Assimilation Modeling of CO2 Fluxes at Niwot Ridge, CO, and Strategy for Scaling up to the Region William J. Sacks (sacks@ucar.edu), David S. Schimel, National Center for Atmospheric Research, Boulder, CO; Russell K. Monson, University of Colorado, Boulder, CO; Bobby H. Braswell, University of New Hampshire, Durham, NH Abstract 3. Results: Optimized Parameters 5. Results: Multiple Data Types 7. Rough Strategy for Scaling up to the Region The net ecosystem exchange of CO2 (NEE) is the small difference between two large fluxes: photosynthesis and ecosystem respiration. Consequently, separating NEE into its component fluxes, and determining the process-level controls over these fluxes, is a difficult problem. In this study, we used a data assimilation approach with the SIPNET flux model to extract process-level information from five years of eddy covariance data at an evergreen forest in the Colorado Rocky Mountains. SIPNET runs at a half-daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a soil moisture sub-model that models both evaporation and transpiration. By optimizing the model parameters before evaluating model-data mismatches, we were able to probe the model structure independently of any arbitrary parameter set. In doing so, we were able to learn about the primary controls over NEE in this ecosystem. We also used this parameter optimization, coupled with a formal model selection criterion, to investigate the effects of making hypothesis-driven changes to the model structure. These experiments lent support to the hypotheses that (1) photosynthesis, and possibly foliar respiration, are down-regulated when the soil is frozen, and (2) the metabolic processes of soil microbes vary in the summer and winter, possibly because of the existence of distinct microbial communities at these two times. Finally, we present a rough strategy for scaling the modeled fluxes up to the region. This scaling approach incorporates data from multiple eddy covariance flux towers and from the satellite-based MODIS sensor to derive NEE estimates for the entire coniferous forest biome of Colorado. Many parameters were well-constrained by the NEE data (e.g. (A), (B)). However, some were poorly-constrained (e.g. (C)), indicating either correlations between parameters or little influence on the model’s NEE predictions, and some were edge-hitting (e.g. (D)). Edge-hitting parameters could indicate unmodeled biases in the data, errors due to the half-daily aggregation, deficiencies in model structure, or priors that were too narrow. In an earlier study, we performed the parameter optimization on a synthetic data set, and found that we were able to retrieve the correct (i.e. truly optimal) values for most model parameters. (1) The inclusion of H2O fluxes in the optimization did NOT significantly change the number of highly correlated parameters. (2) The inclusion of H2O fluxes in the optimization did NOT significantly change the separation of NEE into GPP and R, despite the fact that GPP is highly correlated with transpiration fluxes. It did lead to a different separation of R into RA and RH, but this caused a less realistic prediction of NPP, as compared with independent measurements. Modeling daytime and nighttime points separately seems to allow adequate separation of NEE into GPP and R. A B Count Count Min. temp. for photosynthesis (°C) Optimum temp. for photosynthesis (°C) C D Count Count PAR attenuation coefficient Soil respiration Q10 1. SIPNET Flux Model ACME CO2 Profiles: Drawdown from Morning to Afternoon STILT flight planning: July 29, 2004 - Estimated locations of air that would be over Niwot Ridge at 2 PM Location at 6 AM Location at 10 AM 6. Results: Model Structural Changes - Tested whether modifications to model structure improve model-data fit in the face of an optimized parameter set, to learn more about controls over NEE in this ecosystem. - Evaluated improvement using Bayesian Information Criterion (BIC): BIC = -2 * LL + K * ln (n) (LL = Log Likelihood; K = # of free parameters; n = # of data points) Major findings (1) Support for hypothesis that photosynthesis, and possibly foliar respiration, are shut down with frozen soils. (2) Improvement gained by allowing seasonally-varying heterotrophic respiration: support for hypothesis of seasonally-varying microbial communities. May 20 9:30 AM - 2:00 PM July 12 9:30 AM - 3:30 PM July 29 10:30 AM - 2:30 PM The 32 SIPNET parameters and initial conditions that were allowed to vary in the optimization, and their estimated values. 4. Results: Optimized Model vs. Data The SIPNET model is based on the PnET family of models developed by Aber et al. We used SIPNET with half-daily daytime and nighttime time steps. In each step, eight climate variables drive the flux dynamics: (1) average air temperature, (2) average soil temperature, (3) precipitation, (4) flux density of photosynthetically-active radiation, (5) atmospheric vapor pressure, (6) atmospheric vapor pressure deficit, (7) vapor pressure deficit between the soil and the atmosphere and (8) wind speed. • CO2 drawdown: 0.8 g C m-2 • Tower estimate: 1.4 g C m-2 (mean net flux, Niwot Ridge, May 18-22, 1999-2003) • CO2 drawdown: 2.5 g C m-2 • Tower estimate: 1.5 g C m-2 (mean net flux, Niwot Ridge, July, 1999-2003) • CO2 drawdown: 2.9 g C m-2 • Tower estimate: 1.0 g C m-2 (mean net flux, Niwot Ridge, July, 1999-2003) 2. Parameter Optimization MODIS GPP: Colorado, July 29, 2004 With seasonally-varying heterotrophic respiration, there was an increase in retrieved Q10 values, suggesting higher intra-seasonal than inter-seasonal temperature sensitivity. The retrieved wintertime Q10 (Q10S,c) was much higher than the summertime Q10 (Q10S,w), in accordance with results from a number of field studies. The retrieved wintertime base respiration rate (KH,c) was also higher than the corresponding summertime parameter (KH,w), possibly indicating missing substrate dynamics in the model. Tc is the soil temperature threshold below which the cold soil parameters were used. g C m-2 day-1 Overview illustrating the parameter optimization approach, and showing how the results of the optimization are used to guide model structural changes. Used Metropolis simulated annealing algorithm to find both the single best parameter set and an estimate of the posterior distribution of the parameters. Basic concept: maximize likelihood, - In most experiments, we used only CO2 fluxes in the optimization; however, in one experiment, we used both CO2 and H2O fluxes. A remaining discrepancy between model and data is that the model underestimates the peak summertime CO2 uptake, especially in May and June, when there is relatively little water limitation, but overestimates mid-summer carbon uptake in 2002, when soil moisture levels were low. This indicates that the model may not be representing water stress properly, so may be forced to make compromises between times of high water stress and times of low water stress.