Earth Observation Data and Carbon Cycle Modelling
Marko ScholzeQUEST, Department of Earth Sciences
University of Bristol
GAIM/AIMES Task Force Meeting, Yokohama, 24-29 Oct. 2004
(an incomplete and subjective view…)
Overview
• Atmospheric CO2 observations– TransCom
• Model-Data Synthesis– Oceanic DIC observations: Inverse Ocean
Modelling Project– Terrestrial observations: Eddy-flux towers– Atmospheric observations: Carbon Cycle Data
Assimilation system
TransCom 3Linear atmospheric transport inversion to calculate CO2 sources and sinks:
• 4 background "basis functions" for land, ocean, fossil fuels 1990 & 1995
• 11 land regions, spatial pattern proportional to terr. NPP
• 11 ocean regions, uniform spatial distribution
Solving for 4 (background) + 22 (regions) * 12 (month) basis functions!
TransCom 3 Seasonal Results(mean over 1992 to 1996)
Guerney et al., 2004
response to background fluxes:
ppm
inversion results:
-35
15 4
-5
Gt
C/y
r
TransCom 3 Interannual Results (1988 - 2003)
red: landblue: ocean
darker bands: within-modeluncertainty
lighter bands: between-model uncertainty
• larger land than ocean variability• interannual changes more robust than seasonal
... but atmosphere well mixed interannually... Baker et al. 2004
Gt
C/y
r
Model-Data Synthesis:The Inverse Ocean Modelling
Project
C* of Gruber, Sarmiento, and Stocker (1996) to estimate anthropogenic DIC.Innumerable data authors, but represented by Feely, Sabine, Lee, Key.
Recent ocean carbon survey, ~ 60.000 observations
The Inverse Ocean Modelling Project
Jacobson, TransCom3Meeting, Jena, 2003
The Inverse Ocean Modelling Project
Gloor et al. 2003
• southward carbon transport of 0.37 Pg C/yr for pre-industrial times• present-day transport -0.06 Pg C/yr (northwards)
Terrestrial observations: Fluxnet a global network of eddy covariance
measurements
Inversion of terrestrial ecosystem parameter values against eddy covariance
measurements by Metropolis Monte Carlo sampling
A Posteriori parameter PDF for Loobos site
ga,v: vegetation factor of atmospheric conductanceEvm: activation energy of Vm
Knorr & Kattge, 2004
Carbon sequestration at the Loobos site during 1997 and 1998
Knorr & Kattge, 2004
CCDASCarbon Cycle Data Assimilation System
CO2 stationconcentration
Biosphere Model:BETHY
Atmospheric Transport Model: TM2
Misfit to observations
Model parameter
Fluxes
Misfit 1 Forward Modeling:
Parameters –> Misfit
Inverse Modeling:
Parameter optimization
CCDAS set-up
2-stage-assimilation:
1. AVHRR data(Knorr, 2000)
2. Atm. CO2 data
Background fluxes:1. Fossil emissions (Marland et al., 2001 und Andres et al., 1996)2. Ocean CO2 (Takahashi et al., 1999 und Le Quéré et al., 2000)3. Land-use (Houghton et al., 1990)
Transport Model TM2 (Heimann, 1995)
Methodology
Minimize cost function such as (Bayesian form):
[ ] [ ] [ ] [ ]DpMDpMpp pppJ D
T
pT rrrrrrrrrrr
−−+−−= )()()( 2
1
2
1 10
10 0
-- C C
where- is a model mapping parameters to observable quantities- is a set of observations- error covariance matrixC
DrMr
pr
need of (adjoint of the model)Jpr∇
1
2
2−
⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧
≈ji,
p pJ
rr
∂∂
C
Uncertainties of parametersT
pX p)p(X
p)p(X
rrr
rrr
rr
∂∂
∂∂
≈ CC
Uncertainties of prognostics X
Figure from Tarantola, 1987
Gradient Method
1st derivative (gradient) ofJ (p) to model parameters p:
yields direction of steepest descent.
pr
pr
ppJrr
∂∂− )(
cost function J (p) pr
Model parameter space (p)pr
2nd derivative (Hessian)of J (p):
yields curvature of J.Approximates covariance ofparameters.
pr
22 ppJrr
∂∂ )(
Data Fit
Seasonal Cycle
Barrow Niwot Ridge
observed seasonal cycle
optimised modeled seasonal cycle
Global Growth Rate
Calculated as:
observed growth rate
optimised modeled growth rate
Atmospheric CO2 growth rate
MLOSPOGLOB CCC 75.025.0 +=
Error Reduction in Parameters
Relative Error Reduction
Carbon Balance
latitude N*from Valentini et al. (2000) and others
Euroflux (1-26) and othereddy covariance sites*
net carbon flux 1980-2000gC / (m2 year)
IAV and processes
Major El Niño events
Major La Niña event
Post Pinatubo period
Interannual VariabilityNormalized CO2 flux and ENSO
Lag correlation(low-pass filtered)
correlation coefficient
Outlook
• Data assimilation: problem better constrained without "artefacts" (e.g. spatial patterns created by station network)
but: cannot resolve processes that are not included in the model (look at residuals and learn about the model)
• Simultaneous inversion of land and ocean fluxes• Isotopes• More data over tropical lands: satellites
• Model-Data-Synthesis: problem better constrained without "artefacts" (e.g. spatial patterns created by station network)
but: cannot resolve processes that are not included in the model (look at residuals and learn about the model)
• Simultaneous inversion of land and ocean fluxes
• Further data constraints (e.g. Isotopes, Inventories)
• More data over tropical lands: satellites
Posterior Uncertainty in Net Flux
Uncertainty in net carbon flux 1980-200gC / (m2 year)
Uncertainty in prior net flux
Uncertainty in net carbon flux from prior values 1980-2000gC / (m2 year)
Atm. Inversion on Grid-cell
• prior and posterior uncertainties• sensitivities (colors)
Rödenbeck et al. 2003
Atm. Inversion on Grid-cell
prior/posterior fluxesand reduction in uncertaintyRödenbeck et al. 2003
Not really at model grid of TM3, but aggregated to TM2 grid, 8° x 10°,Underdetermined problem correlation matrix (e.g. l=1275 km for NEE)
CO2 Satellite Measurements
Vertical weighting functions
Sciamachy, OCO
Airs (U) (=Upper limit)
Airs (L) (=Lower limit)
Houweling et al. 2003
Pseudo Satellite Data Inversion
posterior/prior uncertainty
Houweling et al. 2003
