A new machine learning based bias correction method is presented and applied to sea level in a regional climate model for the Baltic and the North Sea. The bias corrections introduced by the method depend on the state of the model it corrects. This contrasts with conventional bias correction methods that operate on distributions of output variables. That is, while conventional correction methods adjusts all modelled sea levels of the same height by the same amount, this method instead adjusts all sea level that occur under the same meteorological conditions by the same amount. Model state dependent corrections allow for better performance on classical skill scores, like correction coefficients, but it also limits the applicability of the method to models that can perform hindcasts. This constrain occurs because the method requires observations and model data from an overlapping time period.

The bias correction method is applied to a large ensemble of dynamically downscaled climate scenario data encompassing many different driving global climate models and representative concentration pathways. The prevalence of significant trends in yearly sea level maximum is found to be independent of emission scenario in our ensemble. This suggests that anthropogenic climate change is not a strong driver of storm surge variability in the area. Moreover, it also suggests that very long datasets of corrected sea levels can be created by merging data from different emission scenarios. A dataset is thus produced that contains over 2600 model years and exists for seven different tide-gauge stations on the Swedish Baltic Sea coast. This dataset is used to estimate return levels for very long return periods by fitting generalized extreme value distributions to block maxima sea level time series. At some stations it is found that the block length used in the return level computation affect the result. This suggests that the commonly used annual maximum approach (i.e. having a block length of one year) is not always applicable for determining return levels for sea level in the area.

How to cite: Hieronymus, M.: A novel machine learning based bias correctionmethod and its application to sea level in an ensemble of downscaled climate projections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5572, https://doi.org/10.5194/egusphere-egu23-5572, 2023.

We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.

How to cite: Rus, M., Fettich, A., Kristan, M., and Ličer, M.: HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3355, https://doi.org/10.5194/egusphere-egu23-3355, 2023.

Urban settlements near to coastal environments are exposed to ocean and cryosphere change, such as sea level rise and extreme sea levels. High-resolution sea level prediction systems have become fundamental tools for taking preventive measures in the face of extreme events, mainly in the most vulnerable coastal locations. Techniques such as Machine Learning (ML) are at the forefront of the development in this sector, as they can reduce the computational time needed to reproduce the results of costly high resolution dynamic models. In this line, different authors have reported results for the prediction of oceanographic variables using ML approaches (Camus et al., 2019; Costa et al., 2020; Zust et al., 2021), mainly for significant wave height, sea level and surge component of sea level. Generally, these works use global and/or regional databases as training data for ML tools.

With the aim of developing a data-driven system for sea level downscaling, by means of very high-resolution circulation model output used as a training data for a ML framework, in this work the results of a long-term numerical modeling of sea level are presented, carried out in the Northern Adriatic. The numerical model implemented correspond to SURF-SHYFEM, a 3-D finite element hydrodynamic model that solves the primitive equations under hydrostatic and Boussinesq approximations. As atmospheric forcing, mean sea level pressure, and meridional and zonal components of wind speed have been included, both from the ERA5 database. For the boundary conditions, sea level has been considered from two databases, the Copernicus Mediterranean Forecasting System (available from November 2020 to present, with tides included in sea level) and the Copernicus Mediterranean Sea Physics Reanalysis (available from 1987 to June 2021, without tides in sea level). Both databases were used on initial analysis in the representation of surge component of sea level when tides are or not included in the boundary condition. The validation of the results has been carried out by comparison with tide gauges located near the Venice Lagoon, from ISPRA[1] and PSMSL[2].

The results show that the model reproduces accurately the sea level (correlation 94% and RMSE 0.09 [m]) and the surge component of sea level (correlation 91% and RMSE 0.06 [m]) measured at the location of the tide gauge. The next step will consist of using such output as a training set for ML-based techniques, with the aim of developing an accurate and cost-effective downscaling tool.

