Estimating biophysical parameters from CO2 flask and flux observationsKevin Schaefer1, P. Tans1, A. S. Denning2, J. Collatz3, L. Prihodko2, I. Baker2, W. Peters1, A. Andrews1, and L. Bruhwiler11NOAA Climate Monitoring and Diagnostics Laboratory, Boulder, Colorado 2Dept. of Atmospheric Science, Colorado State University, Fort Collins, Colorado 3Goddard Space Flight Center, Greenbelt, Maryland

ObjectiveUnderstand processes driving terrestrial CO2 fluxesTechnique: estimate model parameters using data assimilationModel: Simple Biosphere (SiB)Carnegie-Ames-Stanford Approach (CASA)Observations:CO2 concentrations from CMDL flask networkCO2 concentrations & fluxes from towers

Status2-year NAS Postdoc fellowship @ CMDLJoint effort: CMDL & CSUSibCasa in final testingSwitching to EnKFPreliminary resultsOffline with SiB2 & TransCom fluxesSingle point @ WLEF

Combined SibCasa ModelSimple Biosphere (SiB)BiophysicalGood photosynthesis modelHigh time resolutionCASABiogeochemicalGood respiration model Coarse time resolutionSibCasa Good GPP ModelGood respiration modelHigh time resolution

Which parameters to estimate?LowHighLowHighUncertaintyInfluenceno botherno problemno wayno excuse

WLEF Tall Tower in WisconsinHourly and monthly average net CO2 fluxes

Monthly Observed vs. SibCasa Fluxes at WLEFNet CO2 Flux (mmole/m2/s)Date (year)

Hourly Observed vs. SibCasa Fluxes at WLEFNet CO2 Flux (mmole/m2/s)Date (year)

SibCasa diurnal cycle too small at WLEFJune 2-5, 1997Net CO2 Flux (mmole/m2/s)Date (year)

Sample Estimate: Respiration Temperature Response (Q10)Q10 = 3.0Q10 = 2.0Q10 = 1.0Soil Temperature (K)Scaling Factor (-)

Data Assimilation: Minimize Cost function (F)Optimize using Marquardt-Levenberg method (variant of inverse Hessian) No model adjoint: approximate F slope

Q10 Cost Function at WLEF (no a priori)Hourly Obs: aliasing Q10 to fix diurnal cycle

Initial Slow Pool Cost Function at WLEFMonthly Obs: aliasing Slow to fix low GPP in 1998Equilibrium Pool Size

ConclusionsWe can estimate model parameters from CO2 dataBe careful about data assimilation correcting for model flaws

What process information can we extract from CO2 flask and flux tower observations?Ocean ProcessesNet FluxBiosphereProcessesFlux TowerFlaskAtmospheric TransportNet FluxFossil Fuel

ObjectivesUse model physics to better understand mechanisms that drive CO2 fluxesOptimize model parameters to best match model output & observationsEstimate hard-to-measure parameters: Q10, turnover, pool sizes, etc.Joint effort: CMDL & CSU

Postdoc Plan6 Months for Software developmentAdd geochemistry from CASA to SiB28 months for simulations and testingFlux towers first, then flasks6 months writing papersStatus: 3 months into SiB-CASA development

DAS SetupCombine SiB3 with CASASiB3: Photosynthesis & turbulent fluxesCASA: biogeochemistry and respirationIntegrate Sibcasa into TM5Use Ensemble Kalman Filter (EnKF)

DAS ExperimentsSingle point: Sibcasa & flux tower dataOffline: Sibcasa & Transcom3 fluxesCompare NCEP, ECMWF, GEOS4 reanalysisIntegrated: Sibcasa in TM5 & flask data

ProblemsParameter EstimationParameter compensationModel/data biasesEnKF3-D [CO2] field from sparse flask observationsHow to incorporate CO2 memoryHow to go from parameter to flaskNumber ensemble members

Data Assimilation: Minimize Cost Function (F)y = observationsf(x) = model outputE = uncertaintyx = parameter to estimate

Data Assimilation: Minimize Cost function (F)Variance between modeled & observed fluxesobserved fluxSiB2 fluxparametera prioriflux uncertaintya priori uncertainty

Data Assimilation: Minimize Cost function (F)Iterate using Marquardt-Levenberg method (variant of inverse Hessian)Approximate Jacobian:

Data Assimilation: Minimize Cost function (F)Iterate

Ensemble Kalman Filter (EnKF)Use ensemble statistics to approximate terms in Kalman gain equationRun ensemble ~100 membersNo adjoint requiredExperimental: still under development

History of Kevin1984: BS in Aerospace Engineering1984-1993: NASASpace Shuttle, Space Station Mission to Planet Earth1994-1997: White House1997-2004: CSU Atmospheric Science

Kevins FamilySusyJason

Simple Biosphere Model, Version 2 (SiB2)TcTgNEE=R-GPPLHSHSnowCanopyCanopy Air SpaceSoilGPPR10-min time step11 to 45-year simulations

SiB2 InputNational Centers for Environmental Prediction (NCEP) reanalysis1958-2002, every 6 hours, 2x2 resolutionEuropean Centre for Medium-range Weather Forecasts (ECMWF) reanalysis 1978-1993, every 6 hours, 1x1 resolutionLeaf Area Index: Fourier-Adjustment, Solar zenith angle corrected, Interpolated Reconstructed (FASIR) Normalized Difference Vegetation Index (NDVI) data1982-1998, monthly, variable resolution

NOAAs global flask networkRun transport backwards to estimate CO2 fluxesCompare estimated & SiB2 regional fluxes

Initial Coarse Woody Debris Pool at WLEFMonthly Obs: aliasing to fix low GPP in 1998Hourly Obs: aliasing to fix diurnal cycleEquilibrium Pool Size

Q10 Estimated from Transcom FluxesTropical broadleaf evergreen forestBroadleaf deciduous forestBroadleaf-needleleaf forestNeedleleaf forestNeedleleaf-deciduous forestTropical GrasslandsSemi-arid grasslandsBroadleaf shrubs with bare soilTundraDesertAgriculture and C3 grasslands1.2 0.12.2 0.31.9 0.12.6 0.12.2 0.11.4 0.01.6 0.11.7 0.22.1 0.22.6 0.31.6 0.0 BiomeQ10 (-)

Flasks: Turnover (T) and Q10Tropical broadleaf evergreen forestBroadleaf deciduous forestBroadleaf-needleleaf forestNeedleleaf forestNeedleleaf-deciduous forestTropical GrasslandsSemi-arid grasslandsBroadleaf shrubs with bare soilTundraDesertAgriculture and C3 grasslands12.8 0.8 1.2 0.113.3 2.2 2.2 0.313.6 0.8 1.9 0.112.9 0.5 2.6 0.112.8 0.4 2.2 0.112.8 0.4 1.4 0.012.4 1.0 1.6 0.116.3 1.9 1.7 0.212.4 1.0 2.1 0.212.9 2.4 2.6 0.312.8 0.4 1.6 0.0 BiomeT (mon)Q10 (-)

Global Estimated T and Q10Global Q10 = 1.670.04Agrees well with published values (1.6-2.4)Q10 increases with shorter time scalesGlobal T = 12.7 0.8 monthsRepresents only fast turnover poolsAverage between autotrophic & heterotrophicNeed more carbon pools in SiB2