Centro de Previsão de Tempo e Estudos Climáticos (CPTEC/INPE) São Paulo, Brazil () Integrated...

Centro de Previsão de Tempo e Estudos Climáticos (CPTEC/INPE)Centro de Previsão de Tempo e Estudos Climáticos (CPTEC/INPE)São Paulo, BrazilSão Paulo, Brazil(www.cptec.inpe.br)(www.cptec.inpe.br)

Integrated observed and modeled atmospheric water budget in the Amazon Basin: How much more can we ask from it?

Jose A. Marengo, Carlos Nobre, Helio Camargo, Luiz Candido, Christopher CastroCPTEC/INPE

Sao Paulo, Brazil

Moisture transport from the tropical Atlantic

Evapotranspiration

RainfallRunoff to Atlantic Ocean

Water balance in the Amazon Basin (perfect!)

Water-Balance Approach (1)

• Terrestrial water balance:

• Atmospheric water balance:

• Combined water balance:measuredstreamflow(Rs+Rg)

Water-Balance Approach (2)

• Assumptions:– The contributions of the liquid and solid phases of

atmospheric water are negligible

– The measured streamflow includes both the contributions of surface and groundwater runoff

• Limitations:– Atmospheric water balance estimations are accurate

only for domains > 105-106 km2 (Rasmusson 1968, Yeh et al. 1998

Water-Balance Approach (3)

• Changes in terrestrial water storage (dS/dt) in a given river basin can be estimated as the sum of three terms:

: Convergence of the vertically integrated water vapour flux

: Change in column storage of water vapour

: Evaporation (Latent Heat flux)

ReanalysisData (NCEP)

E

: Measured rainfall and streamflow ObservationsP, R

ADJF-CMAP

BDJF-CRU

CDJF-NCEP

DMAM-CMAP

EMAM-CRU

FRMAM-NCEP

Precipitation (mm/day)

EJJA

CMAM

BNDJ

ASON

Evaporation (mm/day)

ASON

BDJF

EJJA

CMAM

Moisture convergence (mm/day)

Amazon Basin

0.0

2.0

4.0

6.0

8.0

10.0

12.0197

0197

1197

2197

3197

4197

5197

6197

7197

8197

9198

0198

1198

2198

3198

4198

5198

6198

7198

8198

9199

0199

1199

2199

3199

4199

5199

6199

7199

8

Year

mm/da

y

NCEP CMAP Rain Gauge GHCN Linear (Rain Gauge)

Northern Amazonia

3.0

4.0

5.0

6.0

7.0

8.0

9.0

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Year

mm/da

y

RAINGAUGE NCEP CMAP Linear (RAINGAUGE)

Southern Amazonia

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Year

RAINGAUGE NCEP CMAP Linear (RAINGAUGE)

Centro de Previsão de Tempo e Estudos Climáticos (CPTEC/INPE)Centro de Previsão de Tempo e Estudos Climáticos (CPTEC/INPE)São Paulo, BrazilSão Paulo, Brazil(www.cptec.inpe.br)(www.cptec.inpe.br)

NortheastTradesET

Amazonia

Energy balance

MCSLa Plata Basin

wind

Ta

Td

LLJ

N

500

200hPa

1000

800900

600

700

400

300

Altiplano

Moisture flux from Amazonia

The Low Level Jet east of the Andes (LLJ) Transports moisture from Amazonia to the Parana La Plata Basin (Marengo et al. 2004)

Water budget 1970-99 the entire Amazon basin (using various rainfall data sets)

Component GHCN

CMAP GPCP NCEP LW CRU Marengo

(2004)

P 8.6 5.6 5.2 6.4 5.9 6.0 5.8

E 4.3 4.3 4.3 4.3 4.3 4.3 4.3

R 2.9 2.9 2.9 2.9 2.9 2.9 2.9

C 1.4 1.4 1.4 1.4 1.4 1.4 1.4

P-E 4.3 1.3 0.9 2.1 1.6 1.6 1.5

P-E-C +2.9 -0.1 -0.5 +0.7 +0.2 +0.3 +0.1

Climatological water budget 1970-99

ComponentMean El Niño

1982/83El Nino 1997/98

La Niña 1988/89

P 5.8 4.9 5.2 6.7

E 4.3 4.5 4.1 4.4

R 2.9 2.1 2.5 2.9

C 1.4 1.3 1.2 3.1

P-E +1.5 +0.4 +0.9 +2.3

P-E-C +0.1 -0.9 -0.1 -0.8

Imbalance=[((C/R)-1)]

