Articles | Volume 17, issue 12
https://doi.org/10.5194/gmd-17-4705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jun 2024
Model description paper |
| 17 Jun 2024
| 17 Jun 2024
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
Slovenian Environment Agency, Ljubljana, Slovenia
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Antonio Ricchi
Department of Physical and Chemical Sciences (DSFC), University of L'Aquila, L'Aquila, Italy
Center of Excellence in Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Sandro Carniel
Institute of Polar Sciences of the National Research Council (CNR-ISP), Venice, Italy
currently at: NATO STO Centre for Maritime Research and Experimentation, La Spezia, Italy
Davide Bonaldo
Institute of Marine Sciences of the National Research Council (CNR-ISMAR), Venice, Italy
Slovenian Environment Agency, Ljubljana, Slovenia
National Institute of Biology, Marine Biology Station, Piran, Slovenia
Related authors
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
Short summary
Short summary
A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Giovanni Scardino, Alok Kushabaha, Mario Marcello Miglietta, Davide Bonaldo, and Giovanni Scicchitano
EGUsphere, https://doi.org/10.5194/egusphere-2025-3260, https://doi.org/10.5194/egusphere-2025-3260, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
This study investigated how Mediterranean cyclones impact chlorophyll-a dynamics. Using reanalysis data, ARGO floats, and satellite imagery, we found that slow-moving cyclones significantly uplift the deep chlorophyll maximum. Upwelling and air-sea heat exchange were key mechanisms driving this uplift, enhancing nutrient availability and primary productivity. These findings highlight the strong influence of cyclone intensity and movement on biogeochemical processes in the Mediterranean Sea.
Amirhossein Barzandeh, Matjaž Ličer, Marko Rus, Matej Kristan, Ilja Maljutenko, Jüri Elken, Priidik Lagemaa, and Rivo Uiboupin
Ocean Sci., 21, 1315–1327, https://doi.org/10.5194/os-21-1315-2025, https://doi.org/10.5194/os-21-1315-2025, 2025
Short summary
Short summary
We evaluated a deep-learning model, HIDRA2, for predicting sea levels along the Estonian coast and compared it to traditional numerical models. HIDRA2 performed better overall, offering faster forecasts and valuable uncertainty estimates using ensemble predictions.
Marko Rus, Matjaž Ličer, and Matej Kristan
EGUsphere, https://doi.org/10.5194/egusphere-2025-3187, https://doi.org/10.5194/egusphere-2025-3187, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper introduces HIDRA-D, a novel deep-learning model for dense, gridded sea level forecasting from sparse satellite altimetry and tide gauge data. By forecasting low-frequency spatial components, HIDRA-D offers a faster alternative to traditional numerical models. Evaluated in the Adriatic Sea, it outperforms the NEMO general circulation model, reducing the mean absolute error by 28.0 %. The model is robust but shows limitations in complex coastal areas.
Davide Bonaldo, Sandro Carniel, Renato R. Colucci, Cléa Denamiel, Petra Pranić, Fabio Raicich, Antonio Ricchi, Lorenzo Sangelantoni, Ivica Vilibić, and Maria Letizia Vitelletti
Ocean Sci., 21, 1003–1031, https://doi.org/10.5194/os-21-1003-2025, https://doi.org/10.5194/os-21-1003-2025, 2025
Short summary
Short summary
We present a high-resolution modelling effort to investigate the possible end-of-century evolution of the main physical processes in the Adriatic Sea in a severe climate change scenario, with an ensemble approach (i.e. use of multiple simulations) allowing us to control the uncertainty of the predictions. Our model exhibits a satisfactory capability to reproduce the recent past and provides a basis for a set of multidisciplinary studies in this area over a multi-decadal horizon.
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208, https://doi.org/10.5194/gmd-2024-208, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
Short summary
Short summary
HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
Florian Pantillon, Silvio Davolio, Elenio Avolio, Carlos Calvo-Sancho, Diego Saul Carrió, Stavros Dafis, Emanuele Silvio Gentile, Juan Jesus Gonzalez-Aleman, Suzanne Gray, Mario Marcello Miglietta, Platon Patlakas, Ioannis Pytharoulis, Didier Ricard, Antonio Ricchi, Claudio Sanchez, and Emmanouil Flaounas
Weather Clim. Dynam., 5, 1187–1205, https://doi.org/10.5194/wcd-5-1187-2024, https://doi.org/10.5194/wcd-5-1187-2024, 2024
Short summary
Short summary
Cyclone Ianos of September 2020 was a high-impact but poorly predicted medicane (Mediterranean hurricane). A community effort of numerical modelling provides robust results to improve prediction. It is found that the representation of local thunderstorms controlled the interaction of Ianos with a jet stream at larger scales and its subsequent evolution. The results help us understand the peculiar dynamics of medicanes and provide guidance for the next generation of weather and climate models.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
Short summary
Short summary
A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, https://doi.org/10.5194/gmd-16-271-2023, 2023
Short summary
Short summary
We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
Nydia Catalina Reyes Suárez, Valentina Tirelli, Laura Ursella, Matjaž Ličer, Massimo Celio, and Vanessa Cardin
Ocean Sci., 18, 1321–1337, https://doi.org/10.5194/os-18-1321-2022, https://doi.org/10.5194/os-18-1321-2022, 2022
Short summary
Short summary
Explaining the dynamics of jellyfish blooms is a challenge for scientists. Biological and meteo-oceanographic data were combined on different timescales to explain the exceptional bloom of the jellyfish Rhizostoma pulmo in the Gulf of Trieste (Adriatic Sea) in April 2021. The bloom was associated with anomalously warm seasonal sea conditions. Then, a strong bora wind event enhanced upwelling and mixing of the water column, causing jellyfish to rise to the surface and accumulate along the coast.
