DEPARTAMENTO DE METEOROLOGIA UNIVERSIDADE FEDERAL DE ALAGOAS

DEPARTAMENTO DE METEOROLOGIA UNIVERSIDADE FEDERAL DE ALAGOAS

molion@radar.ufal.br

REGIONAL MEETING ON CLIPS AND REGIONAL MEETING ON CLIPS AND AGROMETEOROLOGICAL APPLICATIONS FOR THE AGROMETEOROLOGICAL APPLICATIONS FOR THE

MERCOSUR COUNTRIESMERCOSUR COUNTRIES

LUIZ CARLOS B. MOLION

LONG-TERM CLIMATE PREDICTION LONG-TERM CLIMATE PREDICTION AS A MARKETING STRATEGYAS A MARKETING STRATEGY

CAMPINAS, SÃO PAULO, BRAZIL - JULY 13 TO 16 2005CAMPINAS, SÃO PAULO, BRAZIL - JULY 13 TO 16 2005

CLIMATE MONITORING AND CLIMATE MONITORING AND PREDICTION: A KEY FACTOR TO PREDICTION: A KEY FACTOR TO INCREASING PRODUCTION WITH INCREASING PRODUCTION WITH REDUCED COSTREDUCED COST

GLOBALIZATION REQUIRES GLOBALIZATION REQUIRES MARKETING STRATEGIESMARKETING STRATEGIES

WORLD’S POPULATION IS WORLD’S POPULATION IS INCREASING AND MEETING THE INCREASING AND MEETING THE FOOD DEMAND IS A CHALLENGEFOOD DEMAND IS A CHALLENGE

EXAMPLE 1 : SOYBEANEXAMPLE 1 : SOYBEAN

TOP SOYBEAN PRODUCING COUNTRIESTOP SOYBEAN PRODUCING COUNTRIES

EXAMPLE 2: SUGAREXAMPLE 2: SUGAR

TOP SUGAR PRODUCING COUNTRIESTOP SUGAR PRODUCING COUNTRIES(x 1.000.000 MTONS)(x 1.000.000 MTONS)

• EUROPEAN COMMUNITY............20EUROPEAN COMMUNITY............20• ÍNDIA..............................................16ÍNDIA..............................................16 • CHINA............................................ 11CHINA............................................ 11• USA................................................ 8USA................................................ 8• THAILAND..................................... 7THAILAND..................................... 7• EASTERN EUROPE...................... 7EASTERN EUROPE...................... 7• AUSTRALIA................................... 5AUSTRALIA................................... 5

• BRASIL......................................... 28BRASIL......................................... 28

CLIMATE ANOMALIES CLIMATE ANOMALIES MONITORINGMONITORING

MONTHLY RAINFALL ANOMALIES - 2003MONTHLY RAINFALL ANOMALIES - 2003

SOURCE:CAMS XIE, CPTEC/INPESOURCE:CAMS XIE, CPTEC/INPE

PREDICTING CLIMATE VARIABILITY OR PREDICTING CLIMATE VARIABILITY OR CLIMATE EXTREMES IS A CHALLENGE CLIMATE EXTREMES IS A CHALLENGE BECAUSE OF ITS STRONG IMPACT ONBECAUSE OF ITS STRONG IMPACT ON

SOCIETY !SOCIETY !

METHODS FOR CLIMATE PREDICTIONMETHODS FOR CLIMATE PREDICTIONSHORT-RANGE:SEASONAL TO INTERANNUALSHORT-RANGE:SEASONAL TO INTERANNUAL

• SUCCESSFUL EXAMPLE: EL NIÑO 1997-98SUCCESSFUL EXAMPLE: EL NIÑO 1997-98• SYSTEMATIC APPROACH: USE AGCM/ARCMSYSTEMATIC APPROACH: USE AGCM/ARCM• SINGLE MODEL: LIMITATIONS DUE TO SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEBTOO LARGE E.G., EASTERN COAST OF NEB

FORECAST OF THE EXPERIMENTAL FORECAST OF THE EXPERIMENTAL CLIMATE PREDICTION CENTER (ECPC), SAN CLIMATE PREDICTION CENTER (ECPC), SAN

