Tuesday, 30 January 2024: 5:00 PM
328 (The Baltimore Convention Center)
The unprecedented escalation of extreme wildfires, driven in part by climate change, necessitates a profound understanding of their role as a major contributor of aerosols and trace gases in the atmosphere. The radiative impact of aerosols emitted from extreme wildfires are predominantly influenced by their long-range transport, highlighting the crucial role of the injection height of these emissions. Most climate models adopt fixed injection height approach, some opt for surface emissions or blending within the boundary layer. These methods, however, frequently overlook dynamic meteorological circumstances and the inconsistent intensity of extreme wildfires, both crucial in determining the injection height. This study endeavors to address this oversight by evaluating the radiative consequences of various plume injection schemes, focusing on Sofiev's method which incorporates atmospheric stability and wildfire radiative power in its calculations. The state-of-the-art AM4/LM4 climate model by GFDL serves as our primary tool for this examination. Our initial results expose that from 2003 to 2020, the use of Sofiev’s dynamic scheme reveals significant regional discrepancies in black carbon concentrations between schemes, especially in Congo & Boreal Asia regions. In studies of extreme wildfires, Sofiev’s calculated injection height exhibits roughly 10% less bias than fixed injection height when set against the median MISR plume heights. Using CALIOP’s aerosol extinction coefficient, it's evident that simulated biomass burning injection height primarily affects southern hemisphere Africa and northern Australia in various altitudes. Interestingly, clear-sky net radiative flux at the surface fluctuates by about ±5 W/m2 in these extreme wildfire hotspots, a direct result of alterations in injection height. This research underscores the imperative for more adaptable schemes to accurately forecast aerosol distribution from wildfires in an evolving climatic landscape.