Begoña Pérez Gómez, Ivica Vilibić, Jadranka Šepić, Iva Međugorac, Matjaž Ličer, Laurent Testut, Claire Fraboul, Marta Marcos, Hassen Abdellaoui, Enrique Álvarez Fanjul, Darko Barbalić, Benjamín Casas, Antonio Castaño-Tierno, Srđan Čupić, Aldo Drago, María Angeles Fraile, Daniele A. Galliano, Adam Gauci, Branislav Gloginja, Víctor Martín Guijarro, Maja Jeromel, Marcos Larrad Revuelto, Ayah Lazar, Ibrahim Haktan Keskin, Igor Medvedev, Abdelkader Menassri, Mohamed Aïssa Meslem, Hrvoje Mihanović, Sara Morucci, Dragos Niculescu, José Manuel Quijano de Benito, Josep Pascual, Atanas Palazov, Marco Picone, Fabio Raicich, Mohamed Said, Jordi Salat, Erdinc Sezen, Mehmet Simav, Georgios Sylaios, Elena Tel, Joaquín Tintoré, Klodian Zaimi, and George Zodiatis
Ocean Sci., 18, 997–1053, https://doi.org/10.5194/os-18-997-2022, https://doi.org/10.5194/os-18-997-2022, 2022
Short summary
Short summary
This description and mapping of coastal sea level monitoring networks in the Mediterranean and Black seas reveals the existence of 240 presently operational tide gauges. Information is provided about the type of sensor, time sampling, data availability, and ancillary measurements. An assessment of the fit-for-purpose status of the network is also included, along with recommendations to mitigate existing bottlenecks and improve the network, in a context of sea level rise and increasing extremes.
Emma Reyes, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Vanessa Cardin, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Maria J. Fernandes, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Pablo Lorente, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Adèle Révelard, Catalina Reyes-Suárez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Alejandro Orfila
Ocean Sci., 18, 797–837, https://doi.org/10.5194/os-18-797-2022, https://doi.org/10.5194/os-18-797-2022, 2022
Short summary
Short summary
This work reviews the existing advanced and emerging scientific and societal applications using HFR data, developed to address the major challenges identified in Mediterranean coastal waters organized around three main topics: maritime safety, extreme hazards and environmental transport processes. It also includes a discussion and preliminary assessment of the capabilities of existing HFR applications, finally providing a set of recommendations towards setting out future prospects.
Pablo Lorente, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Alejandro Orfila, Adèle Révelard, Emma Reyes, Jorge Sánchez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Laura Ursella, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Vanessa Cardin
Ocean Sci., 18, 761–795, https://doi.org/10.5194/os-18-761-2022, https://doi.org/10.5194/os-18-761-2022, 2022
Short summary
Short summary
High-frequency radar (HFR) is a land-based remote sensing technology that can provide maps of the surface circulation over broad coastal areas, along with wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network as well as present and future applications of this sensor for societal benefit such as search and rescue operations, safe vessel navigation, tracking of marine pollutants, and the monitoring of extreme events.
Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 14, 2057–2074, https://doi.org/10.5194/gmd-14-2057-2021, https://doi.org/10.5194/gmd-14-2057-2021, 2021
Short summary
Short summary
Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.
Matjaž Ličer, Solène Estival, Catalina Reyes-Suarez, Davide Deponte, and Anja Fettich
Nat. Hazards Earth Syst. Sci., 20, 2335–2349, https://doi.org/10.5194/nhess-20-2335-2020, https://doi.org/10.5194/nhess-20-2335-2020, 2020
Short summary
Short summary
In 2018 windsurfer’s mast broke about 1 km offshore during a scirocco storm in the northern Adriatic. He was drifting in severe conditions until he eventually beached alive and well in Sistiana (Italy) 24 h later. We conducted an interview with the survivor to reconstruct his trajectory. We simulate his trajectory in several ways and estimate the optimal search-and-rescue area for a civil rescue response. Properly calibrated virtual drifter properties are key to reliable rescue area forecasting.
Alexander Barth, Aida Alvera-Azcárate, Matjaz Licer, and Jean-Marie Beckers
Geosci. Model Dev., 13, 1609–1622, https://doi.org/10.5194/gmd-13-1609-2020, https://doi.org/10.5194/gmd-13-1609-2020, 2020
Short summary
Short summary
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images.
Christian Ferrarin, Andrea Valentini, Martin Vodopivec, Dijana Klaric, Giovanni Massaro, Marco Bajo, Francesca De Pascalis, Amedeo Fadini, Michol Ghezzo, Stefano Menegon, Lidia Bressan, Silvia Unguendoli, Anja Fettich, Jure Jerman, Matjaz̆ Ličer, Lidija Fustar, Alvise Papa, and Enrico Carraro
Nat. Hazards Earth Syst. Sci., 20, 73–93, https://doi.org/10.5194/nhess-20-73-2020, https://doi.org/10.5194/nhess-20-73-2020, 2020
Short summary
Short summary
Here we present a shared and interoperable system to allow a better exchange of and elaboration on information related to sea storms among countries. The proposed integrated web system (IWS) is a combination of a common data system for sharing ocean observations and forecasts, a multi-model ensemble system, a geoportal, and interactive geo-visualization tools. This study describes the application of the developed system to the exceptional storm event of 29 October 2018.
Sandro Carniel, Judith Wolf, Vittorio E. Brando, and Lakshmi H. Kantha
Ocean Sci., 13, 495–501, https://doi.org/10.5194/os-13-495-2017, https://doi.org/10.5194/os-13-495-2017, 2017
Renata Archetti, Agnese Paci, Sandro Carniel, and Davide Bonaldo
Nat. Hazards Earth Syst. Sci., 16, 1107–1122, https://doi.org/10.5194/nhess-16-1107-2016, https://doi.org/10.5194/nhess-16-1107-2016, 2016
Short summary
Short summary
An application to monitor the response of a beach to single storms, in order to predict shoreline changes and to plan the defence of the shore zone is presented. On the study area, Jesolo (Italy), video and current stations were installed. The methodology, which is economically attractive, proves to be a valuable system for providing detailed indications on beach erosion processes and can be used for improving the collaboration between coastal scientists and managers to solve beach-maintenance problems.