How to cite: Campos-Caba, R., Mentaschi, L., Pinardi, N., Alessandri, J., Camus, P., and Tondello, M.: Long-term numerical modeling of sea level in the Northern Adriatic for a machine learning downscaling system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6824, https://doi.org/10.5194/egusphere-egu23-6824, 2023.

Marine Heat Waves (MHWs) have significant social and ecological consequences. There is a growing need to predict these extreme events to prevent and possibly mitigate their negative impacts and to better inform decision-makers on MHWs-related risks. In this context, we applied Long Short-Term Memory (LSTM) networks to predict Sea Surface Temperature (SST) time-series and, in turn, MHWs, over the Mediterranean Sea. LSTM networks are types of recurrent neural networks capable of learning order dependence in sequence prediction problems, and they have been widely applied in temperature forecasting problems. The model is a multi-step LSTM model, which means that it predicts seven time-steps of SST into the future. In order to build an efficient prediction model, as input times-series, LSTM exploits SST together with other relevant atmospheric variables (e.g. Geopotential Height, Incoming solar radiation), selected as potential MHWs drivers. The datasets used to train and to test the model are the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature (SST) v2.1 for SST and ERA5 reanalysis for the atmospheric components from 1981 to 2016.  Preliminary results over target areas suggest that, besides the SST itself, the incoming solar radiation has the highest predictive skill on SST variability with respect to the other atmospheric variables. The model is accurate in predicting the occurrence of MHWs in the test dataset at the earliest days of forecast. In addition, the root mean square error analysis between predicted and actual SST time-series shows that LSTM models errors compare favorably with respect to the Copernicus Mediterranean Forecasting System (MedFS, i.e. dynamical model) errors, at least at the earliest days of forecast.

How to cite: Bonino, G., Masina, S., Galimberti, G., and Clementi, E.: A deep machine learning approach to predict Sea Surface Temperature and Marine Heatwaves occurrences over the Mediterranean Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12113, https://doi.org/10.5194/egusphere-egu23-12113, 2023.

This study aims to generalize the synthetic effects of TS, LA, and coastal geometries on a maximum surge height (MSH) along the coast through the numerical simulations of a series of idealized scenarios as well as real-scale event. A two-dimensional model in Delft3D-FM was adopted in this study to simulate the MSHs. The model domain consists of multi-resolution grids ranging from 16 km to 1 km considering the cyclone’s landfall spot at the center of the coastline. All these simulations were implemented without tides and waves since this study aims to investigate the synthetic effects on main surge levels. The hypothetical cyclones were generated under various TSs and LAs conditions. The TSs were altered while LAs ranged varied from 0° to 180°. Additionally, the coastal layout and bathymetry were also controlled. For bathymetry, a constant bed, beds with different continental shelf widths, and a multi-sloped bed were also considered. For coastal layout, an open coast and bays characterized by the morphological ratio were considered. Totally, 763 idealized scenarios were simulated to obtain the MSHs distributions along the coast. The realistic scenarios based on historical typhoon Maemi in 2003 was additionally simulated with various TSs and LAs conditions to apply developed idea from idealized scenario cases. The effects of the TS, LA, and coastal geometries on MSH were analyzed by simulating idealized and realistic scenarios. The trends of MSHs along the open coast extracted from each scenario were found to be almost identical despite minor discrepancies. The results revealed that MSHs along the open coast were amplified by fast-moving TSs. For constant bed, the results exhibited a distinct characteristic that generated the Kelvin wave propagating in a down-coast direction, while certain types of sharp LAs with a slow-moving cyclone might generate other types of resonant waves. For the beds with different dimensions of continental shelves, trends of the MSHs were distinguishable between slow and faster-moving TSs. Nevertheless, the continental shelf with narrow width led to a peak at a sharp LA under all TSs, implying that the shelf geometry can limitedly affect the MSH. As for the multi-sloped bed, slope geometry strongly influenced the surge process in that it enhanced the MSH due to the Greenspan resonance. For bays, the trends of MSHs were essentially coincident with those of open coast, while an enhancement of MSHs was observed when L/E was close to 1. Additionally, the realistic scenarios based on a historical typhoon were simulated to validate outcomes from idealized scenarios, indicating that super typhoons like Maemi with a  LA, tend to slow down and generate an extreme surge in Masan Bay in the future sight.

