消除相关性迷雾：生物燃烧气溶胶辐射效应的因果分解：南大西洋地区
Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic
AI总结：
研究通过因果图方法分析生物燃烧气溶胶对南大西洋短波辐射的影响，发现其辐射冷却效应可分解为三个路径，并提出消除观测偏差以改进气候模型。
摘要：
生物燃烧气溶胶（BBAs）从南非季节性覆盖南大西洋（SEA）层积云层，通过复杂的气溶胶-云-辐射-气象相互作用影响该地区能量预算。气候模型比较计划，如气溶胶观测与模型比较（AeroCom），已突出指出BBAs辐射效应在SEA存在较大的模型间变异性，尤其是在气溶胶排放建模和烟雾属性参数化方面。观测约束对于减少这些不确定性至关重要，但相关性观测研究通常受气象影响干扰。我们提出一种基于因果图的统计方法，应用于卫星观测，以分离BBAs对SEA短波辐射的影响，并识别观测研究中主要的统计偏差来源。我们发现，在火灾季节，BBAs导致区域短波冷却-2.5 W m$^{-2}$，可分解为三个物理路径的等贡献：气溶胶辐射相互作用（ARI）、对ARI的调整和气溶胶云相互作用（ACI）。我们还通过图变体进行消融实验，研究主要干扰源，如大尺度风、湿度偏置检索或数据空间聚合，并显示这些会导致辐射效应估计偏差（-50%至+15%）。一旦消除这些偏差，我们推导出的因果估计的烟雾辐射效应可作为观测约束来改进气候模型。
英文摘要：
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.
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