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Modélisation des bilans hydrologiques continentaux: variabilité interannuelle et

tendances. Comparaison aux observations

ModelingModeling the continental the continental hydrologic cycle: interannual hydrologic cycle: interannual

variability and trends.variability and trends.Comparison with observationsComparison with observations

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Ph.D. Defense

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Introduction

Our climate is changing, which direct consequences for the Earth.

Variations in greenhouse gases, aerosols, and land use/cover force changes in climate…

……but, most of but, most of consequencesconsequences of climate of climate change are change are realized through the water realized through the water cyclecycle : : flood, drought, sea level rise, etc.flood, drought, sea level rise, etc.

Brésil, 1997 ©IRD/ photo Bernard Osès

Bolivie, 1983 ©IRD photo Denis Wirrmann

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Introduction

This thesis aims to study the variability of continental hydrologic This thesis aims to study the variability of continental hydrologic cycle by using numerical models and observations.cycle by using numerical models and observations.

Solar heat

Precip. 387

water vapor

Evaporation427

Ocean

Net mouvement of water by wind

40Evaporation 71

water vapor

surface water andground waterflow of water

40

Water cycle×1012m3/yr

Water exchanged volume estimated by Baumgartner et Reichel (1975)

Precip. 111

?GRACE

model

http://www.wilkes.edu

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Introduction

The ORCHIDEE land surface model (LSM)

SECHIBA : surface energy and water balancesSTOMATE : surface biochemical processesLPJ : dynamical evolution of the vegetation and the carbon budget

ORCHIDEE is the new land-surface scheme of the IPSL. It is composed of:

ORCHIDEE: Organising Carbon and Hydrology in Dynamic EcosystEmsSECHIBA : Schématisation des Echanges Hydriques à l’Interface entre la Biosphère et l’AtmosphèreSTOMATE : Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems LPJ: Lund –Postdam-Jena

Only SECHIBA is used.

Inclusion of a Inclusion of a routing schemerouting scheme, which , which routes the water to the oceans through routes the water to the oceans through a cascade of linear reservoirs.a cascade of linear reservoirs.

2 m

river discharge

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Plan

Seasonal variations using

GRACE, basin scale

Thesis

Seasonal/interannual

variations using Topex/Posédion,continental

scale

off-line simulation 1987-1988

coupled simulation1997-1998

Ngo-Duc et al. (JGR, 2005a)

Applications

land water & sea level

Ngo-Duc et al.

(GRL, 2005c)

Construction & validation

Ngo-Duc et al.(JGR, 2005b)

Decadal/interdecadal timescales,

the NCC forcing data set,

basin/continental scale

(NCC:NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) Corrected by CRU (Climate Research Unit)

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http://www.aviso.oceanobs.com

Radar altimeters transmit signals to Earth, and receive the echo from the sea surface.

Measuring the time span between sending and receiving of the pulses allows to determine the height of the satellite above sea-level.

The distance from the satellite to the mean earth ellipsoid is known.

the height of the sea-level above the ellipsoid

Principe of altimetry

Launched: 10/08/1992Orbit: quasi-circular, 66°, 1336 km of altitude

Topex/Poséidon (T/P)Topex/Poséidon (T/P)

I. Topex/Poséidon & ORCHIDEE

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I. Topex/Poséidon & ORCHIDEE

Causes of sea level variations:Causes of sea level variations:

thermal expansion of the oceans (steric effect)

water mass exchanged with other reservoirs

NCEP/NCAR vapor

T/P-steric-vapor

Ishii stericT/P

mean seasonal variations 1993-1998, expressed in sea level

equivalent

8.0

4.0

0.0

- 4.0

- 8.0

mm

NCEP/NCAR vapor

T/P-steric-vapor

Ishii stericT/P

mean seasonal variations 1993-1998, expressed in sea level

equivalent

8.0

4.0

0.0

- 4.0

- 8.0

mm

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I. Topex/Poséidon & ORCHIDEEa) forced simulation for 1987 & 1988

ISLSCP-I (International Satellite Land-Surface Climatology Project, Initiative I) produced the atmospheric forcing over the continents for 1987 and 1988.

