Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning

Abstract

Aerosol–cloud interactions (ACI) have a pronounced influence on the Earth's radiation budget but continue to pose one of the most substantial uncertainties in the climate system. Marine boundary-layer clouds (MBLCs) are particularly important since they cover a large portion of the Earth's surface. One of the biggest challenges in quantifying ACI from observations lies in isolating adjustments of cloud fraction (CLF) to aerosol perturbations from the covariability and influence of the local meteorological conditions. In this study, this isolation is attempted using 9 years (2011–2019) of near-global daily satellite cloud products in combination with reanalysis data of meteorological parameters. With cloud-droplet number concentration (Nd) as a proxy for aerosol, MBLC CLF is predicted by region-specific gradient boosting machine learning (ML) models. By means of SHapley Additive exPlanation (SHAP) regression values, CLF sensitivity to Nd and meteorological factors as well as meteorological influences on the Nd–CLF sensitivity are quantified. The regional ML models are able to capture, on average, 45 % of the CLF variability. Based on our statistical approach, global patterns of CLF sensitivity suggest that CLF is positively associated with Nd, particularly in the stratocumulus-to-cumulus transition regions and the Southern Hemispheric midlatitudes. However, Nd retrieval bias may contribute to non-causality in these positive sensitivities, and hence they should be considered upper-bound estimates. CLF sensitivity to estimated inversion strength (EIS) is ubiquitously positive and strongest in tropical and subtropical regions topped by stratocumulus and within the midlatitudes. Globally, increased sea-surface temperature (SST) reduces CLF, particularly in stratocumulus regions. The spatial patterns of CLF sensitivity to horizontal wind components in the free troposphere may point to the impact of synoptic-scale weather systems and vertical wind shear on MBLCs. The Nd–CLF relationship is found to depend more on the selected thermodynamical variables than dynamical variables and in particular on EIS and SST. In the midlatitudes, a stronger inversion is found to amplify the Nd–CLF relationship, while this is not observed in the stratocumulus regions. In the stratocumulus-to-cumulus transition regions, the Nd–CLF sensitivity is found to be amplified by higher SSTs, potentially pointing to Nd more frequently delaying this transition in these conditions. The expected climatic changes in EIS and SST may thus influence future forcings from the CLF adjustment. The novel data-driven framework, whose limitations are also discussed, produces a quantification of the response of MBLC CLF to aerosols, taking into account the covariations with meteorology.