1. Model-Data Synthesis of CO 2 Fluxes at Niwot Ridge, Colorado Bill Sacks, Dave Schimel NCAR Climate & Global Dynamics Division Russ Monson CU Boulder Rob Braswell University of New Hampshire

2. Motivation What processes do CO 2 flux data contain information about? Can we separate NEE into its component fluxes? Scale up CO 2 fluxes in space and time Improve parameterization of regional & global models, like CCSM Derive general process-level information from eddy covariance data

3. Outline Methods overview Which parameters/processes are constrained by NEE data? Exploration of optimized model-data fit: What do we get right? What do we get wrong? Partitioning the net CO 2 flux What do we gain by including an additional data type (H 2 O fluxes) in the optimization? Using model selection to explore controls over NEE Scaling up (briefly)

4. SIPNET Model Twice-daily time step (day & night) Goal: keep model as simple as possible Photosynthesis: f (Leaf C, T air, VPD, PAR, Soil Moisture) Autotrophic Respiration: f (Plant C, T air ) Heterotrophic Respiration: f (Soil C, T soil, Soil Moisture)

5. http://spot.colorado.edu/~monsonr/Ameriflux.html Data 5 years of half-hourly data from Niwot Ridge, a 100 year-old subalpine forest just below the continental divide –Climate drivers (air & soil temp., precip., PAR, humidity, wind speed) –Net CO 2 flux (NEE) from eddy covariance Gaps in climate drivers and NEE filled using a variety of methods Half-hourly data aggregated up to day/night time step –Optimization only uses time steps with at least 50% measured data

6. Parameter Optimization 32 parameter values optimized to fit NEE data –Initial conditions (e.g. initial C pools) –Rate constants (e.g. max. photosynthetic rate, respiration rates) –Climate sensitivities (e.g. respiration Q 10 ) –Climate thresholds (e.g. minimum temp. for photosynthesis) Optimization performed using variation of Metropolis Algorithm: minimize sum of squares difference between model predicted NEE and observations Each parameter has fixed allowable range (uniform dist’n) Ran 500,000 iterations to generate posterior distributions

7. Parameter Histograms Initial guess PAR attenuation coefficient Count Min. temp. for photosynthesis Count Optimum temp. for photosynthesis Count Soil respiration Q 10 Count

8. Parameter Correlations Base soil respiration rate (g C g -1 C day -1 ) C content of leaves per unit area (g C m -2 ) Initial soil C content (g C m -2 ) PAR half-saturation point (mol m -2 day -1 ) Some parameters can not be estimated well because of correlations with other parameters:

9. Parameter Behavior 13 well-constrained parameters, 5 poorly-constrained parameters, 14 edge-hitting parameters Initial conditions: mostly edge-hitting Parameters governing carbon dynamics: mostly well- constrained. Exceptions: –PAR attenuation coefficient –Parameters governing C allocation/turnover rate –Base soil respiration rate –Soil respiration Q 10 Parameters governing soil moisture dynamics: mostly poorly-constrained or edge-hitting

10. Optimized Model: Range of Predictions Observations Model Daytime NEE (g C m -2 ) Daytime NEE residual (g C m -2 ) Day of Year Nighttime NEE (g C m -2 ) Nighttime NEE residual (g C m -2 )

11. Model vs. Data: Initial Guess NEE (g C m -2 ) Cumulative NEE (g C m -2 ) Days after Nov. 1, 1998 Observed nighttime NEE (g C m -2 ) Observed daytime NEE (g C m -2 ) Modeled nighttime NEE (g C m -2 ) Modeled daytime NEE (g C m -2 ) Observations Model

12. Unoptimized vs. Optimized Model Unoptimized nighttime NEE (g C m -2 ) Unoptimized daytime NEE (g C m -2 ) Optimized nighttime NEE (g C m -2 ) Optimized daytime NEE (g C m -2 )

