Regional Climate Simulations and Regional Climate Simulations and

Decision Making: Decision Making:

The Experience of The Experience of

PesqueclimaPesqueclima

I Ibero-American Workshop on Climate Dynamics, I Ibero-American Workshop on Climate Dynamics, Climate Change, and Regional Climate ModelingClimate Change, and Regional Climate Modeling

São P

au

lo,

SP,

São P

au

lo,

SP,

Bra

zil, 2

0-2

3

Bra

zil, 2

0-2

3

Au

gu

st,

20

07

.A

ug

ust

, 2

00

7.

Departamento de GeociênciasDepartamento de GeociênciasFundação Universidade Federal de Rio GrandeFundação Universidade Federal de Rio Grande

Nisia KruscheNisia Krusche

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Regional climate simulations Regional climate simulations may aid minimize the may aid minimize the vulnerability of local vulnerability of local populations.populations.

• Fishery Fishery communitiescommunities

• Impact of climate Impact of climate variationsvariations

• RegionalRegional climate climate simulations simulations

PesqueclimPesqueclimaa

Vulnerability of Vulnerability of Fishery Communities to Fishery Communities to

Climate Variability, in the Climate Variability, in the Estuary of dos Patos LagoonEstuary of dos Patos Lagoon

I Ibero-American Workshop on Climate Dynamics, I Ibero-American Workshop on Climate Dynamics, Climate Change, and Regional Climate ModelingClimate Change, and Regional Climate Modeling

São P

au

lo,

SP,

São P

au

lo,

SP,

Bra

zil, 2

0-2

3

Bra

zil, 2

0-2

3

Au

gu

st,

20

07

.A

ug

ust

, 2

00

7.

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Pesqueclima Pesqueclima

• Evaluate the impact of climate variability in social and economical vulnerability of communities that fish shrimp in the estuarine region of dos Patos Lagoon; • Provide climate forecasting design-ed to the needs of those com-munities, and contingency plans to climate situations that are not ade-quate to perform their economical activities.

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30 o S

LocationLocation ofof dos Patos dos Patos LagoonLagoon

Fonte: NASA

30 o S

30 o S

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Fishery Communities ofFishery Communities of dos Patos dos Patos LagoonLagoon

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Fishery Artesian Fishery Artesian Systems ofSystems of dos Patos dos Patos

LagoonLagoon

1) Exclusive skilled fishers, living in small communities and specialize in fishing in interior waters;

2) Exclusive skilled fishers, living in communities near the entrance channel and specialize in fishing in interior waters and coastal sea waters;

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Fishery Artesian Systems Fishery Artesian Systems ofof

dos Patos dos Patos LagoonLagoon3) Sporadic fishers, living in urban communities and special-ized in fishing in interior waters; 4) Exclusive skilled fishers, living in near urban centers and specialized in fishing in interior waters;5) Fishers of São Lourenço do Sul,

defeso period is in spring, while for the others it is in winter;

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Fishery Artesian Systems ofFishery Artesian Systems of dos Patos dos Patos LagoonLagoon

6)Fisher-farmer, in the rural areas sur-rounding the estuary.

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Vulnerability of Vulnerability of Fishery CommunitiesFishery Communities

Factors Fishery System

1 2 3 4 5 6

Social Cohesion Low Low Low Usual Low Low

Organization Fair Fair Fair Fine Low Low

Change in arts Higher vulnerability, due to larger fishing effort and lower agreement among fishers.

Uncertainties Higher vulnerability, due to uncertainties on the success of catches.

Adaptation Low adaptation and learning in all systems, except 4.

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Fishers use weather Fishers use weather forecasting:forecasting:

• navigation, • security, • net setting and collecting, • planning, • identify best catch.

• radio and television;• home- radio and cellular to

• other fishers, • other communities, and • Uruguayan fishers.

• Brazilian Navy.

ObtaineObtained fromd from

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Vulnerability and Vulnerability and resilience of those artisan resilience of those artisan fishery com-munities depend fishery com-munities depend strongly on their degree of strongly on their degree of organization.organization.

• Strengthen of organization; • Program to forecast shrimp catches;• Plan of alternate options in case of negative results;• Improvement on fish commerce;• Better distribution of financial resources;• Register again all fishers.

Climate

Climate

Foreca

st

Foreca

st

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Total Fishery Catch in dos Total Fishery Catch in dos Patos Lagoon, 1945-1990Patos Lagoon, 1945-1990

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1945 1955 1965 1975 1985 1995Ano

Ca

ptu

ra (

ton

)

Total artesanal

camarao

corvina

bagre

tainha

IBAMA, organized by Marcelo Vasconcellos, 2000.

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Artisan Shrimp CatchArtisan Shrimp Catch

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 20000

1000

2000

3000

4000

5000

6000

7000

8000

Ca

ptu

ra A

rte

sana

l An

ua

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Cam

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ton

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Tempo (anos)

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Wavelet TransformWavelet Transform

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995012345678

Pes

ca (

10

00 to

n)

Captura Artesanal de Camarão

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 19952

3

5

8

12

18

28

Tempo (anos)

Per

íod

o (a

no

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Espectro dos Coeficientes Reais de Ondeleta

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Energy SpectrumEnergy Spectrum

2.3 2.6 3 3.5 4 4.6 5 6 7 8 9 11 12 14 16 18 21 24 28 32 370

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

6

Período (anos)

Var

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Shrimp Life CycleShrimp Life Cycle

M ESTUARY PLATAFORM

SET

Estuarine inflow and growing of shrimp fry

Reproduction of shrimps

in SCOCT

NOV

DEC

Lowconcentration of shrimps

JAN Growing

FEBGrowing F

ISH

MAR

APR Females go to the ocean

MAY Males go to the ocean

East winds and East winds and low precipitationlow precipitation

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Drainage area of dos Patos Drainage area of dos Patos LagoonLagoon

Organized by Allan de Oliveira, 2006.

