Transcript Estimating biophysical parameters from CO2 flask and flux observations Kevin Schaefer1, P.

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, Colorado
of Atmospheric Science, Colorado State University, Fort Collins, Colorado
3Goddard Space Flight Center, Greenbelt, Maryland
2Dept.
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
•
•
•
•
•
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?
Influence
High
no way
no excuse
no problem
no bother
Low
Low
Uncertainty
High
WLEF Tall Tower in Wisconsin
WLEF
• Hourly and monthly average net CO2 fluxes
Monthly Observed vs. SibCasa Fluxes at WLEF
Observed
Net CO2 Flux (mmole/m2/s)
1.5
SibCasa
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
1996.0
1996.5
1997.0
1997.5
Date (year)
1998.0
1998.5
1999.0
Hourly Observed vs. SibCasa Fluxes at WLEF
Observed
SibCasa
(micromole
C/m2/s)
(mmole/m2/s)
Net CO2NEEFlux
20
15
10
5
0
-5
-10
-15
-20
1996
1996.5
1997
1997.5
Time (year)
Date (year)
1998
1998.5
1999
SibCasa diurnal cycle too small at WLEF
Observed
SibCasa
(micromole
C/m2/s)
(mmole/m2/s)
Net CO2NEEFlux
15
June 2-5, 1997
10
5
0
-5
-10
-15
-20
-25
1997.500
1997.501
1997.502
1997.503
1997.504
1997.505
Date (year)
1997.506
Date (year)
1997.507
1997.508
1997.509
1997.510
Sample Estimate: Respiration Temperature
Response (Q10)
Scaling Factor (-)
Q10 = 3.0
ST  Q10
T Tref 
10
Q10 = 2.0
Q10 = 1.0
Soil Temperature (K)
Data Assimilation: Minimize Cost function (F)
T 1
T 1








F  x  x a Ea x  x a  Y  f x E y Y  f x
• Optimize using Marquardt-Levenberg
method (variant of inverse Hessian)
• No model adjoint: approximate F slope
Q10 Cost Function at WLEF (no a priori)
Normalized Cost (-)
Q10 (mon)
Q10 (mon)
Q10 (hr)
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
1.20
1.40
1.60
1.80
2.00
2.20
2.40
2.60
Q10
• Hourly Obs: aliasing Q10 to “fix” diurnal cycle
Initial Slow Pool Cost Function at WLEF
slow (mon)
slow (hr)
Equilibrium Pool Size
Normalized Cost (-)
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0
50
100
150
200
250
300
Pool Size (mole/m2)
• Monthly Obs: aliasing Slow to “fix” low GPP in 1998
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 CO2
flask and flux tower observations?
Atmospheric Transport
Net Flux
Flux
Tower
Fossil Fuel
Net Flux
Biosphere
Processes
Ocean
Processes
Flask
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
• 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 [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 (F)
F
 y  f ( x) 
2
E
2
y = observations
f(x) = model output
E = uncertainty
x = parameter to estimate
Data Assimilation: Minimize Cost function (F)
• Variance between modeled & observed fluxes
observed flux SiB2 flux
F
 y  f x 
2
E
2
y
flux
uncertainty
parameter a priori

x  xa 
2
E
2
a
a priori
uncertainty
Data Assimilation: Minimize Cost function (F)
F  x  x a  E x  x a   Y  f x Ey1 Y  f x
T
1
a
T
• Iterate using Marquardt-Levenberg method
(variant of inverse Hessian)

xi 1  xi   E  K E K i
1
a
T
i
1
y
 E x
1
1
a

T 1


x

K
i
a
i E y Y  f x 
f x i  x   f x i 
• Approximate Jacobian: K i 
x
Data Assimilation: Minimize Cost function (F)
CO2 Flask Measurements
Transport Models
LAI
Weather
TransCom Inversion
SiB2
Estimated NEE
Modeled NEE
Iterate
Assimilation
T
Q10
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
Kevin’s Family
Susy
Jason
Simple Biosphere Model, Version 2 (SiB2)
NEE=R-GPP
SH LH
Tc
Canopy
Canopy
Air Space
GPP
CO2
Ta
Rha
R
Snow
Tg
W1
W2
Soil
11 to 45-year simulations
T6
T5
T4
T3
T2
W3
T1
10-min time step
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
• Run transport backwards to estimate CO2 fluxes
• Compare estimated & SiB2 regional fluxes
Initial Coarse Woody Debris Pool at WLEF
cwd (mon)
cwd (hr)
Equilibrium Pool Size
Normalized Cost (-)
0.10
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0
50
100
150
200
250
300
Pool Size (mole/m2)
• Monthly Obs: aliasing to fix low GPP in 1998
• Hourly Obs: aliasing to “fix” diurnal cycle
Q10 Estimated from Transcom Fluxes
Biome
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
Q10 (-)
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 Q10
Biome
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
T (mon)
12.8 ± 0.8
13.3 ± 2.2
13.6 ± 0.8
12.9 ± 0.5
12.8 ± 0.4
12.8 ± 0.4
12.4 ± 1.0
16.3 ± 1.9
12.4 ± 1.0
12.9 ± 2.4
12.8 ± 0.4
Q10 (-)
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
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