Francesco Marcello Falcieri, Lakshmi Kantha, Alvise Benetazzo, Andrea Bergamasco, Davide Bonaldo, Francesco Barbariol, Vlado Malačič, Mauro Sclavo, and Sandro Carniel
Ocean Sci., 12, 433–449, https://doi.org/10.5194/os-12-433-2016, https://doi.org/10.5194/os-12-433-2016, 2016
Short summary
Short summary
Between January 30th and February 4th we collected the first turbulence observations in the Gulf of Trieste under different wind forcing and water column structure. The vertical profiles of the turbulence kinetic energy dissipation rates showed that the presence near the sea floor of different water masses, inflowing from the open sea, can prevent the complete mixing of the water column. This dumping effect is enhanced when these masses present higher suspended sediment concentrations.
Francesco Barbariol, Francesco Marcello Falcieri, Carlotta Scotton, Alvise Benetazzo, Sandro Carniel, and Mauro Sclavo
Ocean Sci., 12, 403–415, https://doi.org/10.5194/os-12-403-2016, https://doi.org/10.5194/os-12-403-2016, 2016
Short summary
Short summary
The analysis presented in the paper aims at extending the classification capabilities of Self-Organizing Maps (SOM) within the context of ocean waves. Indeed, the intrinsic SOM difficulty in representing extremes of the wave climate is discussed and alternative strategies are proposed in order to represent the whole wave climate at a given location. Among them, a two-step SOM together with a double-side map provides the best results.
M. Ličer, P. Smerkol, A. Fettich, M. Ravdas, A. Papapostolou, A. Mantziafou, B. Strajnar, J. Cedilnik, M. Jeromel, J. Jerman, S. Petan, V. Malačič, and S. Sofianos
Ocean Sci., 12, 71–86, https://doi.org/10.5194/os-12-71-2016, https://doi.org/10.5194/os-12-71-2016, 2016
Short summary
Short summary
We compare the northern Adriatic response to an extreme bora event, as simulated by one-way and two-way (i.e. with ocean feedback to the atmosphere) atmosphere-ocean coupling. We show that two-way coupling yields significantly better estimates of heat fluxes, most notably sensible heat flux, across the air-sea interface. When compared to observations in the northern Adriatic, two-way coupled system consequently leads to a better representation of ocean temperatures throughout the event.
V. E. Brando, F. Braga, L. Zaggia, C. Giardino, M. Bresciani, E. Matta, D. Bellafiore, C. Ferrarin, F. Maicu, A. Benetazzo, D. Bonaldo, F. M. Falcieri, A. Coluccelli, A. Russo, and S. Carniel
Ocean Sci., 11, 909–920, https://doi.org/10.5194/os-11-909-2015, https://doi.org/10.5194/os-11-909-2015, 2015
Short summary
Short summary
Sea surface temperature and turbidity, derived from satellite imagery, were used to characterize river plumes in the northern Adriatic Sea during a significant flood event in November 2014. Circulation patterns and sea surface salinity, from an operational coupled ocean-wave model, supported the interpretation of the plumes' interaction with the receiving waters and among them.
P. Malanotte-Rizzoli, V. Artale, G. L. Borzelli-Eusebi, S. Brenner, A. Crise, M. Gacic, N. Kress, S. Marullo, M. Ribera d'Alcalà, S. Sofianos, T. Tanhua, A. Theocharis, M. Alvarez, Y. Ashkenazy, A. Bergamasco, V. Cardin, S. Carniel, G. Civitarese, F. D'Ortenzio, J. Font, E. Garcia-Ladona, J. M. Garcia-Lafuente, A. Gogou, M. Gregoire, D. Hainbucher, H. Kontoyannis, V. Kovacevic, E. Kraskapoulou, G. Kroskos, A. Incarbona, M. G. Mazzocchi, M. Orlic, E. Ozsoy, A. Pascual, P.-M. Poulain, W. Roether, A. Rubino, K. Schroeder, J. Siokou-Frangou, E. Souvermezoglou, M. Sprovieri, J. Tintoré, and G. Triantafyllou
Ocean Sci., 10, 281–322, https://doi.org/10.5194/os-10-281-2014, https://doi.org/10.5194/os-10-281-2014, 2014
H. Mihanović, I. Vilibić, S. Carniel, M. Tudor, A. Russo, A. Bergamasco, N. Bubić, Z. Ljubešić, D. Viličić, A. Boldrin, V. Malačič, M. Celio, C. Comici, and F. Raicich
Ocean Sci., 9, 561–572, https://doi.org/10.5194/os-9-561-2013, https://doi.org/10.5194/os-9-561-2013, 2013
Related subject area
Oceanography
Wave forecast investigations on downscaling, source terms, and tides for Aotearoa New Zealand
Impacts of the CICE sea ice model and ERA atmosphere on an Antarctic MetROMS ocean model, MetROMS-UHel-v1.0
Comparing an idealized deterministic–stochastic model (SUP model, version 1) of the tide- and wind-driven sea surface currents in the Gulf of Trieste to high-frequency radar observations
PIBM 1.0: an individual-based model for simulating phytoplankton acclimation, diversity, and evolution in the ocean
An effective communication topology for performance optimization: a case study of the finite-volume wave modeling (FVWAM)
GREAT v1.0: Global Real-time Early Assessment of Tsunamis
Using automatic calibration to improve the physics behind complex numerical models: an example from a 3D lake model using Delft3D (v6.02.10) and DYNO-PODS (v1.0)
Improvements to the Met Office's global ocean–sea ice forecasting system including model and data assimilation changes
Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models
PyESPERv1.01.01: A Python implementation of empirical seawater property estimation routines (ESPERs)
Modelling Microplastic Dynamics in Estuaries: A Comprehensive Review, Challenges and Recommendations
A new global high-resolution wave model for the tropical ocean using WAVEWATCH III version 7.14
DINO: A Diabatic Model of Pole-to-Pole Ocean Dynamics to Assess Subgrid Parameterizations across Horizontal Scales
sedInterFoam 1.0: a three-phase numerical model for sediment transport applications with free surfaces
Offline Fennel: A High-Performance and Computationally Efficient Biogeochemical Model within the Regional Ocean Modeling System (ROMS)
The Ross Sea and Amundsen Sea Ice–Sea Model (RAISE v1.0): a high-resolution ocean–sea ice–ice shelf coupling model for simulating the Dense Shelf Water and Antarctic Bottom Water in the Ross Sea, Antarctica
Sensitivity of the tropical Atlantic to vertical mixing in two ocean models (ICON-O v2.6.6 and FESOM v2.5)
CRITER 1.0: A coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data
HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures
A wave-resolving two-dimensional vertical Lagrangian approach to model microplastic transport in nearshore waters based on TrackMPD 3.0
HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
ISWFM-NSCS v2.0: advancing the internal solitary wave forecasting model with background currents and horizontally inhomogeneous stratifications
