ICOS Science Conference 2020, Session 18

1. Spatial and temporal distribution of
European CH4 emissions from process-
based models and CTE-CH4 atmospheric
inverse model
Tsuruta Aki1
, Leif Backman1
, Tiina Markkanen1
, Maarit Raivonen2
, Antti
Leppänen2
, Sebastian Lienert3
, Fortunat Joos3
, Jurek Müller3
, Hugo
Denier van der Gon4
, Janssens-Maenhout Greet5
, European and global
atm. CH4 station PIs, Tuula Aalto1
17/09/2020 ICOS Science Conference 2020, Online

2. 2
Background
European emissions
●
Largest contribution from
agriculture and waste sectors
●
Second most from fossil fuel
production and use
●
Emissions from wetlands are
the largest natural source
Source categ EU
Map

3. 3
Background
European emissions: seasonal cycle
●
Large uncertainty in emissions from
wetlands
– Seasonal cycle amplitude (SCA) vary
significantly by different inversions and
process-based models.
– Monthly median from TD shows very
small SCA, while 95th percentile (upper
limit) show amplitude of approx. 0.7 Tg
CH4 month-1
– BU SCA tends to be higher than that of
TD
– Some BU models show high winter-
spring emissions, close to summer level
Average monthly European* fluxes during
2008-2017
Top-down (TD) Bottom-up (BU)
●
European domain: [35°N-73°N, 13°W-38°E]
●
Prognostic: models used their own internal approach
to estimate wetland area
●
Diagnostic: wetland surface areas from Wetland Area
Dynamics for Methane Modeling (WAD2M)
Solid line: median of model ensemble, Dotted lines: individual model
Shaded areas: between 5th and 95th percentiles

4. 4
Background
European emissions: spatial distribution
●
High anthropogenic emissions in
cities, agricultural areas → high in
central Europe
– TD estimates do not vary so
significantly between models
●
Biospheric emissions are high in
northern and north-east Europe
– Locations of hot spots vary much
between TD, BU-Prognostic and BU-
Diagnostic
– Rage in estimates is significantly
higher than that of anthropogenic
emissions
(Max. - Min.) / MeanMean
TD
Anthropogenic
TD
Biospheric
BU
Prognostic
BU
Diagnostic
TD
Anthropogenic
BU
Prognostic
BU
Diagnostic
TD
Biospheric
Mean and rage of CH4 emission estimates over
Europe, 2005-2017 average
*Mean of model ensembles, is calculated from 2005-2017 monthly data.
*Min. and Max. is minimum and maximum of model ensembles.

5. 5
● Optimize European CH4 using CarbonTracker Europe-CH4 atmospheric inverse
model
– Grid-based optimization over Europe: 1° x 1°, 3° x 2°, 6° x 4°
– Spatial correlation: 100-500 km
●
Use two distinct sets of wetland priors emissions
●
LPX-Bern v1.4 (global, orig. resolution 0.5° x 0.5° x monthly) (Lienert and Joos,
2018)
– Inundated wetlands, wet soil, soil sinks, peatlands
– Wetland/vegetation distributions calculated with DYPTOP model
●
JSBACH-HIMMELI (Europe only, orig. resolution 0.1° x 0.1° x daily) (Raivonen et al.,
2017)
– Mineral soils (can be sinks or sources), peatlands
– Wetland/vegetation distributions based on EU Corine
Methods
1x1, 100 km
3x2, 200 km
6x4, 500 km

6. 6
CarbonTracker Europe-CH4

7. 7
●
Atmospheric CH4 as constraints:
mainly NOAA + ICOS
observations over Europe
●
Good spatial coverage of
continuous stations over central
and northern Europe, especially
for late years
●
Continuous hourly data are pre-
processed before inversion:
– Filtered by taking only “good
quality” observations
– Afternoon 12-16 LT averages
– Night time 0-4 LT averages for
mountain sites
Atmospheric CH4 observations
Locations of atmospheric CH4 observational sites,
data available from 2000-2018

8. 8
●
Prior differences
– Largest in the northern peatland
area, and hot-spot in Scotland and
east Hungary
●
Posterior
– Northern peatland area: increase
from LPX-Bern v1.4, decrease from
JSBACH-HIMMELI → differences are
smaller than the prior
– Central and Southern Europe:
JSBACH-HIMMELI negative fluxes
are smaller in posterior, but an
increase in LPX-Bern v1.4 emissions
→ differences still remain/increase
from the prior
Results Biospheric (wetlands as net total) flux estimates and
their differences, 2005 mean
Posterior
Prior

9. 9
●
Anthropogenic emissions
– Same priors are used (EDGAR v5.0)
– Higher emissions in the inversion
using JSBACH-HIMMELI at central
Europe, i.e. compensating effect from
the biospheric emissions.
Results Anthropogenic emission estimates and their
differences, 2005 mean
Posterior
Prior
EDGAR v5.0 EDGAR v5.0

10. 10
●
Anthropogenic emissions
– Same priors are used (EDGAR v5.0)
– Higher emissions in the inversion
using JSBACH-HIMMELI at central
Europe, i.e. compensating effect from
the biospheric emissions.
●
Total emissions
– Differences are reduced for northern
Europe, and slightly in central Europe
– Differences of hot-spots (Scotland
and east Hungary) remains
– Differences in the north eastern
Europe remain
Results Total emission estimates and their differences,
2005 mean
Posterior
Prior

11. 11
Results
Biospheric emissions seasonal cycle
●
Europe, 60°N>
– Both priors and posteriors show small
winter emissions and clear emission
maximum in summer
– Seasonal max. in LPX-Bern v1.4 is
much later than of JSBACH-HIMMELI
→ after inversion, both has summer
max. in August.
– Emission magnitudes are close in
posterior, but still differ approx. twice.
●
Whole European domain
– Very different shape of seasonal
cycle in the prior and posterior
Monthly total biospheric fluxes, 2005
Whole European domain Europe, 60°N>
Solid lines: posterior, dashed lines: prior

12. 12
Conclusion
●
Seasonal cycle and spatial distribution of European emissions vary significantly by
different bottom-up and top-down estimates
– Wetlands are the main source of uncertainty
● CTE-CH4 could constrain the wetlands emissions for northern Europe to some
extent, but had difficulties in east Europe due to luck of atmospheric observation.
●
Soil sink is small as magnitude, hence is difficult for inversion to turn signs →
leads to compensating changes in other emission sources
●
Way forward
– Not all process-based models estimate wetland extent & distribution themselves, or take
their temporal changes into account. → Consider such as one uncertainty in inversion or a
parameter to be constrained
– Isotope observations (δ13
C-CH4) can be considered as additional source of constraint, but
they are still limited in numbers and frequencies, or yet synthesized between laboratories.

13. 13
Global atm. CH4 sites

14. 14
Mean flux scaling
factors, biospheric
Mean flux scaling
factors,
anthropogenic