DIEGO, CA, USADIEGO, CA, USA

J. ROADSJ. ROADS

METHODS FOR CLIMATE PREDICTIONMETHODS FOR CLIMATE PREDICTIONSHORT-RANGE:SEASONAL TO INTERANNUALSHORT-RANGE:SEASONAL TO INTERANNUAL

• SUCCESSFUL EXAMPLE: EL NIÑO 1997-98SUCCESSFUL EXAMPLE: EL NIÑO 1997-98• SYSTEMATIC APPROACH: USE AGCM/ARCMSYSTEMATIC APPROACH: USE AGCM/ARCM• SINGLE MODEL: LIMITATIONS DUE TO SINGLE MODEL: LIMITATIONS DUE TO TEMPORAL AND SPATIAL SCALES BEING TEMPORAL AND SPATIAL SCALES BEING TOO LARGE E.G., EASTERN COAST OF NEBTOO LARGE E.G., EASTERN COAST OF NEB• POOLED MULTI - MODEL ENSEMBLES: IRI POOLED MULTI - MODEL ENSEMBLES: IRI GENERATES PROBABILITIES DISTRIBUTION GENERATES PROBABILITIES DISTRIBUTION FORECASTSFORECASTS IMPROVED FORECASTS !IMPROVED FORECASTS !

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

85%85%

T. BARNSTONT. BARNSTON

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

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

METHODS FOR CLIMATE PREDICTIONMETHODS FOR CLIMATE PREDICTION

PACIFIC DECADAL PACIFIC DECADAL OSCILLATIONOSCILLATION

PACIFIC DECADAL OSCILLATIONPACIFIC DECADAL OSCILLATION

WARM PHASECOLD PHASE

DATA SOURCE: NOAA CIRES / CDCDATA SOURCE: NOAA CIRES / CDC

SST PDO: WARM PHASE MINUS COLD PHASE

>1.0°C>1.0°C

< - 0.4°C< - 0.4°C

1947-1976

1977-1998 1925-1946 WARMWARM WARMWARM

COLDCOLD

PACIFIC DECADAL OSCILLATIONPACIFIC DECADAL OSCILLATION

WORLD CLIMATEWORLD CLIMATE

GLOBAL MEAN TEMPERATURE ANOMALIES AND PDO PHASES

---------------------------------------------------------------------

------------------------------------------------------------------------------------

---------------------------------

---------------------------------

WARMWARM ---------------------------------------------------- COLDCOLD--------------------------

--------------------------

WARMWARM

COINCIDENCE.....????COINCIDENCE.....????

LITTLE ICE AGELITTLE ICE AGE

SOURCE: CRU / EAU /UKSOURCE: CRU / EAU /UK

1947-1976

1977-1998

1925-1946 WARMWARM WARMWARM

COLDCOLD COLDCOLD

PACIFIC DECADAL OSCILLATIONPACIFIC DECADAL OSCILLATION

-------------------------------------------------------------------------------------------------------------------------

- 0,14°C- 0,14°C

1947-1976 COLDCOLD

GLOBAL MEAN TEMPERATURE ANOMALIES AND PDO PHASES

1976 1998

COLDCOLD

WARMWARM

YEARSYEARS

STAN

DARD

DEV

IATI

ONS

STAN

DARD

DEV

IATI

ONS

MULTIVARIATE ENSO INDEXMULTIVARIATE ENSO INDEX(MEI)(MEI)

1976 (MEI)(MEI)

MULTIVARIATE ENSO INDEXMULTIVARIATE ENSO INDEX

STAN

DARD

DEV

IATI

ONS

STAN

DARD

DEV

IATI

ONS

YEARSYEARS

SOUTH AMERICA CLIMATE SOUTH AMERICA CLIMATE IMPACTSIMPACTS

SLP 1948/76 – 1948/98SLP 1948/76 – 1948/98 SLP 1977/98 – 1948/98SLP 1977/98 – 1948/98

++-- ++

COLD PHASECOLD PHASE WARM PHASEWARM PHASE

++--

(hPa)(hPa)