DalROMS-NWA12 v1.0, a coupled circulation–ice–biogeochemistry modelling system for the northwest Atlantic Ocean: development and validation
A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature
Evaluating an accelerated forcing approach for improving computational efficiency in coupled ice sheet–ocean modelling
An optimal transformation method for inferring ocean tracer sources and sinks
Data-driven rolling model for global wave height
PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks
Experimental design for the Marine Ice Sheet–Ocean Model Intercomparison Project – phase 2 (MISOMIP2)
Development of a total variation diminishing (TVD) sea ice transport scheme and its application in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids
Spurious numerical mixing under strong tidal forcing: a case study in the south-east Asian seas using the Symphonie model (v3.1.2)
Modelling the water isotope distribution in the Mediterranean Sea using a high-resolution oceanic model (NEMO-MED12-watiso v1.0): evaluation of model results against in situ observations
LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry
Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0
A simple approach to represent precipitation-derived freshwater fluxes into nearshore ocean models: an FVCOM4.1 case study of Quatsino Sound, British Columbia
An optimal transformation method applied to diagnose the ocean carbon budget
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 2: Towards a better representation of total alkalinity when modeling the carbonate system and air–sea CO2 fluxes
Development of a novel storm surge inundation model framework for efficient prediction
Skin sea surface temperature schemes in coupled ocean–atmosphere modelling: the impact of chlorophyll-interactive e-folding depth
StraitFlux – precise computations of water strait fluxes on various modeling grids
Comparison of the Coastal and Regional Ocean COmmunity model (CROCO) and NCAR-LES in non-hydrostatic simulations
Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System
CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago
Implementation of additional spectral wave field exchanges in a three-dimensional wave–current coupled WAVEWATCH-III (version 6.07) and CROCO (version 1.2) configuration: assessment of their implications for macro-tidal coastal hydrodynamics
Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system
LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2
New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4
MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries
Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts
Great Lakes wave forecast system on high-resolution unstructured meshes
Rafael Santana, Richard Gorman, Emily Lane, Stuart Moore, Cyprien Bosserelle, Glen Reeve, and Christo Rautenbach
Geosci. Model Dev., 18, 4877–4898, https://doi.org/10.5194/gmd-18-4877-2025, https://doi.org/10.5194/gmd-18-4877-2025, 2025
Short summary
Short summary
This research explores improving wave forecasts in Aotearoa New Zealand, particularly at Banks Peninsula and Baring Head. We used detailed models and found that forecasts at Baring Head improved significantly due to its narrow geography, but changes at Banks Peninsula were minimal. The study demonstrates that local conditions greatly influence the effectiveness of wave prediction models, highlighting the need for tailored approaches in coastal forecasting to enhance accuracy in the predictions.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David Anthony Bailey, and Petteri Uotila
Geosci. Model Dev., 18, 4823–4853, https://doi.org/10.5194/gmd-18-4823-2025, https://doi.org/10.5194/gmd-18-4823-2025, 2025
Short summary
Short summary
The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Sofia Flora, Laura Ursella, and Achim Wirth
Geosci. Model Dev., 18, 4685–4712, https://doi.org/10.5194/gmd-18-4685-2025, https://doi.org/10.5194/gmd-18-4685-2025, 2025
Short summary
Short summary
We developed a hierarchy of idealized deterministic–stochastic models to simulate sea surface currents in the Gulf of Trieste. They include tide- and wind-driven sea surface current components, resolving the slowly varying part of the flow, and a stochastic signal, representing the fast-varying small-scale dynamics. The comparison with high-frequency radar observations shows that the non-Gaussian stochastic model captures key dynamics and mimics the observed fat-tailed probability distribution.
Iria Sala and Bingzhang Chen
Geosci. Model Dev., 18, 4155–4182, https://doi.org/10.5194/gmd-18-4155-2025, https://doi.org/10.5194/gmd-18-4155-2025, 2025
Short summary
Short summary
Phytoplankton, tiny photosynthetic organisms, produce nearly half of Earth's oxygen. To analyze their physiology, diversity, and evolution in the ocean, we developed a model that treats phytoplankton as individual particles. Moreover, our model considers phytoplankton size, temperature, and light traits and allows for mutations in phytoplankton cells. Thus, our model provides a valuable tool for advancing the study of phytoplankton physiology, diversity, and evolution.
Renbo Pang, Fujiang Yu, Yuanyong Gao, Ye Yuan, Liang Yuan, and Zhiyi Gao
Geosci. Model Dev., 18, 4119–4136, https://doi.org/10.5194/gmd-18-4119-2025, https://doi.org/10.5194/gmd-18-4119-2025, 2025
Short summary
Short summary
The application of distributed graph communication topology in Earth models has rarely been studied. We tested and compared this topology with the traditional point-to-point communication method using a wave model. We found that this topology with the Intel MPI (message-passing interface) library is more efficient. Additionally, using this topology can greatly improve the performance of the wave model and could help improve the performance of other Earth models.
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Geosci. Model Dev., 18, 3487–3507, https://doi.org/10.5194/gmd-18-3487-2025, https://doi.org/10.5194/gmd-18-3487-2025, 2025
Short summary
Short summary
The GREAT v1.0 software introduces a novel tsunami warning technology for global real-time analysis. It leverages acoustic signals generated by tsunamis, which propagate faster than the tsunami itself, enabling real-time detection and assessment. Integrating various models, the software provides reliable and rapid assessment, maps risk areas, and estimates tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.
Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard, and Marco Toffolon
Geosci. Model Dev., 18, 3473–3486, https://doi.org/10.5194/gmd-18-3473-2025, https://doi.org/10.5194/gmd-18-3473-2025, 2025
Short summary
Short summary
Models simplify reality using assumptions, which can sometimes introduce flaws and affect their accuracy. Properly calibrating model parameters is essential, and although automated tools can speed up this process, they may occasionally produce incorrect values due to inconsistencies in the model. We demonstrate that by carefully applying automated tools, we were able to identify and correct a flaw in a widely used model for lake environments.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
Short summary
Short summary
The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Larissa Marie Dias and Brendan Rae Carter
EGUsphere, https://doi.org/10.5194/egusphere-2025-458, https://doi.org/10.5194/egusphere-2025-458, 2025
Short summary
Short summary
The increasing availability of oceanographic physical and chemical data necessitates accompanying methods for optimizing use of this data. This project produced algorithms (PyESPERs) for estimating biogeochemical seawater properties in Python, a freely available coding language. These algorithms were based on Empirical Seawater Property Estimation Routines (ESPERs), which were originally written in the proprietary MATLAB coding language and can be used in studies of marine carbonate chemistry.
Betty John Kaimathuruthy, Isabel Jalón-Rojas, and Damien Sous
EGUsphere, https://doi.org/10.5194/egusphere-2025-529, https://doi.org/10.5194/egusphere-2025-529, 2025
Short summary
Short summary
Studies on plastic pollution have emerged as a rapidly growing field of research. Modelling microplastic transport in estuaries stems from their complex hydrodynamics and diverse particle behaviours affecting the dispersion and retention of microplastics. Our paper reviews key modelling approaches applied in estuaries analyzing their setups and parameterizations. We provide recommendations and future directions to improve the accuracy and modelling strategies for estuarine microplastic research.
Axelle Gaffet, Xavier Bertin, Damien Sous, Héloïse Michaud, Aron Roland, and Emmanuel Cordier
Geosci. Model Dev., 18, 1929–1946, https://doi.org/10.5194/gmd-18-1929-2025, https://doi.org/10.5194/gmd-18-1929-2025, 2025
Short summary
Short summary
This study presents a new global wave model that improves predictions of sea states in tropical areas by using a high-resolution grid and corrected wind fields. The model is validated globally with satellite data and nearshore using in situ data. The model allows for the first time direct comparisons with in situ data collected at 10–30 m water depth, which is very close to shore due to the steep slope usually surrounding volcanic islands.
David Kamm, Julie Deshayes, and Gurvan Madec
EGUsphere, https://doi.org/10.5194/egusphere-2025-1100, https://doi.org/10.5194/egusphere-2025-1100, 2025
Short summary
Short summary
We propose an idealized model of pole-to-pole ocean dynamics designed as a testbed for eddy parameterizations across a range of horizontal scales. While computationally affordable, it is able to capture key metrics of the climate system. By comparing simulations at low, intermediate and high horizontal resolution, we demonstrate its utility for evaluating eddy parameterizations, both in terms of their effect on the mean-state and by diagnosing the unresolved eddy fluxes they aim to represent.
Antoine Mathieu, Yeulwoo Kim, Tian-Jian Hsu, Cyrille Bonamy, and Julien Chauchat
Geosci. Model Dev., 18, 1561–1573, https://doi.org/10.5194/gmd-18-1561-2025, https://doi.org/10.5194/gmd-18-1561-2025, 2025
Short summary
Short summary
Most of the tools available to model sediment transport do not account for complex physical mechanisms such as surface-wave-driven processes. In this study, a new model, sedInterFoam, allows us to reproduce numerically complex configurations in order to investigate coastal sediment transport applications dominated by surface waves and to gain insight into the complex physical processes associated with breaking waves and morphodynamics.
Júlia Crespin, Jordi Solé, and Miquel Canals
EGUsphere, https://doi.org/10.5194/egusphere-2025-865, https://doi.org/10.5194/egusphere-2025-865, 2025
Short summary
Short summary
This study presents the Offline Fennel model, a tool designed to simulate ocean biogeochemical processes efficiently. By using existing hydrodynamic data, the model significantly reduces computation time from 6 hours to just 30 minutes. We tested its accuracy in the Northern Gulf of Mexico and found it closely matches physical-biogeochemical coupled simulations. This model allows researchers to conduct more tests and simulations without the need for extensive computational resources.
Zhaoru Zhang, Chuan Xie, Chuning Wang, Yuanjie Chen, Heng Hu, and Xiaoqiao Wang
Geosci. Model Dev., 18, 1375–1393, https://doi.org/10.5194/gmd-18-1375-2025, https://doi.org/10.5194/gmd-18-1375-2025, 2025
Short summary
Short summary
A coupled fine-resolution ocean–ice model is developed for the Ross Sea and adjacent regions in Antarctica, a key area for the formation of global ocean bottom water, the Antarctic Bottom Water (AABW), which affects global ocean circulation. The model has a high skill level in simulating sea ice production driving the AABW source water formation and AABW properties when assessed against observations. A model experiment shows a significant impact of ice shelf melting on the AABW characteristics.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
Short summary
Short summary
Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208, https://doi.org/10.5194/gmd-2024-208, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
Short summary
Short summary
HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
Isabel Jalón-Rojas, Damien Sous, and Vincent Marieu
Geosci. Model Dev., 18, 319–336, https://doi.org/10.5194/gmd-18-319-2025, https://doi.org/10.5194/gmd-18-319-2025, 2025
Short summary
Short summary
This study presents a novel modeling approach for understanding microplastic transport in coastal waters. The model accurately replicates experimental data and reveals key transport mechanisms. The findings enhance our knowledge of how microplastics move in nearshore environments, aiding in coastal management and efforts to combat plastic pollution globally.
