Preprints
https://doi.org/10.5194/egusphere-2023-1667
https://doi.org/10.5194/egusphere-2023-1667
08 Aug 2023
 | 08 Aug 2023

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

Yichen Jia, Hendrik Andersen, and Jan Cermak

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 nine 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 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. Global patterns of CLF sensitivity show that CLF is positively associated with Nd, in particular in the stratocumulus-to-cumulus transition regions and in the Southern Ocean. 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 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 of EIS and SST may thus influence future forcings from ACIs. The near-global ML framework introduced in this study produces a better quantification of the response of MBLC CLF to aerosols taking into account the covariations with meteorology.

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Yichen Jia, Hendrik Andersen, and Jan Cermak

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1667', Anonymous Referee #1, 29 Aug 2023
  • RC2: 'Comment on egusphere-2023-1667', Anonymous Referee #2, 30 Sep 2023
  • AC1: 'Comment on egusphere-2023-1667', Yichen Jia, 27 Oct 2023
Yichen Jia, Hendrik Andersen, and Jan Cermak
Yichen Jia, Hendrik Andersen, and Jan Cermak

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Short summary
We present a near-global observation-based explainable machine-learning framework to quantify the response of cloud fraction (CLF) of marine low clouds to cloud droplet number concentration (Nd) considering the covariations with meteorological factors. The CLF sensitivity to Nd and numerous meteorological factors as well as the dependence of the Nd–CLF sensitivity on the meteorology reveal the underlying physical mechanisms, providing a novel approach to constrain aerosol-cloud interactions.