Cyclones’ tracking algorithms are commonly used to study the life cycle of extratropical cyclones and their evolution with climate change in both reanalyses and climate models. Such a framework allows the computation of valuable diagnostics such as maps of genesis, lysis, and track densities, statistical analyses or also centered-cyclone composites. Cyclone phase space diagrams, i.e representations of a cyclone in a multidimensional parameters’ space, can be also diagnosed and are particularly illuminating. This has been extensively investigated for tropical cyclones, but much less in the context of extratropical cyclones. The cyclone phase space diagrams of Hart (2003) have shown to be useful to study tropical cyclones and particularly extratropical transitions, but they are not well adapted to study extratropical cyclones themselves and to separate them into different categories, as we will illustrate with different individual cases of mid-latitude and Arctic cyclones. Our aim is to present a new set of cyclone phase space diagrams that are as simple as possible and can be easily computed from climate model or reanalyses outputs. It includes a refinement of the cyclone core temperature of Hart (2003) that takes into account the non-axisymmetric character of extratropical cyclones, the thermal asymmetry parameter of Hart (2003), and additional parameters, related to baroclinic growth such as the baroclinic conversion rate, the baroclinicity, and the vertical tilt of baroclinic disturbances. Such diagrams will be presented with a large set of extratropical cyclones tracked with the ‘TempestExtremes’ algorithm applied to ECMWF-ERA5 reanalysis datasets. A few statistical analyses conducted with the diagrams will be presented as well. 

How to cite: Besson, M., Besson, G., and Sébastien, F.: Cyclone phase space diagrams dedicated to extratropical cyclones studies, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-485, https://doi.org/10.5194/ems2025-485, 2025.

Seasonal climate forecasts offer valuable insights into expected climate conditions up to several months in advance, supporting decision-making in sectors such as agriculture, energy, and disaster management. However, seasonal forecasts still struggle to capture the spatial and temporal variability of atmospheric circulation, particularly in regions prone to climate extremes. Improving their reliability requires better methods for assessing how well they reproduce key features of synoptic-scale dynamics. Classifying synoptic patterns allows for a more comprehensive statistical assessment of atmospheric circulation, enabling the identification of dominant weather regimes and their evolution over time.

In this study  we developed an updated approach to classify weather patterns in Eastern Mediterranean using z500 geopotential heights and vertical velocity data. The classification method identifies five anticyclonic and seven cyclonic types. Our findings indicate that during the cold season, cyclonic and anticyclonic weather types occur at similar frequencies, whereas anticyclonic types dominate in the summer. To evaluate the ability of seasonal forecast models to predict synoptic patterns up to three months in advance, we apply the classification to seasonal hindcast simulations produced by the state-of-the-art Advanced Research WRF (WRF-ARW) model for the period 1981-2016. The classified weather types from the seasonal model outputs are compared against those derived from ERA5 to assess forecast skill, biases, and limitations in capturing large-scale atmospheric circulation. Preliminary findings indicate that synoptic conditions in the study region can be reliably predicted three months in advance (lead time 3), with the seasonal model achieving an accuracy rate of over 75% in estimating the likelihood of anticyclonic or cyclonic conditions. This classification-based approach provides a process-oriented  alternative for evaluating seasonal forecast performance.

Acknowledgments

The work was supported by PREVENT project. This project has received funding from Horizon Europe programme under Grant Agreement No: 101081276.

How to cite: Papadopoulos Zachos, A., Velikou, K., Manios, E. M., Tolika, K., and Anagnostopoulou, C.: Improving Seasonal Forecasts: Evaluating Synoptic Pattern Predictability Using Weather Type Classification, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-26, https://doi.org/10.5194/ems2025-26, 2025.

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.

How to cite: Soós, V. and Breuer, H.: Synoptic Weather Type Variability Explains Regional Cloud Cover Changes in ERA5 Reanalysis, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-422, https://doi.org/10.5194/ems2025-422, 2025.

This study, using a dataset consisting of in-situ daily temperature records and ERA5 reanalysis data, analyses the extreme temperature events occurred in the Apennine Mountains (Italy) over the period 1961-2022. The available dataset has been employed to meet the following two main goals: i) to assess the linear trends of the heat waves (hereafter, HWs) and warm spells in terms of number of events, duration, and severity by applying the Seasonal Kendall test; ii) to shed light, on a seasonal basis, on the synoptic climatology of such events. From the linear trend analysis, it emerged that the Apennines, as many other regions of the world, experienced an increasing trend in extreme temperature episodes. In the last 30-year reference period (1991-2020), the number of regional extreme heat events increased by 134% in summer and 102% in spring compared to the 1961-1990 period, while in winter and autumn the increase in warm spells is smaller and generally not statistically significant in terms of duration and severity. Using Principal Component Analysis and k-means clustering, several synoptic-scale patterns that can trigger extreme hot conditions in the study area have been identified. In the last 30-year period, notable changes in the synoptic climatology of extreme heat events have been detected in summer, as well as in spring and autumn. Specifically, in summer the large-scale patterns characterised by a cyclonic area over the eastern North Atlantic (over the British Islands or off the coasts of Ireland) and by a ridge from North Africa to the eastern Europe provide a larger relative contribution to the total number of HWs. Such patterns promote the advection, over the study area, of hot subtropical air masses, mainly at mid-tropospheric levels. In addition, a relationship between HWs that occurred in the Apennines and the Sea Surface Temperature (SST) anomalies in the North Atlantic has been identified. In the days preceding the HWs, a negative SST anomaly in the eastern North Atlantic is generally observed. This feature can be considered as one of the factors that trigger, amplify, and prolong summer extreme heat events in the Central Mediterranean.