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Catch and Precipitation Catch and Precipitation CorrelationsCorrelations

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

Pre

cipi

taçã

o (m

m) Capt <= 3030 ton

Setembro

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22Capt>3030 ton

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

Com

p. Z

onal

(m

/s)

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-38

-34

-30

-26

-22

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

Com

p. M

erid

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l (m

/s)

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-38

-34

-30

-26

-22

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22Capt<= 3030 ton

Outubro

-1

0

1

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22Capt>3030 ton

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

-1

0

1

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

-1

0

1

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

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Catch and Precipitation Catch and Precipitation CorrelationsCorrelations

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

Pre

cip

itaca

o (

mm

)

Capt <= 3030 tonJaneiro

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-38

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-30

-26

-22Capt>3030 ton

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

Co

mp

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l (m

/s)

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-38

-34

-30

-26

-22

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

Co

mp

. Me

rid

ion

al (

m/s

)

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-38

-34

-30

-26

-22

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22Capt<= 3030 ton

Fevereiro

-1

0

1

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22Capt>3030 ton

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

-1

0

1

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

-1

0

1

-62 -58 -54 -50 -46 -42-42

-38

-34

-30

-26

-22

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D’Incao (2007) observed D’Incao (2007) observed periods with favorable periods with favorable climate conditions and low climate conditions and low shrimp production.shrimp production.

• Official registers are of low confidence.

•Factors:• biological,• oceanographic,• economical, and• climate.

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RegCM3 RegCM3 SimulationSimulation

• Horizontal resolution of 40 km;• Grell convective parameterization and Arakawa-Schubert closure.

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Topography in Topography in Extended DomainExtended Domain

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Simulation DomainSimulation Domain

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Periods of simulationPeriods of simulation

EXTREM EVENT INICIAL AND FINAL DATE M

WET

FEB 1990 1.12.1989 a 1.04.1990 JFM

JUL 1995 1.05.1995 a 1.09.1995 JJA

DEC 1997 1.12.1997 a 1.04.1998 DJF

JAN 2002 1.01.2002 a 1.04.2002 JFM

JUL 1998 1.06.1998 a 1.09.1996 JJA

DRY

MAY 1996 1.05.1996 a 1.08.1996 MJJ

SEP 1998 1.08.1998 a 1.12.1998 SON

JAN 2004 1.11.2003 a 1.02.2004 NDJ

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Wet Simulation – Summer 1998Wet Simulation – Summer 1998

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Wet Simulation – Summer 1998Wet Simulation – Summer 1998

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Comparação entre simulação do RegCM3 Comparação entre simulação do RegCM3 e análise do NCDC, subdomínio SU1, preci-e análise do NCDC, subdomínio SU1, preci-pitação média mensal M e desvio padrão pitação média mensal M e desvio padrão , em mm dia, em mm dia-1-1, e erro médio relativo BR., e erro médio relativo BR.

MRCM3 RCM3 MNCDC NCDC BR (%)

NOV 1997 12,1 4,7 7,7 1,3 57,0

DEZ 1997 11,7 4,3 7,1 1,8 64,7

JAN 1998 12,8 3,9 7,2 1,5 77,8

FEV 1998 10,1 3,1 9,6 1,8 5,2

MAR 1998 6,7 2,2 6,9 1,3 -2,9

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Comparação entre simulação do RegCM3 e Comparação entre simulação do RegCM3 e análise do NCDC, subdomínio SU2, precipitação análise do NCDC, subdomínio SU2, precipitação média mensal M e desvio padrão média mensal M e desvio padrão , em mm dia, em mm dia--

11, erro médio relativo BR e chuva em Rio , erro médio relativo BR e chuva em Rio Grande.Grande.

MRCM3 RCM3 MNCDC NCDC BR (%) RG

NOV 1997 7,8 1,5 6,5 1,5 20,0 5,8

DEZ 1997 11,7 2,2 7,9 1,6 48,1 6,6

JAN 1998 10,5 4,2 6,4 0,9 64,0 6,3

FEV 1998 6,1 2,4 7,5 2,4 18,7 3,5

MAR 1998 6,0 1,5 5,2 0,9 15,3 4,9

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Dry Simulation – Summer Dry Simulation – Summer 20042004

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Extreme Events of Extreme Events of Precipitation in Rio Precipitation in Rio

GrandeGrande

• Observed precitation in Rio Grande is not always that of the drainage area in NCDC. • Spatial and temporal patterns are simulated well by RegCM3 in most events;• Mensal forecast errors often exceed ±5-30%. • Errors decrease in seasonal (3months) average, especially in SU2 region;

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Extreme Events of Extreme Events of Precipitation in Rio Precipitation in Rio

GrandeGrande

• Error in region SU1 for seasonal average is high, due to boundary distance.• Gas parameterization produces errors smaller or equal to Gfc ones.• The analysis should be extend to establish a better representation of cumulus convention in the southern region of Brazil.

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ResearchersNisia Krusche – DGEO-FURGDaniela C. Kalikoski – FAO, RomaRosmeri P. da Rocha – DCA-IAG-USPPedro Quevedo Neto – DGEO –FURG

TeamTeam

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gwww.pesqueclima.furgwww.pesqueclima.furg.br.br

AcknowledgementsAcknowledgements• CNPq: Ed. 019/2004, no 477124/2004-6, e Ed. 057/2005, no 502090/2005-7

• FAPERGS: Ed. PROCOREDES II 001/2005, no 05/1843.7