Studies have shown that the forecasting of mean sea level by both physics-based and data-driven models produces reasonable results. The challenge lies in accurate forecasting of sea level maxima. This task includes handling of extreme events which are often influenced by compound factors (e.g. winds, pressure gradients and prefilling of semi-enclosed basins, such as the Baltic Sea), interactions of which should be adequately resolved. Another challenge is that return periods of extreme events are long. Such events thus occur infrequently in the existing data sets. To address these challenges, we explore the options of combinations of data driven approaches, such as machine and Deep Learning (ML/DL) methods, with statistical extreme value theory to forecast short-term (one day ahead) and long term (years and decades) sea level maxima in the Baltic Sea.
We employ water level data from six Baltic Sea tide gauge stations from 1971 to 2022. The quality of short-term forecasting of sea level maxima is examined using both machine learning (Random Forest) and deep learning (Convolutional neural network-gated recurrent unit, CNN-GRU) models. Further data analysis by means of mutual index and background knowledge from previous studies indicates that wind speed (zonal and meridional), surface air pressure, Baltic Sea Index (BSI), and significant wave height are the most influential input features. The models' hyperparameters were estimated using a Bayesian optimization algorithm. For long-term forecasting, extreme value analysis based on block maximum method and location, scale, and shape parameters of a General Extreme Value (GEV) distribution was employed to compute the frequency of extreme values for each season and tide gauge.
We demonstrate that the CNN-GRU model performs the best with RMSE values from 7 to 14.5 cm. The performance of this model for storm events was reasonable, however, high sea level peaks were often underestimated. The highest extremes (>150 cm over the long-term mean) tend to occur in the eastern and northern Baltic Sea during the winter season with a return time period >5–7 years (winter) and >20 years (spring). On most occasions, the ML/DL models were not able to forecast these events adequately. However, the knowledge of their magnitude, return period and seasonality can assist in marine planning of these events which are vital for coastal communities and infrastructures design.
How to cite: Delpeche-Ellmann, N., Rajabi-Kiasari, S., Soomere, T., and Ellmann, A.: Forecasting of Sea Level Extremes using Deep Learning and Extreme Value Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13350, https://doi.org/10.5194/egusphere-egu25-13350, 2025.
Intense high-frequency sea-level oscillations (HFOs) in the Mediterranean Sea, sometimes leading to destructive meteotsunamis, occur due to specific and spatially limited meteorological conditions. Despite the understanding of their physical dynamics, current forecasting systems based on hydrodynamic models are unreliable and computationally demanding. To address this problem, we built deep-learning models of HFOs for the Adriatic tide-gauge stations with long measurement records (Bakar and Ploče) and transferred these models to meteotsunami-prone locations with limited data (Stari Grad, Vela Luka and Sobra). We trained deep convolutional neural networks using simulated data (hourly mean sea-level pressure, geopotential heights, specific humidity, wind speed, air temperature from ERA5, and the calculated Richardson number) alongside measurements (1-min sea levels). We will present the model's architecture, transfer learning results, and predictions of HFO amplitudes based on: (i) forecasting horizons (ranging up to several days with different time windows; 6 h vs. 24 h), (ii) data inputs (total sea level vs. sea level decomposed into components), and (iii) various refinement strategies through inclusions of additional U-net based refinement heads. The results demonstrate that the developed models can predict the highest expected HFO amplitudes for the next three days with reasonable accuracy. Accuracy improves when using the ‘wet’ Richardson number instead of the ‘dry’ version, extending time windows (e.g., targeting the largest amplitude in the overall next 24 h rather than every 6 h), and reducing the input dataset. Performance also varies depending on the station from which the model was transferred. In all cases, the forecast accuracy is higher for smaller HFO amplitudes, with refinements primarily improving predictions of smaller amplitude HFOs.
How to cite: Međugorac, I., Metličić, N., Rus, M., Čupić, S., Mihanović, H., Šepić, J., Kristan, M., and Ličer, M.: Deep-learning models for predicting high-frequency sea-level oscillations in the Adriatic Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4952, https://doi.org/10.5194/egusphere-egu25-4952, 2025.
The deep Southern Adriatic Pit (dSAP) is a Mediterranean region highly sensitive to climate change, influenced by dense water cascading from the northern Adriatic and heat/salt transport from the Eastern Mediterranean. Historical (since 1957) and modern (permanent and opportunistic CTD sampling, Argo floats, fixed moorings) measurements reveal a mid-2000s transition in dSAP thermohaline properties. Previously marked by steady increases in temperature, salinity, and density, with substantial saw-tooth decadal variability, the dSAP has experienced unprecedented warming (0.8°C) and salinization (0.2) over the past decade, accelerating in time and reversing density trends. The inflow of much more saline waters reduced SAP stratification and altered dense water properties at its source in the northern Adriatic. This at least fivefold acceleration of the high-emission regional climate projections may have substantial effects on the Adriatic biogeochemistry and living organisms, increasing sea level rise trends and more.
