DEPARTAMENTO DE METEOROLOGIA UNIVERSIDADE FEDERAL DE ALAGOAS

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

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

3. EXAMPLE 1 : SOYBEAN

5. TOP SOYBEAN PRODUCING COUNTRIES   

6. EXAMPLE 2: SUGAR

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

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 • SYSTEMATIC APPROACH: USE AGCM/ARCM • 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 • SYSTEMATIC APPROACH: USE AGCM/ARCM • 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 FORECASTSIMPROVED 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 • SYSTEMATIC APPROACH: USE AGCM/ARCM • 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 FORECASTSIMPROVED FORECASTS ! • “SIGNS” OF NATURE:FARMERS ALMANACK ALLIGATOR, DUCK, JOÃO-DE-BARRO (“OVENBIRD”)

20. METHODS FOR CLIMATE PREDICTION LONG-RANGE:DECADAL TO INTERDECADAL • 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

21. PACIFIC DECADAL OSCILLATION

22. WARM PHASE PACIFIC DECADAL OSCILLATION     COLD PHASE DATA SOURCE: NOAA CIRES / CDC

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

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

25. WORLD CLIMATE

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

27. PACIFIC DECADAL OSCILLATION COLD COLD   1947-1976   1925-1946   1977-1998  WARM WARM

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

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

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

31. SOUTH AMERICA CLIMATE IMPACTS

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

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

34. SLP JFM 1977/98 – 1948/76 SLP JJA 1977/98 – 1948/76 SUMMER WINTER

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

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

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

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

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

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

41. CONCLUDING REMARKS • ARE DECISION MAKERS PREPARED TO USE FORECASTS AS ISSUED? • DO FARMERS BENEFIT FROM FORECAST INFORMATION? • METHODS OF ESTIMATING IMPACTS OF CLIMATE VARIABILITY ADN CLIMATE FORECASTS ON SOCIETY ARE NEEDED • 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.

42. THE END