Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low resolution global inventories and the high resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.

Aerosol-cloud interactions are the most uncertain component of the Earth system, due to their major influence on cloud properties, and as a result, Earth’s energy budget. We need to better characterize these interactions, which requires constraining the cloud condensation nuclei (CCN) budget and disentangling the influences of aerosol microphysics from meteorology. Observational data are essential for evaluating and improving climate models, but airborne field campaigns have, until recently, been limited to a few (mostly continental) regions worldwide. CCN measurements over the remote ocean are scarce and only occur during extensive field missions involving airborne or ship-based measurements of limited spatial and temporal extent. Polar-orbiting satellite observations hold great promise for expanding the spatial coverage of observations to remote regions, however, it is currently not well understood to what extent these active and passive remote sensing observations can be considered adequate proxies for CCN. Recent literature make use of column integrated retrievals, such as aerosol optical depth or aerosol index, to characterize aerosol concentration and CCN, and the utility of vertically resolved optical properties from active sensors is only now becoming more fully understood. The NASA ACTIVATE, NAAMES, CAMP2EX and ORACLES field campaigns are particularly well suited for evaluating the skill of advanced satellite aerosol and cloud microphysical retrievals, given the comprehensive suite of airborne aerosol, cloud, and trace gas measurements, combined with airborne High Spectral Resolution Lidar (HSRL) and polarimetric imaging instruments that will be the basis for the next generation of space-based remote sensors. Here, we characterize the properties of aerosol and CCN from these NASA field campaigns and critically assess methods for deriving CCN and CCN proxies using visible and infrared satellite remote sensing retrievals.