The differences could be due to : incompatibility of the compared periods data/model uncertainties

In the simulation, there are interannual variations between 1987 and 1988

next study: use GCM simulations, for 1997 and 1998

The model outputs are comparable to the observations (phase, amplitude)

T/P derived value(T/P-steric-vapor)

ORCHIDEE forced by ISLSCP-I

continental water variations expressed in sea level

equivalent (mm)

12.0

8.0mean seasonal cycle for the period 1993-1998

4.0

0.0

- 4.0

- 8.0

- 12.0

1987 1988

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I. Topex/Poséidon & ORCHIDEEb) coupled simulation for 1997 & 1998

AMIP SimulationAMIP Simulation• LMD GCM, version 3.3 ; 96×72×19● Forced by SST form 1979 to 1999

Ngo-duc, T., K. Laval, J. Polcher and A. Cazenave (JGR, 2005a)

AMIP : Atmospheric Model Intercomparison Project

Contribution of continental water to sea level variations

AMIP

T/P derived value

AMIP

1997 1998

mm

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I. Topex/Poséidon & ORCHIDEEb) coupled simulation for 1997 & 1998

Our results don’t agree with the analysis of Chen et al. (2002) who attributed the contrast between 1997 and 1998 to a change in snow cover at high latitudes.

A major part of the interannual variability of the continental water storage comes from the strong variability of precipitation on the tropical continents.

Seasonal variations of tropical continental water expressed in terms of equivalent sea level

- 4.0

- 2.0

0.0

2.0

4.0m

m

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I. Topex/Poséidon & ORCHIDEEc) Limitations and perspectives

forced simulation: the period of the ISLSCP-I forcing data is incomparable with the Topex/Poséidon

coupled simulation: uncertainty of the precipitation fields, in particular when looking at geographical details.

Limitations of this partLimitations of this part

next study: forced simulation over a long period

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Plan

Thesis



scale


GRACE, basin scale




Applications


Ngo-Duc et al.

(GRL, 2005c)






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II. The NCC atmospheric forcing dataa. Construction

NCEP/NCAR NCEP/NCAR ReanalysisReanalysis

NPRE

CRU (Climate Research Unit) precipitation0.5°×0.5°, 1901-2000

NCRU

CRU temperature Specific humidity, pressure, precipitation

NCEP

Interpolation to the grid 1°×1°, differences in

elevation between the grids were taken into account

6-hourly, ~1.875°, 1948-present

(NCEP Corrected by CRU) NCC 6-hourly, 1°x1°, 1948-

2000

Radiation: SRB (Surface Radiation Budget)

http://dods.lmd.jussieu.fr/cgi-bin/nph-dods/Dods/NCC/ (~40GB)

Ngo-duc, T., J. Polcher and K. Laval (JGR, 2005)

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II. The NCC atmospheric forcing datab. Validation

The world's 10 biggest rivers (by the estimated river mouth flow rate)

Observed dischargeObserved discharge GRDC (Global Runoff Data Center) Data at UCAR (the University Corporation for Atmospheric Research)

Station Obidos

55.51°W, 1.95°S

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OBS

NCEP

NCC

NCC

NCEP

OBS


quality of the NCC forcing data is improved compared to NCEP/NCAR

high flow in simulated mean seasonal signal is too early

the interannual signal is well described by the NCC experiment

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Taylor diagramTaylor diagram

(Taylor, 2001)

The quality of

forcing data is

improved after

each adjustment.

NCEP NPRENCRU NCC

1. Amazon 2. Congo3. Orinoco4. Changjiang5. Brahmaputra

6. Mississippi7. Yenisey8. Parana9. Lena10. Mekong

Sta

ndar

d de

viat

ion

Sta

ndar

d de

viat

ion

Sta

ndar

d de

viat

ion

OBS OBS

OBS

Precipitation: most

important improvement

Temperature: significant

effect only at high

latitudes

Radiation: improves

discharge amplitudes

Precipitation: most

important improvement

Temperature: significant

effect only at high

latitudes

Radiation: improves

discharge amplitudes

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Comparison between NCC and GSWP2 (Global Soil Wetness

Project)

Sta

ndar

d de

viat

ion

Sta

ndar

d de

viat

ion

Sta

ndar

d de

viat

ion

OBS

GSWP2NCC

1. Amazon 2. Congo3. Orinoco4. Changjiang5. Mississippi6. Yenisey7. Paranaseries

mean seasonal signal anomaly

Discharge is better simulated using NCC than GSWP2.