Such results supply new insights about the links between extreme heat events in the central Mediterranean area and large-scale atmospheric types as well as useful tools to improve the predictability of HWs and warm spells at both meteorological and climatological time scales. Finally, this study demonstrates the importance of having reliable and quality-checked long-term climatological time series in mountainous areas. If these sites were properly managed guaranteeing sufficient spatial and temporal coverage, it would be possible to carry out capillary studies to investigate how climate change affects high altitudes and, therefore, how it impacts mountain ecosystems and human activities.

How to cite: Capozzi, V., Di Bernardino, A., and Budillon, G.: Synoptic climatology of extreme heat events in the Apennines (Italy), EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-511, https://doi.org/10.5194/ems2025-511, 2025.

Changes in atmospheric circulation are key drivers of regional climate variability and long-term trends in the midlatitudes, particularly across Europe. This study investigates the concurrence and temporal trends in the frequency and persistence of atmospheric circulation types (CTs) over Central Europe for the period 1951–2020. We apply five distinct classifications: the large-scale Grosswetterlagen (GWL), and four regional-scale schemes—Lityński (LIT), Niedźwiedź (TN), Péczely (PEC), and the objective Jenkinson–Collinson (JCT) classification adapted to the study area.

The analysis is conducted year-round as well as for the four meteorological seasons, offering a comprehensive perspective on synoptic-scale variability and its evolution over time. Results show substantial methodological differences between individual classification schemes. To assess the consistency and co-occurrence of CTs across methods, we employ a suite of statistical measures including contingency tables, mutual probabilities, Cramér’s V, Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). These measures highlight both areas of agreement and systematic discrepancies, underscoring the risk of overinterpretation when relying on a single classification.

Despite differences, some robust trends emerge: notably, a consistent increase in westerly types and decline in meridional flow patterns during winter, in agreement with a strengthened and eastward-shifted North Atlantic Oscillation (NAO). In contrast, summer and autumn display weaker or more inconsistent trends. Persistence analysis also reveals classification-dependent behavior, with some methods overestimating the duration of synoptic types.

Our findings support earlier research emphasizing the necessity of multi-classification approaches in climatological analyses. We advocate for using classification ensembles to improve the robustness of circulation trend detection, especially in studies related to climate change attribution, regional modelling, and statistical downscaling. The results contribute to refining synoptic-scale diagnostics and improving the interpretability of long-term atmospheric circulation changes over Europe.

How to cite: Wypych, A., Ustrnul, Z., Łanecki, F. O., Řehoř, J., and Mika, J.: Robust Assessment of Atmospheric Circulation Trends over Central Europe Using a Multi-Classification Approach, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-600, https://doi.org/10.5194/ems2025-600, 2025.

Understanding the large-scale variability of atmospheric circulation over Europe has always been a key challenge for interpreting weather extremes and climate trends. The classical Euro-Atlantic weather regimes, usually defined by clusters of geopotential height anomalies, put the emphasis primarily on Western Europe and the connection to the North Atlantic Oscillation. However, they often fail to explain much of the variability of basic meteorological variables in Central Europe and their spatial patterns across the entire European continent. Therefore, we have implemented a new, machine learning-based approach to define the main modes of European circulation variability. Mean daily fields of 500 hPa geopotential height anomalies were calculated from the ERA5 reanalysis for domain centered unusually towards Central Europe (40°W–60°E, 30°–80°N) for the 1981–2023 period. Then 300 individual Self-Organizing Map (SOM) neural networks were trained on this daily dataset, using a topology of 25×25 nodes with 248 training cycles (epochs) and subsequently combined into a single SOM model, by averaging weights in individual nodes. The entire model was clustered using Wards hierarchical clustering method and tested by silhouette analysis, which lead to identification of four main modes of European circulation variability: Atlantic blocking (ABL), Continental blocking (CBL), Southern zonal track (SZT), Northern zonal track (NZT). Then, one mode was assigned to each day, using calculation of the Best Matching Unit (BMU), representing the SOM node with the highest similarity (smallest Euclidean distance) to the given day, including days outside the SOMs’ training period, therefore the time series was extended to the 1940-2023 period. Employing fields from ERA5 and E-OBS, it was discovered that each mode represents a very distinctive spatial patterns of weather conditions over the entire Europe. In particular, SZT/NZT represent very dry/wet patterns over Central and Western Europe (documented by change in soil moisture), even though both modes occur more often during positive NAO phase. We believe these new modes of European circulation variability have the potential to be a useful tool in dynamic climatology and in season-to-season weather forecast.

How to cite: Řehoř, J., Jones, P. D., Brázdil, R., and Trnka, M.: Modes of European Circulation Variability from a Central European Perspective, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-622, https://doi.org/10.5194/ems2025-622, 2025.