++ --

--

SLP 1977/98 – 1948/76SLP 1977/98 – 1948/76

>-0.5>-0.5

> +1.0> +1.0

SLP JJA 1977/98 – 1948/76SLP JJA 1977/98 – 1948/76SLP JFM 1977/98 – 1948/76SLP JFM 1977/98 – 1948/76SUMMERSUMMER WINTERWINTER

RAIN 1948/76 – 1948/98RAIN 1948/76 – 1948/98 RAIN 1977/98 – 1948/76RAIN 1977/98 – 1948/76

-- ++

++ --

COLD PHASECOLD PHASE WARM PHASEWARM PHASE

RAINFALL 1977/98 – 1948/76RAINFALL 1977/98 – 1948/76

(mm/day)(mm/day)

> 4> 4

< - 1< - 1

SURF. TEMP 1948/76 – 1948/98SURF. TEMP 1948/76 – 1948/98 SURF. TEMP 1977/98 – 1948/98SURF. TEMP 1977/98 – 1948/98

++

++--

--

COLD PHASECOLD PHASE WARM PHASEWARM PHASE

++ --

SURF AIR TEMP 1977/98 – 1948/76SURF AIR TEMP 1977/98 – 1948/76

> 1°C> 1°C

~ 1.0~ 1.0

TSM 1977/98 – 48/98TSM 1977/98 – 48/98TSM 1946/76 – 48/98TSM 1946/76 – 48/98

++ ++-- --

WARM PHASEWARM PHASECOLD PHASECOLD PHASE

--++

CONCLUDING REMARKSCONCLUDING REMARKS• THE VULNERABILITY OF SOCIETY INCREASES WITH THE VULNERABILITY OF SOCIETY INCREASES WITH POPULATION GROWTH AND THE ABILITY TO MEET POPULATION GROWTH AND THE ABILITY TO MEET SUSTAINABLE FOOD SUPPLY BECOMES QUESTIONABLESUSTAINABLE FOOD SUPPLY BECOMES QUESTIONABLE

• FORECAST DELIVERY TO USER HAVE TO BE IMPROVEDFORECAST 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 USERS HAVE TO LEARN ABOUT RISK OF FORECAST FAILING AND ITS CONSEQUENCESAND ITS CONSEQUENCES..

• CLIMATE PREDICTION IS A KEY FACTOR FOR ACHIEVING CLIMATE PREDICTION IS A KEY FACTOR FOR ACHIEVING SUSTAINABILITY ! HOWEVER......SUSTAINABILITY ! HOWEVER......

• USE OF ARCMs FOR DOWNSCALING CALL FOR BETTER USE OF ARCMs FOR DOWNSCALING CALL FOR BETTER SURFACE MET NETWORK.SURFACE MET NETWORK.

• SUGGEST TO PERFORM DIAGNOSTIC STUDIES ON THE SUGGEST TO PERFORM DIAGNOSTIC STUDIES ON THE INFLUENCE OF INFLUENCE OF PDOPDO ON LOCAL AND REGIONAL CLIMATE ON LOCAL AND REGIONAL CLIMATE AND THEIR RESULTS TO BE USED IN COMBINATION WITH AND THEIR RESULTS TO BE USED IN COMBINATION WITH FORECASTS. EXAMPLES: ONSET OF RAINY SEASON, FORECASTS. EXAMPLES: ONSET OF RAINY SEASON, FREQUNCY OF SEVERE FROST OR DROUGHTS.FREQUNCY OF SEVERE FROST OR DROUGHTS.

• ARE DECISION MAKERS PREPARED TO USE FORECASTS ARE DECISION MAKERS PREPARED TO USE FORECASTS AS ISSUED? AS ISSUED?

• DO FARMERS BENEFIT FROM FORECAST INFORMATION?DO FARMERS BENEFIT FROM FORECAST INFORMATION?• METHODS OF ESTIMATING IMPACTS OF CLIMATE METHODS OF ESTIMATING IMPACTS OF CLIMATE VARIABILITY ADN CLIMATE FORECASTS ON SOCIETY ARE VARIABILITY ADN CLIMATE FORECASTS ON SOCIETY ARE NEEDEDNEEDED

CONCLUDING REMARKSCONCLUDING REMARKS

THE THE ENDEND