Estimating biophysical parameters from co 2 flask and flux observations
Download
1 / 34

Estimating biophysical parameters from CO 2 flask and flux observations - PowerPoint PPT Presentation


  • 155 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Estimating biophysical parameters from CO 2 flask and flux observations' - adanne


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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


ad