Interpretable models capture the complex relationship between climate indices and fire season intensity in maritime Southeast Asia

Fires, in turn, are a major driver of atmospheric carbon monoxide (CO) variability, especially in the Southern Hemisphere. Here we attempt to maximize the amount of CO variability that can be explained during fire season in MSEA (defined as September through December) via human-interpretable statistical models that use only climate mode indices as predictor variables and are trained on data from 2001 to 2019.

We expand upon previous work through the complexity at which we study the connections between climate mode indices and atmospheric CO (an indicator of fire intensity). Specifically, we present three modeling advancements. First, we analyze five different climate modes at a weekly timescale, increasing explained variability by 15% over models a monthly timescale.