How to cite: Son, S. and Qian, X.: On the effect of tropical cyclones' translation speed and landfall trajectory on the storm surge dynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16745, https://doi.org/10.5194/egusphere-egu23-16745, 2023.

On 12 November 2019, an exceptional flood event occurred in Venice, second only to the one that had occurred on 4 November 1966. The event caused the flooding of about 90% of the streets in the historic centre and the sea level reached a value of 189 cm compared to the local mean-sea-level datum. Subsequent analysis of the meteorological event highlighted the contributions at different temporal and spatial scales. A sub-synoptic cyclone, centred in the Tyrrhenian Sea, caused a strong Sirocco wind along the entire Adriatic basin, with a fairly typical atmospheric configuration. However, embedded in the first cyclone, a second meso-beta scale cyclone developed and moved in the north-westward direction over the Adriatic Sea along the Italian coast. This cyclone had a speed of about 12 m/s, very close to the speed of the shallow water waves for the depth of the northern Adriatic basin. The perturbation then triggered a Proudman resonance, as confirmed by the numerical simulations, and caused a meteotsunami-like wave that affected the north-western coasts of the Adriatic Sea. Through model simulations, we have estimated that the mesoscale cyclone contributed about 40 cm, of which about 40% may be attributed to the air-pressure forcing, amplified through the Proudman resonance, and the rest to the wind forcing influencing both the open Adriatic Sea and the shallow Venetian lagoon. Finally, we have also analysed the propagation and transformation of the perturbation upon its entrance into the Venetian lagoon. This work is part of the COST action CA19109 MEDCYCLONES (European Network for Mediterranean Cyclones in weather and climate) and the Interreg Italy-Croatia STREAM project (Strategic development of flood management, project ID 10249186).

How to cite: Bajo, M., Ferrarin, C., Orlić, M., Davolio, S., Umgiesser, G., and Lionello, P.: The exceptional flood in Venice on 12 November 2019: contribution of a meteotsunami, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5835, https://doi.org/10.5194/egusphere-egu23-5835, 2023.

Infragravity waves have been identified as driving force behind various nearshore processes including beach and dune erosion, the development of seiches in harbors, and wave-driven coastal inundation when not accounted for in the design calculations. These waves have been observed to cause extreme water levels and resulting damage in various locations around the world.

Field observations and measurements are essential for understanding the behavior and impacts of infragravity waves, which are long surface waves with low and with significantly longer periods than the peak frequency of the incident wave spectrum. The period is typically between 30 and 300 seconds (0.03 – 0.003 Hz), the amplitude ranges from a few millimeters to tens of centimeters and has a wavelength scale of kilometers.

In this study, we first revisit field observations, instrumentation, and sampling techniques that have been used to study this phenomenon. The advantages and limitations of different approaches are discussed, as well as the challenges and best practices for collecting high-quality data in the field are addressed.

Field observations were conducted using multipurpose mooring frames equipped with both ADCP-based acoustic surface tracking and high-accuracy quartz pressure sensors. Data were collected continuously for 3 months, covering storm and moderate wave conditions. The measurements from ADCP and pressure sensor were combined and the infragravity wave characteristics were determined. Algorithms to calculate the wave characteristics were developed and combined with data from tide gauges and wave buoys to calibrate the sensors and cross-validate the results.

The observations showed that infragravity waves can be effectively monitored using ADCP and high-accuracy quartz pressure sensors, providing useful information regarding impacts on the coastal environment. The results showed the relevance and occurrence of these waves along the Belgian coast and valuable insights into their generation and propagation and the interaction with Sea-swell waves, including with relation to their spatio-temporal variability.

