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 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 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) SIPNET Model Photosynthesis Autotrophic Respiration PLANT WOOD CARBON PLANT LEAF CARBON Leaf Creation VEGETA TION Leaf Litter Wood Litter Heterotrophic Respiration SOIL CAR BON Precipitation Interception & Evaporation No: Snow Yes: Rain Snow melt Evaporation Heterotrophic Respiration: Surface Layer Drainage SOIL WATER: ROOT ZONE Root Zone Drainage Autotrophic Respiration: f (Plant C, Tair) Infiltration SOIL WATER: SURFACE LAYER f (Leaf C, Tair, VPD, PAR, Soil Moisture) SNOW PACK Throughfall Fast flow (Drainage) • Goal: keep model as simple as possible Photosynthesis: Sublimation Tai r > 0? • Twice-daily time step (day & night) Transpiration f (Soil C, Tsoil, Soil Moisture) 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 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 Count Count Parameter Histograms Initial guess Initial guess PAR attenuation coefficient Optimum temp. for photosynthesis Count Count Min. temp. for photosynthesis Initial guess Initial guess Soil respiration Q10 Parameter Correlations Base soil respiration rate (g C g-1 C day-1) C content of leaves per unit area (g C m-2) Some parameters can not be estimated well because of correlations with other parameters: Initial soil C content (g C m-2) PAR half-saturation point (mol m-2 day-1) Parameter Behavior • 13 well-constrained parameters, 5 poorly-constrained parameters, 14 edge-hitting parameters • Initial conditions: mostly edge-hitting • Parameters governing carbon dynamics: mostly wellconstrained. 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 Nighttime NEE residual (g C m-2) Daytime NEE residual (g C m-2) Daytime NEE (g C m-2) Observations Model Day of Year Nighttime NEE (g C m-2) Optimized Model: Range of Predictions Day of Year Observations Model Days after Nov. 1, 1998 Modeled nighttime NEE (g C m-2) Cumulative NEE (g C m-2) NEE (g C m-2) Modeled daytime NEE (g C m-2) Model vs. Data: Initial Guess Observed daytime NEE (g C m-2) Observed nighttime NEE (g C m-2) Optimized nighttime NEE (g C m-2) Optimized daytime NEE (g C m-2) Unoptimized vs. Optimized Model Unoptimized daytime NEE (g C m-2) Unoptimized nighttime NEE (g C m-2) Observations Model Days after Nov. 1, 1998 Modeled nighttime NEE (g C m-2) Cumulative NEE (g C m-2) NEE (g C m-2) Modeled daytime NEE (g C m-2) Model vs. Data: Optimized Parameters Observed daytime NEE (g C m-2) Observed nighttime NEE (g C m-2) Model vs. Data: Optimized Parameters 5 Modeled Daytime Observed Daytime Modeled Nighttime Observed Nighttime 4 NEE (g C m -2 -1 day ) 3 2 1 0 -1 -2 -3 -4 -5 J F M A M J J A S O N D NEE Interannual Variability (g C m -2 -1 yr ) Model vs. Data: Optimized Parameters 60 40 Modeled Daytime Observed Daytime Modeled Nighttime Observed Nighttime 20 0 -20 -40 -60 1999 2000 2001 2002 2003 Nighttime NEE (g C m-2 day-1) Missing Variability in Nighttime Respiration Observations Model Air temperature (°C) Pool Dynamics Optimized Days after Nov. 1, 1998 Days after Nov. 1, 1998 Fractional soil wetness Fraction of initial pool size Initial Guess Days after Nov. 1, 1998 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 Almost all parameters are now well-constrained Fraction of initial pool size New Parameter Optimization Fractional soil wetness Days after Nov. 1, 1998 Days after Nov. 1, 1998 600 500 GPP Rtot NEE (modeled) 400 300 200 100 0 -100 -200 Initial Opt. New Opt. -1 700 600 -2 800 Mean Annual Flux (g C m yr ) Mean Annual Flux (g C m-2 yr-1) Partitioning the Net Flux 500 400 RA RH NPP 300 200 100 0 Initial Opt. New Opt. Partitioning the Net Flux Flux partitioning using the optimization with fewer free parameters -1 3 Mean Monthly Flux (g C m 4 -2 day ) 5 GPP Rtot NEE (modeled) 2 1 0 -1 -2 J F M A M J J A S O N D Partitioning the Net Flux Flux partitioning using the optimization with fewer free parameters GPP Rtot NEE (modeled) 800 Annual Flux (g C m -2 -1 yr ) 700 600 500 400 300 200 100 0 -100 -200 1999 2000 2001 2002 2003 Mean Optimization on H2O Fluxes Optimized simultaneously on H2O fluxes and CO2 fluxes H2O fluxes also measured using eddy covariance Hypotheses: • 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 Optimization on H2O Fluxes Optimized CO2 fluxes: similar to optimization on CO2 only, although slightly worse fit to observations when optimize on both fluxes Observations Model H2O flux (cm precip. equiv.) Opt. on CO2 only: Days after Nov. 1, 1998 Fractional soil wetness H2O flux (cm precip. equiv.) Optimized H2O fluxes: Opt. on CO2 & H2O: Days after Nov. 1, 1998 Days after Nov. 1, 1998 Optimization on H2O Fluxes Parameter correlations: 800 700 120 600 111 CO2 only CO2 and H2O 100 500 400 GPP Rtot NEE (modeled) 300 200 100 Count -2 -1 Mean Annual Flux (g C m yr ) Flux breakdown: 80 80 60 40 2525 20 0 9 15 4 8 4 1 0 2 3 1 1 3 .4 .5 .5 .6 .6 .7 .7 .8 .8 - .9 - 1 .9 0 -100 -200 CO2 only CO2 & H2O .1 .2 .2 .3 .3 .4 |r| 1 2 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) 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 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 Scaling Up Niwot Ridge Flux Data SIPNET Flux Model Satellite Data SIPNET Optimized for Niwot Ridge (e.g. MODIS LAI) Spatially-explicit Estimate of GPP/NEE Across Colorado Coniferous Forest Biome Comparisons with Top-down Flux Estimates (e.g. Flux Estimates from Airborne Carbon in the Mountains Experiment (ACME), MODIS GPP) 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 Rtot = 600 - 700 g C m-2 yr-1 = 550 - 600 g C m-2 yr-1 • Including H2O fluxes in optimization does NOT help us learn more about controls over CO2 flux