Examining the Wildfire Aerosol-Atmosphere Interactions and Impacts on Radiative Energy Budget

Fire is one important component of the earth system. It can greatly affect the Earth’s climate, air quality, and public health and welfare. Because of the climate change, more intensive wildfires with higher plume injection heights are expected to occur.

Despite great importance, the magnitude and even the sign (warming or cooling) of wildfire smoke on the Earth’s climate are still unknown. To address these issues, the project will predict the fire intensity and plume injection height in a timely manner as well as quantify the impacts of fire smoke on the Earth’s energy budget and climate, which are critically important for the policy makers and stakeholders.

The improved estimation of fire radiative effects will enhance our ability to predict relative warming/cooling effects on the Earth system and greatly benefit teaching and mentoring of undergraduate and graduate students at the Texas A&M University.

This project will investigate the importance of two fire properties, i.e., plume injection height and absorption of dark brown carbon (dBrC) for the radiative effects of biomass burning aerosols (BBAs). For that, the researchers will identify the dominant factors driving the fire intensity distribution which will affect both plume injection height and dBrC absorption.

The project will focus on the three scientific issues: (1) radiative effects of BBAs associated with fire plume injection height and dBrC absorption; (2) controlling factors that determine the wildfire intensity distribution; and (3) potential positive feedback between extreme wildfires (wildfires with high intensity) and climate change (e.g., through diminished polar ice/snow cover). To address these scientific issues, the researchers will: (1) incorporate the existing modeling efforts of plume-rise model and dBrC absorption into the NCAR Community Earth System Model version 2 (CESM2).

By conducting a suite of model experiments, the researchers will quantify the uncertainties in estimating the radiative effect of BBAs associated with plume injection height and dBrC absorption; (2) build a machine learning (ML) emulator fed by observed fire intensity and modeled meteorological and land fields as the predictors to identify the controlling factors that determine the wildfire intensity distribution; (3) conduct fully-coupled simulations using CESM2 with ML emulated fire intensity, plume injection height and dBrC absorption, to test the positive feedback hypothesis between fire intensity and polar ice/snow cover.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.