Greig Oldford, Tereza Jarníková, Villy Christensen, and Michael Dunphy
Geosci. Model Dev., 18, 211–237, https://doi.org/10.5194/gmd-18-211-2025, https://doi.org/10.5194/gmd-18-211-2025, 2025
Short summary
Short summary
We developed a 3D ocean model called the Hindcast of the Salish Sea (HOTSSea v1) that recreates physical conditions throughout the Salish Sea from 1980 to 2018. It was not clear that acceptable accuracy could be achieved because of computational and data limitations, but the model's predictions agreed well with observations. When we used the model to examine ocean temperature trends in areas that lack observations, it indicated that some seasons and areas are warming faster than others.
Yankun Gong, Xueen Chen, Jiexin Xu, Zhiwu Chen, Qingyou He, Ruixiang Zhao, Xiao-Hua Zhu, and Shuqun Cai
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-165, https://doi.org/10.5194/gmd-2024-165, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
A new internal solitary wave forecasting (ISW) model in the northern South China Sea (ISWFM-NSCS v2.0) improves ISW predictions by incorporating background currents and inhomogeneous stratifications. Additionally, viscosity and diffusivity coefficients are optimized to maintain stable stratifications, extending the forecasting period. Sensitivity experiments illustrate that ISWFM-NSCS v2.0 significantly enhances predictions of various wave properties.
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
Geosci. Model Dev., 17, 8697–8733, https://doi.org/10.5194/gmd-17-8697-2024, https://doi.org/10.5194/gmd-17-8697-2024, 2024
Short summary
Short summary
We developed a modelling system of the northwest Atlantic Ocean that simulates the currents, temperature, salinity, and parts of the biochemical cycle of the ocean, as well as sea ice. The system combines advanced, open-source models and can be used to study, for example, the ocean capture of atmospheric carbon dioxide, which is a key process in the global climate. The system produces realistic results, and we use it to investigate the roles of tides and sea ice in the northwest Atlantic Ocean.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
Short summary
Short summary
Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
Short summary
Short summary
We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
Jan D. Zika and Taimoor Sohail
Geosci. Model Dev., 17, 8049–8068, https://doi.org/10.5194/gmd-17-8049-2024, https://doi.org/10.5194/gmd-17-8049-2024, 2024
Short summary
Short summary
We describe a method to relate fluxes of heat and freshwater at the sea surface to the resulting distribution of seawater among categories such as warm and salty or cold and salty. The method exploits the laws that govern how heat and salt change when water mixes. The method will allow the climate community to improve estimates of how much heat the ocean is absorbing and how rainfall and evaporation are changing across the globe.
Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-181, https://doi.org/10.5194/gmd-2024-181, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini
Geosci. Model Dev., 17, 7347–7364, https://doi.org/10.5194/gmd-17-7347-2024, https://doi.org/10.5194/gmd-17-7347-2024, 2024
Short summary
Short summary
Monitoring the ocean is essential for studying marine life and human impact. Our new software, PPCon, uses ocean data to predict key factors like nitrate and chlorophyll levels, which are hard to measure directly. By leveraging machine learning, PPCon offers more accurate and efficient predictions.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
Short summary
Short summary
Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Qian Wang, Yang Zhang, Fei Chai, Y. Joseph Zhang, and Lorenzo Zampieri
Geosci. Model Dev., 17, 7067–7081, https://doi.org/10.5194/gmd-17-7067-2024, https://doi.org/10.5194/gmd-17-7067-2024, 2024
Short summary
Short summary
We coupled an unstructured hydro-model with an advanced column sea ice model to meet the growing demand for increased resolution and complexity in unstructured sea ice models. Additionally, we present a novel tracer transport scheme for the sea ice coupled model and demonstrate that this scheme fulfills the requirements for conservation, accuracy, efficiency, and monotonicity in an idealized test. Our new coupled model also has good performance in realistic tests.
Adrien Garinet, Marine Herrmann, Patrick Marsaleix, and Juliette Pénicaud
Geosci. Model Dev., 17, 6967–6986, https://doi.org/10.5194/gmd-17-6967-2024, https://doi.org/10.5194/gmd-17-6967-2024, 2024
Short summary
Short summary
Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations is made challenging by errors that result in unwanted mixing, compromising simulation realism. Here we illustrate the spurious effect that tides can have on simulations of south-east Asia. Although they play an important role in determining the state of the ocean, they can increase numerical errors and make simulation outputs less realistic. We also provide insights into how to reduce these errors.
Mohamed Ayache, Jean-Claude Dutay, Anne Mouchet, Kazuyo Tachikawa, Camille Risi, and Gilles Ramstein
Geosci. Model Dev., 17, 6627–6655, https://doi.org/10.5194/gmd-17-6627-2024, https://doi.org/10.5194/gmd-17-6627-2024, 2024
Short summary
Short summary
Water isotopes (δ18O, δD) are one of the most widely used proxies in ocean climate research. Previous studies using water isotope observations and modelling have highlighted the importance of understanding spatial and temporal isotopic variability for a quantitative interpretation of these tracers. Here we present the first results of a high-resolution regional dynamical model (at 1/12° horizontal resolution) developed for the Mediterranean Sea, one of the hotspots of ongoing climate change.
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, https://doi.org/10.5194/gmd-17-6415-2024, 2024
Short summary
Short summary
Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
Geosci. Model Dev., 17, 6123–6136, https://doi.org/10.5194/gmd-17-6123-2024, https://doi.org/10.5194/gmd-17-6123-2024, 2024
Short summary
Short summary
Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, GPU-accelerated computing is introduced in WAve Modeling Cycle 6 (WAM6). With this effort, global high-resolution wave simulations can now run on GPUs up to tens of times faster than the currently available models can on a CPU node with results that are just as accurate.