How to cite: Terzić, E., Vilibić, I., Cardin, V., Le Meur, J., Dunić, N., and Vodopivec, M.: The Deep Adriatic Transient, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1801, https://doi.org/10.5194/egusphere-egu25-1801, 2025.
Sea Surface Salinity (SSS) plays an important role in the global water cycle, which appears to be intensifying due to climate change and influences the vertical distribution of heat in the ocean because of its influence on water density. The role of SSS in the onset and offset of Marine Heat Waves (MHWs) and cold spells, or the changes induced on SSS by these extreme events has not been adequately addressed.
In this work we analyse the variations of SSS before, during and after MHWs and cold spells in the North Atlantic Ocean, in order to address the role of SSS and the atmospheric conditions in the evolution of MHW events. We also assess the occurrence of compound extreme temperature /salinity events and determine the regions and conditions under which these occur. Both high and low SSS anomalies are detected during MHWs, which indicates different oceanic and atmospheric processes are at play during each event. Large differences are observed between satellite SSS estimates and reanalyses, especially in coastal regions. Therefore, the first step when assessing SSS extreme high and low values consists on an intercomparison of the different products available in order to establish a reference climatology.
Compound events can cause more damage to the ecosystem than individual events. It is therefore necessary to establish the relation between extreme temperature and salinity compound events, and establish their spatio-temporal patterns in different regions, in order to understand the origin of these events and which are the drivers that lead to their formation.
How to cite: Alvera-Azcárate, A., Barth, A., and Mohamed, B.: Analysis of the variability of sea surface salinity and temperature extremes in the North Atlantic ocean., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18897, https://doi.org/10.5194/egusphere-egu25-18897, 2025.
Sea surface salinity (SSS) plays a crucial role in upper-ocean stratification and marine ecosystems. Since the mid-20th century, SSS patterns have intensified, with saline regions becoming saltier and fresher regions fresher, driven by rising global sea surface temperatures. Using datasets like EN4, GODAS, GLORYS, and in situ observations (e.g., BATS), we assessed global high SSS extremes from 1982 to 2023. Results show significant increases in intensity (0.2 PSU/decade), duration (4 months/decade), and frequency (4 counts/decade) across most oceans, except in regions like the tropical Atlantic. High salinity extremes often compound with marine heatwaves, especially in mid- to high-latitudes, highlighting their growing impact under global warming.
How to cite: Ma, J. and Wang, C.: Global increase in high salinity extremes and their compounding with marine heatwaves, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8166, https://doi.org/10.5194/egusphere-egu25-8166, 2025.
Marine heatwaves (MHWs) are periods of persistently elevated sea temperatures that pose significant threats to marine ecosystems and coastal economies. In this study, we provide a quantitative assessment of the influence of anthropogenic global warming on the intensity and persistence of MHWs using a novel counterfactual climate framework. This approach removes the effects of long-term global air temperature increases from observed sea surface temperature records while preserving natural variability.
Our analysis reveals that anthropogenic global warming has caused a threefold increase in the duration of MHW conditions globally, with oceans experiencing an average of 34 additional days per year under extreme heat conditions since 1940. Furthermore, the maximum intensity of these events has increased by 1°C on average, with regional hotspots such as the Mediterranean Sea experiencing amplified impacts. These findings highlight the dominant role of human-induced warming in driving observed changes in MHW characteristics and underscore the need for targeted mitigation and adaptation strategies.
How to cite: Amores, A., Marcos, M., Agulles, M., Robson, J., and Feng, X.: Quantifying the impact of anthropogenic warming on observed marine heatwaves, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8445, https://doi.org/10.5194/egusphere-egu25-8445, 2025.
Marine heatwaves (MHWs)—prolonged periods of anomalously warm sea surface temperatures (SST)—pose significant ecological and economic challenges, particularly for aquaculture sectors sensitive to temperature variability around Ireland. This study integrates 43 years of historical daily SST data (1982–2024) from NOAA, ICES, and the Marine Institute to develop a comprehensive deep-learning framework for predicting SST and detecting MHWs in the Irish maritime region.
