Lecture 9: Assimilating Blackness Through Love and Friendship
Assimilating observed seasonal cycles of CO 2 to CASA model parameters
description
Transcript of Assimilating observed seasonal cycles of CO 2 to CASA model parameters
Assimilating observed seasonal cycles of CO2
to CASA model parameters
Yumiko NakatsukaNikolay Kadygrov and Shamil Maksyutov
Center for Global Environmental ResearchNational 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.
→ 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.
Optimizing Seasonal Cycle
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)
10
30),(
10
txT
heterotr QR
W)f(T,εIPARRGPPNPP plant
=Light Use Efficiency
• Q10 = Increase in heterotrophic respiration for ΔT= +10 ºC.
• Larger Q10 → Rheterotr is more sensitive to changes in temp.
Carnegie-Ames-Stanford Approach (CASA)
10
30),(
10
txT
heterotr QR
W)f(T,εIPARRGPPNPP plant
=Light Use Efficiency
EBF: evergreen broadleaf forest BSG: Broadleaf trees and shrubs (ground cover)DBF: deciduous braodleaf forest GSL: GrasslandMBNF: mixed broadleaf and needle leaf forest BSB: Broadleaf shrubs with bare soilENF: evergreen needle leaf forest TUN: TundraDNF: deciduous needle leaf forest DST: Desert
AGR: Agriculture
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.
CASA (non linear wrt p)
NIES Transport Model
Inverse calculation (Minimization of cost function)
NEE (1ºx 1º)dNEE/ dpi (1ºx 1º)
Cmod (2.5ºx 2.5º)dCmod/ dpi (2.5ºx 2.5º)
Output: Optimized pi’s and Cmod (liniarized approximation).
pi (11 x 2 parameters); i.e. ε and Q10 for each biome type
Outline of Parameter Optimization
Cobs(s, m)
• Initially: ε=0.55 gC/ MJ and Q10=1.50 globally.
→Total global NPP=56.5 Pg/ yr
Step 1a: CASA
Figure: NPP predicted by CASA and average of 17 models used in Potsdam Intercomparison (Cramer et al. 1999)
N S
Step 1b: CASA NEP sensitivities
•CASA NEE sensitivities approximated linear:
x ε Q10
x0 0.55 gC/ MJ 1.50
x1 0.5525 gC/MJ 1.525
x2 0.5475 gC/MJ 1.475
Table: Parameters used to calculate first approximation of CASA NEP sensitivities.
21
21
xx
)NEE(x)NEE(x
dx
dNEE
0
xx
Figure: Seasonal cycles of CASA NEP sensitivities for each biome type. Total sensitivities for northern hemisphere.
• 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)
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.
• Minimize the following cost function, G(p) to reduce the mismatches between Cmod and Cobs:
Step 3: Minimization of mismatches
222
i p
0i
d
2
obs
oceanfossilCASAobs )σ
pp(
σ
)ff)((fT̂(d)C)G(
pp
22
i p
0i
d
222
ii
iCASAioceanfossil0CASAobs
σ
pp]
dp
)(pdfT̂p)ff)((fT̂[(d)C
)G(obs
p
p
approximate Cmod
Exact Cmod
EBF
DBF
MBNF
ENF
DNF
BSG
GSL
BSB
TUN
DST
AGR
Step 3: Minimization of mismatches
-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.
Result: Parameter Optimizations
ε, gC/ MJ
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
N S
Result: Uncertainty Reductions
prior x,
post x,x σ
σ1R
-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
Result: Improved Uncertainty ReductionsE
εE
Q1
0
(noSib)R
(Sib)RE
x
xx
Ex: the enhancement of uncertainty reduction due to the use of data from Siberian stations in the optimization:
Deciduous broad leaf forest (DBF), deciduous needle leaf forest (DNF) and Tundra (TUN): particularly good reductions of uncertainty
EBF: evergreen broadleaf forest BSG: Broadleaf trees and shrubs (ground cover)DBF: deciduous braodleaf forest GSL: GrasslandMBNF: mixed broadleaf and needle leaf forest BSB: Broadleaf shrubs with bare soilENF: evergreen needle leaf forest TUN: TundraDNF: deciduous needle leaf forest DST: Desert
AGR: Agriculture
Biome types in CASA
EBF
DBF
MBNF
ENF
DNF
BSG
GSL
BSB
TUN
DST
AGR
CASA (non linear wrt p)
NIES Transport Model
Inverse calculation (Minimization of cost function)
NEE (1ºx 1º)dNEE/ dpi (1ºx 1º)
Cmod (2.5ºx 2.5º)dCmod/ dpi (2.5ºx 2.5º)
Output: Optimized pi’s and Cmod (liniarized approximation).
pi (11 x 2 parameters); i.e. ε and Q10 for each biome type
Outline of Parameter Optimization
Cobs(s, m)
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.
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0.1
0.2
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0.6
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0.8
0.9
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1
M(Prior)
M(P
oste
rior
)
3rd iteration, no parameters constrained
3rd iteration, Q10 of MBNF constraine
reference line (slope=1)
212
m obs
modelobspost/prior ]
(J)σ
J)(m,CJ)(m,C[
12
1(J)M
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.
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
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.
N S
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)