Estimating biophysical parameters from CO 2 flask and flux observations Kevin Schaefer 1, P. Tans 1, A. S. Denning 2, J. Collatz 3, L. Prihodko 2, I. Baker.

Estimating biophysical parameters from CO 2 flask and flux observations Kevin Schaefer 1, P. Tans 1, A. S. Denning 2, J. Collatz 3, L. Prihodko 2, I. Baker 2, W. Peters 1, A. Andrews 1, and L. Bruhwiler 1 1 NOAA Climate Monitoring and Diagnostics Laboratory, Boulder, Colorado 2 Dept. of Atmospheric Science, Colorado State University, Fort Collins, Colorado 3 Goddard Space Flight Center, Greenbelt, Maryland

Objective Understand processes driving terrestrial CO 2 fluxes Technique: estimate model parameters using data assimilation Model: Simple Biosphere (SiB) Carnegie-Ames-Stanford Approach (CASA) Observations: CO 2 concentrations from CMDL flask network CO 2 concentrations & fluxes from towers

Status 2-year NAS Postdoc fellowship @ CMDL Joint effort: CMDL & CSU SibCasa in final testing Switching to EnKF Preliminary results Offline with SiB2 & TransCom fluxes Single point @ WLEF

Combined SibCasa Model Simple Biosphere (SiB) Biophysical Good photosynthesis model High time resolution CASA Biogeochemical Good respiration model Coarse time resolution SibCasa Good GPP Model Good respiration model High time resolution

Which parameters to estimate? Low High Low High Uncertainty Influence no botherno problem no way no excuse

WLEF Tall Tower in Wisconsin Hourly and monthly average net CO 2 fluxes WLEF

Monthly Observed vs. SibCasa Fluxes at WLEF Net CO 2 Flux ( mole/m2/s) Date (year) SibCasa Observed

Hourly Observed vs. SibCasa Fluxes at WLEF Net CO 2 Flux ( mole/m2/s) Date (year) SibCasa Observed

SibCasa diurnal cycle too small at WLEF June 2-5, 1997 SibCasa Observed Net CO 2 Flux ( mole/m2/s) Date (year)

Sample Estimate: Respiration Temperature Response (Q 10 ) Q 10 = 3.0 Q 10 = 2.0 Q 10 = 1.0 Soil Temperature (K) Scaling Factor (-)

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

Q 10 Cost Function at WLEF (no a priori) Hourly Obs: aliasing Q 10 to fix diurnal cycle

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

Conclusions We can estimate model parameters from CO 2 data Be careful about data assimilation correcting for model flaws

What process information can we extract from CO 2 flask and flux tower observations? Ocean Processes Net Flux Biosphere Processes Flux Tower Flask Atmospheric Transport Net Flux Fossil Fuel

Objectives Use model physics to better understand mechanisms that drive CO 2 fluxes Optimize model parameters to best match model output & observations Estimate hard-to-measure parameters: Q 10, turnover, pool sizes, etc. Joint effort: CMDL & CSU

Postdoc Plan 6 Months for Software development Add geochemistry from CASA to SiB2 8 months for simulations and testing Flux towers first, then flasks 6 months writing papers Status: 3 months into SiB-CASA development

DAS Setup Combine SiB3 with CASA SiB3: Photosynthesis & turbulent fluxes CASA: biogeochemistry and respiration Integrate Sibcasa into TM5 Use Ensemble Kalman Filter (EnKF)

DAS Experiments Single point: Sibcasa & flux tower data Offline: Sibcasa & Transcom3 fluxes Compare NCEP, ECMWF, GEOS4 reanalysis Integrated: Sibcasa in TM5 & flask data

Problems Parameter Estimation Parameter compensation Model/data biases EnKF 3-D [CO 2 ] field from sparse flask observations How to incorporate CO 2 memory How to go from parameter to flask Number ensemble members

Data Assimilation: Minimize Cost Function ( ) y = observations f(x) = model output E = uncertainty x = parameter to estimate

