Improving estimates of carbon sources, sinks, and atmospheric transport using global airborne data and a transformed isentropic coordinate

Estimating natural sources and sinks of greenhouse gases using atmospheric observations (e.g., at surface stations or from satellites) traditionally relies on inversion methods using atmospheric transport models (ATMs), but is limited by large errors in ATMs. Airborne observations, however, can simultaneously provide constraints on sources/sinks and atmospheric circulation through observed tracer spatial gradients and inventory changes. Recent studies (Jin et al., 2023, GBC; Jin et al., 2024, PNAS) have used airborne data from the HIPPO, ORCAS, and ATom campaigns to identify biases in ATMs and provide improved estimates of Southern Ocean (SO) seasonal air-sea CO₂ fluxes and global-scale seasonal air-sea O₂ fluxes. These applications build upon a box-model framework organized by a mass-indexed isentropic coordinate. We are now applying this framework to other compelling challenges in carbon cycle science. These include, for example, quantifying SO air-sea O₂ fluxes to gain insights into ocean biogeochemical controls on atmospheric CO₂, quantifying photochemical CH₄ loss rates in the remote atmosphere, quantifying seasonal Arctic CH₄ sources, and quantifying the latitudinal distribution of CO₂ fluxes. This framework also provides a new metric to systematically test ATMs globally. This presentation aims to summarize the use of global airborne observations and the isentropic coordinate for carbon cycle research, highlighting new results, future analysis pathways, current challenges, and strategies for future airborne campaigns.