A comparative analysis of two MHW detection methodologies—Hobday et al. (2016) and Darmaraki et al. (2019)—was conducted, highlighting regional trends and spatial patterns of MHW characteristics like frequency, duration, and intensity. The Darmaraki method, with its 99th percentile threshold and flexible event merging criteria, was found to better capture localized and extreme temperature anomalies relevant to aquaculture, while the Hobday method identified a broader range of moderate events. The findings show that MHW frequency has increased significantly over time, particularly in the southeastern and northern waters, with some regions experiencing a doubling of annual MHW events as detected by the Darmaraki method. Long-duration MHWs, exceeding 60 days, are frequently observed along the western and southeastern coasts, demonstrating persistent thermal stress in these areas. The most intense MHWs, with temperature anomalies surpassing 2.5°C above climatological baselines, are concentrated in the southwestern and offshore regions. These areas emerge as critical hotspots, underlining the need for targeted monitoring and adaptive strategies for aquaculture management.
Deep learning models were introduced to predict SST and assess MHW risks to address the need for actionable forecasts. Long Short-Term Memory (LSTM) networks are particularly well-suited for analyzing time series data, as they effectively capture temporal dependencies and long-range patterns in sequential datasets. When coupled with the PyTorch framework, these models offer flexibility and scalability, making them ideal for large and complex SST datasets. Furthermore, combining LSTM with Convolutional Neural Networks (LSTM-CNN) enables the integration of both temporal and spatial features, which is crucial for understanding the intricate dynamics of MHWs.
The LSTM and LSTM-CNN frameworks demonstrated their effectiveness in forecasting SST across various temporal horizons, with predicted values evaluated against MHW criteria to identify potential events and their impacts. By leveraging these models, this study transitions from reactive to proactive MHW detection, providing early warnings and enabling aquaculture stakeholders to implement timely mitigation measures.
This interdisciplinary study bridges marine science and data engineering, combining observational data, machine learning, and robust detection frameworks to enhance the monitoring, forecasting, and management of extreme ocean events. The outcomes provide critical tools for sustainable aquaculture management and contribute to the broader understanding of climate impacts on marine environments.
How to cite: Baxla, M. A., Lyashevska, O., Conway, A., and Farinas-Franco, J.: Marine Heatwave Analysis and Prediction Using Deep Learning: A Case Study Around Ireland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17598, https://doi.org/10.5194/egusphere-egu25-17598, 2025.
Tropical cyclones (TCs) are among the most powerful and destructive atmospheric phenomena, significantly influencing the ocean's surface and subsurface dynamics. While individual TC interactions with the upper ocean are well-studied, the effects of binary or multiple TC interactions on the physical setting of the upper ocean remain relatively underexplored. In this study, we have investigated the coupled dynamics of the two co-occurring TCs Seroja and Odette in the southwestern Indian Ocean, focusing on their binary interactions and the impacts on the upper ocean layer (0-200 m). For the first time, we examined the impact on the upper ocean during stalling and complete merging of TCs by using a combination of observational data and numerical simulations.
During the interaction of the weak TCs Seroja and Odette, we observed cooling of up to 3.0°C within 72 hours, typical known only for strong TCs. This cooling persisted for at least 8 days and was associated with significant upwelling processes in the upper 200 m of the ocean. Our analysis revealed drastic changes in vertical ocean velocities, with sudden reversals from downward to upward velocities of up to 30 m d-1, observed down to depths of at least 750 m. These changes were particularly pronounced during the merging of the two TCs, highlighting the extreme nature of such binary interactions.
This research contributes to our understanding of how even weaker TCs, when interacting, can cause extraordinary transport of deeper water masses to the ocean surface. With the potential increase in TC frequency and intensity due to climate change, our findings underscore the importance of studying these extreme events for better prediction and risk assessment in marine environments.
How to cite: Wurl, O. and Meyerjürgens, J.: Upper Ocean Dynamics During Binary Interaction of Tropical Cyclones: A Case Study in the Southeastern Indian Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2691, https://doi.org/10.5194/egusphere-egu25-2691, 2025.
This study investigates the capabilities of the Surface Water and Ocean Topography (SWOT) satellite to observe and analyze storm surges, a major driver of extreme sea level events that result in devastating coastal flooding. Storm surges, caused by wind setup, the inverse barometer effect, and wave setup, lead to rapid sea level rises, as demonstrated in events like Storm Gloria in the Mediterranean and Hurricane Milton in the Gulf of Mexico. Traditionally, tide gauges (TGs) have been the primary tool for studying these phenomena. While TGs provide high-frequency data, they are sparsely distributed, fixed to shorelines, and unable to capture the full spatial footprint of storm surges in the open ocean or along complex coastlines. Satellite altimetry has advanced surge detection, but missions like TOPEX/Poseidon and Jason series are constrained by narrow ground tracks and large gaps, limiting their ability to resolve fine-scale surge dynamics.
