Articles | Volume 20, issue 5
https://doi.org/10.5194/os-20-1087-2024
© 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/os-20-1087-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Sep 2024
Research article |
| 06 Sep 2024
| 06 Sep 2024
Investigating the long-term variability of the Red Sea marine heatwaves and their relationship to different climate modes: focus on 2010 events in the northern basin
GeoHydrodynamics and Environment Research (GHER), University of Liège, Liège, Belgium
Oceanography Department, Faculty of Science, Alexandria University, Alexandria, Egypt
Aida Alvera-Azcárate
GeoHydrodynamics and Environment Research (GHER), University of Liège, Liège, Belgium
George Krokos
Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Ocean Physics, Institute of Oceanography, Hellenic Centre for Marine Research, Anavyssos, Greece
Ibrahim Hoteit
Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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Ibrahim Hoteit, Eric Chassignet, and Mike Bell
State Planet, 5-opsr, 21, https://doi.org/10.5194/sp-5-opsr-21-2025, https://doi.org/10.5194/sp-5-opsr-21-2025, 2025
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This paper explores how using multiple predictions instead of just one can improve ocean forecasts and help prepare for changes in ocean conditions. By combining different forecasts, scientists can better understand the uncertainty in predictions, leading to more reliable forecasts and better decision-making. This method is useful for responding to hazards like oil spills, improving climate forecasts, and supporting decision-making in fields like marine safety and resource management.
Andreas Schiller, Simon A. Josey, John Siddorn, and Ibrahim Hoteit
State Planet, 5-opsr, 18, https://doi.org/10.5194/sp-5-opsr-18-2025, https://doi.org/10.5194/sp-5-opsr-18-2025, 2025
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The study illustrates the way atmospheric fields are used in ocean models as boundary conditions for the provisioning of the exchanges of heat, freshwater, and momentum fluxes. Such fluxes can be based on remote sensing instruments or provided directly by numerical weather prediction systems. Air–sea flux datasets are defined by their spatial and temporal resolutions and are limited by associated biases. Air–sea flux datasets for ocean models should be chosen with the applications in mind.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
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Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Ségolène Berthou, John Siddorn, Vivian Fraser-Leonhardt, Pierre-Yves Le Traon, and Ibrahim Hoteit
State Planet, 5-opsr, 20, https://doi.org/10.5194/sp-5-opsr-20-2025, https://doi.org/10.5194/sp-5-opsr-20-2025, 2025
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Ocean forecasting is traditionally done independently from atmospheric, wave, or river modelling. We discuss the benefits and challenges of bringing all these modelling systems together for ocean forecasting.
Aida Alvera-Azcárate, Dimitry Van der Zande, Alexander Barth, Antoine Dille, Joppe Massant, and Jean-Marie Beckers
Ocean Sci., 21, 787–805, https://doi.org/10.5194/os-21-787-2025, https://doi.org/10.5194/os-21-787-2025, 2025
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This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.
Bayoumy Mohamed, Alexander Barth, Dimitry Van der Zande, and Aida Alvera-Azcárate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1578, https://doi.org/10.5194/egusphere-2025-1578, 2025
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We quantified the role of climate change and internal variability on marine heatwaves (MHWs) in the North Sea over more than four decades (1982–2024). A key finding is the 2013 climate shift, which was associated with increased warming and MHWs. Long-term warming accounted for 80 % of the observed trend in MHW frequency. The most intense MHW event in May 2024 was attributed to an anomalous anticyclonic atmospheric circulation. We also explored the impact of MHWs on chlorophyll concentrations.
Cécile Pujol, Alexander Barth, Iván Pérez-Santos, Pamela Muñoz-Linford, and Aida Alvera-Azcárate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1421, https://doi.org/10.5194/egusphere-2025-1421, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
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Marine heatwaves and cold spells are periods of extreme sea temperatures. This study focuses on Chilean Northern Patagonia, a fjord region vulnerable due to its aquaculture. It aims to understand these events' distribution and identify the most affected basins. Results show higher intensity in enclosed areas like Reloncaví Sound and Puyuhuapi Fjord. Marine heatwaves are becoming more frequent over time, while cold spells are decreasing.
Ehsan Mehdipour, Hongyan Xi, Alexander Barth, Aida Alvera-Azcárate, Adalbert Wilhelm, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2025-112, https://doi.org/10.5194/egusphere-2025-112, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Phytoplankton are vital for marine ecosystems and nutrient cycling, detectable by optical satellites. Data gaps caused by clouds and other non-optimal conditions limit comprehensive analyses like trend monitoring. This study evaluated DINCAE and DINEOF gap-filling methods for reconstructing chlorophyll-a datasets, including total chlorophyll-a and five major phytoplankton groups. Both methods showed robust reconstruction capabilities, aiding pattern detection and long-term ocean colour analysis.
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
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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 %.
Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers
Ocean Sci., 20, 1567–1584, https://doi.org/10.5194/os-20-1567-2024, https://doi.org/10.5194/os-20-1567-2024, 2024
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Most satellite observations have gaps, for example, due to clouds. This paper presents a method to reconstruct missing data in satellite observations of the chlorophyll a concentration in the Black Sea. Rather than giving a single possible reconstructed field, the discussed method provides an ensemble of possible reconstructions using a generative neural network. The resulting ensemble is validated using techniques from numerical weather prediction and ocean modelling.
Yasser O. Abualnaja, Alexandra Pavlidou, James H. Churchill, Ioannis Hatzianestis, Dimitris Velaoras, Harilaos Kontoyiannis, Vassilis P. Papadopoulos, Aristomenis P. Karageorgis, Georgia Assimakopoulou, Helen Kaberi, Theodoros Kannelopoulos, Constantine Parinos, Christina Zeri, Dionysios Ballas, Elli Pitta, Vassiliki Paraskevopoulou, Afroditi Androni, Styliani Chourdaki, Vassileia Fioraki, Stylianos Iliakis, Georgia Kabouri, Angeliki Konstantinopoulou, Georgios Krokos, Dimitra Papageorgiou, Alkiviadis Papageorgiou, Georgios Pappas, Elvira Plakidi, Eleni Rousselaki, Ioanna Stavrakaki, Eleni Tzempelikou, Panagiota Zachioti, Anthi Yfanti, Theodore Zoulias, Abdulah Al Amoudi, Yasser Alshehri, Ahmad Alharbi, Hammad Al Sulami, Taha Boksmati, Rayan Mutwalli, and Ibrahim Hoteit
Earth Syst. Sci. Data, 16, 1703–1731, https://doi.org/10.5194/essd-16-1703-2024, https://doi.org/10.5194/essd-16-1703-2024, 2024
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We present oceanographic measurements obtained during two surveillance cruises conducted in June and September 2021 in the Red Sea and the Arabian Gulf. It is the first multidisciplinary survey within the Saudi Arabian coastal zone, extending from near the Saudi–Jordanian border in the north of the Red Sea to the south close to the Saudi--Yemen border and in the Arabian Gulf. The objective was to record the pollution status along the coastal zone of the kingdom related to specific pressures.
Pamela Linford, Iván Pérez-Santos, Paulina Montero, Patricio A. Díaz, Claudia Aracena, Elías Pinilla, Facundo Barrera, Manuel Castillo, Aida Alvera-Azcárate, Mónica Alvarado, Gabriel Soto, Cécile Pujol, Camila Schwerter, Sara Arenas-Uribe, Pilar Navarro, Guido Mancilla-Gutiérrez, Robinson Altamirano, Javiera San Martín, and Camila Soto-Riquelme
Biogeosciences, 21, 1433–1459, https://doi.org/10.5194/bg-21-1433-2024, https://doi.org/10.5194/bg-21-1433-2024, 2024
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The Patagonian fjords comprise a world region where low-oxygen water and hypoxia conditions are observed. An in situ dataset was used to quantify the mechanism involved in the presence of these conditions in northern Patagonian fjords. Water mass analysis confirmed the contribution of Equatorial Subsurface Water in the advection of the low-oxygen water, and hypoxic conditions occurred when the community respiration rate exceeded the gross primary production.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
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In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
M. G. Ziliani, M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, 2022
Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers
Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, https://doi.org/10.5194/gmd-15-2183-2022, 2022
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Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Estrella Olmedo, Verónica González-Gambau, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Marilaure Gregoire, Aida Álvera-Azcárate, Luminita Buga, and Marie-Hélène Rio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-364, https://doi.org/10.5194/essd-2021-364, 2021
Revised manuscript not accepted
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We present the first dedicated satellite salinity product in the Black Sea. We use the measurements provided by the European Soil Moisture and Ocean Salinity mission. We introduce enhanced algorithms for dealing with the contamination produced by the Radio Frequency Interferences that strongly affect this basin. We also provide a complete quality assessment of the new product and give an estimated accuracy of it.
Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 24, 5251–5277, https://doi.org/10.5194/hess-24-5251-2020, https://doi.org/10.5194/hess-24-5251-2020, 2020
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The agricultural sector in Saudi Arabia has expanded rapidly over the last few decades, supported by non-renewable groundwater abstraction. This study describes a novel data–model fusion approach to compile national-scale groundwater abstractions and demonstrates its use over 5000 individual center-pivot fields. This method will allow both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.
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
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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.
Rui Sun, Aneesh C. Subramanian, Arthur J. Miller, Matthew R. Mazloff, Ibrahim Hoteit, and Bruce D. Cornuelle
Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, https://doi.org/10.5194/gmd-12-4221-2019, 2019
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A new regional coupled ocean–atmosphere model, SKRIPS, is developed and presented. The oceanic component is the MITgcm and the atmospheric component is the WRF model. The coupler is implemented using ESMF according to NUOPC protocols. SKRIPS is demonstrated by simulating a series of extreme heat events occurring in the Red Sea region. We show that SKRIPS is capable of performing coupled ocean–atmosphere simulations. In addition, the scalability test shows SKRIPS is computationally efficient.
Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio
Geosci. Model Dev., 11, 3071–3088, https://doi.org/10.5194/gmd-11-3071-2018, https://doi.org/10.5194/gmd-11-3071-2018, 2018
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One of the most important challenges seismologists and earthquake engineers face is reliably estimating ground motion in an area prone to large damaging earthquakes. This study aimed at better understanding the relationship between characteristics of geological faults (e.g., hypocenter location, rupture size/location, etc.) and resulting ground motion, via statistical analysis of a rupture simulation model. This study provides important insight on ground-motion responses to geological faults.
Khan Zaib Jadoon, Muhammad Umer Altaf, Matthew Francis McCabe, Ibrahim Hoteit, Nisar Muhammad, Davood Moghadas, and Lutz Weihermüller
Hydrol. Earth Syst. Sci., 21, 5375–5383, https://doi.org/10.5194/hess-21-5375-2017, https://doi.org/10.5194/hess-21-5375-2017, 2017
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In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity in an agriculture field irrigated with a drip irrigation system. Electromagnetic model parameters and uncertainty were estimated using adaptive Bayesian Markov chain Monte Carlo (MCMC). Application of the MCMC-based inversion to the synthetic and field measurements demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil.
Mohamad E. Gharamti, Johan Valstar, Gijs Janssen, Annemieke Marsman, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 4561–4583, https://doi.org/10.5194/hess-20-4561-2016, https://doi.org/10.5194/hess-20-4561-2016, 2016
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The paper addresses the issue of sampling errors when using the ensemble Kalman filter, in particular its hybrid and second-order formulations. The presented work is aimed at estimating concentration and biodegradation rates of subsurface contaminants at the port of Rotterdam in the Netherlands. Overall, we found that accounting for both forecast and observation sampling errors in the joint data assimilation system helps recover more accurate state and parameter estimates.
Boujemaa Ait-El-Fquih, Mohamad El Gharamti, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 3289–3307, https://doi.org/10.5194/hess-20-3289-2016, https://doi.org/10.5194/hess-20-3289-2016, 2016
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We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The derivation is based on the one-step-ahead smoothing formulation, and unlike the standard dual EnKF, it is consistent with the Bayesian formulation of the state-parameter estimation problem and uses the observations in both state smoothing and forecast. This is shown to enhance the performance and robustness of the dual EnKF in experiments conducted with a two-dimensional synthetic groundwater aquifer model.
J.-M. Beckers, A. Barth, I. Tomazic, and A. Alvera-Azcárate
Ocean Sci., 10, 845–862, https://doi.org/10.5194/os-10-845-2014, https://doi.org/10.5194/os-10-845-2014, 2014
Related subject area
Approach: Remote Sensing | Properties and processes: Climate and modes of variability
Tracking Marine Heatwaves in the Balearic Sea: Temperature Trends and the Role of Detection Methods
Assessing heat and freshwater changes in the Southern Ocean using satellite-derived steric height
Blanca Fernández-Álvarez, Bàrbara Barceló-Llull, and Ananda Pascual
EGUsphere, https://doi.org/10.5194/egusphere-2024-4065, https://doi.org/10.5194/egusphere-2024-4065, 2025
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Marine heatwaves (MHWs) standard detection method uses a fixed baseline, showing rising MHW frequency and intensity due to global warming, eventually reaching saturation. To address this, alternative approaches separate long-term warming from extreme events. Here we compare two in the Balearic Sea: moving baseline and detrending data. We found a warming trend of 0.036 °C/year, with major MHWs in 2003 and 2022 identified by all methods. Only the fixed baseline shows rising MHW frequency.
Jennifer Cocks, Alessandro Silvano, Alice Marzocchi, Oana Dragomir, Noémie Schifano, Anna E. Hogg, and Alberto C. Naveira Garabato
EGUsphere, https://doi.org/10.5194/egusphere-2023-3050, https://doi.org/10.5194/egusphere-2023-3050, 2023
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Heat and freshwater fluxes in the Southern Ocean mediate global ocean circulation and abyssal ventilation. These fluxes manifest as changes in steric height: sea level anomalies from changes in ocean density. We compute the steric height anomaly of the Southern Ocean using satellite data and validate it against in-situ observations. We analyse interannual patterns, drawing links to climate variability, and discuss the effectiveness of the method, highlighting issues and suggesting improvements.
Cited articles
Alduchov, O. A. and Eskridge, R. E.: Improved Magnus Form Approximation of Saturation Vapor Pressure, J. Appl. Meteorol. Climatol., 35, 4, https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2, 1996.
Amaya, D. J., Miller, A. J., Xie, S.-P., and Kosaka, Y.: Physical drivers of the summer 2019 North Pacific marine heatwave, Nat. Commun., 11, 1903, https://doi.org/10.1038/s41467-020-15820-w, 2020.
