| A parametric and process-oriented view of the carbon system |
| The challenge: explain the controls over the systemÕs response |
| Carbon emissions and uptakes
since 1800 (Gt C) |
| Expanding the model: |
| A Hierarchical view of the carbon system |
| Drivers (weather, nutrients, fires) |
| 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 ÒtransformÓ the observation (NEE) into physically modeling components. | |
| This is neglecting complex but different processes such as fire and forest harvest. | |
| Ecosystem Model Structure |
| 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. |
| 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 |
| Slide 10 |
| Control variables |
| Temperature | |
| Soil moisture | |
| Nutrient availability | |
| Fire regime | |
| Light interception | |
| Land management | |
| Atmospheric CO2 | |
| etc |
| Concentrations have less information about processes and parameters than do fluxes |
| Why? | |
| They are Òone step more removedÓ (by transport) | |
| That step includes ÒinvertibleÓ (advective) processes and irreversible (diffusive) processes | |
| There is information loss along the chain of causation |
| Get closer to the answer: measure fluxes |
| FLUXNET |
| Slide 15 |
| More gadgets |
| More gadgets |
| CO2, H2O T, u,v,w |
| Slide 18 |
| Time-scale character of carbon modeling |
| Observed variability of fluxes |
| Analyzed variability of processes |
| Analysis of controls |
| Self-consistent parameter sets |
| Slide 24 |
| Slide 25 |
| "Evaluation against an independent water..." |
| Evaluation against an independent water flux measurement |
| Normal Model Parameterization Method |
| Step 2É.. |
| Self-consistent parameter sets |
| Analysis of controls |
| What does this type of local study contribute to global modeling? |
| We can use this to understand the information in different types of observation |
| Carbon from space |
| Day and Night |
| Remember, weÕve shown a huge loss of process information without diurnal information |
| Future active CO2 experiments make day and night observations |
| Process priors for global models |
| The global scale is very distant from processes |
| Distributed local measurements and innovative measurement approaches can bridge the gap |
| Slide 37 |
| Slide 38 |
| Slide 39 |
| Vertical profiles and CO2 ÒlakesÓ |
| "Carbon data assimilation" |
| Carbon data assimilation |
| 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) |