Decomposing Cloud Trends: A Satellite Perspective on Size–Occurrence Compensation and Cloud Overlap Biases in Low-Level Clouds

Long-term satellite observations of cloud amount provide critical constraints on climate sensitivity and are essential for reducing uncertainties in climate projections. Different cloud types exert distinct radiative effects and exhibit divergent responses to warming. Yet, their relative contributions to trends in total cloud amount remain inadequately quantified. This study analyzes 17 years (2003–2019) of MODIS observations and reveals that, although global and zonal (60°S–60°N) mean trends in total cloud amount are weak, significant local signals persist. Trend decomposition demonstrates that free-tropospheric cloud occurrence dominates the magnitude of total cloud amount trends, whereas low-level cloud (LowC) size primarily dictates their spatial distribution. Nevertheless, this LowC size signal is substantially suppressed by an opposing LowC occurrence signal, resulting in a significant decoupling between LowC and total cloud amounts. To address the inherent limitations of passive satellite retrievals, we further utilize ERA5 reanalysis data. This data further indicates that approximately half of the trend in LowC occurrence arises from cloud overlap biases unresolved by passive satellite sensors. Correcting for this bias should yield a coherent shift in LowC amount trends over most oceans, suggesting that passive satellite retrievals may have systematically obscured the response of low-level clouds to recent climate change.