The Tibetan Plateau, the world’s highest plateau, is known as the ‘Third Pole’ on earth. Evaluating the impact of Tibetan Plateau topographic forcing on Arctic stratospheric circulation and ozone is a crucial step toward achieving a deeper comprehension of the role of topography uplift in driving paleoclimatic evolution and stratospheric dynamics and chemistry. Through topography experiments in the Whole Atmosphere Community Climate Model version 6, this study reveals that the Tibetan Plateau’s topographic influence on Arctic stratospheric circulation and ozone is strongest in winter, followed by spring and autumn, and weakest in summer. During winter, the Tibetan Plateau can generate more quasi-stationary planetary waves that propagate into the Arctic stratosphere, causing the Arctic stratospheric polar vortex to weaken and shift toward Eurasia. Specifically, the upper Arctic stratosphere experiences a 42.7% increase in the amplitude of planetary waves with wavenumber 1. Compared to the flattened topography, the Tibetan Plateau’s topography also increases significantly the ozone by 15% in the lower Arctic stratosphere, accompanied by the highest accumulation of total ozone appearing in the polar region north to the North American continent. The intensified residual mean circulation transport is principally responsible for the increase in ozone, and topography has a stronger impact on the vertical residual mean transport than on the meridional residual mean transport. Further examination demonstrates that the robust strengthening of planetary waves caused by topography significantly contributes to the enhancement of the residual mean circulation. In contrast, during summer, the planetary waves generated by the Tibetan Plateau’s topography struggle to propagate toward high latitudes, resulting in a minimal impact on Arctic stratospheric circulation and ozone.

How to cite: Duan, A., Wang, Q., and Pan, Z.: Impact of Tibetan Plateau Topographic Forcing on Arctic Stratospheric Circulation and Ozone, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-39, https://doi.org/10.5194/ems2025-39, 2025.

Vegetation is a key component of the Earth system, regulating surface energy and water fluxes through its influence on surface roughness, albedo, and evapotranspiration. Its high spatial and temporal variability—ranging from seasonal to decadal timescales—plays a fundamental role in shaping both local climate and large-scale atmospheric circulation. Despite its importance, vegetation dynamics remain a challenging component to represent realistically in many Earth system models.
In this study, we assess the impact of improved vegetation representation on the simulated climate system by introducing a new parameterization of effective vegetation cover. This parameterization is derived from the latest satellite-based vegetation datasets, offering a more physically consistent and temporally responsive characterization of vegetation states.
The new parameterization is implemented in the land surface scheme HTESSEL, used within the IFS/OpenIFS modeling framework. A set of sensitivity experiments is carried out to evaluate the role of vegetation in modulating surface climate and atmospheric circulation patterns. Results show that the enhanced vegetation representation leads to a substantial reduction in surface climate biases, with notable improvements in near-surface temperature (T2M), mean sea level pressure, and zonal wind across mid-to-high latitudes.
Beyond local improvements, the new vegetation dynamics induce changes in large-scale atmospheric teleconnections. In particular, over Siberia—where vegetation changes exert the most pronounced influence on surface temperature—a circulation response is initiated, extending across the Northern Hemisphere. This includes a marked enhancement in the simulation of the North Atlantic Oscillation (NAO), with significantly stronger correlations between the modeled NAO index and its observational counterpart from ERA5.
These findings highlight the central role of vegetation–atmosphere interactions in controlling not only local energy balance but also hemispheric-scale circulation and predictability. The results underscore the importance of accurately representing land surface processes in Earth system models to enhance both seasonal forecasting skills and long-term climate projections. This study supports continued efforts in land surface model development and provides a clear pathway toward more realistic and skillful climate simulations.

How to cite: Di Carlo, E., Alessandri, A., Cherchi, A., and Corti, S.: The Impact of Vegetation Dynamics on Atmospheric Teleconnections and Predictability., EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-411, https://doi.org/10.5194/ems2025-411, 2025.

The Indian and African monsoons are large scale circulations fueled by the latent heat released by the marine air masses, which cyclonically spiral inland from nearby tropical Atlantic and Indian oceans. The catch basin of the Indian monsoon is in the Indian ocean, whilst the catch basin of the African monsoon is in the Atlantic.