How to cite: Pepi, Y., Ponsoni, L., and Boone, W.: Field observations of Infragravity waves along the Belgian coast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7002, https://doi.org/10.5194/egusphere-egu23-7002, 2023.

Superimposed on long-term ocean acidification are ocean acidity extremes (OAXs), i.e., periods of unusual acidity in the marine environment. Such extremes form through the interplay of the various spatio-temporal scales of variability associated with the ocean’s carbonate system, ranging from multi-decadal trends to subseasonal dynamics. Using a high-resolution regional ocean model coupled to a biogeochemical-ecosystem model (ROMS-BEC) we assess the role of five mechanisms associated with different scales of variability – namely atmospheric CO₂ rise, the decadal North Pacific Gyre Oscillation (NPGO), the interannual El-Niño-Southern Oscillation (ENSO), seasonal upwelling, and mesoscale eddies – in the occurrence of OAXs in a 300 km nearshore band along the U.S. West Coast and the Alaskan coast over the period 1984 to 2019. We find that the annual fraction of the upper 250 m depth that is hit by OAXs increases at a rate of 0.16 % units.μatm^-1 driven by the increase in atmospheric CO₂ concentration. In addition, our analysis reveals that Pacific climate variability substantially modulates OAXs occurrence on interannual timescales. The fraction of the upper 250 m depth that is hit by OAXs increases by 0.53 % units per unit decrease in the ENSO index, and 0.39 % units per unit increase in the NPGO index. Last but not least, we find that seasonal upwelling and mesoscale cyclonic eddies are key regional drivers of OAXs along the U.S. West Coast. Our results show that coastal upwelling forms intense and shallow OAXs near the coast, while mesoscale cyclonic eddies drive large and long-lasting OAXs that propagate over hundreds of kilometers from the coast to the offshore. Altogether, our results quantify the respective imprint of five mechanisms associated with different scales of variability on the occurrence of OAXs in coastal regions. This knowledge opens new perspectives for improving the predictability of OAXs in the highly productive coastal regions of the northeast Pacific.  

How to cite: Desmet, F., Gruber, N., Münnich, M., Vogt, M., and Köhn, E. E.: Ocean acidity extremes in the northeast Pacific, from multi-decadal trends to mesoscale drivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13205, https://doi.org/10.5194/egusphere-egu23-13205, 2023.

The development and implementation of Early Warning Systems (EWSs) are decisive as they allow for timely measures before the arrival of the flooding waters. EWS enhances the prevention and preparedness activities that mitigate the effects of disasters on lives, property, and the environment. Forecasting outcomes supply decision-makers at local, regional, or national levels with relevant information and comprise an essential part of monitoring and warning procedures. These decisions must be taken quickly to allow mitigation measures, so computer time acceleration is decisive. 
The proposed innovation for implementing an Early Warning System (EWS) is the parallelization model. The proposed parallelization model is based on different parallel sub-schemes. Each parallelization sub-schema can be combinable with each other, providing a hierarchical parallelization scheme. The problem size (the number of transects along the Campania coast) is divided into lots and distributed to several executors. Each executor is an instance of a computer program (process) in charge of computing the partition of the problem in its duty. Due to CPUs being composed of more computing cores, each process can decompose its part of the problem to each computing core running concurrently (threads). While the threads of the same process share the same memory, processes communicate by exchanging data messages. As demonstrated later in the paper, the problem decomposition makes the overall computing performance remarkable as the problem size increase.
The EWS has been designed and developed, leveraging a high-performance computing system. This approach is motivated by the goal of managing and running scientific workflows. Therefore, the performance evaluation has been performed considering a production workflow executing diverse and different numerical and A.I. models): the community Weather Research and Forecasting (WRF),  the Wavewatch III (WW3) numerical models, and, finally, 3) the novel Shoreline Alert Model (SAM).
SAM implements the empirical approach to evaluate the alert level as a function of the shoreline characteristics. The workflow starts with the WRF numerical model to forecast the atmospheric forcing needed to fuel the WW3 model for estimating the offshore waves, which drives the initial and boundary conditions for modeling waves in shallow water. Then, according to the wave decay submodel, these conditions assess the run-up height and overtopping discharge. The results are associated with an alert system triggered by the duration and intensity of storm events forecasted by the models. Finally, it is obtained considering the geomorphology of the area of interest and the presence/absence of protection structures.
The case study under examination covers a coastal stretch located in the municipality of Torre del Greco, in the Gulf of Naples, consisting of a beach varying in width from 10 to 20 meters, which is protected by an artificial reef up of natural blocks. In recent years, the succession of extreme weather events has created coastal flooding, like during the extreme storm of October 2018, which caused considerable damage in this area.

