Synoptic Weather Type Variability Explains Regional Cloud Cover Changes in ERA5 Reanalysis

Cloud cover variability remains one of the key uncertainties in regional climate trend detection. In this study, we analyze ERA5 reanalysis data over Central and Southeastern Europe to investigate the extent to which cloud cover changes are driven by variations in synoptic-scale atmospheric circulation patterns. Building on previous research that found no clear long-term trends in atmospheric instability indicators such as LFC (Level of Free Convection) or CAPE (Convective Available Potential Energy) due to large internal variability and synoptic modulation, we shift focus to the explanatory power of Grosswetterlagen-type weather regime classifications.

We apply a combination of dynamically derived weather pattern indicators—including lower- and mid-tropospheric vorticity, wind direction, and total column water vapor—to classify daily circulation patterns into six GWL clusters using hierarchical clustering. Using a gridded approach, we calculate monthly cloud type anomalies (low, medium, and high cloud cover) and assess their spatial correlation with the frequency of GWL cluster occurrences.

The results show clear and spatially coherent relationships between cloud anomalies and weather type frequency. In particular, high cloud cover anomalies correlate significantly with specific GWL clusters, especially in summer months and at southern latitudes, with spatial R² values exceeding 0.6 locally.

Additionally, the effect of GWL clusters on cloud anomalies varies between months, with some clusters showing consistent negative correlations across southern regions, and others influencing only specific cloud layers. Hovmöller diagrams of latitudinal cloud anomaly trends suggest periodic signals in cloud cover that are not well explained by ENSO or NAO indices alone but align well with GWL-based predictors.

Our findings highlight the importance of incorporating synoptic variability when analyzing cloud cover trends. Apparent long-term trends in cloudiness can in fact result from changes in the frequency of distinct synoptic regimes. This synoptic masking likely contributes to the limited detectability of trends in traditional convective instability metrics.

We conclude that variability in GWL-type weather regimes plays a crucial role in modulating monthly and seasonal cloud cover across the region, offering a mechanistic explanation for observed cloud changes and their spatial structure in ERA5. These results emphasize the need to account for synoptic-scale drivers when evaluating regional climate variability and trends.