Constraining terrestrial carbon fluxes by
assimilating the SMOS soil moisture product
into a model of the global terrestrial
biosphere
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
ICOS Science Conference
27-29 September 2016, Helsinki
Global Carbon Budget
8.3±0.4 PgC/yr
+
1.0±0.5 PgC/yr
90%
4.3±0.1 PgC/yr
Atmosphere
46%
2.6±0.8 PgC/yr
Land
28%
10%
2.5±0.5 PgC/yr
Oceans
26%
The case for data assimilation
Available Observations
C flux to land (Pg C/yr)
Large uncertainty from land
to predict C-balance (GCP)
Le Quéré et al. 2013
 Carbon Cycle Data Assimilation System
= ecophysiological constraints from forward modelling
+ observational constraints from inverse modelling
Previous results


Water and Carbon Cycles tightly coupled
Assimilation of FAPAR and atmospheric CO2 constrains water
fluxes
High
Plant water
stress
Low
Kaminski et al. (2012)
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
PFT composition
ecosystem parameters initial conditions
Climate
ysoil moist
J(X)
Terrestrial
ecosystem
model
parameters
( X)
Optimizer
J(X) and dJ(X)/X
M(X)
SMOS
soil moisture

yCO2
Adjoint model
dJ(X)/X
In‐situ
CO2
t
1
Cost function: J(x)   y  M (x) C 1  y  M (x)  (x  x p )t C p1 (x  x p )
2
y
 Need to define the error matrices Cy-1, Cp-1
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 only
Photosynthesis Eco. Respiration Phenology Soil Hydrology
& C balance
CO2 & SMOS
Photosynthesis Eco. Respiration Phenology Soil Hydrology
& C balance
Scholze et al. (2016)
Results: atm CO2 (also for validation)
MLO
CO2 only
CO2 & SMOS
ALT
Results: soil moisture (RMS)
CO2 only
1
2
3
4
CO2 & SMOS
5
6
1
2
3
4
5
6
Results: CO2 fluxes (NEP)
CO2 only
-400 -300 -200 -100
0
g C / m2
100
CO2 & SMOS
200
300 400
-400 -300 -200 -100
0
g C / m2
100
200
300 400
Results: CO2 fluxes (NPP)
CO2 only
-3000 -2000 -1000
CO2 & SMOS
0
1000 2000 3000
-3000 -2000 -1000
0
1000 2000 3000
Validation: soil moisture at site level
CO2 only
CO2 & SMOS
Relative flux (NEP & NPP) uncertainty
reduction for 6 regions
Percentage
Red: CO2 only Blue: CO2 & SMOS
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