How to cite: Florio, A., Di Luccio, D., De Vita, C. G., Mellone, G., Benassai, G., Budillon, G., and Montella, R.: A Shoreline Alert Model for coastal early warning system in the Gulf of Naples (Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6673, https://doi.org/10.5194/egusphere-egu23-6673, 2023.

Marine heat waves (MHWs) cause devastating damage to ecosystems and ocean services, with effects being identified mostly below the ocean surface. Current forecasting efforts, however, focus only on metrics based on sea surface temperature. To create a more user-relevant detection system, it is necessary to provide forecasts of subsurface events. Here, we demonstrate the feasibility of seasonal forecasting of subsurface MHWs by using ocean heat content, a more relevant indicator than surface temperature for marine stakeholders. We validate summer MHW indicators in a fully-coupled seasonal forecast system against a global ocean reanalysis and satellite data. Our main result is that subsurface summer MHWs are predicted with greater skill than surface MHWs across much of the global ocean. Sub-surface MHWs are typically longer-lasting than surface events, rendering them easier targets for forecasting systems. Despite the long-lasting nature of subsurface MHWs, we also show that the dynamical forecast system used here typically outperforms a MHW-persistence model, indicating the capability for capturing the onset and decay of MHWs. Lastly, we highlight the role of warming oceans in MHW detection skill, by removing linear trends. This work highlights the need for a wider appreciation of subsurface ocean phenomena and the increased uptake of seasonal forecasting indicators by marine stakeholders such as marine protected areas and fisheries.

How to cite: McAdam, R., Masina, S., and Gualdi, S.: Seasonal forecasting of subsurface marine heat waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14540, https://doi.org/10.5194/egusphere-egu23-14540, 2023.

Atmospheric and marine heatwaves (AHW/MHW) have been observed around the world and are expected to increase in intensity and frequency under future climate change. Despite numerous studies that have examined AHW or MHW independently, only few regional studies investigated potential associations between these two types of extreme events. However, the co-occurrence of AHW and MHW could have broader and greater environmental, human, and economic impacts than an individual event, such as changes in species distributions, land and marine mass mortalities, or increased heat stress in coastal areas due to interactions between warm and moist air over the ocean. Based on research on AHW and MHW, we propose a comprehensive and globally applicable definition that relates the two extreme events and the two realms, and allows comparison with past and present concurrent and single events. Our definition is based on a conditional approach: We define a concurrent heatwave as an extreme event where sea surface temperature (SST) and 2 m air temperature (T_air) exceed their daily 90th percentiles, based on a 30-year historical baseline period, for at least 5 and 3 consecutive days, respectively (Perkins & Alexander 2013; Hobday et al. 2016). Thereby, we account for a potential lagged relationship between the two extremes by calculating and choosing the lag that provides the maximum probability of observing a MHW and an AHW simultaneously or delayed. In this work, we show the results of the most common heatwave metrics, such as duration, frequency, intensity, and cumulative intensity, for concurrent and single heatwaves in the Mediterranean Sea, Western Australia, and the Northwest Atlantic. We use SSTs from Advanced Very High-Resolution Radiometer (AVHRR) satellite data (NOAA OISST V2) as well as T_air from the ECMWF Reanalysis v5 (ERA5), both provided daily and globally on a high resolution (0.25°) for the period 1982 – 2022. In the Mediterranean Sea, we find concurrent heatwaves to be shorter and less frequent, but more intense and cumulatively intense than their single variants. For concurrent events, the MHW component (SST) is observed to be most intense in summer and spring, and the AHW component (T_air) in fall and winter. Moreover, the MHW appears to determine the strength of the concurrent heatwave in that region.