The two monsoons are dynamically inter connected in the upper atmosphere, where the Indian monsoon exerts its influence on the African monsoon with its westwards propagating long planetary waves, whilst the African monsoon exerts its influence on the Indian monsoon with its eastwards propagating Kelvin waves. In the lower atmosphere, the Somali mountains physically separate the catch basin of marine air masses of the African monsoon from that of the Indian monsoon. However, since these mountains are a barrier of limited height and limited longitudinal length, they are partially permeable to the marine air masses transported eastwards by the African easterly jet, when the Saharan heat low is strong.

Thus, in his work we show the results of an analysis of the role played by the African desert and of the Somali mountains in interrupting or favoring air mass exchange between the two basins. This on-off marine particle exchange can induce free oscillations and damped forced oscillations of the monsoon-desert system at intra seasonal time scales.

Results show that the monsoon-desert does not stay in its average climatological state, because this state is metastable. When the Saharan desert is at its maximum and retreating, the African monsoon grows from its average climatological position; and when the monsoon retreats from its average climatological position, the Saharan desert grows form its minimum.

Results also show that oscillations of the monsoon-desert system can be triggered by a small external perturbation.

Thus, in the presence of weather perturbations introduced in parametric form as stochastic noise, these transitions become quasi-periodic, even when the interaction coefficient are weak, because of induced stochastic resonance.

In this work, the monsoonal dynamics are simulated using Gill's theory, whilst the monsoon-desert feedbacks are analyzed in terms of Lotka-Volterra's theory.

How to cite: Dalu, G. and Baldi, M.: Interactions within the Asia-Africa monsoonal system and with the Saharan desert, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-296, https://doi.org/10.5194/ems2025-296, 2025.

How to cite: Trombini, I., Weitzel, N., Valdes, P. J., Baudouin, J.-P., Armstrong, E., and Rehfeld, K.: Atmospheric and Oceanic Pathways Drive Separate Modes of Southern Hemisphere Climate in Simulations of Spontaneous Dansgaard-Oeschger-Type Oscillations, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-616, https://doi.org/10.5194/ems2025-616, 2025.

The potential collapse of North Atlantic subpolar gyre (SPG) deep convection under global warming has emerged as an increasingly important research topic and a significant source of public concern in the context of climate risk. Although both conceptual and coupled climate models have indicated the possibility of abrupt changes in SPG circulation, a comprehensive understanding of the mechanisms behind the convection shutdown remains incomplete, despite existing dynamical interpretations. Preindustrial control simulations from coupled climate models, designed to simulate a stable preindustrial climate state over time periods of the order of 10^3 years, have been shown to provide meaningful insights about the behavior of SPG in absence of anthropogenic global warming. In this study, we investigate the potential collapse of SPG deep convection in the preindustrial control simulation of six models that contribute to the Climate Model Intercomparison Project 6 (CMIP6). By analyzing the time series of mixed layer depth, we select events of winter SPG shallow convection with return period exceeding 50 years. The temporal evolution of the SPG states leading to convection shutdown exhibits common features across different model simulations. Notably, a positive sea surface temperature anomaly emerges in the SPG region the year before the event, coupled with a strong and persistent negative phase of the North Atlantic Oscillation which is followed by an abrupt freshwater release in the Labrador sea. Defining a causal chain, as aimed in this work, could be valuable for spoiling the major feedback mechanisms involved in the process as well as for detecting dynamical early warning signals, with a possible improvement in the predictability of such convection collapse events. Future steps include testing this hypothesis in forced simulations to explore parallels between the autonomous (preindustrial) and non-autonomous cases.

How to cite: Buccellato, M., Bellucci, A., Corti, S., and Zappa, G.: Abrupt shifts in Subpolar Gyre deep convection under stable climate conditions , EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-567, https://doi.org/10.5194/ems2025-567, 2025.

