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
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%
+
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
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
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.
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
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)
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
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
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
Results: atm CO2 (also for validation)CO2 only
CO2 & SMOS
MLO ALT
Results: soil moisture (RMS)
CO2 & SMOSCO2 only
1 2 3 4 5 6 1 2 3 4 5 6
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
Results: CO2 fluxes (NPP)
CO2 & SMOSCO2 only
-3000 -2000 -1000 0 1000 2000 3000 -3000 -2000 -1000 0 1000 2000 3000
Validation: soil moisture at site level
CO2 & SMOSCO2 only
Relative flux (NEP & NPP) uncertainty reduction for 6 regions
Red: CO2 only Blue: CO2 & SMOS
Percen
tage
NEP NPP
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