How to cite: Behr, L., Luther, N., Xoplaki, E., Petalas, S., Tragou, E., and Zervakis, V.: A global approach to defining Concurrent Atmospheric and Marine Heatwaves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6094, https://doi.org/10.5194/egusphere-egu23-6094, 2023.

How to cite: Verbrugge, N., Pisano, A., Augot, J., Greiner, E., Landolfi, A., Leonelli, F., Da Toma, V., Organelli, E., Marullo, S., and Santoleri, R.: deteCtion and threAts of maRinE Heat waves (CAREHeat) ESA project:  How to better characterize Marine Heatwaves ? , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7735, https://doi.org/10.5194/egusphere-egu23-7735, 2023.

Marine heatwaves (MHWs) are described as anomalously warm temperature events over a portion of the ocean during at least five consecutive days, developing in both coastal and open-ocean environments.

MHWs have been subject to numerous studies over the last years and it has been proved that their frequency and intensity is increasing through the decades in connection with human-induced global warming. Most of the studies are focusing on open-ocean MHW events and few in coastal environments, principally due to the lack of adequate data. Indeed, the detection of MHWs requires a long-term climatology of the ocean’s surface temperature, generally made with satellite data. Nevertheless, the complexity of coastal environments makes the use of satellite data non-optimal because of insufficient temporal coverage with high resolution data and interferences with land systems.

The primary purpose of this study is to detect MHWs in a semi enclosed sea, with the study case of the Sea of Chiloé, North Chilean Patagonia. This sea is characterised by multiple fjords and channel systems, and has a cloudy and rainy climate; consequently, this kind of environment is not compatible with the use of satellite data to build the long-term climatology of the sea temperature at a high resolution required to detect the MHWs. Here, we use another way to calculate the climatology, using in situ data and interpolating them in order to have a continuous field. Indeed, the inner seas of North Patagonia have been quite well sampled across the years, with measurements realised since the 1950s, spatially scattered in all the regions at both surface and depth (including fjords and channels). To spatially interpolate these data, we used the tool DIVAnd (Data-Interpolating Variational Analysis) which allows to spatially interpolate in an optimal way discrete observations onto a regular grid, taking advantage of the information in the 4 dimensions. Doing this interpolation, we got a monthly climatology at 32 different depths, from the surface to 400m. MHWs were then detected by comparing the climatology to the local temperature in the Reloncaví Sound, in the Northern part of the Sea of Chiloé, where an anchored buoy recording the temperature of the sea surface since 2017 is present. We focused on MHWs that occurred during the last five years. Strong ones were detected during summers 2021 and 2022: two successive very intense and brief events occurred in January and February 2021, and several short successive events with increasing intensity from November 2021 to February 2022. We also realised the comparison between MHWs detected using in situ data and detected using satellite data.

How to cite: Pujol, C., Pérez-Santos, I., Barth, A., Linford, P., and Alvera-Azcárate, A.: How to detect marine heatwaves in a fjord-like environment ? Study case of the semi-enclosed inner seas of North Patagonia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12509, https://doi.org/10.5194/egusphere-egu23-12509, 2023.

