Persistence of marine heat waves for coral bleaching and their spectral characteristics around Andaman coral reef

Mohanty, P. C.; Kushabaha, A.; Mahendra, R. S.; Nayak, R. K.; Sahu, Bijaya Kumar; Rao, E. Pattabhi Rama; Kumar, T. Srinivasa

doi:10.1007/s10661-021-09264-y

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“…A phenomenon known as marine heatwave (MHW), characterized by a prolonged period of excessive warmth, has devastating impacts on the biodiversity loss of marine species (Smale et al., 2019; Wernberg et al., 2012, 2016), coral reef bleaching (Hughes et al., 2017; Mohanty et al., 2021), and fishing management (Caputi et al., 2016; Cheung & Frölicher, 2020; Mills et al., 2013). For example, the northern Great Barrier Reef experienced anomalous temperatures in the summers of 2016 and 2017, leading to back‐to‐back mass bleaching of corals (Hughes et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Global Warming has Increased the Distance Traveled by Marine Heatwaves

Zhang

Xia

et al. 2023

Geophysical Research Letters

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Section: Introductionmentioning

confidence: 99%

Global Warming has Increased the Distance Traveled by Marine Heatwaves

Zhang

Xia

et al. 2023

Geophysical Research Letters

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Analysis of Sea Surface Temperature Variability Using Machine Learning

Ouala,

Chapron,

Collard

et al. 2023

Mathematics of Planet Earth

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Sea surface temperature (SST) is a critical factor in the global climate system and plays a key role in many marine processes. Understanding the variability of SST is therefore important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management. In this study, we use machine learning techniques to analyze SST anomaly (SSTA) data from the Mediterranean Sea over a period of 33 years. The objective is to best explain the temporal variability of the SSTA extremes. These extremes are revealed to be well explained through a non-linear interaction between multi-scale processes. The results contribute to better unveil factors influencing SSTA extremes, and the development of more accurate prediction models.

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