Using harmonized radon (222Rn) observations in a dual-tracer inversion to estimate CH4 emissions

Radon (²²²Rn) is an ideal tracer for studying atmospheric mixing and evaluating atmospheric transport models because its lifetime is comparable to the timescale of atmospheric ventilation. A persistent challenge for atmospheric transport models is accurately representing vertical mixing especially under stable boundary layer conditions. Errors in this representation directly propagate into biases in greenhouse gas flux estimates derived from inverse modelling. Here, we demonstrate the potential of consistent, harmonized atmospheric ²²²Rn observations to assess transport model performance and improve methane (CH₄) emission estimates using a joint CH₄-²²²Rn inversion framework.

To this end, we compiled and harmonized ²²²Rn activity concentration measurements – alongside concurrent CH₄ observations – from multiple atmospheric sites across central Europe. Using the Stochastic Time-Inverted Lagrangian Transport (STILT) model and prior flux estimates, we calculated the differences between observed and modelled concentrations (the so-called model-data mismatches, MDMs) for both ²²²Rn and CH₄. We found significant correlations between the MDMs of ²²²Rn and CH₄, indicating shared errors in their simulations, which primarily originate from the transport model’s inadequate representation of vertical mixing. To exploit this information, we conducted a dual-tracer CH₄-²²²Rn inversion using the CarboScope-Regional inversion framework. We present the latest CH₄ flux estimates from this dual-tracer approach and compare them with results from a single-tracer CH₄-only inversion without additional ²²²Rn information. Finally, we assess how biases and uncertainties in ²²²Rn observations and ²²²Rn flux maps propagate into the dual-tracer inversion and affect the derived CH₄ flux estimates. Our findings highlight the critical need for harmonized, spatially and temporally extensive ²²²Rn data, as well as accurate ²²²Rn flux maps, to fully leverage the dual-tracer approach and improve the reliability of CH₄ flux estimates.