Extreme weather and climate events - such as floods - are among the most impactful hazards for both human society and natural systems. Their accurate representation and prediction remain challenging, especially when such events are driven by interacting climatic factors that operate on different spatial and temporal scales. In this context, large-scale atmospheric circulation patterns and associated teleconnections play a crucial role in modulating regional hydro-meteorological extremes, offering a potential source of predictability that is still not fully exploited or understood. This study examines extreme flood events in Emilia-Romagna, a flood-prone region of northern Italy, with a particular focus on the link between local flood dynamics and broader atmospheric variability.

Floods are here categorised into two types: preconditioned floods, which occur when extreme precipitation is preceded by abnormally high levels of soil moisture, and non-preconditioned floods, in which no such antecedent conditions are present. To detect these extremes, a peak-over-threshold method is applied to ERA5-Land reanalysis data spanning the period from 1979 to 2024, capturing significant anomalies in both precipitation and soil moisture. The role of atmospheric low-frequency variability is explored through the analysis of geopotential height anomalies at 500 hPa, derived from ERA5: results indicate that preconditioned flood events are frequently associated with persistent and more intense negative geopotential height anomalies near the affected region, particularly during the winter months, highlighting the role of seasonal atmospheric circulation patterns in enhancing flood risk. Additionally, hydrological impacts are assessed using EFAS river discharge data (1992–2024), showing that preconditioned events are associated with widespread and elevated discharge levels across Emilia-Romagna. Finally, an event coincidence analysis reveals that soil moisture preconditioning is more frequent during winter months, consistent with enhanced synoptic-scale persistence during this season.

Overall, the study offers new insight into how persistent large-scale circulation anomalies modulate regional flood risk through both dynamic and hydrological pathways. These findings underscore the importance of incorporating atmospheric teleconnections into flood early-warning systems, and suggest avenues for improving predictive capabilities not only in Emilia-Romagna but also in other areas with comparable climatic and hydrological settings.

How to cite: Butera, C., Giordani, A., Ruggieri, P., and Di Sabatino, S.: Linking large-scale low-frequency atmospheric variability to compound flood events in Emilia-Romagna, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-544, https://doi.org/10.5194/ems2025-544, 2025.

Tropical modes of variability, such as the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), exert a strong influence on the interannual variability of tropical precipitation, as well as on precipitation across large regions of the extra-tropics. However, widely used indices of ENSO and IOD variability exhibit substantial co-variability, making it challenging to robustly quantify the independent contribution of each mode to precipitation anomalies. A common approach to this issue involves statistically removing either ENSO or IOD variability from precipitation fields prior to calculating teleconnection patterns.

Here, we estimate the independent contributions of these dominant tropical modes by conducting a suite of coupled and uncoupled (atmospheric-only) modeling experiments, in which the sea surface temperature (SST) variability associated with either ENSO or IOD is suppressed. The partially coupled experiments include a fully dynamic ocean, but SSTs are restored to the model’s monthly mean climatology (CTRL-coupled) within specified regions. We apply two different restoring masks: the first spans the eastern Pacific (from 180°W to the American coast, 20°S–20°N) to suppress ENSO-related SST variability (noENSO-coupled experiment), and the second covers the Indian Ocean and western Pacific (from the African coast to the Maritime Continent, 20°S to the Asian coast) to suppress IOD-related SST variability (noIOD-coupled experiment).

Using Australia as a case study, we show that precipitation patterns attributed to ENSO, when computed by statistically removing the IOD influence, significantly underestimate the true impact of ENSO on precipitation variability. Conversely, we find that IOD teleconnections estimated by regressing the Dipole Mode Index (DMI) onto June–October mean precipitation anomalies tend to overestimate the role of the IOD. Motivated by these findings, we propose a conceptual framework that offers a more effective approach for disentangling the independent contributions of ENSO and IOD to precipitation variability.

How to cite: Liguori, G.: Disentangling ENSO and IOD Teleconnections in Precipitation Variability, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-269, https://doi.org/10.5194/ems2025-269, 2025.

