1. 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

2. 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

3. 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.

4. 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.

5. Carnegie-Ames-Stanford Approach (CASA)
=Light Use Efficiency
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.

6. Carnegie-Ames-Stanford Approach (CASA)
=Light Use Efficiency
Q10 = Increase in heterotrophic respiration for ΔT= +10 ºC.
Larger Q10 → Rheterotr is more sensitive to changes in temp.

7. Biome types in CASA EBF DBF MBNF ENF DNF BSG GSL BSB TUN DST AGR
A set of 2 parameters (ε and Q10) for each biome is used for optimization.
EBF: evergreen broadleaf forest BSG: Broadleaf trees and shrubs (ground cover)
DBF: deciduous braodleaf forest GSL: Grassland
MBNF: mixed broadleaf and needle leaf forest BSB: Broadleaf shrubs with bare soil
ENF: evergreen needle leaf forest TUN: Tundra
DNF: deciduous needle leaf forest DST: Desert
AGR: Agriculture

8. 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).

9. 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)

10. Step 1b: CASA NEP sensitivities
CASA NEE sensitivities approximated linear:
Figure: Seasonal cycles of CASA NEP sensitivities for each biome type. Total sensitivities for northern hemisphere.
Table: Parameters used to calculate first approximation of CASA NEP sensitivities.

11. 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).

12. 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.

13. Step 3: Minimization of mismatches
Minimize the following cost function, G(p) to reduce the mismatches between Cmod and Cobs:
Exact Cmod
approximate Cmod

14. Step 3: Minimization of mismatches
Cobs: obtained from GLOBALVIEW 2006 and several observation network by NIES.
Data by NIES fills the data-deficiency in Siberian region.
Parameter
Values used
σobs
Based on the GLOBALVIEW-2006 algorithm* σε
15 % of prior value
σQ10
15% of prior value

15. 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 CO2-observing stations.
Results of iterative calculation are presented for the case when all the data points are considered.

16. Result: Parameter Optimizations

17. Map of optimized ε
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).
Biomes near equator have smaller ε → Reduced NPP
Without Siberian data N S
With Siberian data
ε, gC/ MJ

18. Result: Uncertainty Reductions
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

19. Result: Improved Uncertainty Reductions
Ex: the enhancement of uncertainty reduction due to the use of data from Siberian stations in the optimization: Eε
Deciduous broad leaf forest (DBF), deciduous needle leaf forest (DNF) and Tundra (TUN): particularly good reductions of uncertainty
EQ10

20. Biome types in CASA EBF DBF MBNF ENF DNF BSG GSL BSB TUN DST AGR
EBF: evergreen broadleaf forest BSG: Broadleaf trees and shrubs (ground cover)
DBF: deciduous braodleaf forest GSL: Grassland
MBNF: mixed broadleaf and needle leaf forest BSB: Broadleaf shrubs with bare soil
ENF: evergreen needle leaf forest TUN: Tundra
DNF: deciduous needle leaf forest DST: Desert
AGR: Agriculture

21. 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).

22. 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.

23. 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.

24. Results: Effects on the misfit of seasonal cycle of CO2
Mprior>0.5
→better match with Cobs after optimization.
Mprior<0.5
→No dramatic reductions.

25. Effects of Iteration on annual NPP
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. N S

26. Conclusions and future work
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.

27. 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)