Marine heatwaves

While we all understand atmospheric heatwaves and their direct effects on our wellbeing, how much is actually known about a similar process occurring in the oceans: marine heatwaves? Are they becoming more intense and severe due to climate change? And what are their consequences? Read more about it in an interactive story.

Marine heatwaves (MHWs) are defined in an analogous way to its close relative atmospheric heatwaves, as a deviation from the expected temperature at a given point of the year: the climatology.1 Instead of the air temperature, for marine heatwaves Sea Surface Temperature (SST) is the variable used, which measures how the temperature of oceans evolves at shallow depths, where most of the human-related activity occurs.

MHW legend

Satellite data has made it possible to have operational SST over the world’s oceans since a few decades ago. In 1991, ESA’s ERS-1 missions started providing satellite SST measurements on a continuous basis. The operational measurements were extended with ERS-2 and ENVISAT missions and are still ongoing with the Sentinel’s satellite generation.

In a recent study being carried out by CLS, Artificial Intelligence was applied directly on satellite imagery to obtain forecasts for future potential marine heatwaves. This is state-of-the-art research in a rapidly evolving field.2 3

Physical models

This study is carried out within the Mediterranean Sea. In this area, observations have allowed scientists to investigate historical daily data.

For example, during the summer of 2003, a strong marine heatwave was observed over almost the entire Mediterranean sea. Take a look at the month of August of that year.

This large amount of data has also allowed scientists to compare year by year how heatwaves evolve. It is particularly alarming to see how in only 30 years, the frequency of marine heatwaves has increased dramatically.4

The map shows the maximum peak of all heatwaves during the month of September 2020.

Marine heatwaves were much less frequent in September 1990, 30 years before.

But what are the consequences of marine heatwaves? Who do they affect?

Reported biological impacts range from geographical species shifts and widespread changes in species composition to harmful algal blooms, mass strandings of mammals and mass mortalities of particular species.

They can also lead to significant political and socioeconomic ramifications when affecting aquaculture or important fishery species.

Finally, they can also perturb atmospheric conditions over land via teleconnections that may persist over weeks or months.5

In order to minimise these consequences, CLS is developing a model for predicting MHWs. This convolutional neural network takes as input past history of maps data covering the Mediterranean: sea surface temperature, temperature at 40 m depth and sea surface height. The objective of the model is to learn to predict the time series of future SSTs  based on this past history, hence making use of both spatial and temporal context. This future SST is then thresholded to obtain marine heatwave predictions.

DL models

The knowledge provided by historical data, models and forecasts should be used in the future to minimise the effects and consequences of marine heatwaves, as well as to further comprehend the effect humans have on the oceans, which are some of the most important sources of life and prosperity.

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The production of this interactive story has been possible thanks to the efforts of CLS (France), Lobelia Earth (Spain) and under ESA funding. Data used for visualisation has been extracted from the Copernicus Marine Service MyOcean Viewer and from CLS datasets. The former consists of Sea Water Potential Temperature at the ocean surface, or Sea Surface Temperature (SST) with product ID GLOBAL_ANALYSIS_FORECAST_PHY_001_024 published on the 14th October 2016 by Mercator Ocean International. The latter consists of a dataset of daily Marine Heatwave categories over the Mediterranean Sea containing data for the last 30 years.

This story was produced in the context of European project Digital Twin Ocean Precursor.


1 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., & Wernberg, T. (2016). A hierarchical approach to defining marine heatwaves. Progress in Oceanography, 141, 227–238. DOI: 10.1016/j.pocean.2015.12.014.

2 Ham, YG., Kim, JH. & Luo, JJ. (2019). Deep learning for multi-year ENSO forecasts. Nature 573, 568–572. DOI: 10.1038/s41586-019-1559-7.

3 Biard, J. C., & Kunkel, K. E. (2019). Automated detection of weather fronts using a deep learning neural network. Advances in Statistical Climatology, Meteorology and Oceanography 5(2), 147–160. DOI: 10.5194/ascmo-5-147-2019.

4 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., & Wernberg, T. (2018). Longer and more frequent marine heatwaves over the past century. Nature Communications 9(1). DOI: 10.1038/s41467-018-03732-9.

5 Frölicher, T. L., & Laufkötter, C. (2018). Emerging risks from marine heat waves. Nature Communications. 9(1). DOI: 10.1038/s41467-018-03163-6.