FastOpt

A prototype Carbon Cycle Data Assimilation System

(CCDAS)

Inferring interannual variations of vegetation-atmosphere CO2 fluxes

Marko Scholze1, Peter Rayner2, Wolfgang Knorr#3, Thomas Kaminski4, Ralf Giering4

#presenting

1 2 3 4

FastOpt

Biosphere Model: BETHY

Parameters: 58

Atmospheric Transport Model: TM2

Fluxes: 800,000

Misfit to Observations

Station Conc. 10,000

Misfit 1

1. Parameter Optimisation:

Forward: Parameters –> Misfit

Adjoint or Tangent linear:

∂ Misfit / ∂ Parameters

2. Parameter Uncertainties:

Hessian: ∂2 Misfit / ∂ Parameters2

Error covariance=Inverse of Hessian

3. Uncertainty of Diagnostics:

Adjoint or Tangent linear

Carbon Cycle Data Assimilationusing automatic differentiation

FastOpt

CCDAS Setup

CCDAS Step 2IMBETHY+TM2

only Photosynthesis, Energy&Carbon Balance

CO2

+ Uncert.

Calibrated Params + Uncert.

Diagnostics + Uncert.

veg. indexSatellite

CCDAS Step 1full BETHY

PhenologyHydrology

AssimilatedPrescribedAssimilated

BackgroundCO2 fluxes*

* * ocean: Takahashi et al. (1999), LeQuere et al. (2000); emissions: Marland et al. (2001), Andres et al. (1996); land use: Houghton et al. (1990)0

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BETHY(Biosphere Energy-Transfer-Hydrology Scheme)

• GPP:

C3 photosynthesis – Farquhar et al. (1980)

C4 photosynthesis – Collatz et al. (1992)

stomata – Knorr (1997)

• Raut:

maintenance respiration = f(Nleaf, T) – Farquhar, Ryan (1991)

growth respiration ~ NPP – Ryan (1991)

• Rhet:

fast/slow pool resp. = wQ10 T/10 C fast/slow / fast/slow

slow –> infin.

average NPP = average Rhet (at each grid point)

<1: source>1: sink

t=1h

t=1h

t=1day

lat, lon = 2 deg

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Concentrations

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Parameters

first guess optimized prior unc. opt.unc. Vm(TrEv) Vm(EvCn) Vm(C3Gr) Vm(Crop)

µmol/m 2s µmol/m 2s % %Vm(TrEv) 60.0 43.2 20.0 10.5 0.28 0.02 -0.02 0.05Vm(EvCn) 29.0 32.6 20.0 16.2 0.02 0.65 -0.10 0.08Vm(C3Gr) 42.0 18.0 20.0 16.9 -0.02 -0.10 0.71 -0.31Vm(Crop) 117.0 45.4 20.0 17.8 0.05 0.08 -0.31 0.80

error covariance

relative error reduction:

examples:

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Processes 1global fluxes

Carbon source anomaly:drop in GPP exceeds drop in resp

Carbon sink anomaly:stronger decr. in resp. than GPP

El Niño events

Pinatubo eruptionLa Niña

Carbon sink:GPP slightly exceeds respiration

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Processes 2normalized CO2 flux and ENSO

4-month lagged:

ENSO and terr. biosph. CO2:correlation seems strong

lag correlation(low-pass filtered)

correlation between Niño-3 SST anomaly and net CO2 flux shows maximum at 4 months lag, forboth El Niño and La Niña states

Pinatubo eruption:shows up as largest deviation in the low-pass filtered curve

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Processes 3

lagged correlationat 99% significance

-0.8 -0.4 0 0.4 0.8

El Niño (>+1)net CO2 flux to atm.

gC / (m2 month)

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net carbon flux 1980-2000gC / (m2 year)

Euroflux (1-26) and othereddy covariance sites*

Carbon Balance

latitude N*from Valentini et al. (2000) and others

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• CCDAS with 58 parameters can already fit 20 years of

CO2 concentration data

• Significant reduction of uncertainty for ~13 parameters,

some important covariances

• terr. biosphere response to climate fluctuations dominated

by ENSO and Pinatubo

• Can be explained by small perturbations of 3 large fluxes

(GPP, Raut, Rhet)

Conclusions

FastOpt

• explore more parameter configurations

• include fire as a process with uncertainties

• include more constraints (isotopes, eddy fluxes)

• extend approach to ocean carbon cycle

Outlook