Data Assimilation: Minimize Cost function ( ) observed fluxSiB2 fluxparametera priori flux uncertainty a priori uncertainty Variance between modeled & observed fluxes

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

Data Assimilation: Minimize Cost function ( ) CO 2 Flask Measurements Transport Models TransCom Inversion Estimated NEE SiB2 Assimilation LAI Weather Modeled NEE T Q 10 Iterate

Ensemble Kalman Filter (EnKF) Use ensemble statistics to approximate terms in Kalman gain equation Run ensemble ~100 members No adjoint required Experimental: still under development

History of Kevin 1984: BS in Aerospace Engineering 1984-1993: NASA Space Shuttle, Space Station Mission to Planet Earth 1994-1997: White House 1997-2004: CSU Atmospheric Science

Kevins Family SusyJason

Simple Biosphere Model, Version 2 (SiB2) TcTc TgTg CO 2 TaTa Rh a NEE=R-GPP LHSH Snow Canopy Canopy Air Space Soil GPP R W1W1 W2W2 W3W3 T1T1 T2T2 T3T3 T4T4 T5T5 T6T6 10-min time step 11 to 45-year simulations

SiB2 Input National Centers for Environmental Prediction (NCEP) reanalysis 1958-2002, every 6 hours, 2x2 resolution European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis 1978-1993, every 6 hours, 1x1 resolution Leaf Area Index: Fourier-Adjustment, Solar zenith angle corrected, Interpolated Reconstructed (FASIR) Normalized Difference Vegetation Index (NDVI) data 1982-1998, monthly, variable resolution

NOAAs global flask network Run transport backwards to estimate CO 2 fluxes Compare estimated & SiB2 regional fluxes

Initial Coarse Woody Debris Pool at WLEF Monthly Obs: aliasing to fix low GPP in 1998 Hourly Obs: aliasing to fix diurnal cycle Equilibrium Pool Size

Q 10 Estimated from Transcom Fluxes Tropical broadleaf evergreen forest Broadleaf deciduous forest Broadleaf-needleleaf forest Needleleaf forest Needleleaf-deciduous forest Tropical Grasslands Semi-arid grasslands Broadleaf shrubs with bare soil Tundra Desert Agriculture and C3 grasslands 1.2 0.1 2.2 0.3 1.9 0.1 2.6 0.1 2.2 0.1 1.4 0.0 1.6 0.1 1.7 0.2 2.1 0.2 2.6 0.3 1.6 0.0 BiomeQ 10 (-)

Flasks: Turnover (T) and Q 10 Tropical broadleaf evergreen forest Broadleaf deciduous forest Broadleaf-needleleaf forest Needleleaf forest Needleleaf-deciduous forest Tropical Grasslands Semi-arid grasslands Broadleaf shrubs with bare soil Tundra Desert Agriculture and C3 grasslands 12.8 0.8 1.2 0.1 13.3 2.2 2.2 0.3 13.6 0.8 1.9 0.1 12.9 0.5 2.6 0.1 12.8 0.4 2.2 0.1 12.8 0.4 1.4 0.0 12.4 1.0 1.6 0.1 16.3 1.9 1.7 0.2 12.4 1.0 2.1 0.2 12.9 2.4 2.6 0.3 12.8 0.4 1.6 0.0 BiomeT (mon) Q 10 (-)

Global Estimated T and Q 10 Global Q 10 = 1.670.04 Agrees well with published values (1.6-2.4) Q 10 increases with shorter time scales Global T = 12.7 0.8 months Represents only fast turnover pools Average between autotrophic & heterotrophic Need more carbon pools in SiB2

Estimating biophysical parameters from CO 2 flask and flux observations Kevin Schaefer 1, P. Tans 1, A. S. Denning 2, J. Collatz 3, L. Prihodko 2, I. Baker.

Documents

fluxes wlef slide

maryland slide

excuse slide

model parameters

sibcasa fluxes

cmdl csu slide

diurnal cycle slide

flux molem2s date year