1. Coherence of parameters governing NEE variability in eastern U. S
Coherence of parameters governing NEE variability in eastern U.S. forests: A multisite data assimilation of eddy covariance data using TRIFFID. Daniel Ricciuto June 5th, 2006 ChEAS meeting IX

2. Motivation Terrestrial models are used to predict future fluxes.
Large uncertainty, grows with time
Generally not constrained by observations
How can flux towers help?
Friedlingstein et al. (in press)

3. Data assimilation and flux towers
Previous work
Braswell (2005), Sacks (2006): Parameter optimization using SipNET model
Improves model estimates of NEE
Captures seasonal cycle
Interannual variability poorly modeled

4. Data assimilation and flux towers
Ricciuto et al. (in press)
Simple model: based on gap-filling routines, includes SWC dependence
reproduces daily sums of NEE reasonably well (example month: Sep 1997)
Also reproduces seasonal cycle of monthly NEE sums reasonably well

5. Data assimilation and flux towers
Interannual variability in NEE sums poorly modeled, but does capture difference.
Nighttime NEE variability modeled reasonably well but model is biased low (because respiration parameters include daytime data)
Daytime NEE modeled reasonably well but model biased high (because respiration parameters include nighttime data)
1997
2001

6. Multiple tower assimilation
No published studies yet using data assimilation with multiple flux towers (others in press)
Key questions:
Can a single set of optimized model parameters:
reproduce observed interannual variability?
reproduce observed intersite variability?
Are parameters coherent across space? Time?
Model: Top-town representation of interactive foliage and flora including dynamics (TRIFFID)

7. The TRIFFID world: 5 PFTs
Source: Hadley Centre (

8. Modified TRIFFID carbon cycle
Climate input:
Precip,
PAR, Tair, RH
Ra1
GPP1
GPP2
Ra2
Broadleaf
PFT (H1, FRAC1,
LAI1)
Needleleaf
PFT (H2, LAI2,
FRAC2)
wood
wood RH
Soil layer (Tsoil, CS, SWC)
root
root

9. Key model parameters Photosynthesis: Autotrophic respiration:
Vmax, a, Tupp, Tlow, Q10VM, SWCopt, SWCdep
Autotrophic respiration:
Rdc, Rgc, Q10RD
Heterotrophic respiration:
Csoil Q10, SWCoptR, SWCdepR
Phenology
Toff, SWCoff, Lburst

10. Nonconvex problem
Nonlinear systems often are nonconvex (multiple maxima in parameter space)
Gradient-based optimization (e.g. Levenberg-Marquardt) misconverges
Need a method that can find global (best) solution

11. Assimilation technique
Stochastic Evolutionary Ranking Strategy (SRES)
Global optimization method
Stochastic – difficult to guarantee convergence
Relatively fast method to find very good solution
No parametric uncertainty (unlike MCMC)
Method:
Start with initial population (parameter sets)
Evaluate goodness of fit (objective function)
Select best-fitting members
Mutate these members, repeat until convergence apparent
Run twice for each tower, compare solutions

12. Method 5 eastern U.S. flux sites with long records (>= 5 years)
Optimize each site individually with hourly data (23 params)
Optimize all 5 sites jointly (single set of parameters)
Soil carbon treated as separate fit parameter for each site

13. Results: seasonal cycle

14. Interannual variability
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15. Results: Joint optimization

16. Conclusions
Optimized TRIFFID model reproduces seasonal cycle at each eddy covariance site well
Interannual variability poorly modeled. Why?
Poor representation of hydrology (fit improved at WLEF when SWC data used… but don’t have everywhere)
Do we need better pools / time lag effects?
Intersite variability modeled somewhat well
Soil carbon as only intersite variable: oversimplification

17. Future work Better representation of hydrology
More data sources as constraints
Hydrology where available
Inventory data
Manipulation experiments
Run model over longer timescales
Optimize parameters related to longer-term effects such as competition, CO2 fertilization
Future predictions
Couple to cheap GCMs, run ensembles of predictions to gauge uncertainty