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