Arafeh-Dalmau, N., Montaño-Moctezuma, G., Martínez, J. A., Beas-Luna, R., Schoeman, D. S., and Torres-Moye, G.: Extreme Marine Heatwaves Alter Kelp Forest Community Near Its Equatorward Distribution Limit, Front. Mar. Sci., 6, 499, https://doi.org/10.3389/fmars.2019.00499, 2019.
Bamston, A. G., Chelliah, M., and Goldenberg, S. B.: Documentation of a highly ENSO-related sst region in the equatorial pacific: Research note, Atmos.-Ocean, 35, 367–383, https://doi.org/10.1080/07055900.1997.9649597, 1997.
Barale, V.: The African Marginal and Enclosed Seas: An Overview, in: Remote Sensing of the African Seas, edited by: Barale, V. and Gade, M., Springer Netherlands, Dordrecht, 3–29, https://doi.org/10.1007/978-94-017-8008-7_1, 2014.
Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns, Mon. Weather Rev., 115, 1083–1126, https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2, 1987.
Barros, V. R., Field, C. B., Dokken, D. J., Mastrandrea, M. D., and Mach, K. J. (Eds.): Climate Change 2014: Impacts, Adaptation and Vulnerability: Working Group II Contribution to the IPCC Fifth Assessment Report of the Integovernmental Panel on Climate Change, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781107415386, 2014.
Bawadekji, A., Tonbol, K., Ghazouani, N., Becheikh, N., and Shaltout, M.: General and Local Characteristics of Current Marine Heatwave in the Red Sea, J. Mar. Sci. Eng., 9, 1048, https://doi.org/10.3390/jmse9101048, 2021.
Bawadekji, A., Tonbol, K., Ghazouani, N., Becheikh, N., and Shaltout, M.: Recent atmospheric changes and future projections along the Saudi Arabian Red Sea Coast, Sci. Rep., 12, 160, https://doi.org/10.1038/s41598-021-04200-z, 2022.
Behera, S. K., Doi, T., and Ratnam, J. V.: 5 – Air–sea interactions in tropical Indian Ocean: The Indian Ocean Dipole, in: Tropical and Extratropical Air-Sea Interactions, edited by: Behera, S. K., Elsevier, 115–139, https://doi.org/10.1016/B978-0-12-818156-0.00001-0, 2021.
Belkin, I. M.: Rapid warming of Large Marine Ecosystems, Prog. Oceanogr., 81, 207–213, https://doi.org/10.1016/j.pocean.2009.04.011, 2009.
Bjerknes, J.: Atmospheric Teleconnections From The Equatorial Pacific, Mon. Weather Rev., 97, 163–172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2, 1969.
Bower, A. S. and Farrar, J. T.: Air–Sea Interaction and Horizontal Circulation in the Red Sea, in: The Red Sea, edited by: Rasul, N. M. A. and Stewart, I. C. F., Springer Berlin Heidelberg, Berlin, Heidelberg, 329–342, https://doi.org/10.1007/978-3-662-45201-1_19, 2015.
Cai, W., Wang, G., Li, Z., Zheng, X., Yang, K., and Ng, B.: Response of the positive Indian Ocean dipole to climate change and impact on Indian summer monsoon rainfall, Chap. 21, in: Indian Summer Monsoon Variability, edited by: Chowdary, J., Parekh, A., and Gnanaseelan, C., Elsevier, 413–432, https://doi.org/10.1016/B978-0-12-822402-1.00010-7, 2021.
Carlson, D. F., Yarbro, L. A., Scolaro, S., Poniatowski, M., McGee-Absten, V., and Carlson, P. R.: Sea surface temperatures and seagrass mortality in Florida Bay: Spatial and temporal patterns discerned from MODIS and AVHRR data, Remote Sens. Environ., 208, 171–188, https://doi.org/10.1016/j.rse.2018.02.014, 2018.
Chaidez, V., Dreano, D., Agusti, S., Duarte, C. M., and Hoteit, I.: Decadal trends in Red Sea maximum surface temperature, Sci. Rep., 7, 8144, https://doi.org/10.1038/s41598-017-08146-z, 2017.
Chefaoui, R. M., Duarte, C. M., and Serrão, E. A.: Dramatic loss of seagrass habitat under projected climate change in the Mediterranean Sea, Glob. Change Biol., 24, 4919–4928, https://doi.org/10.1111/gcb.14401, 2018.
Chen, W. Y. and den Dool, H. V.: Sensitivity of Teleconnection Patterns to the Sign of Their Primary Action Center, Mon. Weather Rev., 131, 2885–2899, https://doi.org/10.1175/1520-0493(2003)131<2885:SOTPTT>2.0.CO;2, 2003.
Cheng, Y., Zhang, M., Song, Z., Wang, G., Zhao, C., Shu, Q., Zhang, Y., and Qiao, F.: A quantitative analysis of marine heatwaves in response to rising sea surface temperature, Sci. Total Environ., 881, 163396, https://doi.org/10.1016/j.scitotenv.2023.163396, 2023.
