Predicting Fire Season Intensity in Maritime Southeast Asia with Interpretable Models

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Authors

William Stewart Daniels , Dorit M Hammerling , Rebecca R Buchholz , Helen M Worden , Fatimah Ahamad

Abstract

There have been many extreme fire seasons in Maritime Southeast Asia (MSEA) over the last two decades, a trend which will likely continue, if not accelerate, due to climate change. Fires, in turn, are a major driver of atmospheric carbon monoxide (CO) variability, especially in the Southern Hemisphere. Previous studies have explored the relationship between climate variability and fire counts, burned area, and atmospheric CO through regression models that use climate mode indices as predictor variables. Here we model the connections between climate variability and atmospheric CO at a level of complexity not yet studied and make accurate predictions of atmospheric CO (a proxy for fire intensity) at useful lead times. To do this, we develop a regularization-based statistical modeling framework that can accommodate multiple lags of a single climate index, which we show to be an important feature in explaining CO. We use this framework to present advancements over previous modeling efforts, such as the inclusion of outgoing longwave radiation (OLR) anomalies, the use of high resolution weekly data, and a stability analysis that adds weight to the scientific interpretation of selected model terms. We find that the El Nino Southern Oscillation (ENSO), the Dipole Mode Index (DMI), and OLR (as a proxy for the Madden-Julian Oscillation) at various lead times are the most significant predictors of atmospheric CO in MSEA. We further show that the model gives accurate predictions of atmospheric CO at leads times of up to 6 months, making it a useful tool for fire season preparedness.

DOI

https://doi.org/10.31223/X59320

Subjects

Atmospheric Sciences, Statistical Models

Keywords

Carbon Monoxide Variability, Fire Season Intensity, Climate Mode Indices, Statistical Modeling

Dates

Published: 2021-09-10 15:22

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
Data available in public Github repository.

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