The SWOT satellite, launched in December 2022, addresses these limitations with its innovative wide-swath interferometric radar, producing two-dimensional sea surface height (SSHA) maps at ~2x2 km resolution. This unprecedented capability is particularly valuable near coastlines, where traditional altimetry struggles due to land interference. SWOT’s ability to observe storm surges in two dimensions provides new opportunities to understand their spatial evolution. By combining SWOT data with TG observations, SCHISM hydrodynamic model, and ERA5 atmospheric reanalysis of wind and pressure fields, this study offers a comprehensive analysis of storm surge dynamics across diverse environments, including the Baltic Sea, North Sea, and regions frequently affected by tropical cyclones as Gulf of Mexico.
The results reveal that SWOT accurately captures the spatial footprint of storm surges and their evolution over time, with strong agreement between SWOT-derived sea level anomalies (SLA) and tide gauge records. Case studies demonstrate SWOT’s capability to monitor storm surges in micro-tidal, macro-tidal, and regions frequently impacted by tropical-cyclones, showcasing its adaptability to various oceanic regimes. SWOT’s high-resolution spatial data significantly enhance coastal hydrodynamic models by providing detailed observations in regions with sparse TG coverage. Unlike traditional altimeters, which provide isolated measurements along predefined tracks, SWOT delivers wide-swath snapshots that unveil the full structure of storm surges, offering a more comprehensive understanding of their dynamics.
This study underscores SWOT’s transformative potential for monitoring, forecasting, and mitigating storm surges. By bridging critical observational gaps and providing high-resolution spatial data, SWOT complements traditional altimetry and ground-based measurements, offering unprecedented tools to improve coastal resilience. Its contributions are particularly significant in the context of climate change, where more frequent and intense extreme sea level events threaten coastal populations and infrastructure. SWOT’s ability to advance our understanding of storm surge processes represents a major step forward in developing strategies to manage and mitigate the risks associated with extreme weather and rising seas.
How to cite: Vega-Gimenez, D., Paris, A., Pascual, A., and Amores, A.: SWOT capabilities for measuring extreme coastal water levels, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3173, https://doi.org/10.5194/egusphere-egu25-3173, 2025.
Assessing the coastal hazards of extreme ocean and weather conditions, remains a difficult task for the scientific community since several limits need to
be overcome. The physical knowledge of extreme events, a precise representation of the input data, as well as reproducing correct and accurate numerical simulations are some of them.
In this work we consider a dataset of more then 1100 cyclones which took place in the period 1994-2020 in the Mediterranean basin. The aim is to evaluate their impact - in terms of sea level and waves - in both the open sea and the coastal regions through a coupled hydrodynamic-wave numerical model. The adopted modelling system consists in the SHYFEM (System of HydrodYnamic Finite Element Modules) hydrodynamic model, two-way coupled with the WW3 (WAVEWATCH III) wave model, thus accounting for the wave-current interaction in deep and shallow waters. An unstructured mesh is used to cover the whole Mediterranean sea with a mesh size varying from 10 km in the open sea to less than 1 km at the coasts. Wind and mean sea level pressure are considered as meteorological forcing at very high horizontal resolution thanks to the Copernicus European Regional ReAnalysis (CERRA) system. The numerical results are extensively validated against tide gauges, wave buoys, and satellite-borne instruments showing a good performance for specific storm events and mean conditions in different areas of the Mediterranean Sea. Then, sea level and wave results are used to compute some storm impact indicators for every cyclone of the entire dataset. These quantitative indexes allow a first classification of Mediterranean cyclones from an ocean and coastal impact point of view.
How to cite: Pavan, S., Ferrarin, C., Bajo, M., Barbariol, F., Benetazzo, A., Davison, S., and Arpaia, L.: The marine and coastal hazards of Mediterranean cyclones, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4078, https://doi.org/10.5194/egusphere-egu25-4078, 2025.
Marine extreme events, such as marine heatwaves (MHW), low oxygen (LOX), and acidity extremes (OAX), must be considered alongside long-term ocean changes as potential ecosystem stressors. In recent years, marine extreme events have been shown to be more frequent, persistent, and intense in response to continued ocean warming, underlining the urgent need for understanding the mechanisms and potential compounding effects. In this study, we investigate the potentially significant role of ocean coherent mesoscale eddies in driving such events by applying an eddy- tracking algorithm to high resolution fields (0.1°) provided by the GFDL CM2.6 ocean model. We quantify the eddy contribution to marine extreme events in target ocean regions near Eastern Boundary Upwelling Systems in both pre-industrial and idealized CO2 forcing experiments. We also use the GFDL CM2-O ensemble to investigate the effect of using different resolution classes (0.1°, 0.25° and 1°) on marine extremes metrics (intensity, duration, and frequency). Here we analyse eddy-rich regions, thus inferring the different effects of eddy-rich, -present and -parameterizing configurations. Our study demonstrates that resolving the mesoscale is the best approach for studying biogeochemical extremes in eddy-rich regions, which presented higher frequency of shorter events with increasing resolution. For LOX the significance of eddies goes to regions beyond those typically characterized as eddy-rich, causing the global LOX frequency to increase significantly by 136% when the resolution was refined from coarse to eddy-rich, shortening the mean duration by 62%. As climate biogeochemical coupled models are very computational and storage-wise costly, it is important to quantify the impacts of mesoscale dynamics onto biogeochemical extremes to improve climate model parameterizations.
