EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Analysis of marine heat waves using machine learning 

Said Ouala1, Bertrand Chapron2, Fabrice Collard3, Lucile Gaultier3, and Ronan Fablet1
Said Ouala et al.
  • 1IMT Atlantique, Lab STICC, Signal et communications, France (
  • 2Ifremer, LOPS, Brest, France
  • 3ODL, Brest, France

Sea surface temperature (SST) is a critical parameter in the global climate system and plays a vital role in many marine processes, including ocean circulation, evaporation, and the exchange of heat and moisture between the ocean and atmosphere. As such, understanding the variability of SST is important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management.

The dynamics of SST is the compound of multiple degrees of freedom that interact across a continuum of Spatio-temporal scales. A first-order approximation of such a system was initially introduced by Hasselmann. In his pioneering work, Hasselmann (1976) discussed the interest in using a two-scale stochastic model to represent the interactions between slow and fast variables of the global ocean, climate, and atmosphere system. In this paper, we examine the potential of machine learning techniques to derive relevant dynamical models of Sea Surface Temperature Anomaly (SSTA) data in the Mediterranean Sea. We focus on the seasonal modulation of the SSTA and aim to understand the factors that influence the temporal variability of SSTA extremes. Our analysis shows that the variability of the SSTA can indeed well be decomposed into slow and fast components. The dynamics of the slow variables are associated with the seasonal cycle, while the dynamics of the fast variables are linked to the SSTA response to rapid underlying processes such as the local wind variability. Based on these observations, we approximate the probability density function of the SSTA data using a stochastic differential equation parameterized by a neural network. In this model, the drift function represents the seasonal cycle and the diffusion function represents the envelope of the fast SSTA response.


How to cite: Ouala, S., Chapron, B., Collard, F., Gaultier, L., and Fablet, R.: Analysis of marine heat waves using machine learning , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16936,, 2023.