Comment on egusphere-2026-569

作者：Yichen Jia, Hendrik Andersen, David Neubauer, Ulrike Lohmann, Corinna Hoose, Jan Cermak · 年份：2026 · DOI：10.5194/egusphere-2026-569-rc2 · 研究领域：Atmospheric sciences、Mathematics、Environmental science、Meteorology、Climatology、Econometrics、Statistical physics

Abstract. Marine low-level clouds substantially cool the climate and are sensitive to aerosols. Observations suggest strong cooling from cloud fraction (CF) adjustments, yet global climate models (GCMs) may underestimate this process. We apply explainable machine-learning with SHapley Additive exPlanations (SHAP) to the ICOsahedral Nonhydrostatic atmosphere model coupled with the Hamburg Aerosol Module (ICON-HAM) to quantify CF sensitivity to cloud droplet number concentration (Nd). We analyze six experiments combining two cloud cover parameterizations, an RH-only scheme (CC–RH) and a cloud-water-dependent scheme (CC–RH–LWC), with three prescribed lower bounds for Nd (Nd,min = 10, 40, 100 cm-3). The lnNd–CF relationships in ICON–HAM are more nonlinear than in satellite observations and exhibit saturation. We quantify sensitivities using piecewise linear regression (PLR) separating low- and high-Nd regimes. Sensitivities are stronger in the low-Nd regime, while inter-scheme and inter-Nd,min differences are negligible at high Nd. Unexpectedly, CC-RH-LWC shows weaker sensitivity than CC-RH at high Nd,min, despite explicit CF–cloud water coupling. We attribute this to a "headroom effect'': higher Nd,min suppresses autoconversion and enhances liquid water path in both schemes, but CC-RH-LWC converts this into higher mean CF, limiting headroom for aerosol-induced CF increases. These findings are robust across three PLR breakpoint strategies. Our results demonstrate that GCM CF resp...