How to cite: Maia Pacheco, M., Andrews, O., Frenger, I., Queste, B., and Monteiro, F.: The role of mesoscale eddies as drivers of marine biogeochemical extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1531, https://doi.org/10.5194/egusphere-egu25-1531, 2025.
Marine heatwaves (MHWs), marked by extended periods of unusually warm seawater, significantly impact marine ecosystems and human communities. They have notably increased in the recent decades especially in the Northwest Pacific, a complex coastal region rich in biodiversity and economic activities. To develop effective policies for sustainable and resilient marine ecosystems in this region, high-resolution and reliable ocean climate information is essential. In this study, we simulate the long-term (1982–2014) North Pacific ocean climate using a high-resolution Regional Climate Model (RCM) driven by eight relatively low-resolution Coupled Model Intercomparison Project 6 (CMIP6) models. The ensemble median of eight RCM simulations reduces warm biases of CMIP6 sea surface temperature by 20–69%. It also improves the spatio-temporal variation of MHW properties, with up to 80–97% improvement in winter MHW frequency in the Northwest Pacific. This improvement is attributed to a more realistic representation of the Kuroshio and its extensions, which increases warm water advection from lower latitudes. This result highlights the importance of high-resolution ocean modeling in providing reliable ocean climate productions, especially for local extreme ocean events influenced by regional ocean circulations.
How to cite: Oh, S.-G., Lim, K.-G., Son, S.-W., and Cho, Y.-K.: Improving Marine Heatwave Simulation Through Realistic Representation of the Kuroshio in High-Resolution Regional Ocean Model Ensemble , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2100, https://doi.org/10.5194/egusphere-egu25-2100, 2025.
Disastrous waves often bring serious economic losses and casualties. Accurate and rapid prediction of sea conditions has an important impact on ship hedging, berthing and operation safety during disastrous waves. The existing wave prediction mostly takes wave height as the main index, and rarely consider the influence of wave period and wavelength on the navigation safety of offshore buildings and ships. Waves with larger wavelengths and periods have stronger penetration, which not only enter the port with more energy, but also may cause harbor resonance, affecting the mooring stability conditions and the number of days that berths that can be operated. In severe cases, it may even lead to the moored vessels accident.
In this study, based on the wavelength and period data simulated by the SWAN wave model, the LSTM-DR model is established to predict the wavelength and period by adding the wave dispersion relationship to the loss function of the LSTM algorithm.The loss function consists of three parts, which are the RMSE of wavelength and period and the error of dispersion relation. The model obtains the optimal simulation results by adjusting the proportion of the three in the loss function.The model was used to input 3 months, 6 months and 12 months of data ( 50 % for training and 50 % for verification ) for sensitivity experiments, and the calculation results were compared with the LSTM model. The results show that the shorter the input data, the more significant the accuracy of the time series prediction results, especially in the coastal water, the correlation coefficient, RMSE and MAPE are significantly improved. This shows that adding physical constraints to the artificial intelligence algorithm can effectively improve the accuracy of the prediction results under the condition of limited data.
How to cite: Wang, J.: The Combined Prediction of Wavelength and Wave Period Based on Wave Dispersion Relation and LSTM Algorithm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2438, https://doi.org/10.5194/egusphere-egu25-2438, 2025.
Gelatinous zooplankton (GZ) has recently been proposed as one of the potential key contributors to the global biological carbon pump, a process that sequesters substantial amounts of CO2 in the deep ocean through sinking organic matter. We derive a first dynamically consistent physical model
coupling GZ sinking speed to its mass, to provide high-resolution visualization of global vertical transport of GZ-derived carbon. We propose an improvement to microbial decay modeling, where the GZ biomass degradation rate is a function of its area rather than mass. We use these models to quantify marine heat wave (MHW) inhibitions of the vertical carbon fluxes into deep global ocean. We find that marine heatwaves accelerate GZ decay and subsequently slow their sinking velocity, which leads to an inhibition of carbon export of up to some 10\% locally. This difference, however, can reduce the global carbon export only up to 5 %. We further repeat all the simulations under ocean warming climate projection SSP2-4.5 and SSP5-8.5 pathways. Here, in contrast to MHW inhibitions, model projections at the end of the \nth{21} century suggest a major decrease in carbon export to the deep ocean of up to 20 % globally.
