Comment on egusphere-2025-2850

Abstract

Abstract. Regional inverse analyses of atmospheric trace gas observations quantify gridded two-dimensional surface fluxes by fitting the observations to simulated concentrations from a chemical transport model (CTM), usually by Bayesian optimization regularized by a gridded prior flux estimates. Regional inversions rely on the specification of background concentrations given by the boundary conditions (BCs) at the edges of the inversion domain, but biases in the BCs propagate to biases in the optimized fluxes. We develop a theoretical framework to explain how errors in the BCs influence the optimized fluxes as a function of the prior and observing system error statistics and of CTM transport. We derive a preview metric to estimate the BC-induced errors before conducting an inversion to support domain specification and a diagnostic metric to accurately quantify these errors after solving the inversion. We compare two methods to correct BC biases as part of an inversion, either directly by optimizing BC concentrations (boundary method) or indirectly by correcting grid cell fluxes outside the domain of interest (buffer method). We demonstrate that the boundary method is generally more accurate, physically grounded, and computationally tractable.

Authors 10

1. Hannah O. Nesser corresponding

   Jet Propulsion Laboratory · Harvard University

   Affiliation as printed

   Harvard University School of Engineering and Applied Sciences, Cambridge, MA, USA

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

2. Kevin W. Bowman

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

3. Matthew D. Thill

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

4. Daniel J. Varon

   Harvard University

   Affiliation as printed

   Harvard University School of Engineering and Applied Sciences, Cambridge, MA, USA

5. Cynthia A. Randles

   ExxonMobil (United States)

   Affiliation as printed

   ExxonMobil Technology and Engineering Company, Annandale, NJ, USA

   now at: Scepter, Inc., San Francisco, CA, USA

6. Ashutosh Tewari

   Amazon (United States) · ExxonMobil (United States)

   Affiliation as printed

   ExxonMobil Technology and Engineering Company, Annandale, NJ, USA

   now at: Amazon Supply Chain Optimization Technologies, Seattle, WA, USA

7. Felipe J. Cardoso‐Saldaña

   ExxonMobil (United States)

   Affiliation as printed

   ExxonMobil Technology and Engineering Company, Annandale, NJ, USA

8. Emily Reidy

   ExxonMobil (United States)

   Affiliation as printed

   ExxonMobil Technology and Engineering Company, Annandale, NJ, USA

9. Joannes D. Maasakkers Aachen SRON Space Research Organization Netherlands

   Space Research Organisation Netherlands

   Affiliation as printed

   SRON Space Research Organization Netherlands, Leiden, the Netherlands

10. Daniel J. Jacob

    Harvard University

    Affiliation as printed

    Harvard University School of Engineering and Applied Sciences, Cambridge, MA, USA