Transcript Optimizations of CASA ecosystem model parameters： Fitting

Assimilating observed seasonal cycles of CO2
to CASA model parameters
Yumiko Nakatsuka
Nikolay Kadygrov and Shamil Maksyutov
Center for Global Environmental Research
National Institute for Environmental Studies
Japan
Outline
• Introduction and motivation for this study
• Brief description of CASA model
• Method of parameter optimization: general
overview
• Details of the optimization method
• Results
• Future plan
Objective
 Flux of CO2 simulated with terrestrial biosphere model (e.g. CASA) = important
prior information for inverse model estimation of regional CO2 fluxes based on
the measurements of atmospheric CO2.
 Our group’s objective is to make the best use of remotely-sensed CO2 data
(GOSAT (Greenhouse gases Observing SATellite) and OCO) → Dramatically
increase the spatial distributions of available CO2 observations.
 Accurate simulation of seasonal cycle of CO2 exchange by terrestrial biosphere
model is one of the important factors for reliable estimation of CO2 fluxes from
CO2 observations.
→ Goal of this study: to optimize the model parameters of a terrestrial
biosphere model (CASA) in order to provide a best fit to the observed seasonal
cycles of CO2.
Optimizing Seasonal Cycle
→ Initial attempt: Optimize the maximum light use efficiency (ε) and Q10
of Carnegie-Ames-Stanford Approach (CASA) model for each biome type
to the observed seasonal cycle of CO2.
Carnegie-Ames-Stanford Approach (CASA)
=Light Use Efficiency
NPP  GPP  R plant  IPAR  ε  f(T, W)
T ( x ,t ) 30
Rheterotr  Q10 10
 CASA simulates NPP as a
function of solar radiation limited
by water and temperature stress
→ε=Maximum light use efficiency
(light use efficiency when there
is no water or temperature
stress).
 Heterotrophic respiration is a
function of temperature.
Carnegie-Ames-Stanford Approach (CASA)
=Light Use Efficiency
NPP  GPP  R plant  IPAR  ε  f(T, W)
T ( x ,t ) 30
Rheterotr  Q10 10
• Q10 = Increase in
heterotrophic respiration for
ΔT= +10 ºC.
• Larger Q10 → Rheterotr is more
sensitive to changes in temp.
Biome types in CASA
EBF
DBF
MBNF
ENF
DNF
BSG
GSL
BSB
TUN
A set of 2 parameters (ε and Q10) for
each biome is used for optimization.
DST
AGR
EBF: evergreen broadleaf forest
DBF: deciduous braodleaf forest
MBNF: mixed broadleaf and needle leaf forest
ENF: evergreen needle leaf forest
DNF: deciduous needle leaf forest
BSG: Broadleaf trees and shrubs (ground cover)
GSL: Grassland
BSB: Broadleaf shrubs with bare soil
TUN: Tundra
DST: Desert
AGR: Agriculture
Outline of Parameter Optimization
pi (11 x 2 parameters); i.e. ε and Q10 for each biome type
CASA (non linear wrt p)
 NEE (1ºx 1º)
dNEE/ dpi (1ºx 1º)
NIES Transport Model
 Cobs(s, m)
 Cmod (2.5ºx 2.5º)
dCmod/ dpi (2.5ºx 2.5º)
Inverse calculation
(Minimization of cost function)
Output: Optimized pi’s and Cmod (liniarized
approximation).
Step 1a: CASA
•
Initially: ε=0.55 gC/ MJ and Q10=1.50 globally.
→Total global NPP=56.5 Pg/ yr
N
S
Figure: NPP predicted by CASA and average of 17 models used in Potsdam
Intercomparison (Cramer et al. 1999)
Step 1b: CASA NEP sensitivities
•CASA NEE sensitivities approximated linear:
x
x0
x1
x2
ε
0.55 gC/ MJ
0.5525 gC/MJ
0.5475 gC/MJ
Q10
1.50
1.525
1.475
Table: Parameters used to calculate first
approximation of CASA NEP
sensitivities.
Figure: Seasonal cycles of CASA NEP
sensitivities for each biome type. Total
sensitivities for northern hemisphere.
NEE(x1 )  NEE(x 2 )
dNEE

dx xx0
x1  x 2
Step 2: Atmospheric Transport Model (NIES99)
• Fluxes: Oceanic (Takahashi et al. 2002,
monthly), anthropogenic, and terrestrial
(CASA, monthly); 1ºx1º
• CASA NEP sensitivities; 1ºx1º
• Resolutions of NIES99: 2.5ºx2.5º, 15
layers (vertical), every 15 minutes.
• Driven by NCEP data (1997 to 1998 for
spin-up and 1999 for analysis).
Step 2: Atmospheric Transport Model (NIES99)
←Figure: Changes in global CO2
concentration at 500 mbar (in
ppm) associated with a unit
change in ε (left, in gC/MJ) and
Q10 (right) of evergreen needle
leaf forest (ENF) for the
indicated month.
Step 3: Minimization of mismatches
•
Minimize the following cost function, G(p) to reduce the mismatches between Cmod and Cobs:
Exact Cmod
G(p) 

d
2
 C obs (d)  Tˆ (f CASA (p)  f fossil  f ocean ) 

 