Diaz-Almela, E., Marbà, N., and Duarte, C. M.: Consequences of Mediterranean warming events in seagrass (Posidonia oceanica) flowering records, Glob. Change Biol., 13, 224–235, https://doi.org/10.1111/j.1365-2486.2006.01260.x, 2007.
Dool, H. M. van den, Saha, S., and Johansson, Å.: Empirical Orthogonal Teleconnections, J. Clim., 13, 1421–1435, https://doi.org/10.1175/1520-0442(2000)013<1421:EOT>2.0.CO;2, 2000.
Eakin, C. M., Morgan, J. A., Heron, S. F., Smith, T. B., Liu, G., Alvarez-Filip, L., Baca, B., Bartels, E., Bastidas, C., Bouchon, C., Brandt, M., Bruckner, A. W., Bunkley-Williams, L., Cameron, A., Causey, B. D., Chiappone, M., Christensen, T. R. L., Crabbe, M. J. C., Day, O., Guardia, E. de la, Díaz-Pulido, G., DiResta, D., Gil-Agudelo, D. L., Gilliam, D. S., Ginsburg, R. N., Gore, S., Guzmán, H. M., Hendee, J. C., Hernández-Delgado, E. A., Husain, E., Jeffrey, C. F. G., Jones, R. J., Jordán-Dahlgren, E., Kaufman, L. S., Kline, D. I., Kramer, P. A., Lang, J. C., Lirman, D., Mallela, J., Manfrino, C., Maréchal, J.-P., Marks, K., Mihaly, J., Miller, W. J., Mueller, E. M., Muller, E. M., Toro, C. A. O., Oxenford, H. A., Ponce-Taylor, D., Quinn, N., Ritchie, K. B., Rodríguez, S., Ramírez, A. R., Romano, S., Samhouri, J. F., Sánchez, J. A., Schmahl, G. P., Shank, B. V., Skirving, W. J., Steiner, S. C. C., Villamizar, E., Walsh, S. M., Walter, C., Weil, E., Williams, E. H., Roberson, K. W., and Yusuf, Y.: Caribbean Corals in Crisis: Record Thermal Stress, Bleaching, and Mortality in 2005, PLOS ONE, 5, e13969, https://doi.org/10.1371/journal.pone.0013969, 2010.
Eladawy, A., Nadaoka, K., Negm, A., Abdel-Fattah, S., Hanafy, M., and Shaltout, M.: Characterization of the northern Red Sea's oceanic features with remote sensing data and outputs from a global circulation model, Oceanologia, 59, 213–237, https://doi.org/10.1016/j.oceano.2017.01.002, 2017.
Garrabou, J., Coma, R., Bensoussan, N., Bally, M., Chevaldonné, P., Cigliano, M., Diaz, D., Harmelin, J. G., Gambi, M. C., Kersting, D. K., Ledoux, J. B., Lejeusne, C., Linares, C., Marschal, C., Pérez, T., Ribes, M., Romano, J. C., Serrano, E., Teixido, N., Torrents, O., Zabala, M., Zuberer, F., and Cerrano, C.: Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave, Glob. Change Biol., 15, 1090–1103, https://doi.org/10.1111/j.1365-2486.2008.01823.x, 2009.
Genevier, L. G. C., Jamil, T., Raitsos, D. E., Krokos, G., and Hoteit, I.: Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea, Glob. Change Biol., 25, 2338–2351, https://doi.org/10.1111/gcb.14652, 2019.
Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses, Remote Sens., 12, 720, https://doi.org/10.3390/rs12040720, 2020.
Hamdeno, M. and Alvera-Azcaráte, A.: Marine heatwaves characteristics in the Mediterranean Sea: Case study the 2019 heatwave events, Front. Mar. Sci., 10, 1093760, https://doi.org/10.3389/fmars.2023.1093760, 2023.
Hamdeno, M., Nagy, H., Ibrahim, O., and Mohamed, B.: Responses of Satellite Chlorophyll-a to the Extreme Sea Surface Temperatures over the Arabian and Omani Gulf, Remote Sens., 14, 4653, https://doi.org/10.3390/rs14184653, 2022.
Hamed, K. H. and Ramachandra Rao, A.: A modified Mann-Kendall trend test for autocorrelated data, J. Hydrol., 204, 182–196, https://doi.org/10.1016/S0022-1694(97)00125-X, 1998.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hobday, A., Oliver, E., Sen Gupta, A., Benthuysen, J., Burrows, M., Donat, M., Holbrook, N., Moore, P., Thomsen, M., Wernberg, T., and Smale, D.: Categorizing and Naming Marine Heatwaves, Oceanography, 31, 162–173, https://doi.org/10.5670/oceanog.2018.205, 2018.
Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Moore, P. J., Scannell, H. A., Sen Gupta, A., and Wernberg, T.: A hierarchical approach to defining marine heatwaves, Prog. Oceanogr., 141, 227–238, https://doi.org/10.1016/j.pocean.2015.12.014, 2016.
