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A Global Carbon Cycle Data Assimilation System (CCDAS) to Infer Atmosphere- Biosphere CO2 Exchanges and Their Uncertainties. Marko Scholze 1 , Peter Rayner 2 , Jens Kattge 3 , Wolfgang Knorr 3 , Thomas Kaminski 4 , Ralf Giering 4 & Heinrich Widmann 3 Tsukuba, 1 st Novembre 2004. 3. 4. 1. - PowerPoint PPT Presentation
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A Global Carbon Cycle Data Assimilation System (CCDAS) to
Infer Atmosphere-Biosphere CO2 Exchanges and
Their Uncertainties
Marko Scholze1, Peter Rayner2, Jens Kattge3, Wolfgang Knorr3, Thomas Kaminski4, Ralf Giering4 & Heinrich
Widmann3
Tsukuba, 1st Novembre 20043
FastOpt421
Overview
• Motivation• Top-down vs. bottom-up approach• CCDAS set-up• Calculation and propagation of
uncertainties• Data fit• Global results• Conclusions and outlook
Motivation
after Joos, 1996
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
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)
eddy flux CO2 & H2O
Monte CarloParam. Inversion
full BETHY
Assimilated
params& uncert.
Pre-step
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
Pre-Step
Inversion of terrestrial ecosystem parameter values against eddy covariance measurements by Monte Carlo sampling
Case study: Loobos site, Netherlands
• temperate oceanic climate, coniferous forest• Halfhourly data of Eddy covariance measurements
from seven days during 1997 and 1998
• Diagnostics: NEE and LE
Estimated parameters and their standard deviations
a priori SD:
0.1
0.25
0.5
A Posteriori parameter PDF for Loobos site
ga,v: vegetation factor of atmospheric conductanceEvm: activation energy of Vm
Propagation of unctertainties to modelled fluxes
Carbon sequestration at the Loobos site during 1997 and 1998
Knorr & Kattge, 2004
CCDAS Step 2: Station network
41 stations from Globalview (2001), no gap-filling, monthly values
1979-1999.
Annual uncertainty values from Globalview (2001).
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 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∇
Calculation of uncertainties
• Error covariance of parameters
1
2
2−
⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧
=ji,
p pJ
rr
∂∂
C = inverse Hessian
T
pX p)p(X
p)p(X
rrr
rrr
rr
∂∂
∂∂
≈ 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.
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 +=
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
Parameters II
Relative Error Reduction
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 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)
NEP anomalies: global and tropical
global flux anomalies
tropical (20S to 20N) flux anomalies
IAV and processes
Major El Niño events
Major La Niña event
Post Pinatubo period
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
Low-resolution CCDAS
• A fully functional low resolution version of CCDAS, BETHY runs on the TM2 grid (appr. 10° x 7.8°)
• 506 vegetation points compared to 8776 (high-res.)• About a factor of 20 faster than high-res. Version -> ideal
for developing, testing and debugging• On a global scale results are comparable (can be used
for pre-optimising)
Including the ocean • A 1 GtC/month pulse lasting for three months is used as
a basis function for the optimisation• Oceans are divided into the 11 TransCom-3 regions• That means: 11 regions * 12 months * 21 yr / 3 months =
924 additional parameters• Test case:
all 924 parameters have a prior of 0. (assuming that our background ocean flux is correct)
each pulse has an uncertainty of 0.1 GtC/month giving an annual uncertainty of ~2 GtC for the total ocean flux
Including the ocean
Seasonality at MLOGlobal land flux
Observations
Low-res incl. ocean basis functions Low resolution model
High resolution standard model
Conclusions
• Eddy covariance measurements can be used to assign prior values and uncertainty distribution for CCDAS step 2.
• CCDAS with 58 parameters can fit 20 years of CO2 concentration data; ~15 directions can be resolved
• 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.
• With the ability of including ocean basis functions in the optimisation procedure CCDAS comprises a ‘normal’ atmospheric inversion.
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 a prognostic ocean carbon cycle
model.
For more information, please visit:http://www.ccdas.org