σ
obs


22
(
i
pi  p0 2
)
σp
approximate Cmod
G(p) 

d

 C obs (d)  [Tˆ (f CASA (p 0 )  f fossil  f ocean ) 


 obs



22
i
2
df
(p ) 
p i Tˆ CASA i ] 
22
dpi
  pi  p0

σp
i



Step 3: Minimization of mismatches
EBF
DBF
MBNF
ENF
DNF
BSG
GSL
BSB
TUN
DST
AGR
-Data from GLOBALVIEW 2006 and a network of stations operated by NIES are used.
-No observations from southern hemisphere: NIES99 has a known problem with seasonal cycle of
CO2 in Southern hemisphere.
-Parameter optimizations were performed with and without data from Siberian CO 2-observing
stations.
-Results of iterative calculation are presented for the case when all the data points are considered.
Result: Parameter Optimizations
Map of optimized ε
1. Generally, vegetation in high
latitude is known to have higher
ε. → This trend is better seen
with the results obtained with
Siberian data (especially for
tundra and deciduous needle
leaf forest).
2. Biomes near equator have
smaller ε → Reduced NPP
Without Siberian data
With Siberian data
ε, gC/ MJ
N
S
Result: Uncertainty Reductions
Rx 1
σ x, post
σ x, prior
-Uncertainties of posterior
parameters were reduced
particularly well for ENF
(evergreen needle leaf forest)
and AGR (agriculture)
- Biome types with suspicious
(i.e. unreasonably low) ε’s
(e.g. EBF, BSB, and DST)
show very low reductions of
uncertainties.
Biome Type
Eε
Result: Improved Uncertainty Reductions
 Ex: the enhancement of uncertainty
reduction due to the use of data from
Siberian stations in the optimization:
EQ10
Ex 
R x (Sib)
R x (noSib)
Deciduous broad leaf forest (DBF),
deciduous needle leaf forest (DNF)
and Tundra (TUN): particularly good
reductions of uncertainty
Biome types in CASA
EBF
DBF
MBNF
ENF
DNF
BSG
GSL
BSB
TUN
DST
AGR
EBF: evergreen broadleaf forest
DBF: deciduous braodleaf forest
MBNF: mixed broadleaf and needle leaf forest
ENF: evergreen needle leaf forest
DNF: deciduous needle leaf forest
BSG: Broadleaf trees and shrubs (ground cover)
GSL: Grassland
BSB: Broadleaf shrubs with bare soil
TUN: Tundra
DST: Desert
AGR: Agriculture
Outline of Parameter Optimization
pi (11 x 2 parameters); i.e. ε and Q10 for each biome type
CASA (non linear wrt p)
 NEE (1ºx 1º)
dNEE/ dpi (1ºx 1º)
NIES Transport Model
 Cobs(s, m)
 Cmod (2.5ºx 2.5º)
dCmod/ dpi (2.5ºx 2.5º)
Inverse calculation
(Minimization of cost function)
Output: Optimized pi’s and Cmod (liniarized
approximation).
Effects of Iteration on Parameters
-Q10 and ε of mixed broadleaf
and needle leaf forest (MBNF)
are fluctuating most
significantly.
-Q10 and ε of AGR are also
fluctuating by quite a bit.
Q10 and ε of a same biome
have same trend.
- dNEP/dε and dNEP/dQ10
have opposite trend
- Possible to achieve similar
Cmod with two different sets of
Q10 and ε when constraints
on parameters by
observations are insufficient.
Results: Iteration
- Q10 of MBNF was fixed at the
value obtained by the 1st
iteration.
→ Amplitudes of oscillation of ε
of MBNF is decreased but not
completely stabilized.
→ Correlation with other
biomes also possible.
- E.g. the amplitude of
oscillation of Q10 of AGR
dramatically reduced.
Results: Effects on the misfit of seasonal cycle of CO2
M post/prior (J) 
1
12
12
[
m
Cobs (m, J)  Cmodel (m, J) 2
]
σ obs (J)
1
3rd iteration, no parameters constrained
0.9
3rd iteration, Q10 of MBNF constraine
0.8
reference line (slope=1)
M(Posterior)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
Mprior<0.5
→No dramatic reductions.
0.5
0.6
M(Prior)
0.7
Mprior>0.5
0.8
1.1
→better0.9
match 1with Cobs
after optimization.
Effects of Iteration on annual NPP
N
S
Cases
Total NPP
Original CASA
56.5 Gt/ yr
Without iteration
43.53 Gt/yr
After 3 inversions
(no parameters
constrained)
51.75 Gt/yr
After 3 inversions (Q10
of MBNF constrained)
54.23 Gt/yr
→ This method is not applicable
globally at this point…
- More data (especially from BSG
(broadleaf trees with shrubs on
the ground cover) might help.
Conclusions and future work
Conclusions
 Maximum light use efficiency (ε) and Q10 of CASA were optimized for
each biome using observed seasonal cycles of CO2.
 Addition of Siberian data enhanced the reduction of uncertainty of the
optimized parameters.
 The observed latitudinal gradient of ε was obtained.
 Optimization is not working near the equator.
 The method is quite general and can be applied to other biosphere
and transport models very easily.
Future works and questions
 Does the oscillation stop if I keep the iteration?
 Try using different transport model (e.g. NICAM).
 It will be interesting to optimize other biospheric models.
Acknowledgement
 Shamil Maksyutov (CGER, NIES)
 Nikolay Kadygrov (CGER, NIES)
 Toshinobu Machida (CGER, NIES)
 Kou Shimoyama (now at Institute of Low Temperature Science at Hokkaido
University)