Assimilating observed seasonal cycles of CO 2 to CASA model parameters

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. EBF DBF MBNF ENF DNF BSG GSL BSBTUNDSTAGR Biome types in CASA 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 Figure: Seasonal cycles of CASA NEP sensitivities for each biome type. Total sensitivities for northern hemisphere. • CASA NEE sensitivities approximated linear: 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 Exact Cmod approximate Cmod • Minimize the following cost function, G(p) to reduce the mismatches between Cmod and Cobs:

14. Step 3: Minimization of mismatches EBF DBF MBNF ENF DNF BSG GSL BSBTUNDSTAGR • 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.

15. Result: Parameter Optimizations

16. Map of optimized ε N S • 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 With Siberian data ε, gC/ MJ

17. 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

18. 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

19. EBF DBF MBNF ENF DNF BSG GSL BSBTUNDSTAGR Biome types in CASA 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

20. 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).

21. 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.

22. 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.

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

24. Effects of Iteration on annual NPP → This method is not applicable globally at this point… - More data (especially from BSG (broadleaf trees with shrubs on the ground cover) might help. S N

25. 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.

26. 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)