLP 1977/98 – 1948/98 + - + COLD PHASE WARM PHASE + - (hPa) +- -

33. SLP 1977/98 – 1948/76 >-0.5 > +1.0

34. SLP JJA 1977/98 – 1948/76 SLP JFM 1977/98 – 1948/76 SUMMERWINTER

35. RAIN 1948/76 – 1948/98 RAIN 1977/98 – 1948/76 - + + - COLD PHASE WARM PHASE

36. RAINFALL 1977/98 – 1948/76 (mm/day) > 4 < - 1

37. SURF. TEMP 1948/76 – 1948/98 SURF. TEMP 1977/98 – 1948/98 + + - - COLD PHASE WARM PHASE +-

38. SURF AIR TEMP 1977/98 – 1948/76 > 1°C ~ 1.0

39. TSM 1977/98 – 48/98 TSM 1946/76 – 48/98 + + -- WARM PHASE COLD PHASE - +

40. CONCLUDING REMARKS THE VULNERABILITY OF SOCIETY INCREASES WITH POPULATION GROWTH AND THE ABILITY TO MEET SUSTAINABLE FOOD SUPPLY BECOMES QUESTIONABLE THE VULNERABILITY OF SOCIETY INCREASES WITH POPULATION GROWTH AND THE ABILITY TO MEET SUSTAINABLE FOOD SUPPLY BECOMES QUESTIONABLE FORECAST DELIVERY TO USER HAVE TO BE IMPROVED. FORECAST DELIVERY TO USER HAVE TO BE IMPROVED. FORECAST HAVE TO MEET USERS’ NEEDS. FORECAST HAVE TO MEET USERS’ NEEDS. USERS HAVE TO LEARN ABOUT RISK OF FORECAST FAILING AND ITS CONSEQUENCES. USERS HAVE TO LEARN ABOUT RISK OF FORECAST FAILING AND ITS CONSEQUENCES. CLIMATE PREDICTION IS A KEY FACTOR FOR ACHIEVING SUSTAINABILITY ! HOWEVER...... CLIMATE PREDICTION IS A KEY FACTOR FOR ACHIEVING SUSTAINABILITY ! HOWEVER...... USE OF ARCMs FOR DOWNSCALING CALL FOR BETTER SURFACE MET NETWORK. USE OF ARCMs FOR DOWNSCALING CALL FOR BETTER SURFACE MET NETWORK.

41. SUGGEST TO PERFORM DIAGNOSTIC STUDIES ON THE INFLUENCE OF PDO ON LOCAL AND REGIONAL CLIMATE AND THEIR RESULTS TO BE USED IN COMBINATION WITH FORECASTS. EXAMPLES: ONSET OF RAINY SEASON, FREQUNCY OF SEVERE FROST OR DROUGHTS. SUGGEST TO PERFORM DIAGNOSTIC STUDIES ON THE INFLUENCE OF PDO ON LOCAL AND REGIONAL CLIMATE AND THEIR RESULTS TO BE USED IN COMBINATION WITH FORECASTS. EXAMPLES: ONSET OF RAINY SEASON, FREQUNCY OF SEVERE FROST OR DROUGHTS. ARE DECISION MAKERS PREPARED TO USE FORECASTS AS ISSUED? ARE DECISION MAKERS PREPARED TO USE FORECASTS AS ISSUED? DO FARMERS BENEFIT FROM FORECAST INFORMATION? DO FARMERS BENEFIT FROM FORECAST INFORMATION? METHODS OF ESTIMATING IMPACTS OF CLIMATE VARIABILITY ADN CLIMATE FORECASTS ON SOCIETY ARE NEEDED METHODS OF ESTIMATING IMPACTS OF CLIMATE VARIABILITY ADN CLIMATE FORECASTS ON SOCIETY ARE NEEDED CONCLUDING REMARKS

42. THE END