ICOS Science Conference 2020: Day 2, Plenary

1. Using SMOS soil moisture data combining CO2 flask samples to
constrain carbon fluxes during 2010-2015 within a carbon cycle
data assimilation system (CCDAS)
MOUSONG WU1,2, MARKO SCHOLZE2, THOMAS KAMINSKI3, MICHAEL VOSSBECK3, TORBERN
TAGESSON2
1. INTERNATIONAL INSTITUTE FOR EARTH SYSTEM SCIENCE, NANJING UNIVERSITY, NANJING, CHINA
2. DEPARTMENT OF PHYSICAL GEOGRAPHY AND ECOSYSTEM SCIENCE, LUND UNIVERSITY, LUND, SWEDEN
3. THE INVERSION LAB, HAMBURG, GERMANY

2. Background—Carbon cycle, climate change
Global Carbon Budget (GCP 2016)
• Terrestrial ecosystems play an important role in absorbing anthropogenic CO2 emissions
and in controlling atmospheric CO2 concentrations
• Large uncertainties exist in modeling terrestrial carbon fluxes
(Projections of anthropogenic CO2
uptake by process-based models (1850-
2100), IPCC 3rd assessment report)
1850 1950 2050

3. Literature reviews—why soil moisture?

4. Water and carbon coupling in ecosystem
(Scholze et al. 2016 ICOS meeting)
Assimilation of soil moisture
A two-year assimilation example=>Impacts of soil moisture assimilation
on NEP&NPP uncertainties reductions (Scholze et al. 2016, RSE)
CO2 only
SMOS+CO2
Hypotheses:
Soil moisture diversifies carbon cycle in different regions and different periods
Adding soil moisture constraints to terrestrial biosphere models can improve model representation of inter-
annual variability (IAV) of global carbon cycle
Scale matters in the study of soil moisture-carbon cycle feedbacks

5. CCDAS (Carbon Cycle Data Assimilation System)
Terrestrial biosphere
model (BETHY, Biosphere,
Energy Transfer and
HYdrology)
+
TM2 atmospheric
transport model
Meteo. (Precp.,
Tmax, Tmin, SWdown)
PFT and soil texture
map
Parameter and uncertainties
Optimized parameters and variables
Variational optimization
method based on
adjoint model
Observed soil moisture/VOD and
uncertainties
Observed eddy covariance data and
uncertainties
Observed CO2 concentrations and
uncertainties

6. Methods: SMOS soil moisture & CO2 data
SMOS soil moisture (Soil Moisture and Ocean Salinity, L3, CATDS, ~25 km, daily)
TCA (Triple Collocation Analysis, Scipal et al.,
2008) to calculate additional SMOS soil
moisture uncertainty (model uncertainty):
SMOS(passive, L-band) + ASCAT(active, C-band)
+ BETHY(forward modeling)
Unc = sqrt(square(data unc)+square(model
data))
CO2 observation sites (Scripps Institution of Oceanography)
(Wu et al., 2020 RSE)
(Wu et al., 2020 RSE)
• Scripps Institution of Oceanography (SIO) 8 observation
sites
• unc = max(diff(SIO, ESRL (Earth System Research
Laboratory, NOAA)), Keeling et al., 2001 fitting deviation)
• 2010-2015 observation period
• GlobalView 41 sites as validation

7. Data assimilation and model validation
(Scholze et al., 2016 RSE)
Cost function:
M(x) denotes the modeled variables, x parameters, d observations, x0 initial
parameters, C0,Cd for parameters and observations uncertainties,
respectively
• Optimization goal:
𝜕𝐽 𝑥
𝜕𝑥
→ 0
• Gradient-based optimization
algorithm, utilizes adjoint code
to compute gradient
• Parameter uncertainty:
𝐶 𝑝𝑜𝑠𝑡
−1
≈ 𝐻 𝑥
• Variable uncertainty:
𝐶 𝑦 = 𝑁′
𝑦 𝐶 𝑝𝑜𝑠𝑡 𝑁′
(𝑦) 𝑇
+ 𝐶 𝑦,𝑚𝑜𝑑
SIO
Data and settings:
2010-2015, global 2×2 degree: “prior”, “co2”, “smos+co2”
ASCAT(TU Wien): soil moisture validation
CARBOSCOPE(Rödenbeck 2015, MPI Jena): NEP validation
FLUXNET(Kumar et al., 2016): GPP validation
MODIS(Zhang et al., 2017): regional GPP validation
DGVMs(Le Quéré, 2017, GCB 2017): global NEP validation
GlobalView (NOAA): CO2 validation

