inferring terrestrial co 2 fluxes from a global-scale carbon cycle data assimilation system (ccdas)...
TRANSCRIPT
Inferring terrestrial CO2 fluxesfrom a global-scale
Carbon Cycle Data Assimilation System (CCDAS)
Marko Scholze1, Peter Rayner2, Wolfgang Knorr1, Thomas Kaminski3, Ralf Giering3 & Heinrich
Widmann1
Atmospheric Science Seminars, Harvard University, 16th January 2004FastOpt1 2 3
Overview
• Motivation• Top-down vs. bottom-up approach• CCDAS set-up• Calculation and propagation of
uncertainties• Data fit• Global results• Conclusions and outlook
Motivation
• Where are the sources/sinks?
• Which are the important processes?
• How do they evolve?
Sketch of the global carbon cycle
Fluxes in Gt C yr-1, pools in Gt C,after Prentice et al., 2001.
„top-down“ vs. „bottom-up“
net CO2
flux at thesurface
Process Model
climate and other driving data
atmospheric inversion
(Transport Model)
atm. CO2 dataAdvantages:• Fluxes consistent with
atm. data• Estimation of uncertainties
Disadvantages:• No process information• Coarse resolution
Advantages:• Process understanding
-> prognostic modeling• High resolution
Disadvantages:• Global validation difficult • Parameter validity
Combined MethodCCDAS – Carbon 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)
Station network
41 stations from Globalview (2001), no gap-filling, monthly values
1979-1999.
Annual uncertainty values from Globalview (2001).
Terminology
GPP Gross primary productivity (photosynthesis)NPP Net primary productivity (plant growth)NEP Net ecosystem productivity (undisturbed C storage)NBP Net biome productivity (C storage)
BETHY(Biosphere Energy-Transfer-Hydrology
Scheme)
• GPP:C3 photosynthesis – Farquhar et al. (1980)C4 photosynthesis – Collatz et al. (1992)stomata – Knorr (1997)
• Plant respiration:maintenance resp. = f(Nleaf, T) – Farquhar, Ryan (1991)
growth resp. ~ NPP – Ryan (1991) • Soil respiration:
fast/slow pool resp., temperature (Q10 formulation) and soil moisture dependant
• Carbon balance:average NPP = average soil resp. (at each grid point)
<1: source>1: sink
t=1h
t=1h
t=1day
lat, lon = 2 deg
Calibration Step
Flow of information in CCDAS. Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.
Prognostic Step
Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.
Methodology
Minimize cost function such as (Bayesian form):
DpMDpMpp pppJ D
T
pT
)()()( 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
DM
p
need of (adjoint of the model)Jp
Calculation of uncertainties
• Error covariance of parameters
1
2
2
ji,
p pJ
C = inverse Hessian
T
pX p)p(X
p)p(X
CC
• Covariance (uncertainties) of prognostic quantities
Figure from Tarantola, 1987
Gradient Method
1st derivative (gradient) ofJ (p) to model parameters p:
yields direction of steepest descent.
p
p
ppJ
)(
cost function J (p) p
Model parameter space (p)p
2nd derivative (Hessian)of J (p):
yields curvature of J.Approximates covariance ofparameters.
p
22 ppJ
)(
Global Growth Rate
Calculated as:
observed growth rate
optimised modeled growth rate
Atmospheric CO2 growth rate
MLOSPOGLOB CCC 75.025.0
Parameters I
• 3 PFT specific parameters (Jmax, Jmax/Vmax and )
• 18 global parameters• 57 parameters in all plus 1 initial value (offset)
Param InitialPredicted
Prior unc. (%) Unc. Reduction (%)
fautleafc-costQ10 (slow)
(fast)
0.41.251.51.5
0.241.271.351.62
2.50.57075
3917278
(TrEv)(TrDec) (TmpDec) (EvCn) (DecCn) (C4Gr) (Crop)
1.01.01.01.01.01.01.0
1.440.352.480.920.731.563.36
25252525252525
7895629591901
Some values of global fluxes
1980-2000 (prior)
1980-2000 1980-1990 1990-2000
GPPGrowth resp.Maint. resp.NPP
135.723.544.0468.18
134.822.3572.740.55
134.322.3172.1340.63
135.322.3973.2840.46
Fast soil resp.Slow soil resp.NEP
53.8314.46-0.11
27.410.692.453
27.610.712.318
27.2110.672.587
Value Gt C/yr
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)
Uncertainty in prior net flux
Uncertainty in net carbon flux from prior values 1980-2000gC / (m2 year)
Interannual Variability I
Normalized CO2 flux and ENSO
Lag correlation(low-pass filtered)
ENSO and terr. biosph. CO2:Correlations seems strong with a maximum at ~4 months lag,for both El Niño and La Niña states.
Interannual Variabiliy II
Lagged correlation on grid-cell basis at 99% significance
correlation coefficient
Conclusions
• CCDAS with 58 parameters can fit 20 years of CO2 concentration data.
• Significant reduction of uncertainty for ~15 parameters.
• Terr. biosphere response to climate fluctuations dominated by El Nino.
• A tool to test model with uncertain parameters and to deliver a posterior uncertainties on parameters and prognostics.
Future
• Explore more parameter configurations.• Include missing processes (e.g. fire).• Upgrade transport model and extend data.• Include more data constraints (eddy
fluxes, isotopes, high frequency data, satellites) -> scaling issue.
• Projections of prognostics and uncertainties into future.
• Extend approach to ocean carbon cycle.