a prototype carbon cycle data assimilation system (ccdas)
DESCRIPTION
A prototype Carbon Cycle Data Assimilation System (CCDAS). Inferring interannual variations of vegetation-atmosphere CO 2 fluxes. Marko Scholze 1 , Peter Rayner 2 , Wolfgang Knorr #3 , Thomas Kaminski 4 , Ralf Giering 4. # presenting. 1. 2. 3. 4. Parameters: 58. Fluxes: 800,000. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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• explore more parameter configurations
• include fire as a process with uncertainties
• include more constraints (isotopes, eddy fluxes)
• extend approach to ocean carbon cycle
Outlook