This study examines how atmospheric mean state biases influence the El Niño-Southern Oscillation (ENSO) teleconnections with the North Atlantic-European (NAE) region, using ERA5 reanalysis and a large ensemble of historical simulations from CMIP5 and CMIP6 models. By isolating the contributions from the Niño 3.4 and Tropical Western-Eastern Indian Ocean (TWEIO) regions, we find that in November, the ENSO teleconnection dominates and projects onto the positive phase of the North Atlantic Oscillation (NAO). Later in December, the TWEIO teleconnection becomes dominant, reinforcing the positive NAO phase through a zonal wavenumber-3 Rossby wave train originating from SouthEast Asia (SEA). In general, a systematic improvement in the simulation of the ENSO teleconnection in CMIP6 models compared to CMIP5 is observed, particularly during December. By implementing a clustering approach, we show that models failing to capture the December ENSO teleconnection with the NAE region consistently exhibit a weak Rossby wave source over SEA and overly strong subtropical jet streams in the Pacific and Atlantic Oceans. Using a Rossby wave ray tracing algorithm, we show that enhanced subtropical waveguides in these models effectively trap Rossby waves at lower latitudes, thereby limiting their influence on the NAE region. These stronger subtropical jets are likely linked, via thermal wind balance, to cold sea surface temperature biases in the North Pacific and North Atlantic sectors, common mean state errors in earlier-generation climate models. On the other hand, the underestimated SEA Rossby wave source might be the consequence of a poorly simulated ENSO inter-basin connection with the Indian Ocean. 

How to cite: Sabatani, D. and Gualdi, S.: ENSO teleconnections with the NAE sector during December in CMIP5/CMIP6 models: impacts of the atmospheric mean state, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-88, https://doi.org/10.5194/ems2025-88, 2025.

This work introduces a statistical post-processing approach designed to enhance the predictive performance of global seasonal forecast systems, developed within the framework of a forecasting assignment led by the Spanish State Meteorological Agency (AEMET).

The study is carried out over the Iberian Peninsula (IP), located in the Northern Hemisphere's mid-latitudes. As happens with midlatitudes, this area shows low predictability on seasonal timescales due to high internal variability and low signal-to-noise ratio. However, some works have found a window of opportunity for North Atlantic Oscillation (NAO) predictability, which, in turn, can lead to improvements in the skill of the forecasts for climatic parameters (Baker, 2018) (Sánchez García et al., 2019) (Trigo, 2004). Following this example, the NAO and other teleconnection patterns are used through the methodology, and the potential for improvement, by weighting the ensemble accordingly, is explored

Two statistical approaches are applied: Empirical Orthogonal Functions (EOFs) and Partial Least Squares (PLS) regression. Both methodologies are employed on various global seasonal models, first without applying a weighted member technique and then applying an ensemble member weighting approach to the whole ensemble. The member-weighted technique uses the prediction of the best-performing models for the analysed variability patterns. These patterns, derived from ERA5 reanalysis data, are treated as "perfect forecasts" of dominant circulation modes.  Deterministic and probabilistic verification metrics are used - according to the definitions provided in the WMO forecast guidance (WMO, 2018) - to evaluate the potential for improvement. 

Results indicate that some variability patterns demonstrate skill for predicting months in advance, suggesting the potential to enhance seasonal forecasting through model weighting methods incorporating variability mode information. However, additional improvements could still be found: the potential for improvement is conditioned by the definition of variability patterns used. 

How to cite: Rodríguez-Guisado, E., Sanfiz, S., Domínguez-Alonso, M., and Senande-Rivera, M.: Statistical Approaches for Applying Member Weighting Techniques in Seasonal Weather Forecasting, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-537, https://doi.org/10.5194/ems2025-537, 2025.

In recent years, hybrid statistical-dynamical approaches have emerged as a promising avenue to enhance seasonal predictions of the extratropical climate (Slater et al., 2023). These methods combine ensemble-based dynamical prediction systems with statistical post-processing techniques, with the aim of amplifying the predictable components of climate variability and, thus, increasing the signal-to-noise ratio in the model ensemble.

