A%20global%20Carbon%20Cycle%20Data%20Assimilation%20System%20(CCDAS)%20to%20infer%20atmosphere-biosphere%20CO2%20exchanges%20and%20their%20uncertainties

1
A global Carbon Cycle Data Assimilation System
(CCDAS) to infer atmosphere-biosphere CO2
exchanges and their uncertainties

• Marko Scholze1, Peter Rayner2, Wolfgang Knorr1,
  Thomas Kaminski3, Ralf Giering3 Heinrich
  Widmann1
• TransCom Tsukuba, 2004

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Overview

• CCDAS set-up
• Calculation and propagation of uncertainties
• Data fit
• Global results
• New developments
• Conclusions and outlook

3
Combined top-down/bottom-up MethodCCDAS
Carbon Cycle Data Assimilation System
Forward Modeling Parameters gt Misfit
Misfit to observations
Atmospheric Transport Model TM2
Biosphere Model BETHY
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CCDAS set-up

• 2-stage-assimilation
• AVHRR data
• (Knorr, 2000)
• Atm. CO2 data

• Background fluxes
• Fossil emissions (Marland et al., 2001 und Andres
  et al., 1996)
• Ocean CO2 (Takahashi et al., 1999 und Le Quéré et
  al., 2000)
• Land-use (Houghton et al., 1990)

Transport Model TM2 (Heimann, 1995)
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Station network
41 stations from Globalview (2001), no
gap-filling, monthly values 1979-1999. Annual
uncertainty values from Globalview (2001).
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Terminology
GPP Gross primary productivity (photosynthesis) NP
P Net primary productivity (plant growth) NEP Net
ecosystem productivity (undisturbed C
storage) NBP Net biome productivity (C storage)
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BETHY(Biosphere Energy-Transfer-Hydrology Scheme)
?lat, ?lon 2 deg

• GPP
• C3 photosynthesis Farquhar et al. (1980)
• C4 photosynthesis Collatz et al. (1992)
• stomata Knorr (1997)
• Plant respiration
• maintenance resp. f(Nleaf, T) Farquhar, Ryan
  (1991)
• growth resp. NPP Ryan (1991)
• Soil respiration
• fast/slow pool resp., temperature (Q10
  formulation) and soil moisture
  dependent
• Carbon balance
• average NPP b average soil resp. (at each grid
  point)

?t1h
?t1h
?t1day
blt1 source bgt1 sink
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Calibration Step
Flow of information in CCDAS. Oval boxes
represent the various quantities. Rectangular
boxes denote mappings between these fields.
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Prognostic Step
Oval boxes represent the various quantities.
Rectangular boxes denote mappings between these
fields.
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Methodology
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Calculation of uncertainties

• Error covariance of parameters

• Adjoint, Hessian, and Jacobian code generated
  automatically from model code by TAF

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Gradient Method
Figure from Tarantola, 1987
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Data fit
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Seasonal cycle
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Global Growth Rate
Atmospheric CO2 growth rate
Calculated as
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Parameters I

• 3 PFT specific parameters (Jmax, Jmax/Vmax and b)
• 18 global parameters
• 57 parameters in all plus 1 initial value
  (offset)

Param Initial Predicted Prior unc. () Unc. Reduction ()
fautleaf c-cost Q10 (slow) t (fast) 0.4 1.25 1.5 1.5 0.24 1.27 1.35 1.62 2.5 0.5 70 75 39 1 72 78
(TrEv) (TrDec) (TmpDec) (EvCn) (DecCn) (C4Gr) (Crop) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.44 0.35 2.48 0.92 0.73 1.56 3.36 25 25 25 25 25 25 25 78 95 62 95 91 90 1
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Parameters II
Relative Error Reduction
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Some values of global fluxes
Value Gt C/yr
1980-2000 (prior) 1980-2000 1980-1990 1990-2000
GPP Growth resp. Maint. resp. NPP 135.7 23.5 44.04 68.18 134.8 22.35 72.7 40.55 134.3 22.31 72.13 40.63 135.3 22.39 73.28 40.46
Fast soil resp. Slow soil resp. NEP 53.83 14.46 -0.11 27.4 10.69 2.453 27.6 10.71 2.318 27.21 10.67 2.587
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Carbon Balance
net carbon flux 1980-2000 gC / (m2 year)
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Uncertainty in net flux
Uncertainty in net carbon flux 1980-200 gC / (m2
year)
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Uncertainty in prior net flux
Uncertainty in net carbon flux from prior values
1980-2000 gC / (m2 year)
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NEP anomalies global and tropical
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IAV and processes
Major El Niño events
Major La Niña event
Post Pinatubo period
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Interannual Variability I
ENSO and terr. biosph. CO2 Correlations seems
strong with a maximum at 4 months lag, for both
El Niño and La Niña states.
Lag correlation (low-pass filtered)
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Interannual Variabiliy II
Lagged correlation on grid-cell basis at 99
significance
correlation coefficient
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Low-resolution CCDAS

• A fully functional low resolution version of
  CCDAS, BETHY runs on the TM2 grid (appr. 10 x
  7.8)
• 506 vegetation points compared to 8776
  (high-res.)
• About a factor of 20 faster than high-res.
  Version -gt ideal for developing, testing and
  debugging
• On a global scale results are comparable (can be
  used for pre-optimising)

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Including the ocean

• A 1 GtC/month pulse lasting for three months is
  used as a basis function for the optimisation
• Oceans are divided into the 11 TransCom-3 regions
• That means 11 regions 12 months 21 yr / 3
  months 924 additional parameters
• Test case
• all 924 parameters have a prior of 0. (assuming
  that our background ocean flux is correct)
• each pulse has an uncertainty of 0.1 GtC/month
  giving an annual uncertainty of 2 GtC for the
  total ocean flux

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Including the ocean
High resolution standard model
Low resolution model
Low-res incl. ocean basis functions
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Conclusions

• CCDAS with 58 parameters can fit 20 years of CO2
  concentration data 15 directions can be
  resolved
• Terr. biosphere response to climate fluctuations
  dominated by El Nino.
• A tool to test model with uncertain parameters
  and to deliver a posterior uncertainties on
  parameters and prognostics.
• With the ability of including ocean basis
  functions in the optimisation procedure CCDAS
  comprises a normal atmospheric inversion.

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Future

• Explore more parameter configurations.
• Include missing processes (e.g. fire).
• Upgrade transport model and extend data.
• Include more data constraints (eddy fluxes,
  isotopes, high frequency data, satellites) -gt
  scaling issue.
• Projections of prognostics and uncertainties into
  future.
• Extend approach to a prognostic ocean carbon
  cycle model.