51% 38% 52% 6%

Component

N. Amazo

n

1982/83

1997/98

1988/89

S. Amazo

n

1982/83

1997/98

1988/89

P 6.1 5.0 6.0 7.4 4.7 4.8 4.6 6.0

E 4.8 5.1 4.9 4.9 4.0 4.3 3.9 4.2

C 0.4 0.1 -0.5 2.3 2.0 2.2 2.2 3.1

P-E +1.3 -0.1 +1.1 +2.5 +0.7 +0.5 +0.7 +1.8

P-E-C +1.0 -0.2 +1.6 +0.2 -1.3 -1.7 -1.5 -1.3

MediumMediumPredictabilityPredictability

Low Predictability

Higher predictability Higher predictability

LBA

PLATIN

Seasonal climate predictability in South America

MONSOON

Medium predictability

Medium predictability

Water balance (mm/day) in the Amazon River Basin

CPTEC COLA AGCM NCEP Rean+Obsv

0

1

2

3

4

5

6

7

8

J F M A M J J A S O N D

P

E

R

0

1

2

3

4

5

6

7

8

J F M A M J J A S O N D

P

E

R

Energy balance (W/m2) in the Amazon River BasinCPTEC-COLA AGCM NCEP Rean+OBSV

-100

-50

0

50

100

150

200

250

J F M A M J J A S O N D

SWLWLEHGS

-100

-50

0

50

100

150

200

250

J F M A M J J A S O N D

SW

LW

LE

H

GS

0

1

2

3

4

5

6

7

Da

ta

No

v-5

1

Oc

t-5

3

Se

p-5

5

Au

g-5

7

Ju

l-5

9

Ju

n-6

1

Ma

y-6

3

Ap

r-6

5

Ma

r-6

7

Fe

b-6

9

Ja

n-7

1

De

c-7

2

No

v-7

4

Oc

t-7

6

Se

p-7

8

Au

g-8

0

Ju

l-8

2

Ju

n-8

4

Ma

y-8

6

Ap

r-8

8

Ma

r-9

0

Fe

b-9

2

Ja

n-9

4

De

c-9

5

No

v-9

7

Oc

t-9

9

Se

p-0

1

ObidosCPTEC NCEP Runoff

0

2

4

6

8

10

12

Ja

n-5

0

Ja

n-5

3

Ja

n-5

6

Ja

n-5

9

Ja

n-6

2

Ja

n-6

5

Ja

n-6

8

Ja

n-7

1

Ja

n-7

4

Ja

n-7

7

Ja

n-8

0

Ja

n-8

3

Ja

n-8

6

Ja

n-8

9

Ja

n-9

2

Ja

n-9

5

Ja

n-9

8

Ja

n-0

1

P (NCEP) P (CPTEC) : P (CRU) Precipitation

0

1

2

3

4

5

6

Ja

n-5

0

Ja

n-5

3

Ja

n-5

6

Ja

n-5

9

Ja

n-6

2

Ja

n-6

5

Ja

n-6

8

Ja

n-7

1

Ja

n-7

4

Ja

n-7

7

Ja

n-8

0

Ja

n-8

3

Ja

n-8

6

Ja

n-8

9

Ja

n-9

2

Ja

n-9

5

Ja

n-9

8

Ja

n-0

1

Amazoni E (CPTEC)

Amazoni E (NCEP)

0

10

20

30

40

50

60

70

80

90

100

Jan

-50

Jan

-52

Jan

-54

Jan

-56

Jan

-58

Jan

-60

Jan

-62

Jan

-64

Jan

-66

Jan

-68

Jan

-70

Jan

-72

Jan

-74

Jan

-76

Jan

-78

Jan

-80

Jan

-82

Jan

-84

Jan

-86

Jan

-88

Jan

-90

Jan

-92

Jan

-94

Jan

-96

Jan

-98

Jan

-00

H CPTEC)

H (NCEP)

Evaporation (Latent heat)

Sensible Heat

0

2

4

6

8

10

12

0 2 4 6 8 10 12

P (CPTEC)

P (CRU)

Linear (P (CRU))

Linear (P (CPTEC)) NCEP-CPTEC

NCEP-CRU

Observed vs modelled precipitation

Conclusions- Imbalances in the water balance (1)

• Major differences in the behavior of the water balance between the northern and southern parts of the basin (seasonal to interannual variability)

• In present climates the entire basin behaves as a sink of moisture, while apparently northern Amazonia can act as a net source for moisture under extreme dry conditions (e.g. the strong 1983 El Niño event)In the future it will become source of moisture (HadCM3)

• Uncertainties in P in Amazonia, especially in the southern section can reach up to +1.0 mm/day. Some differences among rainfall data sets can reach up to 30% in rainfall and 15% in runoff.

• Estimates show a basin-wide imbalance of 51%, exhibiting an interannual variability.

• The choice of rainfall data set also has an impact in the imbalance in the water budget. Thus, significant uncertainties exist in these results and they are sensitive to the data used, in particular the atmospheric can hydrological data.

• CPTC AGCM underestimates P, R, E, overestimates H

Conclusions- Imbalances in the water balance (2)

• The accuracy of the computed water balances depends critically on the domain size and on regional characteristics (climate, density of radiosonde data, topography?).

• The combined water-balance approach is a promising tool for estimating large-scale changes in terrestrial water storage

• Some limitations:– Domain size needs to be at least > 2*105 km2– Additional validation data would be needed (E from

Observations LBA Reference sites?)• Aerological estimates of evaporation might be a useful proxy of

reality and, when confronted with model evaporation, expose physical parametrization problems (we should take advantage of LBA reference site evaporation data).

• Nonetheless, the possible applications and uses are numerous given the dearth of observations of terrestrial water storage and its components