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Constraining terrestrial carbon fluxes by assimilating the SMOS soil moisture product
into a model of the global terrestrial biosphere
ICOS Science Conference 27-29 September 2016, Helsinki
M. Scholze1, T. Kaminski2, S. Blessing3, W. Knorr1, M. Vossbeck2, J. Grant1* & K. Scipal4
1 Lund University, 2 Inversion Lab (previously at FastOpt),
3 FastOpt, 4 ESA, * now at Netherlands Space Office
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Global Carbon Budget
1.0±0.5 PgC/yr 10%
2.6±0.8 PgC/yrLand28%
4.3±0.1 PgC/yrAtmosphere
46%
2.5±0.5 PgC/yrOceans
26%
8.3±0.4 PgC/yr 90%
+
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The case for data assimilation
Carbon Cycle Data Assimilation System = ecophysiological constraints from forward modelling+ observational constraints from inverse modelling
Large uncertainty from landto predict C-balance (GCP)
C fl
ux to
land
(Pg
C/y
r)
Available Observations
Le Quéré et al. 2013
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Previous results
Kaminski et al. (2012)
Assimilation of FAPAR and atmospheric CO2 constrains water fluxes
Water and Carbon Cycles tightly coupled
Plant water stress
LowHigh
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Objective of this study
Assimilation of SMOS soil moisture observation together with atmospheric CO2 concentration: To quantify the added value of remotely sensed soil moisture
observations (as provided by SMOS) on constraining terrestrial C fluxes.
To assess the potential of a SMOS-based NEE product.
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C-cycle data assimilation system
OptimizerJ(X) and dJ(X)/X
SMOSsoil moisture
PFT compositionecosystem parameters initial conditions
parameters(X)
J(X) M(X)
ysoil moist
Terrestrialecosystemmodel
J(X) J(X)
Climate
J(x) 12
yM (x) t Cy1 yM (x) (x xp )t Cp
1(x xp )
Cost function:
Need to define the error matrices Cy-1, Cp
-1
In‐situCO2yCO2Adjoint model
dJ(X)/X
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CCDAS methodology Based on process-based terrestrial ecosystem model (BETHY) Optimizing parameter values (~100) based on gradient method Hessian (2nd deriv.) to estimate posterior parameter uncertainty Error propagation by using linearised model
Scholze et al. (2007)
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Site-scale experiments Substantial model development to simulate surface soil moisture Joint assimilation of SMOS daily SM data for 5 sites 5 member ensembles from different starting points All 5 converge to same minimum
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Global Experiments Coarse resolution, 2 years (2010/11) Running 3-member ensembles from different starting points Baseline: in-situ atm. CO2 (10 sites) concentrations only
Baseline + SMOS daily soil moisture with variance/mean scaling
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Results: process-parameters
CO2 & SMOSCO2 only
Scholze et al. (2016)
Photosynthesis Eco. Respiration& C balance
Phenology Soil Hydrology Photosynthesis Eco. Respiration& C balance
Phenology Soil Hydrology
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Results: atm CO2 (also for validation)CO2 only
CO2 & SMOS
MLO ALT
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Results: soil moisture (RMS)
CO2 & SMOSCO2 only
1 2 3 4 5 6 1 2 3 4 5 6
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Results: CO2 fluxes (NEP)
CO2 & SMOSCO2 only
-400 -300 -200 -100 0 100 200 300 400
g C / m2
-400 -300 -200 -100 0 100 200 300 400
g C / m2
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Results: CO2 fluxes (NPP)
CO2 & SMOSCO2 only
-3000 -2000 -1000 0 1000 2000 3000 -3000 -2000 -1000 0 1000 2000 3000
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Validation: soil moisture at site level
CO2 & SMOSCO2 only
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Relative flux (NEP & NPP) uncertainty reduction for 6 regions
Red: CO2 only Blue: CO2 & SMOS
Percen
tage
NEP NPP
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Conclusions• CCDAS combines process understanding with observations,
provides an integrated view on global carbon cycle and delivers elaborated products based on ICOS data (among others)
• Site scale experiments to test and validate assimilation system => demonstrates capabilities of CCDAS to assimilate SM
• First global experiments assimilating remotely sensed SM and atm. CO2 simultaneously
• Significant added value (unc. reduction) compared to CO2 only• Further work using SMOS observations together with atm. CO2:
– assimilate the full 6 years of SM data at high resolution– extend the assimilation system to additionally include
microwave Vegetation Optical Depth data as a proxy for above ground biomass (vegetation water content) over various land-cover types