How to cite: Perharic Bailey, C. E., Vodopivec, M., Herndl, G., Tinta, T., and Licer, M.: Marine heat wave and global warming inhibition of gelatinous zooplankton related carbon fluxes into the deep ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2483, https://doi.org/10.5194/egusphere-egu25-2483, 2025.
Human-induced climate change is expected to increase the intensity and frequency of major storms. Explosive cyclogenesis (“bomb cyclone”) is among the most violent of atmospheric events and occurs when there is a rapid deepening of the pressure at the centre of a cyclonic system over a period of 24h. Bomb cyclones generally form over the ocean in winter and are relatively common on the Atlantic coast of North America, where they can be manifested in nor’easters in the form of blizzards up north and hurricanes down south – Hurricane Milton experienced explosive cyclogenesis.
In this study, we examine the bomb cyclone that impacted the British Columbia (BC) coast of Canada during 18-21 November, 2024. This extreme weather event was accompanied by hurricane strength wind gusts of up to 170 km/h and extreme storm waves. Atmospheric pressure in the cyclone centre fell as low as 940 hPa and the storm caused large-scale power outages and strongly affected coastal infrastructure. The cyclone and associated storm produced a strong storm surge, significant seiches, infragravity waves and modified the oceanic circulation, impacting inlet and coastal ecological habitats. We examine real-time observations recorded by tide gauges along with simultaneous atmospheric microbarographs from the Canadian Hydrographic Service to provide estimates of the statistical and extreme parameters of the sea level and atmospheric pressure oscillations. Additional observations of water properties, oceanic circulation, acoustic backscatter and undersea video from the Ocean Networks Canada coastal sub-sea networks provide a comprehensive view of the impact on inlet and coastal habitat by this extreme weather event.
How to cite: Mihaly, S., Rabinovich, A., Sepic, J., Hannah, C., and Thomson, R.: The impact of extreme storms on coastal oceanographic conditions on the west coast of British Columbia: A case study of the 18-21 November 2024 Bomb Cyclone., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13376, https://doi.org/10.5194/egusphere-egu25-13376, 2025.
The presented work analyses the results of an innovative numerical simulation system over the Mediterranean Sea basin developed in the context of the PROMETO project, aimed at producing early-warning indicators for coastal protection and navigation safety.
The numerical system is exploited to simulate the evolution of extreme weather and sea events in the Mediterranean Sea over the decade 2010-2020, using both the downscaling of the global ERA5 model by means of the WRF (Weather Research and Forecasting) model at 5 km horizontal resolution and the reforecast with the same model at 3 km resolution (with sea surface temperature updates every 6 hours). Resulting atmospheric fields are used to force the SHYFEM hydrodynamic model coupled to the WAVEWATCHIII wave model at very high resolution and to produce the relevant environmental variables for early-warning indicators over the entire Mediterranean basin.
To test the system in an operational early-warning context, using the Ensemble reforecast approach based on 50 ECMWF members, two case studies of extreme weather-sea events are simulated, namely the high-impact storms 'Vaia' (2018) and 'Detlef' (2019). For each event, we can evaluate the model uncertainty of wind speed, rain and ocean-wave fields and we can assess the impact of the uncertainty provided by the ensemble approach on the predictions of wind, waves, sea level and derived early-warning indicators. In addition, a ‘member selection’ technique is used in order to assess how the selection of a few, potentially, more significant ensemble members is impactful in statistical and forecasting terms, allowing also to reduce the computational load of high-resolution ensemble meteo-marine forecasts at regional scale.
How to cite: Barbariol, F., Ferretti, R., Favaretto, C., Redaelli, G., Ricchi, A., Nastasi, M., Pant, M., Benetazzo, A., Ferrarin, C., Falcieri, F., Menegon, S., Ruol, P., and Cavaleri, L.: Extreme meteo-marine events in the Mediterranean: numerical modeling approaches for early-warning , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19504, https://doi.org/10.5194/egusphere-egu25-19504, 2025.
The Mediterranean Sea is a semi-enclosed basin highly sensitive to climate variability and anthropogenic influences. Understanding its response to climate forcings is crucial for assessing future environmental and socio-economic impacts. In this study, we analyze the Mediterranean Sea's response to climate forcings in 2024 by leveraging observational data, remote sensing products, and numerical model outputs. Key parameters such as sea surface temperature, sea level anomalies, and wave patterns are examined to identify trends and anomalies relative to historical baselines. We employ high-resolution regional models to investigate the interplay between atmospheric and oceanic dynamics, with a focus on extreme events such as marine heatwaves and anomalous weather patterns. Preliminary results indicate significant deviations and anomalies from historical norms and confirm sea temperature and sea level positive trends. The study highlights the influence of large-scale climate drivers, including teleconnections, on the Mediterranean basin's hydrodynamics and ecosystem. Our findings contribute to improved climate impact assessments and inform adaptive management strategies for the region's coastal communities and marine biodiversity.
