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Parametric and Process-oriented View of the Carbon System: Explaining Controls and Response

This study explores the controls and response of the carbon system since 1800, focusing on carbon emissions, uptakes, and the expansion of a model for photosynthesis, respiration, and fire. The hierarchical view, causation, drivers, fluxes, concentrations, and inverse models are discussed, along with key model equations and estimation methods. Tower-based measurements, FLUXNET, and CO2 observation from space are analyzed, as well as the contribution of local studies to global modeling and the potential of carbon data assimilation. This study provides insights into understanding and parameterizing the carbon system.

ernestbarnes
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Parametric and Process-oriented View of the Carbon System: Explaining Controls and Response

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  1. A parametric and process-oriented view of the carbon system

  2. The challenge: explain the controls over the system’s response

  3. Carbon emissions and uptakes since 1800 (Gt C)

  4. Expanding the model: A model for (Fba-Fab) Fab = G(Di, pi, S i) = photosynthesis Fba = G(Di, pi, S i) = respiration and fire

  5. A Hierarchical view of the carbon system Causation goes in this direction Drivers (weather, nutrients, fires) Fluxes Concentrations Inverse models do something is this direction

  6. A-R: A key feature of the system What we measure: Net Ecosystem Exchange (the flux of CO2 across an imaginary plane above the canopy) But: NEE cannot be directly parameterized NEE = Photosynthesis - Respiration The model (or observation equation) must “transform” the observation (NEE) into physically modeling components. This is neglecting complex but different processes such as fire and forest harvest.

  7. Ecosystem Model Structure Photosynthesis (Phenology,Soil Moisture, Tair, VPD, PAR) Plant Respiration (Plant C, Tair) Plant Carbon Precip. Transpiration Litterfall (Plant C, Phenology) Soil Respiration (Soil C, Soil Moisture, Tsoil) Soil Moisture Soil Carbon Drainage

  8. Some key model equations NEE = Ra +Rh - GPP GPPmax = AamaxAd+Rleaf GPPpot = GPPmaxDtempDvpdDlight Rh = CsKhQ10sTsoil/10(W/Wc) GPP = canopy photosynthesis, R denotes respiration, Amax = max leaf-level carbon assimilation, Ds are scalars for environmental factors, Ad, a scaling factor over time, Cs = substrate, K, rate constant, Q10 the temperature scalar and W, water scalars.

  9. Estimation (zj - H(Fapj,Fpaj))tR-j1 (zj - H(Fapj,Fpaj))/2 + (pj - Pj)tR-j1 (pj - Pj) /2 The rubber bands are the prior estimates of parameters

  10. Assimilation of fluxes provides consistency between prior knowledge and observed carbon exchange

  11. Control variables • Temperature • Soil moisture • Nutrient availability • Fire regime • Light interception • Land management • Atmospheric CO2 • etc

  12. Concentrations have less information about processes and parameters than do fluxes Why? They are “one step more removed” (by transport) That step includes “invertible” (advective) processes and irreversible (diffusive) processes There is information loss along the chain of causation

  13. Get closer to the answer: measure fluxes Tower-based measurements

  14. FLUXNET

  15. More gadgets My little flux tower….

  16. More gadgets CO2, H2O T, u,v,w w

  17. Time-scale character of carbon modeling Variability is at a maximum on the strongly forced time scales They have an annual sum of ~0 Modeling the carbon storage time scales (years) is the goal Diurnal Seasonal

  18. Observed variability of fluxes

  19. Analyzed variability of processes

  20. Analysis of controls Warm springs accelerate growth but also evaporation. Despite the overall positive response shown earlier, the annual relationship of flux to temperature is negative

  21. Self-consistent parameter sets Fit to the diurnal cycle (~12 hour time steps) Fit to daily data: 24 hour time steps

  22. Assimilating water and carbon Just water Carbon only or carbon plus water

  23. Adding water doesn’t help carbon, but it helps water Carbon only Carbon and water

  24. Evaluation against an independent water flux measurement

  25. Normal Model Parameterization Method

  26. Step 2…..

  27. KH (g g-1 y-1) CS,0 (g m-2) Self-consistent parameter sets Range from prior knowledge Validate-tune Second parameter dictated First parameter

  28. Analysis of controls The emergent Relationship of temperature and carbon uptake. Note the multiple Regimes. The lower lines are the water-limited response Realized T response wet Realized T response, dry

  29. What does this type of local study contribute to global modeling? We can use this to understand the information in different types of observation

  30. Carbon from space OCO uses reflected sunlight to make measurements during the day

  31. Day and Night Remember, we’ve shown a huge loss of process information without diurnal information

  32. Future active CO2 experiments make day and night observations LIDAR

  33. Process priors for global models Tower-based estimates of parameters can be used as priors to invert global concentration data to estimate parameters controlling fluxes instead of fluxes (Knorr, Wofsy, Rayner)

  34. The global scale is very distant from processes Distributed local measurements and innovative measurement approaches can bridge the gap

  35. ACME prepares for its first flight

  36. Vertical profiles and CO2 “lakes”

  37. Carbon dataassimilation Carbon data assimilation and parametric estimation are fast-moving fields

  38. A few references • Vukicevic, T., B.H. Braswell and D.S. Schimel. 2001. A diagnostic study of temperature controls on global terrestrial carbon exchange. Tellus (B) 53:150-170. (variational) • Braswell, B.H., W.J. Sacks, E. Linder and D.S. Schimel. 2004. Estimating ecosystem process parameters by assimilation of eddy flux observations of NEE. Global Change Biol. 11:335-355 (MCMC) • Williams, M. Schwarz, B.E. Law, J. Irvine, and M.R. Kurpius. 2005. An improved analysis of forest carbon dynamics using data assimilation. Glov=bal Change Biol. 11:85-105 (EKF) • Wang, Y-P. and D Barrett. 2003. stimating regional terrestrial carbon fluxes for the Australian continent using a multiple-constraint approach. I. Using remotely sensed data and ecological observations of net primary production. Tellus (B) 55:270-289 (Synthesis inversion)

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