Krysten Rutherford, Laura Bianucci, and William Floyd
Geosci. Model Dev., 17, 6083–6104, https://doi.org/10.5194/gmd-17-6083-2024, https://doi.org/10.5194/gmd-17-6083-2024, 2024
Short summary
Short summary
Nearshore ocean models often lack complete information about freshwater fluxes due to numerous ungauged rivers and streams. We tested a simple rain-based hydrological model as inputs into an ocean model of Quatsino Sound, Canada, with the aim of improving the representation of the land–ocean connection in the nearshore model. Through multiple tests, we found that the performance of the ocean model improved when providing 60 % or more of the freshwater inputs from the simple runoff model.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
Short summary
Short summary
The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 17, 5851–5882, https://doi.org/10.5194/gmd-17-5851-2024, https://doi.org/10.5194/gmd-17-5851-2024, 2024
Short summary
Short summary
The carbonate system is typically studied using measurements, but modeling can contribute valuable insights. Using a biogeochemical model, we propose a new representation of total alkalinity, dissolved inorganic carbon, pCO2, and pH in a highly dynamic Mediterranean coastal area, the Bay of Marseille, a useful addition to measurements. Through a detailed analysis of pCO2 and air–sea CO2 fluxes, we show that variations are strongly impacted by the hydrodynamic processes that affect the bay.
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, https://doi.org/10.5194/gmd-17-5497-2024, 2024
Short summary
Short summary
Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
Short summary
Short summary
Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, https://doi.org/10.5194/gmd-17-4095-2024, 2024
Short summary
Short summary
Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
Short summary
Short summary
The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
Short summary
Short summary
In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
Short summary
Short summary
Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
Short summary
Short summary
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
Short summary
Short summary
The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
Short summary
Short summary
A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
Short summary
Short summary
We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
Short summary
Short summary
Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024, https://doi.org/10.5194/gmd-17-1023-2024, 2024
Short summary
Short summary
This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
Cited articles
Antonioli, F., Anzidei, M., Amorosi, A., Lo Presti, V., Mastronuzzi, G., Deiana, G., De Falco, G., Fontana, A., Fontolan, G., Lisco, S., Marsico, A., Moretti, M., Orrù, P., Sannino, G., Serpelloni, E., and Vecchio, A.: Sea-level rise and potential drowning of the Italian coastal plains: Flooding risk scenarios for 2100, Quaternary Sci. Rev., 158, 29–43, https://doi.org/10.1016/j.quascirev.2016.12.021, 2017. a
Astariz, S. and Iglesias, G.: The economics of wave energy: A review, Renew. Sust. Energ. Rev., 45, 397–408, https://doi.org/10.1016/j.rser.2015.01.061, 2015. a
Bellafiore, D., Bucchignani, E., Gualdi, S., Carniel, S., Djurdjević, V., and Umgiesser, G.: Assessment of meteorological climate models as inputs for coastal studies, Ocean Dynam., 62, 555–568, https://doi.org/10.1007/s10236-011-0508-2, 2012. a
Benetazzo, A., Bergamasco, A., Bonaldo, D., Falcieri, F., Sclavo, M., Langone, L., and Carniel, S.: Response of the Adriatic Sea to an intense cold air outbreak: Dense water dynamics and wave-induced transport, Prog. Oceanogr., 128, 115–138, https://doi.org/10.1016/j.pocean.2014.08.015, 2014. a
Benetazzo, A., Davison, S., Barbariol, F., Mercogliano, P., Favaretto, C., and Sclavo, M.: Correction of ERA5 Wind for Regional Climate Projections of Sea Waves, Water, 14, 1590, https://doi.org/10.3390/w14101590, 2022. a
Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M., and Gentine, P.: Machine learning for clouds and climate (invited chapter for the agu geophysical monograph series “clouds and climate”), Earth and Space Science Open Archive [preprint], 27, https://doi.org/10.1002/essoar.10506925.1, 2021. a
Boehme, L. and Rosso, I.: Classifying oceanographic structures in the Amundsen Sea, Antarctica, Geophys. Res. Lett., 48, e2020GL089412, https://doi.org/10.1029/2020GL089412, 2021. a
Bonaldo, D., Benetazzo, A., Bergamasco, A., Campiani, E., Foglini, F., Sclavo, M., Trincardi, F., and Carniel, S.: Interactions among Adriatic continental margin morphology, deep circulation and bedform patterns, Mar. Geol., 375, 82–98, https://doi.org/10.1016/j.margeo.2015.09.012, 2016. a
Bonaldo, D., Bucchignani, E., Ricchi, A., and Carniel, S.: Wind storminess in the adriatic sea in a climate change scenario, Acta Adriat., 58, 195–208, https://doi.org/10.32582/aa.58.2.1, 2017. a
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions: 1. Model description and validation, J. Geophys. Res., 104, 7649, https://doi.org/10.1029/98JC02622, 1999. a
Bucchignani, E., Montesarchio, M., Zollo, A. L., and Mercogliano, P.: High-resolution climate simulations with COSMO-CLM over Italy: Performance evaluation and climate projections for the 21st century, Int. J. Climatol., 36, 735–756, https://doi.org/10.1002/joc.4379, 2016. a, b
Cavaleri, L., Alves, J. H., Ardhuin, F., Babanin, A., Banner, M., Belibassakis, K., Benoit, M., Donelan, M., Groeneweg, J., Herbers, T. H., Hwang, P., Janssen, P. A., Janssen, T., Lavrenov, I. V., Magne, R., Monbaliu, J., Onorato, M., Polnikov, V., Resio, D., Rogers, W. E., Sheremet, A., McKee Smith, J., Tolman, H. L., van Vledder, G., Wolf, J., and Young, I.: Wave modelling – The state of the art, Prog. Oceanogr., 75, 603–674, https://doi.org/10.1016/j.pocean.2007.05.005, 2007. a