The Mediterranean Sea has suffered accelerated warming in the last 40 years, bringing higher temperatures mainly in the extended summer season. Sea surface temperature (SST) is not only experiencing higher extremes but also persistent high values. In this context, marine heat waves (MHW), considered as persistent and spatially extensive SST anomalies, have emerged as a key global change-induced high impact on the oceans. Hence, the characterization and trend analysis of MHWs has become of major interest. In this work MHWs in a relatively small, but complex, basin such as the Mediterranean, have been characterized and long-term trends assessed from SST satellite data analysis. In this study, a minimum area threshold, 5 % of the area basin, has been applied to avoid heat spikes or small-scale events. A trend to more frequent, intense, and longer MHWs is found in the 1982–2021 period in the Mediterranean. In the analysis, regional differences were apparent in MHWs characteristics and trends across the different sub-basins evidencing the fact that, even in a relatively small basin such as the Mediterranean, it is necessary to include a spatial perspective in the MHW analysis. Regarding the characterization of MHWs and trend analysis in the Mediterranean, a growing trend has been found in terms of frequency, duration, and intensity that accelerated since 2000 and especially in the last decade. This indicates not only the intensification and higher frequency of MHWs but the emergence of a new type of more intense, long-lasting and spatially extensive MHWs in recent years.

How to cite: Pastor, F. and Khodayar, S.: Marine heat waves: Characterizing a major climate impact in the Mediterranean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13058, https://doi.org/10.5194/egusphere-egu23-13058, 2023.

The Mediterranean Sea, a global climate change hot-spot region, has experienced an increase in marine heatwave (MHW) frequency and intensity since the early 1990s. These extreme events have been associated with a range of local-ecological impacts in the basin, which hosts a large marine biodiversity in addition to 480 million inhabitants along its coasts. According to 21^st century projections, the increasing MHW trends are likely to continue in the Mediterranean Sea. This calls for a deeper understanding of MHW drivers that can lead to a higher degree of MHW predictability. Therefore, this study investigates the dominant physical processes behind an ensemble of past Mediterranean MHWs, using the output of a dedicated, fully-coupled regional climate system model in hindcast mode, over the 1980-2018 period. In particular, we explore the vertical signature of multiple events in a local scale through “online” diagnostics of a mixed layer heat budget, where we disentangle the relative role of local-scale dynamics (e.g air-sea interactions, ocean currents, entrainment, mixing) during their development and decline. Preliminary results indicate a key role of atmosphere heat fluxes, wind forcing and vertical mixing on most events and a predominant horizontal advection presence only at smaller-scale. We present here a statistical overview of the dominant MHW drivers at the regional scale, across seasons and different regions in the Mediterranean basin, providing stakeholders and economy sectors affected by these marine extreme events, with critical information on their causes.

How to cite: Darmaraki, S., Waldman, R., Sevault, F., and Somot, S.: Dominant drivers of Past Mediterranean Marine Heatwaves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13986, https://doi.org/10.5194/egusphere-egu23-13986, 2023.

Under a warming climate, extreme ocean warming events, namely Marine Heatwaves (MHW), have become more frequent and stronger in global ocean regions. This study examines how the long-term variability of global marine heatwave characteristics is affected by global warming. We quantify the long-term trends (1982-2022) of MHW and investigate the connection between mean climate change and MHW trends. Since 1982, MHW properties over most global regions have increased positive signals during winter and summer. We investigate the rapidly variation of marine heatwave duration and intensity over the global ocean regions compared to the global average change. In addition, this study reveals the possible atmospheric and oceanic processes driving these rapidly changes in ocean areas where MHW occurs dramatically increasing. For example, during winter, the MHW has increased rapidly over the northern East Sea region (over 600 %) compared to the past two decades and this region is influenced on the northward shift of warm ocean current.  

How to cite: Lee, S., Park, M.-S., Kwon, M., Park, Y. G., Kim, Y.-H., and Choi, N.: Variation and possible causes of Marine Heatwaves under global warming condition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16991, https://doi.org/10.5194/egusphere-egu23-16991, 2023.

