Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion

Nitrous oxide is a potent greenhouse gas and ozone depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with non-Gaussian distributions and model errors, in order to examine the drivers of

5 the atmospheric surface growth rate. We show that both meteorology and emissions are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0° and 30° N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95% credible intervals) TgN yr−1 , comprising of 12.0 (11.2–12.8) TgN yr−1 from land and 5.2 (4.5–5.9) TgN yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained

10 for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the Northern Hemisphere extra-tropics that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterising the uncertainties in the system, for example in the prior emission fields, is crucial to be able to derive posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than

15 solely through expert judgment. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.

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