Results from a multi-year, Results from a multi-year, multi-site AmeriFlux data assimilation multi-site AmeriFlux data assimilation

with the TRIFFID modelwith the TRIFFID model

Daniel RicciutoDaniel RicciutoOak Ridge National LaboratoryOak Ridge National Laboratory

June 14, 2007June 14, 2007

Carbon cycle uncertaintyCarbon cycle uncertainty• C4MIP: comparison of 10 coupled “bottom-up” climate/carbon models

• No statistical assimilation of existing carbon cycle observations.

• Different parametric/structural representation of key feedbacks

• How to incorporate observations in a statistically meaningful way?

Friedlingstein et al. (2006)

Motivation

• Overall goal: to determine predictive uncertainty in CO2 sink strength• Questions assimilation can answer: Given a set of observations,

– How uncertain are model parameters?– Within a set of models, which is the most likely to be correct?

• The problems:– Most CO2 flux observations cover small spatial scales and/or time scales– Are model parameters relevant over multiple spatial scales?

Model predictionsParameters from Ecosystem-scale

observations

Past Future

uncertainty

Forcing and Global constraints

Spatiotemporal scales of current Spatiotemporal scales of current observations and modeling techniquesobservations and modeling techniques

< model gridscale > model gridscaleFIA

Cha

mbe

r flu

x

Tow

er f

lux

Global synthesis inversions

Airborne flux

Biogeochemical modeling

Regional inversions

CO2

Legend:

Observation

Top-down technique

Bottom-up technique

C4MIP

Spatiotemporal scales of current Spatiotemporal scales of current observations and modeling techniquesobservations and modeling techniques

< model gridscale > model gridscaleFIA

Cha

mbe

r flu

x

Tow

er f

lux

Global synthesis inversions

Airborne flux

Biogeochemical modeling

Regional inversions

CO2

Legend:

Observation

Top-down technique

Bottom-up technique

C4MIP

?

Multiple tower data assimilationMultiple tower data assimilation

FIAC

ham

ber

flux

Tow

er f

lux

Global synthesis inversions

Airborne flux

Biogeochemical modeling

Regional inversions

< model gridscale > model gridscale

CO2

Tower sitesTower sitesSite-years analyzed

(37 total)

WLEF: 1997-2004

Harvard: 1992-2003

Howland: 1996-2003

UMBS: 1999-2003

M. Monroe: 1999-2003

Forcing: wind, [CO2], PAR, Tair,

precip

Wood(NL)

Wood(BL)

Leaf(BL)

Root (NL) Root (BL)

GPP (NL,BL)

Leaf(NL) NEE

NPP(NL,BL)

RH

RaRa

Soil carbon (CS)

Simplified TRIFFID ModelSimplified TRIFFID Model• 22 model parameters

- initial soil carbon

- Photosynthesis

- autotrophic respiration

- Phenology

- soil moisture

• 4 PFT-specific

- Tlow, Tupp, nl,

Canopy structure

- initial NL/BL fraction

- initial canopy height

Why TRIFFID?Why TRIFFID?• Dynamic Global Vegetation Model: can be used to predict

- calculates phenology, LAI

• Used in Cox et al. (2000) – strong feedbacks. Are model parameters realistic? Is model structure appropriate?

.

Friedlingstein et al. (2006)

Cox et al. (2000)

Cox et al. (2000)

Friedlingstein et al. (2006)

Data assimilation methodologyData assimilation methodology

• Experiment 1: optimize 5 sites individually (separate)– Are parameters coherent across space

• Experiment 2: optimize 5 sites simultaneously (joint)– Allow soil carbon and leaf nitrogen to vary among sites (adds 8

parameters)– Do these parameters explain cross-site variability?

• This is a nonlinear, nonconvex problem requiring a global optimization algorithm

• Gradient-based techniques will fail!

• Stochastic Evolutionary Ranking Strategy (SRES) – genetic algorithm• Full parametric uncertainty with MCMC underway, no results yet

Convergence diagnosticsConvergence diagnostics

Separate optimization: parameters

Table 0-1: Results of the parameter optimization for each separate optimization at HW (Howland), HV (Harvard), WL (WLEF), MM (Morgan Monroe) and UM (UMBS). Well-constrained parameters are shown in bold, and are defined as values that are within 0.5% of each other in all three SRES runs and are not edge-hitting (upper and lower bounds and parameter units are shown in Table 4-2)

