Interpretable Models Capture the Complex Relationship Between Climate Indices and Fire Season Intensity in Maritime Southeast Asia. Issue 17 (5th September 2022)

• Record Type: Journal Article
• Authors: Daniels, William S.; Buchholz, Rebecca R.; Worden, Helen M.; Ahamad, Fatimah; Hammerling, Dorit M.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 17(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-05
• Subjects: carbon monoxide -- climate modes -- climate connections -- statistical modeling -- Maritime Southeast Asia -- biomass burning; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD036774
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: There have been many extreme fire seasons in Maritime Southeast Asia (MSEA) over the last two decades. 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. Second, we accommodate multiple lead times for each climate mode index, finding that some indices have very different effects on CO at different lead times. Finally, we model the interactions between climate mode indices at a weekly timescale, providing a framework for studying more complex interactions than previous work. Furthermore, we perform a stability analysis and show that our model for the MSEA region is robust, adding weight to the scientific interpretation of selected model terms. We believe the relationships quantified here provide new understanding of a significant mode of variability in MSEA, specific lead times for use in forecasts, and a method for evaluating climate mode‐CO relationships in climate model output. Key Points: We explain 70% of atmospheric carbon monoxide variability (an indicator of fire intensity) at a weekly timescale using climate mode indices Our statistical model finds lead times for five climate mode indices (or their proxies) that most influence atmospheric carbon monoxide The impact of certain indices on atmospheric carbon monoxide changes as their lead time in the model increases