Transcript TRIFFID model diagram - Pennsylvania State University

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
Motivation
• Terrestrial models are
used to predict future
fluxes.
• Large uncertainty,
grows with time
Friedlingstein et al. (in press)
• Generally not
constrained by
observations
• How can flux towers
help?
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
Data assimilation and flux towers
Ricciuto et al. (in press)
• Simple model: based on gapfilling 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
Data assimilation and flux towers
1997
2001
• Interannual variability in NEE sums
poorly modeled, but does capture
1997-2001 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)
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)
The TRIFFID world: 5 PFTs
Source: Hadley Centre (http://www.metoffice.com/research/hadleycentre/models/carbon_cycle/models_terrest.html)
Modified TRIFFID carbon cycle
Ra1
GPP1
Broadleaf
PFT (H1, FRAC1,
LAI1)
wood
root
GPP2
Ra2
Climate input:
Precip,
PAR, Tair, RH
Needleleaf
PFT (H2, LAI2,
FRAC2)
wood
root
Soil layer (Tsoil, CS, SWC)
RH
Key model parameters
• Photosynthesis:
– Vmax, a, Tupp, Tlow, Q10VM, SWCopt, SWCdep
• Autotrophic respiration:
– Rdc, Rgc, Q10RD
• Heterotrophic respiration:
– Csoil Q10, SWCoptR, SWCdepR
• Phenology
– Toff, SWCoff, Lburst
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
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
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
Results: seasonal cycle
Interannual variability
• fff
Results: Joint optimization
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
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