Hoerling, M. P., Kumar, A., and Xu, T.: Robustness of the Nonlinear Climate Response to ENSO's Extreme Phases, J. Clim., 14, 1277–1293, https://doi.org/10.1175/1520-0442(2001)014<1277:ROTNCR>2.0.CO;2, 2001.
Holbrook, N. J., Scannell, H. A., Sen Gupta, A., Benthuysen, J. A., Feng, M., Oliver, E. C. J., Alexander, L. V., Burrows, M. T., Donat, M. G., Hobday, A. J., Moore, P. J., Perkins-Kirkpatrick, S. E., Smale, D. A., Straub, S. C., and Wernberg, T.: A global assessment of marine heatwaves and their drivers, Nat. Commun., 10, 2624, https://doi.org/10.1038/s41467-019-10206-z, 2019.
Holbrook, N. J., Sen Gupta, A., Oliver, E. C. J., Hobday, A. J., Benthuysen, J. A., Scannell, H. A., Smale, D. A., and Wernberg, T.: Keeping pace with marine heatwaves, Nat. Rev. Earth Environ., 1, 482–493, https://doi.org/10.1038/s43017-020-0068-4, 2020.
Hoteit, I., Abualnaja, Y., Afzal, S., Ait-El-Fquih, B., Akylas, T., Antony, C., Dawson, C., Asfahani, K., Brewin, R. J., Cavaleri, L., Cerovecki, I., Cornuelle, B., Desamsetti, S., Attada, R., Dasari, H., Sanchez-Garrido, J., Genevier, L., Gharamti, M. E., Gittings, J. A., Gokul, E., Gopalakrishnan, G., Guo, D., Hadri, B., Hadwiger, M., Hammoud, M. A., Hendershott, M., Hittawe, M., Karumuri, A., Knio, O., Köhl, A., Kortas, S., Krokos, G., Kunchala, R., Issa, L., Lakkis, I., Langodan, S., Lermusiaux, P., Luong, T., Ma, J., Maitre, O. L., Mazloff, M., Mohtar, S. E., Papadopoulos, V. P., Platt, T., Pratt, L., Raboudi, N., Racault, M.-F., Raitsos, D. E., Razak, S., Sanikommu, S., Sathyendranath, S., Sofianos, S., Subramanian, A., Sun, R., Titi, E., Toye, H., Triantafyllou, G., Tsiaras, K., Vasou, P., Viswanadhapalli, Y., Wang, Y., Yao, F., Zhan, P., and Zodiatis, G.: Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea, Bull. Am. Meteorol. Soc., 102, E99–E122, https://doi.org/10.1175/BAMS-D-19-0005.1, 2021.
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons, J. Clim., 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1, 2017.
Hughes, T. P., Anderson, K. D., Connolly, S. R., Heron, S. F., Kerry, J. T., Lough, J. M., Baird, A. H., Baum, J. K., Berumen, M. L., Bridge, T. C., Claar, D. C., Eakin, C. M., Gilmour, J. P., Graham, N. A. J., Harrison, H., Hobbs, J.-P. A., Hoey, A. S., Hoogenboom, M., Lowe, R. J., McCulloch, M. T., Pandolfi, J. M., Pratchett, M., Schoepf, V., Torda, G., and Wilson, S. K.: Spatial and temporal patterns of mass bleaching of corals in the Anthropocene, Science, 359, 80–83, https://doi.org/10.1126/science.aan8048, 2018.
Karnauskas, K. B. and Jones, B. H.: The Interannual Variability of Sea Surface Temperature in the Red Sea From 35 Years of Satellite and In Situ Observations, J. Geophys. Res.-Ocean., 123, 5824–5841, https://doi.org/10.1029/2017JC013320, 2018.
Kirch, W. (Ed.): Pearson's Correlation Coefficient, in: Encyclopedia of Public Health, Springer Netherlands, Dordrecht, 1090–1091, https://doi.org/10.1007/978-1-4020-5614-7_2569, 2008.
Krokos, G., Papadopoulos, V. P., Sofianos, S. S., Ombao, H., Dybczak, P., and Hoteit, I.: Natural Climate Oscillations may Counteract Red Sea Warming Over the Coming Decades, Geophys. Res. Lett., 46, 3454–3461, https://doi.org/10.1029/2018GL081397, 2019.
Krokos, G., Cerovecki, I., Zhan, P., Hendershott, M., and Hoteit, I.: Seasonal Evolution of Mixed Layers in the Red Sea and the Relative Contribution of Atmospheric Buoyancy and Momentum Forcing, https://doi.org/10.48550/arXiv.2112.08762, 2021.
Langodan, S., Cavaleri, L., Vishwanadhapalli, Y., Pomaro, A., Bertotti, L., and Hoteit, I.: The climatology of the Red Sea – part 1: the wind, Int. J. Climatol., 37, 4509–4517, https://doi.org/10.1002/joc.5103, 2017a.
Langodan, S., Cavaleri, L., Pomaro, A., V, Y., Bertotti, L., and Hoteit, I.: The climatology of the Red Sea – part 2: The waves, Int. J. Climatol., 37, 4518–4528, https://doi.org/10.1002/joc.5101, 2017b.
Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C. S., and Frölicher, T. L.: Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period, Biogeosciences, 18, 2119–2137, https://doi.org/10.5194/bg-18-2119-2021, 2021.
Liu, X. and Yao, F.: Relationship of the Warming of Red Sea Surface Water over 140 Years with External Heat Elements, J. Mar. Sci. Eng., 10, 846, https://doi.org/10.3390/jmse10070846, 2022.
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers, J. Geophys. Res.-Ocean., 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997.
McPhaden, M. J., Zebiak, S. E., and Glantz, M. H.: ENSO as an Integrating Concept in Earth Science, Science, 314, 1740–1745, https://doi.org/10.1126/science.1132588, 2006.
Meziere, Z., Rich, W., Carvalho, S., Benzoni, F., Morán, X. A., and Berumen, M.: Stylophora under stress: A review of research trends and impacts of stressors on a model coral species, Sci. Total Environ., 816, 151639, https://doi.org/10.1016/j.scitotenv.2021.151639, 2021.
Mohamed, B., Nagy, H., and Ibrahim, O.: Spatiotemporal Variability and Trends of Marine Heat Waves in the Red Sea over 38 Years, J. Mar. Sci. Eng., 9, 842, https://doi.org/10.3390/jmse9080842, 2021.
Mohamed, B., Nilsen, F., and Skogseth, R.: Marine Heatwaves Characteristics in the Barents Sea Based on High Resolution Satellite Data (1982–2020), Front. Mar. Sci., 9, 821646, https://doi.org/10.3389/fmars.2022.821646, 2022.
Mohamed, B., Barth, A., and Alvera-Azcárate, A.: Extreme marine heatwaves and cold-spells events in the Southern North Sea: classifications, patterns, and trends, Front. Mar. Sci., 10, 1258117, https://doi.org/10.3389/fmars.2023.1258117, 2023.
Nagy, H., Elgindy, A., Pinardi, N., Zavatarelli, M., and Oddo, P.: A nested pre-operational model for the Egyptian shelf zone: Model configuration and validation/calibration, Dynam. Atmos. Ocean., 80, 75–96, https://doi.org/10.1016/j.dynatmoce.2017.10.003, 2017.
Nagy, H., Mohamed, B., and Ibrahim, O.: Variability of Heat and Water Fluxes in the Red Sea Using ERA5 Data (1981–2020), J. Mar. Sci. Eng., 9, 1276, https://doi.org/10.3390/jmse9111276, 2021.
Oliver, E. C. J., Donat, M. G., Burrows, M. T., Moore, P. J., Smale, D. A., Alexander, L. V., Benthuysen, J. A., Feng, M., Sen Gupta, A., Hobday, A. J., Holbrook, N. J., Perkins-Kirkpatrick, S. E., Scannell, H. A., Straub, S. C., and Wernberg, T.: Longer and more frequent marine heatwaves over the past century, Nat. Commun., 9, 1324, https://doi.org/10.1038/s41467-018-03732-9, 2018.
Oliver, E. C. J., Benthuysen, J. A., Darmaraki, S., Donat, M. G., Hobday, A. J., Holbrook, N. J., Schlegel, R. W., and Sen Gupta, A.: Marine Heatwaves, Annu. Rev. Mar. Sci., 13, 313–342, https://doi.org/10.1146/annurev-marine-032720-095144, 2021.
Papadopoulos, V., Abualnaja, Y., Josey, S., Bower, A., Dionysios, Raitsos, E., Kontoyiannis, H., and Hoteit, I.: Atmospheric Forcing of the Winter Air–Sea Heat Fluxes over the Northern Red Sea, J. Clim., 26, 1685–1701, https://doi.org/10.1175/JCLI-D-12-00267.1, 2013.
Patten, M. L. and Newhart, M.: Understanding Research Methods: An Overview of the Essentials, 10th Edn., Routledge, New York, Routledge, https://doi.org/10.4324/9781315213033, 2017.
Pujol, C., Pérez-Santos, I., Barth, A., and Alvera-Azcárate, A.: Marine Heatwaves Offshore Central and South Chile: Understanding Forcing Mechanisms During the Years 2016–2017, Front. Mar. Sci., 9, 800325, https://doi.org/10.3389/fmars.2022.800325, 2022.
Raitsos, D., Hoteit, I., Prihartato, P., Chronis, T., Triantafyllou, G., and Abualnaja, Y.: Abrupt warming of the Red Sea, Geophys. Res. Lett., 38, L14601, https://doi.org/10.1029/2011GL047984, 2011.
Rivetti, I., Fraschetti, S., Lionello, P., Zambianchi, E., and Boero, F.: Global Warming and Mass Mortalities of Benthic Invertebrates in the Mediterranean Sea, PLOS ONE, 9, e115655, https://doi.org/10.1371/journal.pone.0115655, 2014.