Cavaleri, L., Abdalla, S., Benetazzo, A., Bertotti, L., Bidlot, J. R., Breivik, Carniel, S., Jensen, R. E., Portilla-Yandun, J., Rogers, W. E., Roland, A., Sanchez-Arcilla, A., Smith, J. M., Staneva, J., Toledo, Y., van Vledder, G. P., and van der Westhuysen, A. J.: Wave modelling in coastal and inner seas, Prog. Oceanogr., 167, 164–233, https://doi.org/10.1016/j.pocean.2018.03.010, 2018. a
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv [preprint], arXiv:1406.1078, 2014. a
Conv1d: PyTorch implementation, https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html, last access: 4 June 2024. a
Di Silvio, G., Dall'Angelo, C., Bonaldo, D., and Fasolato, G.: Long-term model of planimetric and bathymetric evolution of a tidal lagoon, Cont. Shelf Res., 30, 894–903, https://doi.org/10.1016/j.csr.2009.09.010, 2010. a
Friedrichs, C. T.: Tidal Flat Morphodynamics: A Synthesis, Vol. 3, Elsevier Inc., https://doi.org/10.1016/B978-0-12-374711-2.00307-7, 2011. a
Giorgi, F. and Lionello, P.: Climate change projections for the Mediterranean region, Global Planet. Change, 63, 90–104, https://doi.org/10.1016/j.gloplacha.2007.09.005, 2008. a
Hendrycks, D. and Gimpel, K.: Gaussian error linear units (gelus), arXiv [preprint], arXiv:1606.08415, 2016. a, b, c
Institute of Information Science, G. o. t. R. o. S.: IZUM, https://izum.si/en/home (last access: 3 March 2023), 2023. a
James, S. C., Zhang, Y., and O'Donncha, F.: A machine learning framework to forecast wave conditions, Coast. Eng., 137, 1–10, https://doi.org/10.1016/j.coastaleng.2018.03.004, 2018. a
Janssens, M. and Hulshoff, S. J.: Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model, J. Adv. Model. Earth Sy., 14, e2021MS002490, https://doi.org/10.1029/2021MS002490, e2021MS002490 2021MS002490, 2022. a
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv [preprint], arXiv:1412.6980, 2014. a
Lionello, P., Conte, D., Marzo, L., and Scarascia, L.: The contrasting effect of increasing mean sea level and decreasing storminess on the maximum water level during storms along the coast of the Mediterranean Sea in the mid 21st century, Global Planet. Change, 151, 80–91, https://doi.org/10.1016/j.gloplacha.2016.06.012, 2017. a
Mallett, H. K. W., Boehme, L., Fedak, M., Heywood, K. J., Stevens, D. P., and Roquet, F.: Variation in the Distribution and Properties of Circumpolar Deep Water in the Eastern Amundsen Sea, on Seasonal Timescales, Using Seal-Borne Tags, Geophys. Res. Lett., 45, 4982–4990, https://doi.org/10.1029/2018GL077430, 2018. a
Mercogliano, P.: 10m wind components of the dynamical downscaling with COSMO-CLM of historical (1979/2005) and future climate (2006/2100) data under scenario RCP4.5 and RCP8.5 at 8 km over Italy, CMCC DDS [data set], https://doi.org/10.25424/cmcc-3hph-jy15, 2023. a
Mlakar, P.: petermlakar/DELWAVE: DELWAVE v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.10990866, 2024. a
Mlakar, P., Ricchi, A., Carniel, S., Bonaldo, D., and Ličer, M.: DELWAVE 1.0: Training and test datasets (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7816888, 2023. a
Morim, J., Trenham, C., Hemer, M., Wang, X. L., Mori, N., Casas-Prat, M., Semedo, A., Shimura, T., Timmermans, B., Camus, P., Bricheno, L., Mentaschi, L., Dobrynin, M., Feng, Y., and Erikson, L.: A global ensemble of ocean wave climate projections from CMIP5-driven models, Scientific Data, 7, 1–10, https://doi.org/10.1038/s41597-020-0446-2, 2020. a
Parker, W. S.: Ensemble modeling, uncertainty and robust predictions, WIREs Climate Change, 4, 213–223, https://doi.org/10.1002/wcc.220, 2013. a
Pomaro, A., Cavaleri, L., Papa, A., and Lionello, P.: Data Descriptor: 39 years of directional wave recorded data and relative problems, climatological implications and use, Scientific Data, 5, 1–12, https://doi.org/10.1038/sdata.2018.139, 2018. a, b
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Rodriguez-Delgado, C. and Bergillos, R. J.: Wave energy assessment under climate change through artificial intelligence, Sci. Total Environ., 760, 144039, https://doi.org/10.1016/j.scitotenv.2020.144039, 2021. a
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, Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, 2023. a
Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Fogli, P. G., Manzini, E., Vichi, M., Oddo, P., and Navarra, A.: Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model, J. Climate, 24, 4368–4384, https://doi.org/10.1175/2011JCLI4104.1, 2011. a
Sonnewald, M., Lguensat, R., Jones, D. C., Dueben, P. D., Brajard, J., and Balaji, V.: Bridging observations, theory and numerical simulation of the ocean using machine learning, Environ. Res. Lett., 16, 073008, https://doi.org/10.1088/1748-9326/ac0eb0, 2021. a
Steppeler, J., Doms, G., Schattler, U., Bitzer, H. W., Gassmann, A., Damrath, U., and Gregoric, G.: Meso-gamma scale forecasts using the nonhydrostatic model LM, Meteorol. Atmos. Phys., 82, 75–96, 2003. a
Tognin, D., D'Alpaos, A., Marani, M., and Carniello, L.: Marsh resilience to sea-level rise reduced by storm-surge barriers in the Venice Lagoon, Nat. Geosci., 14, 906–911, https://doi.org/10.1038/s41561-021-00853-7, 2021. a
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The Changing Character of Precipitation, B. Am. Meteorol. Soc., 84, 1205–1217, 2003. a
USACE: Coastal Engineering Manual, Engineer Manual 1110-2-1100, U. S. Army Corps of Engineers, Washington, USA, 2002. a
Žust, L., Fettich, A., Kristan, M., and Ličer, M.: HIDRA 1.0: deep-learning-based ensemble sea level forecasting in the northern Adriatic, Geosci. Model Dev., 14, 2057–2074, https://doi.org/10.5194/gmd-14-2057-2021, 2021. a
Zollo, A. L., Rillo, V., Bucchignani, E., Montesarchio, M., and Mercogliano, P.: Extreme temperature and precipitation events over Italy: assessment of high-resolution simulations with COSMO-CLM and future scenarios, Int. J. Climatol., 36, 987–1004, https://doi.org/10.1002/joc.4401, 2016. a
Short summary
We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which...
