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