Download Results from the Carbon Cycle Data Assimilation System (CCDAS)

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Results from the
Carbon Cycle Data Assimilation System
(CCDAS)
Marko Scholze1, Peter Rayner2, Wolfgang Knorr1
Heinrich Widmann3, Thomas Kaminski4 & Ralf Giering4
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4
FastOpt
Methodology sketch
CCDAS – Carbon Cycle Data Assimilation System
Misfit 1
Misfit to
observations
Forward Modeling:
Parameters –> Misfit
CO2 station
concentration
Atmospheric Transport
Model: TM2
Fluxes
Biosphere Model:
BETHY
Model parameter
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)
BETHY
(Biosphere Energy-Transfer-Hydrology Scheme)
lat, lon = 2 deg
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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
moisture dependant
Carbon balance:
average NPP =  average soil resp. (at each grid point)
t=1h
t=1h
t=1day
soil
<1: source
>1: sink
Methodology
Minimize cost function such as (Bayesian form):

T
1
1
-1
J (p )  p  p 0  C p 0 p  p 0  M (p )  D
2
2

T

C-1D M (p )  D

where


- M is a model mapping parameters p to observable quantities
- D is a set of observations
- C error covariance matrix
 need of  p J (adjoint of the model)
Calculation of uncertainties
• Error covariance of parameters
  J
C p    2
 p i, j
2



1
= inverse Hessian
• Covariance (uncertainties) of prognostic quantities
 
  T

X
(
p
)

X
(
p
)
C X 
 C p

p
p
Improvements and further applications since
Rayner et al. 2005
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Improved carbon balance
Improved spin-up of fast soil pool
Weaker prior constraint on parameters
Fate of terrestrial C under climate change
Including biomass burning
Uncertainties of prognostic (2000-2004) net
fluxes (still calculating)
Seasonal cycle of CO2 at Barrow, Alaska
The red line is the simulation of R05 while the green line
Is the improved simulation. Observations are shown by
diamonds.
Global atmospheric growth rate
Weighted sum of Mauna Loa (0.75) and
South Pole (0.25) concentrations
Parameters I
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3 PFT specific parameters (Jmax, Jmax/Vmax and )
18 global parameters
56 parameters in all plus 1 initial value (offset)
Parameters II
Relative Error Reduction
Some values of global fluxes
Value Gt C/yr
1980-2000
(prior)
1980-1999
R05
New
GPP
NPP
Fast Resp.
Slow Resp.
135.7
68.18
53.83
14.46
134.8
40.55
27.4
10.69
144.7
64.92
25.7
36.9
NEP
-0.11
2.45
2.32
Carbon Balance
Uncertainty in net carbon flux
1980-2000 gC / (m2 year)
net carbon flux 1980-2000
gC / (m2 year)
Terrestrial C cycling under climate change
Off-line model for prognostic slow pool
Some equations:
P
fs
R

rF  P
t 1. f s

Ta /10
R    Q10,s
NEP  NPP  rF  P
R
P: slow pool, rF: fast resp.,
fS: allocation fast to slow pool
: soil moisture
Ta: air temperature

Finding :
• Assume P(t = 1979)
• Adjust  to yield NEP(t = 1979-200)  iterative process
Initial slow pool size
Decadal mean global NEP 1980-2090
Red lines indicate simulations with climate change and black
lines with no climate change. Solid lines indicate simulations
with optimized parameters and broken lines with a priori parameters.
Including biomass burning
• A biomass burning climatology (monthly resolved) based on the
v. d. Werf data is used as a yearly basis function for the
optimisation
• Land is divided into the 11 TransCom-3 regions
• That means: 11 regions * 21 yr = 231 additional parameters
van der Werf et al., 2004, Continental-Scale Partitioning of fire emissions
during the 1997 to 2001 El Niño/La Niña Period. Science, 303, 73-76.
Parameters revisited
Parameter
Prior
No fire
Inc. fire
fR,leaf
ccost
fS

Q10,f
Q10,s
f
0.4
1.25
0.2
1.0
1.5
1.5
1.5
0.22
1.09
0.32
0.63
2.06
1.31
8.7
0.3
1.23
0.78
0.34
2.08
1.46
7.35
Global fluxes revisited
Mean value 1980-1999 Gt C/yr
Prior
No fire
Inc. fire
GPP
NPP
Fast Resp.
Slow Resp.
Fire
135.7
68.18
53.83
14.46
144.7
64.92
25.7
36.9
143.9
57.89
13.26
39.28
2.96
NEP
-0.11
2.32
2.39
Global growth rate revisited
Atmospheric CO2 growth rate
observed
no fire
with fire
Calculated as:
C GLO B  0.25C SPO  0.75C MLO
Interannual variability in biomass burning
estimate
4.50
4.00
3.00
2.50
2.00
1.50
1.00
0.50
0.00
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
19
99
Gt C/yr
3.50
year
blue bars
red bars
CCDAS
v. d. Werf et al.
Conclusions & Outlook
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Prognostic future net carbon flux under climate
change: more productive & more sensitive
More processes: fire (‘weak constraint’ as a first step)
More components: ocean (not-shown, but “free”
optimization indicates no big changes, ideally also
process-based)
Prognostic uncertainties on net carbon flux for 20002004: calculations finished by now..
More data: inventories, regional inversions and
budgets, satellite CO2 columns, isotopes, O2/N2