Results from a multi-year, multi-site AmeriFlux data assimilation with the TRIFFID model

1. Results from a multi-year, multi-site AmeriFlux data assimilation with the TRIFFID model Daniel Ricciuto Oak Ridge National Laboratory June 14, 2007

2. Carbon 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)

3. 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? Parameters from Ecosystem-scale observations Forcing and Global constraints Model predictions uncertainty Future Past

4. Legend: Observation Top-down technique Bottom-up technique Spatiotemporal scales of current observations and modeling techniques < model gridscale > model gridscale C4MIP FIA Global synthesis inversions Tower flux Chamber flux Biogeochemical modeling CO2 Regional inversions Airborne flux

5. Legend: Observation Top-down technique Bottom-up technique Spatiotemporal scales of current observations and modeling techniques < model gridscale > model gridscale C4MIP ? FIA Global synthesis inversions Tower flux Chamber flux Biogeochemical modeling CO2 Regional inversions Airborne flux

6. FIA Global synthesis inversions Tower flux Chamber flux Biogeochemical modeling Regional inversions Airborne flux Multiple tower data assimilation < model gridscale > model gridscale CO2

7. Tower sites Site-years analyzed (37 total) WLEF: 1997-2004 Harvard: 1992-2003 Howland: 1996-2003 UMBS: 1999-2003 M. Monroe: 1999-2003

8. GPP (NL,BL) Forcing: wind, [CO2], PAR, Tair, precip NPP (NL,BL) Ra Ra Leaf (NL) Leaf (BL) NEE Wood (BL) Wood (NL) RH Root (NL) Root (BL) Soil carbon (CS) Simplified TRIFFID Model • 22 model parameters • - initial soil carbon • - Photosynthesis • - autotrophic respiration • - Phenology • - soil moisture • 4 PFT-specific • - Tlow, Tupp, nl, a • Canopy structure - initial NL/BL fraction - initial canopy height

9. 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? • . Cox et al. (2000) Cox et al. (2000) Friedlingstein et al. (2006) Friedlingstein et al. (2006)

10. Data assimilation methodology • 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 • 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?

11. Convergence diagnostics

12. Separate optimization: parameters Well-constrained parameters: leaf nitrogen quantum efficiency Tlower phenology (Toff) Poorly constrained parameters autotrophic respiration soil moisture parameters Difference from published Coherence across space

13. Model performance: seasonal cycle

14. Model performance: Cross-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

15. Joint optimization parameters • Soil carbon • HV, MM unrealistic • compensating for other effects • Leaf nitrogen, a • Comparable to separate optimizations • Constrained parameters • - Q10VM, TlowBL, DC • - Toff, gp • fall within ranges of separate optimization

16. Model performance:Interannual variability

17. 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?

18. 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

19. 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

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

22. Carbon fertilization predictions: Are they reasonable?