Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic

TL;DR

This study introduces a causal graph-based statistical method to accurately quantify the radiative effects of biomass burning aerosols over the South-East Atlantic, reducing observational biases and aiding climate model improvements.

Contribution

It presents a novel causal decomposition approach using satellite data to disentangle aerosol effects and identify sources of observational bias in radiative effect estimates.

Findings

BBAs cause a -2.5 W/m² shortwave cooling during fire season.

Radiative effects are decomposed into aerosol-radiation and aerosol-cloud interactions.

Biases from meteorological confounders can distort radiative effect estimates by up to 50%.

Abstract

Biomass burning aerosols (BBAs) from Southern Africa seasonally overlie the semi-permanent South-East Atlantic (SEA) stratocumulus deck, impacting the region's energy budget through complex aerosol-cloud-radiation-meteorology interactions. Climate model intercomparison initiatives, like the Aerosol Comparisons between Observations and Models (AeroCom), have highlighted the large inter-model variability for BBA radiative effects, especially over the SEA, due to parameterization of emission modeling and smoke properties. Observational constraints are needed to reduce these uncertainties, but correlative observational studies are typically affected by confounding meteorological influences. We propose a physically informed statistical approach, based on causal graphs applied to satellite observations, to disentangle BBA influences on shortwave radiation over the SEA and identify the main sources of statistical biases plaguing observational studies. We find that, during the fire season, BBAs cause a regional shortwave cooling of -2.5 W m$^{-2}$, which can be decomposed into equal contributions from three physical pathways: aerosol-radiation interactions (ARI), adjustments to ARI, and aerosol-cloud interactions (ACI). We also perform ablation experiments with graph variants to investigate the main sources of confounding - like large-scale winds, humidity-biased retrievals or spatial aggregation of data - and show that they result in biased radiative effect estimates (between -50 $\%$ and +15 $\%$). Once free of such biases, our derived causal estimates of smoke radiative effects can be used as observational constraints to improve climate models.