8. Results: soil moisture at global and site scales
SMOS soil moisture assimilation can improve global soil
moisture simulation in most regions
Tundra, wetland, and croplands with irrigation show
large uncertainties
Soil moisture RMSE from different experiments
(Wu et al., 2020 RSE)
Site level soil moisture show improvements after
the assimilation of SMOS soil moisture
prior
co2
sm+co2

9. Results: CO2 concentrations at stations
Sites used for assimilation
Sites used for validation
Large improvement from “prior” experiment after the
assimilation of atmospheric CO2 concentrations and SMOS
soil moisture, both for assimilated sites and validation sites

10. NEP & GPP performance
RMSENEP (gC/m2/month):
26.0(prior), 24.0(co2), 21.3(smos+co2)
RMSEGPP (gC/m2/month):
81.6(prior), 81.4(co2), 69.3(smos+co2)
• NEP simulated by CCDAS shows larger differences to atmospheric inversion in Europe and Oceania
• Global GPP possibly overestimated by CCDAS, especially in Asia and South America
NEP: 2.16, 2.14, 1.79 PgC/yr, for “prior”, “co2”, “smos+co2”
GPP: 109, 95, 168 PgC/yr, for “prior”, “co2”, “smos+co2”
RMSE for NEP and GPP

11. GPP vs. SIF (GOME-2)
Correlation between SIF and GPP:
prior : 0.93
co2 : 0.95
smos+co2: 0.96
Regions with correlation increased
when soil moisture is assimilated, in
comparison with only CO2
assimilation

12. Inter-annual variability of NEP & GPP
Year
RMSENEP CCDAS vs. CARBOSCOPE RMSEGPP CCDAS vs. FLUXNET
“prior” “co2” “smos+co2” “prior” “co2” “smos+co2”
2010 35.3162 31.3267 23.8069 107.0076 105.2350 78.1522
2011 35.5045 30.6459 23.2497 104.6475 103.6549 79.6863
2012 42.4098 32.2532 23.0053 99.2730 96.9280 81.2455
2013 36.0176 32.4883 22.8798 85.5687 84.7727 82.8235
2014 33.3660 30.7491 23.1707 98.9762 96.9952 83.9701
2015 38.3588 33.2934 23.3738 / / /
Monthly mean NEP and GPP
NEP and GPP performance for each year
NEP simulation results at site scale
Better representation of IAV at both global
and site scales

13. NEP, GPP vs. ENSO indices
The boundary of continents defined in this study
(Wu et al., 2020 RSE)
An improved correlation with ENSO indices is
obtained for all continents, the 2015/2016 El
Nino impacts on regional carbon cycle are
detectedNEP correlation with ENSO indices at six continents

14. Assimilation of SMOS soil moisture and JRC-TIP
FAPAR(global)—a diagnosis for tropical regions
Assimilation of FAPAR results in slight degradation in RMSE, but much better
seasonality and amplitude in CO2 concentrations

15. Preliminary results
• Adding of JRC-TIP FAPAR in assimilation can reduce
soil moisture simulation errors in tropical regions
• Assimilation of FAPAR results in much better GPP
both at global and continental scalesRMSE for soil moisture in two experiments
before and after assimilating FAPAR

16. Take-home messages
• Remotely sensed soil moisture assimilation provides a better constraint on global carbon
cycle than assimilation of CO2 alone
• Assimilation of soil moisture improves inter-annual variability of carbon fluxes, resulting
in high correlations with ENSO indices
• Carbon fluxes in tropical regions remain uncertain, depicting larger uncertainties than
northern hemisphere
• Multiple sources of satellite observations have potential to constrain carbon cycle from
different aspects

17. Acknowledgements
• SNSB project, ESA SMOS+VEGETATION project
• CRUNCEP data processing by Johan Nord (Lund University)
• ASCAT data processing by Guillaume Monteil (Lund University)
• DGVMs results provided by Wenxin Zhang (Lund University)
• GOME-2 data processing by Songhan Wang (Nanjing University)
• Continuing funding support from China (Nantional Key Research and
Development Program of China, NSFC, etc)

18. Thank you
for
your attention
Questions?