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Prabir K. Patra, Shamil Maksyutov, A. Ito
and TransCom-3 modellers
Jena; 13 May 2003
An evaluation of an ecosystem model for studying CO2 seasonal cycle
TransCom-3 (Level-1) related activities
at FRSGC
Goals…To configure optimal observation system
– Measurement network optimisation (surface)
– Estimate benefits of satellite data in inversion
– Evaluate of their relative performance
Tools
Inverse Modelling
Least squares fitting of observed data and model simulations
Matrix multiplication and SVD
TransCom-3 setup for 11 land and 11 ocean regions
HiRes setup for 42 land and 11 ocean regions
Forward Modelling
16 global transport models of TransCom-3
Advection, PBL, Convection etc. are treated differently
ECMWF, NCEP, GCM meteorological fields
Simulation of monthly-mean source/basis functions
Network Optimization
Patra and Maksyutov, GRL, 29, 28 May 2002 CD=RSD2
Incremental Optimization of Surface Network (Case 1)
O basic[] Model Ensemble
Average uncertainty for TransCom-3 models
Total Source Covar C = CS; Average Unc = C/ No. of Region
Signal gradients at optimal stations
Model Dependent Uncertainty Reduction
1:UCB 2:UCI 3:UCI:s 4:UCI:b 5:JMA 6:MATCH:b 7:MATCH:c 8:MATCH:l 9:NIES:FRSGC10:NIRE-CTM11:RPN:SEF12:SKIHI13:TM214:TM315:CSU
Patra et al., Tellus, 55B(2), 2003
Signal gradients within NH regions
Occultation based satellite measurements (Case 2)
CD =
RS
D2 +In
st. Err. 2
Regional flux
uncertainty at several satellite
data precision
Satellite vs Surface data inversion
(inst err=0)
Ecosystem production
distribution: a justification for high resolution inverse model
The fossil fuel emission do not have seasonality.Oceanic sources and sinks are weaker compared to the land and less heterogeneous.
HiRes Inverse Model(42 Land and 11 Ocean Regions)
0 0
0
0
( )( ) ( )*2.0*
( )S S
S newC new C old
S old
Inverse Model Intercomparison
Optimal Networks:TransCom-3 vs HiRes
Comparison of average flux uncertainty
C_D=RSD^2
Satellite vs Surface Observations
TransCom-3 HiRes setup
C_D=RSD^2 + P^2
Multimodel Inversion of SOFIS data
Three model groups: 1. High, Low and Intermediate signal in the “global” middle-upper troposphere
High C_Ds compared to the signal – flat flux unc. with precision
Multimodel Inversion (no RSDs)
Is the use of RSDs (derived from NIES model only) in satellite data inversion justified?
Com
pari
son
s fo
r diff
ere
nt
lati
tude b
elt
s
Flux uncertainty reduction with surface network extension depends on vertical profiles near the surface
Diving the Tracom-3 region into four smaller regions do seem to pose a severe aggregation problem
The use to different ATM simulations effect the pseudo-satellite inversion results
Conclusions
An evaluation of an ecosystem model for studying CO2 seasonal cycle
Tests with an Ecosystem Model Outputs
Optimisation of SimCYCLE model parameters:
– 1. Q10 for respiration change with temperature
– 2. Leaf-level Photosynthetic Capacity (PC)
Both parameters were changed by -20%, -10%,
-5%, -3%, -1%, +1%, +3%, +5%, +10%, and +20%
SimCYCLE: SIMulation model of the Carbon cYCle in Land Ecosystem (Ito and Oikawa, Eco. Mod., 2002)
Flowchart of SimCYCLE model
Source: A
. Ito
Light-photosynthesis relationship with different maximum rate
Source: A
. Ito
Temperature-respiration relationship with different Q10
Source: A
. Ito
Procedure Monthly-mean SimCYCLE outputs are
transported using NIES/FRSGC model Signals are sampled at 8 background stations in
NH high latitude:• Alert, Greenland 82.45 297.48 210. • Zeppelin St., Norway 78.90 11.88 474. • Mould Bay, Canada 76.25 240.65 58. • Barrow, Alaska 71.32 203.40 11. • Atlantic Ocean, Norway 66.00 2.00 7. • Storhofdi, Iceland 63.25 339.85 100. • Baltic Sea, Poland 55.50 16.67 7. • Cold Bay, Alaska 55.20 197.28 25. • Mace Head 53.33 350.10 26. • Shemya Island, Alaska 52.72 174.10 40.
The simulations are then fitted to the Observed seasonal cycles of CO2
Fitting at Alert
Q10 is not so sensitive
Best fit at PSR=-10%
(Bad)Fitting
at Baltic Sea
Best fit at PSR=-10%
(Good)Fitting
at Mace Head
Good fit atPSR=-5%
Summary
Recommended:5 to 10% Q10 &-5 to -10% PSR
Thanks for your attention
TransCom-3 Modellers:
D. Baker (NCAR), P. Bousquet (LSCE), L. Bruhwiler (CMDL), Y-H. Chen (MIT), P. Ciais (LSCE), A. S. Denning (CSU), S. Fan (PU), I. Y. Fung (UCB), M. Gloor (MPI), K. R. Gurney (CSU), M. Heimann (MPI), K. Higuchi (MSC), J. John (UCB),R. M.Law (CSIRO), T. Maki (JMA), P. Peylin (LSCE), M. Prather (UCI), B. Pak (UCI), P. J. Rayner (CSIRO), J. L. Sarmiento (PU), S. Taguchi (NIAIST), T. Takahashi (LDEO),
C-W. Yuen (MSC)