A global Carbon Cycle Data Assimilation System CCDAS
A global Carbon Cycle Data Assimilation System (CCDAS) to infer atmospherebiosphere CO 2 exchanges and their uncertainties • Marko Scholze 1, Peter Rayner 2, Wolfgang Knorr 1, Thomas Kaminski 3, Ralf Giering 3 & Heinrich Widmann 1 1 • Trans. Com Tsukuba, 2004 2 3 Fast. Opt
Overview • • • CCDAS set-up Calculation and propagation of uncertainties Data fit Global results New developments Conclusions and outlook
Combined ‘top-down’/’bottom-up’ Method CCDAS – Carbon Cycle Data Assimilation System Misfit 1 Misfit to observations Forward Modeling: Parameters –> Misfit CO 2 station concentration Atmospheric Transport Model: TM 2 Fluxes Biosphere Model: BETHY Model parameter Inverse Modeling: Parameter optimization
CCDAS set-up 2 -stage-assimilation: 1. AVHRR data (Knorr, 2000) 2. Atm. CO 2 data Background fluxes: 1. Fossil emissions (Marland et al. , 2001 und Andres et al. , 1996) 2. Ocean CO 2 (Takahashi et al. , 1999 und Le Quéré et al. , 2000) 3. Land-use (Houghton et al. , 1990) Transport Model TM 2 (Heimann, 1995)
Station network 41 stations from Globalview (2001), no gap-filling, monthly values 1979 -1999. Annual uncertainty values from Globalview (2001).
Terminology GPP NEP NBP Gross primary productivity (photosynthesis) Net primary productivity (plant growth) Net ecosystem productivity (undisturbed C storage) Net biome productivity (C storage)
BETHY (Biosphere Energy-Transfer-Hydrology Scheme) lat, lon = 2 deg • • GPP: C 3 photosynthesis – Farquhar et al. (1980) C 4 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 (Q 10 formulation) and moisture dependent Carbon balance: average NPP = b average soil resp. (at each grid point) t=1 h t=1 day soil b<1: source b>1: sink
Calibration Step Flow of information in CCDAS. Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.
Prognostic Step Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.
Methodology Minimize cost function such as (Bayesian form): where is a model mapping parameters is a set of observations error covariance matrix need of to observable quantities (adjoint of the model)
Calculation of uncertainties • Error covariance of parameters = inverse Hessian • Covariance (uncertainties) of prognostic quantities • Adjoint, Hessian, and Jacobian code generated automatically from model code by TAF
Gradient Method 1 st derivative (gradient) of J (p) to model parameters p: cost function J (p) yields direction of steepest descent. 2 nd derivative (Hessian) of J (p): yields curvature of J. Approximates covariance of parameters. Model parameter space (p) Figure from Tarantola, 1987
Data fit
Seasonal cycle Barrow Niwot Ridge observed seasonal cycle optimised modeled seasonal cycle
Global Growth Rate Atmospheric CO 2 growth rate Calculated as: observed growth rate optimised modeled growth rate
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 Q 10 (slow) t (fast) 0. 4 1. 25 1. 5 0. 24 1. 27 1. 35 1. 62 2. 5 0. 5 70 75 39 1 72 78 b (Tr. Ev) b (Tr. Dec) b (Tmp. Dec) b (Ev. Cn) b (Dec. Cn) b (C 4 Gr) b (Crop) 1. 0 1. 44 0. 35 2. 48 0. 92 0. 73 1. 56 3. 36 25 25 78 95 62 95 91 90 1
Parameters II Relative Error Reduction
Some values of global fluxes Value Gt C/yr 1980 -2000 (prior) 1980 -2000 1980 -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
Carbon Balance Euroflux (1 -26) and other eddy covariance sites* net carbon flux 1980 -2000 g. C / (m 2 year) *from Valentini et al. (2000) and others latitude N
Uncertainty in net flux Uncertainty in net carbon flux 1980 -200 g. C / (m 2 year)
Uncertainty in prior net flux Uncertainty in net carbon flux from prior values 1980 -2000 g. C / (m 2 year)
NEP anomalies: global and tropical global flux anomalies tropical (20 S to 20 N) flux anomalies
IAV and processes Major El Niño events Major La Niña event Post Pinatubo period
Interannual Variability I Normalized CO 2 flux and ENSO Lag correlation (low-pass filtered) ENSO and terr. biosph. CO 2: Correlations seems strong with a maximum at ~4 months lag, for both El Niño and La Niña states.
Interannual Variabiliy II Lagged correlation on grid-cell basis at 99% significance correlation coefficient
Low-resolution CCDAS • A fully functional low resolution version of CCDAS, BETHY runs on the TM 2 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 preoptimising)
Including the ocean • A 1 Gt. C/month pulse lasting for three months is used as a basis function for the optimisation • Oceans are divided into the 11 Trans. Com-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 Gt. C/month giving an annual uncertainty of ~2 Gt. C for the total ocean flux
Including the ocean Global land flux Seasonality at MLO Observations High resolution standard model Low resolution model Low-res incl. ocean basis functions
Conclusions • CCDAS with 58 parameters can fit 20 years of CO 2 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.
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.
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