Parameter Published HV HW WL MM UM

CS 10.0 4.81 4.827 9.81 10.7 7.37

Q10H 2.0 1.47 2.394 3.28 1.92 2.65

opt 0.55 0.843 0.717 0.745 0.685 0.764

fac 0.8 1.00 0.320 0.858 1.00 1.00

c 0.30 0.247 0.129 0.240 0.285 0.239

w 0.13 0.00 0.0498 0.0534 0.00 0.00

nl0BL 0.036 0.149 0.250 0.130 0.100 0.112

nl0 NL 0.030 0.140 0.045 0.0430 0.0500 0.0516

BL 0.06 0.0535 0.117 0.0314 0.0498 0.0353

NL 0.06 0.0274 0.021 0.483 0.249 0.340

Q10VM 2.0 2.23 1.74 2.36 2.15 2.04

TlowBL -5.0 1.48 -40.0 -9.70 6.06 -1.83

TlowNL -15.0 3.55 3.41 -1.80 10.0 -20.0

TuppBL 33.0 50.0 50.0 50.0 50.0 50.0

TuppNL 28.0 11.66 50.0 50.0 10.0 49.1

Dc 0.09 0.0192 0.0266 0.0249 0.0371 0.0282

Rg 0.25 0.107 0.0500 0.122 0.0500 0.0500

Rdc 0.015 0.00822 0.0208 0.0183 0.0019 0.00866

Q10RD 2.0 1.49 1.61 2.00 1.100 1.33

Toff 0.0 11.6 0.80 9.30 14.3 11.4

off 0.90 0.678 0.850 0.672 0.172 0.517

p

14.5

18.2 13.4 16.5 20.0 38.7

-(Log L)

Well-constrained parameters:

leaf nitrogen

quantum efficiency

Tlower

phenology (Toff)

Poorly constrained parameters

autotrophic respiration

soil moisture parameters

Difference from published

Coherence across space

Model performance: Model performance: seasonal cycleseasonal cycle

Model performance: Model performance: Cross-site variabilityCross-site variability

• Site-specific parameters

• Soil carbon

• leaf nitrogen

• Joint assimilation captures cross-site variability of site-mean

• parameters

• climate

• forest structure

• Fails to capture interannual variability

Joint optimization parameters

Parameter Published optimized Parameter published optimized CS (HV) 10.0 0.00 BL 0.06 0.0446

CS (HW) 10.0 5.59 NL 0.06 0.0239 CS (WL) 10.0 12.0 Q10VM 2.0 2.26 CS (MM) 10.0 0.00 TlowBL -5.0 -1.05 CS (UM) 10.0 2.97 TlowNL -15.0 50.0

Q10H 2.0 4.00 TuppBL 33.0 1.475 opt 0.55 0.447 TuppNL 28.0 30.0 fac 0.80 0.980 Dc 0.09 0.0246 c 0.30 0.216 Rg 0.25 0.005 w 0.13 0.00 Rdc 0.015 0.0143

nl0(HV) 0.036 0.112 Q10RD 2.0 1.515 nl0 (HW) 0.030 0.087 Toff 0.0 11.5 nl0(WL) 0.036 0.080 off 0.90 0.00 nl0 (MM) 0.036 0.083 p 20.0 15.8 nl0(UM) 0.036 0.113 -(Log L) N/A 465957

Soil carbon

- HV, MM unrealistic

- compensating for other effects

Leaf nitrogen,

- Comparable to separate optimizations

Constrained parameters

- Q10VM, TlowBL, DC

- Toff, p

- fall within ranges of separate optimization

Model performance:Model performance:Interannual variabilityInterannual variability

Model limitations

• Why do we fail to reproduce the interannual signal?– Problem with separate and joint optimizations– Limitations of the model structure (hydrology?)– Spatial mismatch: TRIFFID is designed for larger

gridscales– Limitations of the data (biases, uncertainty)

• Questions– Correct subset of model parameters?– Are observed fluxes coherent across space?

Observed interannual variability: spatially coherent?

Gap-filled fluxes from the 5 sites used in TRIFFID analysis

Harvard and Howland: Coherent between 1996 and 2000, then breaks down (similar climate)

UMBS and Morgan Monroe: coherent (similar PFT, climate)

WLEF: 2002 missing, coherent with UMBS and Morgan Monroe

Future Work

• Use MCMC to derive parameter PDFs, correlations, probabilistic predictions

• Use additional constraints – Latent heat flux

– Experimental manipulations (T, CO2, precip)

• Make regional predictions– Drive with remote sensing, reanalysis– Compare with top-down estimates

The TRIFFID world

Source: Hadley Centre (http://www.metoffice.com/research/hadleycentre/models/carbon_cycle/models_terrest.html)

Carbon fertilization predictions: Carbon fertilization predictions: Are they reasonable?Are they reasonable?