With the atmosphere serving as an integrator for surface-atmosphere exchange processes across scales, monitoring and interpretation of atmospheric greenhouse gas (GHG) signals provides fundamental information on carbon, energy and water fluxes from natural and anthropogenic sources. Combining observations with modeling frameworks in process-based studies can reveal key mechanisms and drivers governing carbon-climate feedback processes, generating vital information to predicting their future evolution in a changing climate.
This session focuses on modeling frameworks (top-down and bottom-up) that investigate GHG exchange processes using observational platforms such as, localized surface networks (e.g. ICOS Atmosphere and Ecosystem, Fluxnet, NOAA,…), aircraft campaigns (e.g. MAGIC, COMET, ), active and passive remote-sensing missions (e.g., ECOSTRESS, OCO-2/3, TROPOMI, GOSAT).
We invite contributions on: 1) estimation of GHG budgets from global to local scales using inverse and direct methods (e.g. eddy-covariance fluxes, fossil fuel inventories, vegetation modeling); 2) examination of the role of errors (e.g. atmospheric transport, measurement errors) on estimated fluxes and associated GHG budgets; 3) innovative use of remote sensing (e.g. SIF), isotopes (e.g. 14CO2, 13CH4), & novel atmospheric tracers (e.g. NOx, carbonyl sulfide, APO) to improve attribution of carbon fluxes to specific processes, and 4) Observing System Simulation Experiments and Machine Learning approaches targeting the optimization of observing system constraints required to advance our understanding of the carbon cycle and carbon-climate feedbacks.