13. Model vs. Data: Optimized Parameters NEE (g C m -2 ) Cumulative NEE (g C m -2 ) Days after Nov. 1, 1998 Observed nighttime NEE (g C m -2 ) Observed daytime NEE (g C m -2 ) Modeled nighttime NEE (g C m -2 ) Modeled daytime NEE (g C m -2 ) Observations Model

14. Model vs. Data: Optimized Parameters

16. Missing Variability in Nighttime Respiration Air temperature (°C) Nighttime NEE (g C m -2 day -1 ) Observations Model

17. Days after Nov. 1, 1998 Fractional soil wetness Pool Dynamics Fraction of initial pool size Days after Nov. 1, 1998 Initial GuessOptimized

18. NEW! IMPROVED! Parameter Optimization Used a single soil water pool Held about 1/2 of parameters fixed at best guess values; estimated 17 parameters Fixed parameters for which: –Value was relatively well known, and/or –NEE data contained little information; and –Fixing the parameter did NOT cause significantly worse model-data fit This included: –Most initial conditions –Many soil moisture parameters –A few parameters that were highly correlated with another parameter –Turnover rate of wood Incorporating knowledge of which parameters/processes are not well constrained by the data

19. Days after Nov. 1, 1998 Fractional soil wetness New Parameter Optimization Fraction of initial pool size Days after Nov. 1, 1998 Almost all parameters are now well-constrained

20. Partitioning the Net Flux

21. Flux partitioning using the optimization with fewer free parameters

22. Partitioning the Net Flux Flux partitioning using the optimization with fewer free parameters

23. Optimization on H 2 O Fluxes Using H 2 O fluxes in the optimization would allow better separation of NEE into GPP and R, since GPP is highly correlated with transpiration fluxes Using multiple data types would allow better estimates of previously highly-correlated parameters Optimized simultaneously on H 2 O fluxes and CO 2 fluxes H 2 O fluxes also measured using eddy covariance Hypotheses:

24. Optimization on H 2 O Fluxes Optimized H 2 O fluxes: Optimized CO 2 fluxes: similar to optimization on CO 2 only, although slightly worse fit to observations when optimize on both fluxes H 2 O flux (cm precip. equiv.) Days after Nov. 1, 1998 Observations Model Opt. on CO 2 only:Opt. on CO 2 & H 2 O: Days after Nov. 1, 1998 Fractional soil wetness

25. Optimization on H 2 O Fluxes Flux breakdown:Parameter correlations:

26. Model Structural Changes Tested whether hypothesis-driven changes to model structure improve model-data fit in the face of an optimized parameter set Goal: learn more about controls over NEE Evaluated improvement using Bayesian Information Criterion (BIC): BIC = -2 * LL + K * ln (n) (LL = Log Likelihood; K = # of free parameters; n = # of data points)

27. Model Structural Changes No longer shut down photosynthesis & foliar respiration with frozen soils Separated summer and winter soil respiration parameters Split soil carbon pool into two pools Made soil respiration independent of soil moisture Four changes:

28. Model Structural Changes: Results No shut down of photosynthesis & foliar respiration with frozen soil: significantly worse fit Separate summer/winter soil respiration parameters: slightly better fit Two soil carbon pools: slightly worse fit Soil respiration independent of soil moisture: little change

29. Scaling Up

30. Conclusions Eddy covariance CO 2 flux data can be used to constrain most model parameters that directly affect CO 2 flux Optimization yields better fit of CO 2 flux data, but can force other model behavior (e.g. pool dynamics) to become unrealistic Parameter optimization can be used to probe model structure and learn about controls over NEE In this ecosystem, it appears that photosynthesis, and possibly foliar respiration, are down-regulated when the soil is frozen NEE partitioning:GPP = 600 - 700 g C m -2 yr -1 R tot = 550 - 600 g C m -2 yr -1 Including H 2 O fluxes in optimization does NOT help us learn more about controls over CO 2 flux