Schenke, H.-W.: GEBCO, in: Encyclopedia of Marine Geosciences, edited by: Harff, J., Meschede, M., Petersen, S., and Thiede, J., Springer Netherlands, Dordrecht, 1–2, https://doi.org/10.1007/978-94-007-6644-0_63-3, 2013.
Schneider, D. P., Deser, C., Fasullo, J., and Trenberth, K. E.: Climate Data Guide Spurs Discovery and Understanding, Eos Trans. AGU, 94, 121–122, https://doi.org/10.1002/2013EO130001, 2013.
Semenov, V. A., Latif, M., Dommenget, D., Keenlyside, N. S., Strehz, A., Martin, T., and Park, W.: The Impact of North Atlantic–Arctic Multidecadal Variability on Northern Hemisphere Surface Air Temperature, J. Clim., 23, 5668–5677, https://doi.org/10.1175/2010JCLI3347.1, 2010.
Sen Gupta, A., Thomsen, M., Benthuysen, J. A., Hobday, A. J., Oliver, E., Alexander, L. V., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Perkins-Kirkpatrick, S., Moore, P. J., Rodrigues, R. R., Scannell, H. A., Taschetto, A. S., Ummenhofer, C. C., Wernberg, T., and Smale, D. A.: Drivers and impacts of the most extreme marine heatwave events, Sci. Rep., 10, 19359, https://doi.org/10.1038/s41598-020-75445-3, 2020.
Shaltout, M.: Recent sea surface temperature trends and future scenarios for the Red Sea, Oceanologia, 61, 484–504, https://doi.org/10.1016/j.oceano.2019.05.002, 2019.
Shukla, J.: Predictability in the Midst of Chaos: A Scientific Basis for Climate Forecasting, Science, 282, 728–731, https://doi.org/10.1126/science.282.5389.728, 1998.
Skliris, N., Sofianos, S., Gkanasos, A., Mantziafou, A., Vervatis, V., Axaopoulos, P., and Lascaratos, A.: Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability, Ocean Dynam., 62, 13–30, https://doi.org/10.1007/s10236-011-0493-5, 2012.
Smale, D. and Wernberg, T.: Satellite-derived SST Data as a Proxy for Water Temperature in Nearshore Benthic Ecology, ECU Publications, 387, 27–37, https://doi.org/10.3354/meps08132, 2009.
Thomson, R. E. and Emery, W. J.: Data Analysis Methods in Physical Oceanography, 3rd Edn., Elsevier, https://doi.org/10.1016/C2010-0-66362-0, 2014.
Trisos, C. H., Merow, C., and Pigot, A. L.: The projected timing of abrupt ecological disruption from climate change, Nature, 580, 496–501, https://doi.org/10.1038/s41586-020-2189-9, 2020.
Visbeck, M. H., Hurrell, J. W., Polvani, L., and Cullen, H. M.: The North Atlantic Oscillation: Past, present, and future, P. Natl. Acad. Sci. USA, 98, 12876–12877, https://doi.org/10.1073/pnas.231391598, 2001.
Viswanadhapalli, Y., Dasari, H. P., Langodan, S., Challa, V. S., and Hoteit, I.: Climatic features of the Red Sea from a regional assimilative model, Int. J. Climatol., 37, 2563–2581, https://doi.org/10.1002/joc.4865, 2017.
Wang, F., Shao, W., Yu, H., Kan, G., He, X., Zhang, D., Ren, M., and Wang, G.: Re-evaluation of the Power of the Mann-Kendall Test for Detecting Monotonic Trends in Hydrometeorological Time Series, Front. Earth Sci., 8, 14, https://doi.org/10.3389/feart.2020.00014, 2020.
Weatherall, P., Marks, K. M., Jakobsson, M., Schmitt, T., Tani, S., Arndt, J. E., Rovere, M., Chayes, D., Ferrini, V., and Wigley, R.: A new digital bathymetric model of the world's oceans, Earth Space Sci., 2, 331–345, https://doi.org/10.1002/2015EA000107, 2015.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, 4th Edn., Elsevier, https://doi.org/10.1016/C2017-0-03921-6, 2019.
Zhang, R.: Anticorrelated multidecadal variations between surface and subsurface tropical North Atlantic, Geophys. Res. Lett., 34, L12713, https://doi.org/10.1029/2007GL030225, 2007.
Zhao, Z. and Marin, M.: A MATLAB toolbox to detect and analyze marine heatwaves, J. Open Source Softw., 4, 1124, https://doi.org/10.21105/joss.01124, 2019.
Short summary
Our study focuses on the characteristics of MHWs in the Red Sea during the last 4 decades. Using satellite-derived sea surface temperatures (SSTs), we found a clear warming trend in the Red Sea since 1994, which has intensified significantly since 2016. This SST rise was associated with an increase in the frequency and days of MHWs. In addition, a correlation was found between the frequency of MHWs and some climate modes, which was more pronounced in some years of the study period.
Our study focuses on the characteristics of MHWs in the Red Sea during the last 4 decades. Using...
