Examining Correlations Between Wildfire Emissions and Black Carbon Deposition

• Coleman Walsh, Computer Science, University of Delaware

• David Rounce, Civil and Environmental Engineering, Carnegie Mellon University

Glaciers in Alaska are experiencing some of the most rapid mass loss rates of glaciers globally and are a significant contributor to sea level rise. Model projections of mass loss are a critical tool to understand and predict these impacts, but current models fail to include several notable melt feedbacks connected with extreme weather events like wildfires, which are worsening with climate change. PyGEM-EB is a glacier model developed to investigate the impacts of wildfire smoke deposition on glacier mass balance in Alaska, which requires deposition data for dust and black carbon from a reanalysis data product. Once calibrated, it will project future melt rates with wildfires as an additional input. We analyzed an ensemble of climate models and identified MERRA-2 and UKESM as the best candidates for deposition data, while GFED4 provided the most accurate historical fire emissions data. Moving forward, we will further process that data into a usable data product for the model, and further explore the corpus of CMIP6 datasets available for future projections of fire emissions.