Estimating biophysical parameters from co 2 flask and flux observations
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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.

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Estimating biophysical parameters from CO 2 flask and flux observations

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Estimating biophysical parameters from co 2 flask and flux observations

Estimating biophysical parameters from CO2 flask and flux observations

Kevin Schaefer1, P. Tans1, A. S. Denning2, J. Collatz3, L. Prihodko2, I. Baker2, W. Peters1, A. Andrews1, and L. Bruhwiler1

1NOAA Climate Monitoring and Diagnostics Laboratory, Boulder, Colorado2Dept. of Atmospheric Science, Colorado State University, Fort Collins, Colorado3Goddard Space Flight Center, Greenbelt, Maryland


Objective

Objective

  • Understand processes driving terrestrial CO2 fluxes

  • Technique: estimate model parameters using data assimilation

  • Model:

    • Simple Biosphere (SiB)

    • Carnegie-Ames-Stanford Approach (CASA)

  • Observations:

    • CO2 concentrations from CMDL flask network

    • CO2 concentrations & fluxes from towers


Status

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

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

Which parameters to estimate?

High

no excuse

no way

Influence

no problem

no bother

Low

Low

High

Uncertainty


Wlef tall tower in wisconsin

Hourly and monthly average net CO2 fluxes

WLEF

WLEF Tall Tower in Wisconsin


Monthly observed vs sibcasa fluxes at wlef

SibCasa

Observed

Monthly Observed vs. SibCasa Fluxes at WLEF

Net CO2 Flux (mmole/m2/s)

Date (year)


Hourly observed vs sibcasa fluxes at wlef

SibCasa

Observed

Hourly Observed vs. SibCasa Fluxes at WLEF

Net CO2 Flux (mmole/m2/s)

Date (year)


Sibcasa diurnal cycle too small at wlef

SibCasa

Observed

SibCasa diurnal cycle too small at WLEF

June 2-5, 1997

Net CO2 Flux (mmole/m2/s)

Date (year)


Sample estimate respiration temperature response q 10

Sample Estimate: Respiration Temperature Response (Q10)

Q10 = 3.0

Q10 = 2.0

Scaling Factor (-)

Q10 = 1.0

Soil Temperature (K)


Data assimilation minimize cost function f

Data Assimilation: Minimize Cost function (F)

  • Optimize using Marquardt-Levenberg method (variant of inverse Hessian)

  • No model adjoint: approximate F slope


Q 10 cost function at wlef no a priori

Q10 Cost Function at WLEF (no a priori)

  • Hourly Obs: aliasing Q10 to “fix” diurnal cycle


Initial slow pool cost function at wlef

Initial Slow Pool Cost Function at WLEF

  • Monthly Obs: aliasing Slow to “fix” low GPP in 1998

Equilibrium Pool Size


Conclusions

Conclusions

  • We can estimate model parameters from CO2 data

  • Be careful about data assimilation “correcting” for model flaws


What process information can we extract from co 2 flask and flux tower observations

What process information can we extract from CO2 flask and flux tower observations?

Atmospheric Transport

Net Flux

Flux Tower

Fossil Fuel

Flask

Net Flux

Ocean Processes

Biosphere

Processes


Objectives

Objectives

  • Use model physics to better understand mechanisms that drive CO2 fluxes

  • Optimize model parameters to best match model output & observations

  • Estimate hard-to-measure parameters: Q10, turnover, pool sizes, etc.

  • Joint effort: CMDL & CSU


Postdoc plan

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

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

DAS Experiments

  • Single point: Sibcasa & flux tower data

  • Offline: Sibcasa & Transcom3 fluxes

    • Compare NCEP, ECMWF, GEOS4 reanalysis

  • Integrated: Sibcasa in TM5 & flask data


Problems

Problems

  • Parameter Estimation

    • Parameter compensation

    • Model/data biases

  • EnKF

    • 3-D [CO2] field from sparse flask observations

    • How to incorporate CO2 memory

    • How to go from parameter to flask

    • Number ensemble members


Data assimilation minimize cost function f1

Data Assimilation: Minimize Cost Function (F)

y = observations

f(x) = model output

E = uncertainty

x = parameter to estimate


Data assimilation minimize cost function f2

Data Assimilation: Minimize Cost function (F)

  • Variance between modeled & observed fluxes

observed flux

SiB2 flux

parameter

a priori

a priori uncertainty

flux uncertainty


Data assimilation minimize cost function f3

Data Assimilation: Minimize Cost function (F)

  • Iterate using Marquardt-Levenberg method (variant of inverse Hessian)

  • Approximate Jacobian:


Data assimilation minimize cost function f4

CO2 Flask Measurements Transport Models

SiB2

TransCom Inversion

Assimilation

T

Q10

Estimated NEE

Modeled NEE

LAI

Weather

Data Assimilation: Minimize Cost function (F)

Iterate


Ensemble kalman filter enkf

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

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


Kevin s family

Kevin’s Family

Susy

Jason


Simple biosphere model version 2 sib2

CO2

Ta

Rha

T6

W1

T5

T4

W2

T3

T2

W3

T1

Simple Biosphere Model, Version 2 (SiB2)

NEE=R-GPP

SH

LH

Tc

Canopy

Canopy Air Space

GPP

R

Snow

Tg

Soil

11 to 45-year simulations

10-min time step


Sib2 input

SiB2 Input

  • National Centers for Environmental Prediction (NCEP) reanalysis

    • 1958-2002, every 6 hours, 2x2º resolution

  • European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis

    • 1978-1993, every 6 hours, 1x1º resolution

  • Leaf Area Index: Fourier-Adjustment, Solar zenith angle corrected, Interpolated Reconstructed (FASIR) Normalized Difference Vegetation Index (NDVI) data

    • 1982-1998, monthly, variable resolution


Noaa s global flask network

NOAA’s global flask network

  • Run transport backwards to estimate CO2 fluxes

  • Compare estimated & SiB2 regional fluxes


Initial coarse woody debris pool at wlef

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

Q10 Estimated from Transcom Fluxes

Biome

Q10 (-)

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


Flasks turnover t and q 10

Flasks: Turnover (T) and Q10

Biome

T (mon)

Q10 (-)

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 ± 0.81.2 ± 0.1

13.3 ± 2.22.2 ± 0.3

13.6 ± 0.81.9 ± 0.1

12.9 ± 0.52.6 ± 0.1

12.8 ± 0.42.2 ± 0.1

12.8 ± 0.41.4 ± 0.0

12.4 ± 1.01.6 ± 0.1

16.3 ± 1.91.7 ± 0.2

12.4 ± 1.02.1 ± 0.2

12.9 ± 2.42.6 ± 0.3

12.8 ± 0.41.6 ± 0.0


Global estimated t and q 10

Global Estimated T and Q10

  • Global Q10 = 1.67±0.04

    • Agrees well with published values (1.6-2.4)

    • Q10 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


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