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Over the past 50 yrs, the rate of global mean sea level rise was on the order of 1.8 mm/yr [Church et al., 2004], where:

Thermal expansion contributes ~ 0.4 mm/yr [Lombard et al., 2005]

Mountain glaciers melting accounts for ~ 0.4 mm/yr [Meier and Duygerov, 2002]

Greenland & Antarctica melting provide ~ 0.5 mm/yr [Thomas et al., 2004]

Effects of land water storage on global mean sea level over the past 50-yrsEffects of land water storage on global mean sea level over the past 50-yrs

Ngo-Duc, T., K. Laval, J. Polcher, A. Lombard et A. Cazenave (GRL, 2005)

What is the land water contribution?

II.c. Applications of NCCEffects of land water storage on global mean sea level

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5-yr moving average of water reservoirs changes

expressed as equivalent global sea level anomalies.


greatest variation is associated with ground water, followed by soil moisture

no significant trend was detected strong decadal

variability driven by precipitation, strong decrease in the beginning of 1970s agreement

between ORCHIDEE and LaD.(Land Dynamics LSM of GFDL)

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5-yr moving average time series of changes in

land water storage for the six study regions

during the past 50 yrs, the northern tropical Africa lost water to the benefit of the oceans

the strong decrease of the global signal in the early 1970s is due to changes in the Amazon basin

regions 2 and 3 seem to be anti-correlated (-0.78), suggesting a possible teleconnection mechanism


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Relations between land water and thermosteric sea level fluctuations

R=-0.84

oceans warmer continents wetter negative feedback to sea level

These results do not confirm the suggestions of Gregory et al. [2004] that decadal fluctuations in ocean heat content are artifacts of the interpolation processes of raw hydrological data.

+ Tocéan

+ Mcontinents

- Mocéan

-

sea level rise

+ Vocéan

+

+Eocean, (P,E,R)land

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Plan

Thesis



scale


GRACE, basin scale




Applications


Ngo-Duc et al.

(GRL, 2005c)






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III. GRACE & ORCHIDEE

GRACE Mission (Gravity Recovery And Climate ExperimentGRACE Mission (Gravity Recovery And Climate Experiment)

GRACE, twin satellites launched in March 2002, are making detailed measurements of Earth's gravity field. They study the movement of water over the surface of the Earth with a level of detail never before possible.

© NASA

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Seasonal variations of continental waterApril/May - November 2002

GRACE LaD model

Figure provided by Ramillien, G., LEGOS

How do ORCHIDEE results compare?

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Numerical experimentsNumerical experiments

- Built a new atmospheric forcing from 2001 to 2003, named NCMAP (NCEP/NCAR-NCC-CMAP): constrained by monthly CMAP precipitation.

- experiment ORCHIDEE-1: ORCHIDEE without routing scheme, forced by NCMAP

- experiment ORCHIDEE-2: ORCHIDEE with routing scheme, forced by NCMAP

CMAP : CPC (Climate Prediction Center) Merged Analysis of Precipitation

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GRACE ORCHIDEE with routing

ORCHIDEE without routing

Seasonal variations of continental water

April/May – November 2002


The routing scheme much improves the signals over tropical basins

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GRACE

ORCHIDEEwith routing

ORCHIDEE without routing


The routing scheme much improves the signals over

tropical basins

Variations of water stock Variations of water stock over the 8 tropical basinsover the 8 tropical basins

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Conclusions

ORCHIDEE is able to reproduce seasonal and interannual variations of continental water reservoirs

The important role of the tropical regions in the variability of the climate was underlined

The NCC data set was found to be reliable in the validations

On studying the variability of land water storage, an hypothesis was proposed: when the oceans are warmer, the continents will be wetter, leading to a negative feedback on sea level changes The role of the routing scheme on simulating land water storage was shown

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Future directions

study the anti-correlation between South AMerica and north tropical Africa

look at smaller scale: soil moistuRe (T. D’Orgeval)

examine land water storage at interannual/dEcadal timescale using GRACE and other LSMs, forcing data sets

Observations: Global soil moisture data bank

[Robock et al., 2000]

Soil moisture index comparison

capacitymoistureSoil

moisturesoilavailablePlant

study the relations with climate

Change

study anthropogenic impact : irrIgation, floodplains, dams, …

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