In recent decades, the Eastern Pacific has been subject to many pronounced marine heatwaves (MHWs), with far-reaching consequences for marine ecosystems. As MHWs are commonly detected at the sea surface, little is known about their vertical structure, let alone the temporal variability of this structure. To fill this gap, we detect and characterise vertically extended MHWs within a high-resolution model hindcast simulation (1979-2019) of the Eastern Pacific. Considering the vertical dimension in the MHW detection furthermore enables the tracking of vertical MHW propagation. We find that 71% of MHWs are on average confined to the mixed layer, while 29% reach at least 10m below. By clustering the MHWs, we identify four main vertical propagation patterns. While the majority of MHWs remains at the surface throughout their lifetime, 18% (13%) of MHWs subduct beneath (shoal towards) the surface, while 12% rather sit beneath the surface and exhibit multi-surfacing behaviour. As a consequence of the vertical propagation, MHWs affect upper ocean ecosystems substantially longer than diagnosed from the sea surface (40 vs. 30 days on average).In the mid-latitude Northeast Pacific, we find a seasonal cycle in the vertical MHW propagation clusters. We find that wintertime MHWs can detrain from the mixed layer, persist in the seasonal thermocline and re-entrain into the ML at a later stage. This finding agrees well with previous work on the role of the re-emergence phenomenon of sea surface temperature anomalies in the North Pacific and suggests potential sources of predictability for MHWs at the sea surface. At lower latitudes, we find that interannual variability associated with the El Niño-Southern Oscillation strongly dominates any seasonality in the occurrence of the different MHW clusters. Lastly, we find that accounting for the vertical MHW propagation almost doubles the average Eastern Pacific area affected by MHWs, compared to the surface only perspective. These new insights regarding MHW depth structures and their temporal variability mark an important step towards a better understanding of MHW drivers and the consequences of MHWs for marine ecosystems.

How to cite: Köhn, E. E., Gruber, N., Münnich, M., and Vogt, M.: On the structure and variability of vertically propagating marine heatwaves in the Eastern Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17173, https://doi.org/10.5194/egusphere-egu23-17173, 2023.

In October 2021, the Sicily Channel and the central-western Ionian Sea were affected by the passage of the tropical-like cyclone, or MEDICANE, Apollo. The system reached its maximum intensity between 29 and 30 October 2021 producing several damages, intense precipitations and huge coastal floodings in Sicily and Calabria regions. The surface circulation in the MEDICANE impacted area was characterized by permanent cyclonic vortices, offering the chance to describe the impact of a tropical-like cyclone on a pre-existing cold circulation structure. Atmospheric and ocean reanalyses (ERA5 and Marine Copernicus Service), as well as in-situ data from Argo floats, were used to describe the temporal evolution of Apollo, the resulting air-sea interaction, the thermohaline and biological response to its passage in the upper layer (0-150 m) of the western Ionian Sea. During the event, the core of the marine cyclone was characterized by a dramatic drop in temperature, corresponding to a local maximum in the wind-stress curl, Ekman pumping and current field relative vorticity. The strengthening of the cyclonic circulation led by the wind stress curl produced a strong vertical mixing in the surface layer (from 0 m to the Mixed Layer Depth - MLD) and an upwelling in the subsurface layer below the thermocline (MLD-150 m). The combined effect of vertical mixing and upwelling resulted in a shoaling of MLD, deep chlorophyll-a maximum, nutricline, and halocline. Oxygen and chlorophyll-a concentrations increased at surface, due to the enhanced oxygen solubility in the cooler water and higher productivity due to the increase of nutrients upwelled to the surface layer. These results show that the pre-existing cyclonic vortex along Apollo's trajectory leads to a different physical response compared to the one observed during previous MEDICANEs, confirming the influence of the conditions in place in driving the ocean’s reply to the extreme weather systems.

How to cite: Martellucci, R., Menna, M., Reale, M., Cossarini, G., Salon, S., Notarstefano, G., Mauri, E., Poulain, P.-M., Gallo, A., and Solidoro, C.: A case study of impacts of the extreme weather system on ocean circulation features in the Mediterranean Sea: Medicane Apollo (2021), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6472, https://doi.org/10.5194/egusphere-egu23-6472, 2023.