How to cite: Benetazzo, A., Ferrarin, C., Umgiesser, G., Cavaleri, L., Barbariol, F., Bonaldo, D., Bajo, M., Bernardi Aubry, F., Pisano, A., Selva, F., Organelli, E., Landolfi, A., Brandini, C., and Arpaia, L.: Assessment of the 2024 Mediterranean Sea response to climate forcings, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21561, https://doi.org/10.5194/egusphere-egu25-21561, 2025.
Extreme sea levels are a global concern, as they can lead to substantial economic losses and pose risks to human communities in coastal regions. Accurate projections of extreme sea levels are essential for effective coastal management and planning. While relative sea level rise, driven by ongoing climate change, is a major factor influencing future extremes, changes in storm surges due to shifts in storm climatology may also have critical impacts.
In this study, a random forest machine learning approach is employed to project daily maximum storm tides (storm surge and tides) for 59 stations across the Baltic Sea. The model uses atmospheric variables, including wind speed, wind direction, and surface pressure derived from climate datasets. Projections for the period 2070–2099 are compared to pre-industrial conditions from 1850–1879 to assess changes in 50-year storm tide return levels.
The results indicate sub-regional variation in projected changes. Increases of up to 10 cm are projected along Sweden’s west coast and the northern Baltic Sea, while decreases down to 6 cm are anticipated along the southern Swedish coast, the Gulf of Riga, and the Gulf of Finland. Other areas are projected to experience negligible change. These spatially varying trends highlight the importance of local analysis for future sea level risk assessments. However, the variability in atmospheric drivers across climate models contributes to significant uncertainty, underscoring the need for further research to refine projections and reduce uncertainties in future climate storm tides projections.
How to cite: Dubois, K., Nilsson, E., Hieronymus, M., Larsen, M. A. D., Karami, M. P., Drews, M., and Rutgersson, A.: Exploring storm tides projections and their return levels around the Baltic Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1574, https://doi.org/10.5194/egusphere-egu25-1574, 2025.
In the Levantine Basin, it has long been known that salinity can reach a maximum in a thin layer near the surface, particularly during the warm season when summer heating, evaporation, and low mixing prevail. This water mass, termed the Levantine Surface Water, has historically been linked to the generation of Levantine intermediate and deep waters, depending on winter heat loss and wind-induced mixing. However, a recent study demonstrated that similar conditions, referred to as 'surface saline lakes' (SSLs), can occur as far north as the Adriatic Sea. To investigate this, we analyzed data from Argo profiling floats across all Mediterranean basins, focusing on the upper layers (up to 200 m in depth), where such lakes are known to form. We developed an objective algorithm to detect SSLs within profiles, defining an SSL by a salinity gradient exceeding -0.01 m⁻¹ at its base, combined with the uppermost salinity value exceeding the base salinity by at least 0.05. This definition allowed us to estimate SSL depth (corresponding to its base), temperature, potential density anomaly (PDA) gradients, and the Schmidt Stability Index, which quantifies the energy needed to mix SSLs. A further condition ensured the quasi-continuity of Argo profiles throughout the year, as SSLs are highly seasonal phenomena. Our analysis revealed that SSLs exhibit minimum or vanishing occurrences between February and April, while peaking between August and October. SSLs were detected in all Mediterranean basins, with the highest prevalence—65–70% of profiles between July and December—occurring in the Levantine Basin. During the August–October peak, SSLs exceeded 35% of monthly profiles in each basin, even in the Western Mediterranean, albeit with varying overall salinity levels and SSL variables ranges. These findings underscore the role of atmospheric heat and water exchange in all Mediterranean basins, influencing deeper thermohaline properties through winter mixing. Despite pronounced interannual and seasonal variability, our analysis of data showed a significant trend in SSL depth, accompanied by decreasing thermohaline gradients (temperature, salinity, PDA) at SSL bases though the investigated period. However, these trends may partly reflect sampling biases due to time-space differences in Argo float coverage, which has been substantial before 2013. The observed changes raise questions about their drivers—whether they indicate ongoing climate-change-induced salinization and shifts in Mediterranean water mass dynamics, or are merely manifestations of decadal variability.
How to cite: Vilibić, I., Terzić, E., and Gardiol, C.: 'Surface saline lakes' in the Mediterranean Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3140, https://doi.org/10.5194/egusphere-egu25-3140, 2025.
Although some authors have shown that wave–current interactions are not negligible, wave setup on
sea level is often not considered in modeling the Adriatic Sea. Other studies have demonstrated that
using a coupled ocean-atmosphere-wave model can improve the simulation of extreme events,
particularly when high-resolution sea surface temperature (SST), consistently updated with ocean
circulation, is essential for determining heat fluxes. Thus, modeling efforts are increasingly moving
towards two-way current–wave, current–atmosphere, and current–wave–atmosphere coupled systems.
