Model-Data Synthesis of CO 2 Fluxes at Niwot Ridge, Colorado

1. Model-Data Synthesis of CO2 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 Derive general process-level information from eddy covariance data • What processes do CO2 flux data contain information about? • Can we separate NEE into its component fluxes? • Scale up CO2 fluxes in space and time • Improve parameterization of regional & global models, like CCSM

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 CO2 flux • What do we gain by including an additional data type (H2O 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, Tair, VPD, PAR, Soil Moisture) Autotrophic Respiration: f (Plant C, Tair) Heterotrophic Respiration: f (Soil C, Tsoil, Soil Moisture)

5. 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 CO2 flux (NEE) from eddy covariance • Gaps in climate drivers and NEE filled using a variety of methods http://spot.colorado.edu/~monsonr/Ameriflux.html • 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 Q10) • 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 Count Count Initial guess Initial guess Min. temp. for photosynthesis Optimum temp. for photosynthesis Count Count Initial guess Initial guess PAR attenuation coefficient Soil respiration Q10

8. Parameter Correlations Some parameters can not be estimated well because of correlations with other parameters: 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)

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 Q10 • Parameters governing soil moisture dynamics: mostly poorly-constrained or edge-hitting

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

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

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

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

14. Model vs. Data: Optimized Parameters

15. Model vs. Data: Optimized Parameters

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

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

18. NEW! IMPROVED! Parameter Optimization Incorporating knowledge of which parameters/processes are not well constrained by the data • 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

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

20. Partitioning the Net Flux

21. Partitioning the Net Flux 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 H2O Fluxes Optimized simultaneously on H2O fluxes and CO2 fluxes H2O fluxes also measured using eddy covariance • Using H2O 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 Hypotheses:

24. Observations Model Fractional soil wetness Days after Nov. 1, 1998 Optimization on H2O Fluxes Optimized CO2 fluxes: similar to optimization on CO2 only, although slightly worse fit to observations when optimize on both fluxes Optimized H2O fluxes: Opt. on CO2 only: Opt. on CO2 & H2O: H2O flux (cm precip. equiv.) H2O flux (cm precip. equiv.) Days after Nov. 1, 1998 Days after Nov. 1, 1998

25. Parameter correlations: Optimization on H2O Fluxes Flux breakdown:

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 Four 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

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 CO2 flux data can be used to constrain most model parameters that directly affect CO2 flux Optimization yields better fit of CO2 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 Rtot = 550 - 600 g C m-2 yr-1 • Including H2O fluxes in optimization does NOT help us learn more about controls over CO2 flux