Within this framework, the teleconnection-based subsampling approach has been shown to significantly improve the seasonal prediction of both winter and summer climate across large portion of Eurasian continent, including the occurrence of summer extreme temperature events (Famooss Paolini et al., 2024). This technique relies on selecting a limited subset of ensemble members whose simulations of summer low-frequency atmospheric variability are consistent with its statistical forecasts derived from springtime predictors.

Based on these promising results, we present here an application of the teleconnection-based subsampling approach to assess its potential for enhancing the seasonal prediction of heat stress indicators during the summer. Specifically, the methodology is implemented to mimic real-time operational forecast environment, thus differing from standard retrospective forecast (hindcast) applications. For this purpose, we use the ECMWF seasonal prediction system, which provides data from 1981 to 2024, and the ERA5 reanalysis as surrogate of observations. The model ensemble is subsampled by retaining few ensemble members that adequately capture the teleconnection pattern associated with the summer North Atlantic Oscillation, which represents the leading mode of summer low-frequency atmospheric variability.

The results of this study are particularly relevant, as they contribute to the development and implementation of innovative methodologies for predicting climate conditions that pose risks to human health. This is especially important in the context of climate change, which is projected to increase heat-related mortality by up to 50% in the coming decades unless strong mitigation and adaptation strategies are adopted (Masselot et al., 2025).

Bibliography

Famooss Paolini et al. (2024). Hybrid statistical-dynamical seasonal prediction of summer extreme temperatures in Europe. Quarterly Journal of the Royal Meteorological Society, 151(766). https://doi.org/10.1002/qj.4900

Masselot et al. (2025). Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities. Nature Medicine, 1-9.  https://doi.org/10.1038/s41591-024-03452-2

Slater et al. (2023). Hybrid forecasting: blending climate predictions with AI models. Hydrology and earth system sciences, 27(9), 1865-1889. https://doi.org/10.5194/hess-27-1865-2023

How to cite: Famooss Paolini, L., Ruggieri, P., Di Napoli, C., Wetterhall, F., Pascale, S., Brattich, E., and Di Sabatino, S.: Enhancing seasonal forecast of heat stress indicators through teleconnection-based subsampling, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-615, https://doi.org/10.5194/ems2025-615, 2025.

How to cite: Gargiulo, F., Ruggieri, P., Famooss Paolini, L., and Di Sabatino, S.: A hybrid statistical–dynamical approach for seasonal prediction of the boreal winter stratosphere  , EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-582, https://doi.org/10.5194/ems2025-582, 2025.

The East African region has increasingly experienced periods of extreme precipitation and drought, indicating changes in its climatological seasonal precipitation cycle. Enhancing the seasonal prediction of precipitation is therefore of critical importance for climate change adaptation. Currently, state-of-the-art global climate models demonstrate good skill in forecasting precipitation during the October-December (OND) season, commonly referred to as the short rains. This study aims to further improve OND precipitation forecasts through a hybrid statistical-dynamical approach that integrates artificial intelligence with teleconnection-based subsampling. The focus is placed on the El Niño Southern Oscillation (ENSO), a primary driver of East Africa rainfall variability.

Specifically, we start from the synthetic ENSO forecast developed by Patil K. R., (2023), which is based on a convolutional neural network trained on observational data. This forecast is used to guide the subselection of ensemble members from five global seasonal forecast models provided by the Copernicus Climate Change Service (C3S) -namely ECMWF, CMCC, UKMO, DWD and Meteo France- for the 1993-2016 period. The subselection process involves comparing the Nino 3.4 index time series of each member from each C3S model's ensemble with the synthetic ENSO forecast. The members whose ENSO evolution most closely matches the synthetic forecast, measured using the Euclidean distance metric, are retained. These selected members form a new ensemble, intended as a new representation of the climate system, which leads to improved prediction skill for OND precipitation when compared to observational data.

Future developments of this work could involve expanding the spatial domain to the entire Sub-Saharan Africa, applying the method to the less predictable March-May (MAM) season and evaluating additional teleconnections such as the Indian Ocean Dipole.

How to cite: Beltrami, S. and Ruggieri, P.: Predicting seasonal rainfall in East Africa through a teleconnection-based subsampling informed by AI models, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-560, https://doi.org/10.5194/ems2025-560, 2025.