Combining top-down and bottom-up approaches to evaluate recent trends
and seasonal patterns in U.K. N2O emissions
Abstract
Atmospheric trace gas measurements can be used to independently assess
national greenhouse gas inventories through inverse modelling. Here,
atmospheric nitrous oxide (N2O) measurements made in the
United Kingdom (U.K.) and Republic of Ireland are used to derive monthly
N2O emissions for 2013-2022 using two different inverse
methods. We find mean U.K. emissions of
90.5±23.0 (1\(\sigma\)) and
111.7±32.1 (1\(\sigma\))
Gg N2O yr-1 for 2013-2022, and corresponding
trends of
-0.68±0.48 (1\(\sigma\))
Gg N2O yr-2 and
-2.10±0.72 (1\(\sigma\)) Gg N2O yr-2, respectively for the two inverse
methods. The U.K. National Atmospheric Emissions Inventory (NAEI)
reported mean N2O emissions of 73.9 Gg N2O
yr-1 across this period, which is
14-33% smaller than the emissions derived from
atmospheric data. We infer a pronounced seasonal cycle in N2O
emissions, with a peak occurring in the spring and a second smaller peak
in the late summer for certain years. The springtime peak has a long
seasonal decline that contrasts with the sharp rise and fall of
N2O emissions estimated from the bottom-up U.K. Emissions
Model (UKEM). Bayesian inference is used to minimize the seasonal cycle
mismatch between the average top-down (atmospheric data-based) and
bottom-up (process model and inventory-based) seasonal emissions at a
sub-sector level. Increasing agricultural manure management and
decreasing synthetic fertilizer N2O emissions reduces some of
the discrepancy between the average top-down and bottom-up seasonal
cycles. Other possibilities could also explain these discrepancies, such
as missing emissions from NH3 deposition, but these require
further investigation.