Presented at the 3rd ICOS Science Conference in Prague, Czech Republic, 11-13 Sep 2018.

1. Synthesising terrestrial and atmospheric data
into national-scale estimates of nitrous oxide
emissions from the UK
Peter Levy, Alistair Manning, Marcel van Oijen …
UK NERC GREENHOUSE/GAUGE project team
Centre for Ecology & Hydrology
UK Met Office
University of Edinburgh
University of Bristol

2. National-scale N2O fluxes
• Nitrous oxide is a powerful greenhouse gas
• Emitted by breakdown of agricultural fertiliser
• Bottom-up approach: IPCC Tier 1 / 2
• Flux = Ninput x Emission Factor
• Top-down approach
• from inverse modelling of atmospheric concentrations on tall-
tower network
• How to reconcile differences?
• How to use both methods to best constrain the
national budget?

3. Bottom-up vs Top-down discrepancies

4. Three complementary measurement methods
1. Flux chamber (mass balance)
~1 m2
2. Eddy covariance
~10000 m2
3. Atmospheric mass balance
~10-100 km2

5. Available data in UK
1. Flux chamber (mass balance)
>400 fertilistion events, 26000 flux obs
2. Eddy covariance
~10 fertilistion events, 7 sites
3. Atmospheric mass balance
5 tower sites

6. Tall-tower sites
NAME footprint
model

7. Bayesian Data Assimilation approach
• Posterior ~ Prior x Likelihood
• Enables us to combine different data sources in a
coherent framework
• All data sources contribute to likelihood
• Needs an underlying model for predicting flux

8. Modelling post-fertilisation N2O flux
Emulate process-based model DNDC

9. Modelling post-fertilisation N2O flux
Fit to measurements

10. Model
• IPCC Tier 1
• Double Lognormal model
• Lognormal distribution in space
• Lognormal form in time
• Add environmental effects
• Temperature, soil moisture

11. Prior – IPCC data (Stehfest 2006)

12. Model inputs

13. Prior predictions

14. Atmospheric inversion (blue)

15. Posterior predictions (purple)
i.e. assimilating flux chambers, eddy covariance and tall-tower
concentrations & atmospheric transport

16. Posterior distribution of emission factor
i.e. assimilating flux chambers, eddy covariance and tall-tower
concentrations & atmospheric transport

17. Conclusions
• Provides a coherent way of synthesising flux
chamber, eddy covariance and tall-tower data
• Provides rigorous uncertainty quantification
• Posterior emission factors are apparently higher
than pre-supposed
• (Prior emission factors are also apparently higher than commonly
assumed)