In this study, we present a high-resolution ocean-atmosphere numerical simulation for the Adriatic Sea,
where the Weather Research and Forecasting (WRF) model is two-way coupled within the COAWST
(Coupled Ocean–Atmosphere–Wave and Sediment Transport) modeling system. The system integrates
ROMS (Regional Ocean Modeling System) for ocean circulation and SWAN (Simulating Waves
Nearshore) as wave driver. The long-term high-resolution simulation has multiple purposes: to
represent Adriatic Sea circulation from the basin scale to the coastal dynamics, to study extreme events
where atmosphere-ocean interactions are crucial, and to provide the starting framework (initial and
boundary conditions) for very high-resolution simulations needed for nearshore applications such as
coastal flooding and erosion.
In this effort, the model's performance will be evaluated, focusing on thermohaline properties and
ocean circulation. Validation results will be presented during the talk.
How to cite: Memmola, F., Coluccelli, A., Neri, F., Garzia, A., Ferretti, R., and Falco, P.: Coupled ocean-atmosphere numerical simulation for the Adriatic Sea: ocean outcomes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18850, https://doi.org/10.5194/egusphere-egu25-18850, 2025.
Ocean carbon uptake, cycling and sequestration are variable on all time scales, and modulated by an interplay of complex physical and biogeochemical drivers, including anthropogenic CO2increase and associated ocean acidification (OA). OA at Station ALOHA is intensified in the subsurface due to increases in both natural and anthropogenic carbon pools, and their interactions. Enhanced subsurface change is found for all OA indicator variables. This includes both the parameters who have previously been reported to be systematically impacted by nonlinear interactions between anthropogenic and natural carbon pools ([H+], pCO2, Revelle Factor), but also those who do not show this generalized response in the ocean interior (pH, aragonite saturation state (ΩAr)). Different parameters have trend maxima in each of the three water masses in the upper 500 m, driven by different mechanisms. Enhanced acidification is noted in the North Pacific Tropical Water (NPTW) between 2015-2020. This steepening is due to the interplay of a circulation slowdown during a prolonged negative phase of the North Pacific Gyre Oscillation (NPGO) with other anomalous atmospheric forcing that altered source water chemistry, including large-scale freshening. Long-term sustained increased acidification is also associated with freshening and cooling in the Subsurface Salinity Minimum (SSM) over the whole time-series, with considerable oxygen loss and nutrient increases. In the North Pacific Intermediate Water (NPIW), a well-documented long-term circulation slowdown has led to enhanced CO2 ingrowth from remineralization, buffered by increasing carbonate dissolution. Local changes seem to play a smaller role than circulation and source water changes. In two water masses, enhanced acidification is associated with cooling and freshening, providing new insights on how OA can accelerate beyond the well documented warming and souring of the ocean.
How to cite: Knor, L., Sabine, C., Dore, J., White, A., and Potemra, J.: Drivers and Variability of Intensified Subsurface Ocean Acidification Trends at Station ALOHA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2191, https://doi.org/10.5194/egusphere-egu25-2191, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 4
EGU25-17499 | ECS | Posters virtual | VPS18
On the role of air-sea-wave interaction in developing destructive Tropical-Like Cyclones DANIELWed, 30 Apr, 14:00–15:45 (CEST) | vP4.6
Between Sept. 4, 2023, and Sept. 12, 2023, a cyclogenesis develops close to the Greek coast in the Ionian Sea. The evolution of this cyclone is divided into two phases: a strongly baroclinic one with intense orographic precipitation in Greece, and a final barotropic phase with the formation of an intense tropical-like cyclone (TLC) impacting Libya. In this work, we investigate this TLC (named “Daniel”) initially using the standalone WRF model with different sea surface temperature sources, untile the use of the coupled atmosphere-ocean models. Preliminary results show that SST plays a crucial role in the intensification and tropicalization of the cyclone, with a strong impact not only along the cyclone track but especially in the neighboring areas, where high values of heat transport a precipitable water are found. We also observe how the use of a coupled model as a digital twin, shows strengths in the quality of the simulation and the physics of the process, but highlights some critical issues in the configuration of the marine model, which at small technical variations produces intense changes in the structure of the ocean and atmosphere.
How to cite: Ricchi, A., Ferretti, R., Pantillon, F., Dafis, S., Menna, M., Martellucci, R., Serafini, P., and Carrió, D. S. C.: On the role of air-sea-wave interaction in developing destructive Tropical-Like Cyclones DANIEL, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17499, https://doi.org/10.5194/egusphere-egu25-17499, 2025.
