Chalmers Climate Dynamics Group

Rising global temperatures have escalated wildfire frequency, and the resulting fine-mode aerosol emissions pose a significant threat to the global ecosystem. However, how these emissions vary with tree genera remains largely unexplored. In this study, we introduce an innovative deep learning model to precisely retrieve satellite-based fine-mode aerosol optical depth (fAOD) data from 2003 to 2023. Our analysis, which aggregated data from 139 tree species into 23 genera across North America and Europe, reveals significant variations in the contributions of different tree species to wildfire-induced fAOD. Notably, the Pseudotsuga genus along the U.S. Northwest Coast showed a significant increase in wildfire-induced contributions to fAOD emissions during the winter months over the past two decades, with a 2.2-fold increase in its atmospheric warming effect in the mid-atmosphere. Furthermore, Picea (coniferous genus), predominantly planted in Canada, demonstrated an even greater increase in radiative forcing effects, with a more than 4-fold rise across all atmospheric layers compared to non-fire conditions. This research provides critical insights into the species-specific contributions to aerosol radiative forcing and highlights the potential long-term climatic consequences of intensified wildfire activity, especially in regions dominated by aforementioned tree genera.