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Fast Opt

A Global Carbon Cycle Data Assimilation System (CCDAS) to Infer Atmosphere- Biosphere CO2 Exchanges and Their Uncertainties. Marko Scholze 1 , Peter Rayner 2 , Jens Kattge 3 , Wolfgang Knorr 3 , Thomas Kaminski 4 , Ralf Giering 4 & Heinrich Widmann 3 Tsukuba, 1 st Novembre 2004. 3. 4. 1.

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Fast Opt

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  1. A Global Carbon Cycle Data Assimilation System (CCDAS) to Infer Atmosphere-Biosphere CO2 Exchanges and TheirUncertainties Marko Scholze1, Peter Rayner2, Jens Kattge3, Wolfgang Knorr3, Thomas Kaminski4, Ralf Giering4 & Heinrich Widmann3 Tsukuba, 1st Novembre 2004 3 4 1 FastOpt 2

  2. Overview • Motivation • Top-down vs. bottom-up approach • CCDAS set-up • Calculation and propagation of uncertainties • Data fit • Global results • Conclusions and outlook

  3. Motivation after Joos, 1996

  4. Motivation Sketch of the global carbon cycle • Where are the sources/sinks? • Which are the important processes? • How do they evolve? Fluxes in Gt C yr-1, pools in Gt C, after Prentice et al., 2001.

  5. „top-down“ vs. „bottom-up“ atm. CO2 data atmospheric inversion (Transport Model) Process Model climateand other driving data • Advantages: • Fluxes consistent with • atm. data • Estimation of uncertainties • Disadvantages: • No process information • Coarse resolution net CO2 flux at the surface • Advantages: • Process understanding • -> prognostic modeling • High resolution • Disadvantages: • Global validation difficult • Parameter validity

  6. Combined MethodCCDAS – Carbon Cycle Data Assimilation System Misfit 1 CO2 station concentration InverseModeling: Parameter optimization Fluxes Model parameter ForwardModeling: Parameters –> Misfit Misfit to observations Atmospheric Transport Model: TM2 Biosphere Model: BETHY

  7. CCDAS set-up Pre-step Assimilated eddy flux CO2 & H2O Monte Carlo Param. Inversion full BETHY params & uncert. • 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)

  8. Terminology GPP Gross primary productivity (photosynthesis) NPP Net primary productivity (plant growth) NEP Net ecosystem productivity (undisturbed C storage) NBP Net biome productivity (C storage)

  9. 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 dependant • Carbon balance: average NPP = b average soil resp. (at each grid point) t=1h t=1h t=1day b<1: source b>1: sink

  10. Pre-Step Inversion of terrestrial ecosystem parameter values against eddy covariance measurements by Monte Carlo sampling

  11. Case study: Loobos site, Netherlands • temperate oceanic climate, coniferous forest • Halfhourly data of Eddy covariance measurements from seven days during 1997 and 1998 • Diagnostics: NEE and LE

  12. Estimated parameters and their standard deviations a priori SD: 0.1 0.25 0.5

  13. A Posteriori parameter PDF for Loobos site ga,v: vegetation factor of atmospheric conductance Evm: activation energy of Vm

  14. Propagation of unctertainties to modelled fluxes

  15. Carbon sequestration at the Loobos site during 1997 and 1998 Knorr & Kattge, 2004

  16. CCDAS Step 2: Station network 41 stations from Globalview (2001), no gap-filling, monthly values 1979-1999. Annual uncertainty values from Globalview (2001).

  17. Calibration Step Flow of information in CCDAS. Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

  18. Prognostic Step Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

  19. Methodology Minimize cost function such as (Bayesian form): • where • is a model mapping parameters to observable quantities • is a set of observations • error covariance matrix  need of (adjoint of the model)

  20. Calculation of uncertainties = inverse Hessian • Covariance (uncertainties) of prognostic quantities • Error covariance of parameters

  21. Gradient Method cost function J (p) 1st derivative (gradient) of J (p) to model parameters p: yields direction of steepest descent. 2nd derivative (Hessian) of J (p): yields curvature of J. Approximates covariance of parameters. Model parameter space (p) Figure from Tarantola, 1987

  22. Data fit

  23. Seasonal cycle Barrow Niwot Ridge observed seasonal cycle optimised modeled seasonal cycle

  24. Global Growth Rate observed growth rate optimised modeled growth rate Atmospheric CO2 growth rate Calculated as:

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

  26. Parameters II Relative Error Reduction

  27. Some values of global fluxes Value Gt C/yr

  28. Carbon Balance Euroflux (1-26) and other eddy covariance sites* latitude N *from Valentini et al. (2000) and others net carbon flux 1980-2000 gC / (m2 year)

  29. Uncertainty in net flux Uncertainty in net carbon flux 1980-200 gC / (m2 year)

  30. Uncertainty in prior net flux Uncertainty in net carbon flux from prior values 1980-2000 gC / (m2 year)

  31. NEP anomalies: global and tropical global flux anomalies tropical (20S to 20N) flux anomalies

  32. IAV and processes Major El Niño events Major La Niña event Post Pinatubo period

  33. Interannual Variability I Normalized CO2 flux and ENSO 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)

  34. Interannual Variabiliy II Lagged correlation on grid-cell basis at 99% significance correlation coefficient

  35. 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 -> ideal for developing, testing and debugging • On a global scale results are comparable (can be used for pre-optimising)

  36. 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

  37. Including the ocean Global land flux Seasonality at MLO Observations High resolution standard model Low resolution model Low-res incl. ocean basis functions

  38. Conclusions • Eddy covariance measurements can be used to assign prior values and uncertainty distribution for CCDAS step 2. • 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.

  39. 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) -> scaling issue. • Projections of prognostics and uncertainties into future. • Extend approach to a prognostic ocean carbon cycle model.

  40. For more information, please visit: http://www.ccdas.org

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