Researchers predict changes in sea levels along many coasts around the world

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Researchers from the Image Processing Laboratory (IPL) of the University of Valencia have developed an approach to machine learning to model and predict short-term sea level changes in the coastal regions of the Pacific, Indian and Atlantic Oceans. The study, particularly useful for coastal protection, was published in Scientific reports on nature.

All ocean basins have experienced significant warming and rising sea levels in recent decades, fueled by climate change. However, there are important regional differences arising from different processes at different time scales, such as those associated with temperature changes due to natural causes.

To better interpret observations of sea level variations in coastal regions at the local level, the team of Verónice Nieves, a distinguished GenT program researcher at the University of Valencia’s Image Processing Laboratory (IPL), developed a machine learning approach that uses sea temperature estimates to model coastal level variability. sea ​​and associated uncertainties ranging in time ranges from months to several years.

The study is now published in the journal Scientific reports on nature also shows that the physical relationships between temperature variables in the upper layers of open sea regions and estimates of sea level anomalies in coastal areas of these regions can be used in combination with machine learning methods to make reasonably accurate short-term sea level predictions (one to several years).

They conclude that to date, short-term regional variations in coastal sea levels are still largely influenced by natural processes in large open ocean regions, such as the open ocean, temperature changes down the water column at 700 meters, which are closely related to internal natural climate variability. These processes are superimposed on the impact of other effects, such as tides, among others.

“Climate is an extremely complex and dynamic system that can change naturally in unexpected ways; and, in that sense, machine learning methods can provide useful insights for better interpretation of data showing complex nonlinear patterns and recognition of near future regional sea level changes,” she said. is Verónica Nieves, the first author of the article and leader of the AI4OCEANS group, at IPL, where this type of research continues. “Our models perform particularly well in coastal areas most affected by internal climate variability, but are widely applicable for estimating sea level rise and fall in many places around the world,” added Cristina Radín, a team member with whom Professor Gustau Camps-Valls he also collaborated.

This is the first study to use artificial intelligence techniques in the oceans to make this type of prediction. Modeling and forecasting sea level changes in the coming years is crucial for short-term decision-making and strategic planning of coastal protection measures.

The team also developed an interactive map, as a support tool that will enable the inspection of individual regions in which machine learning model prediction has been made.

Researchers from the Image Processing Laboratory (IPL) of the University of Valencia have developed an approach to machine learning to model and predict short-term sea level changes in the coastal regions of the Pacific, Indian and Atlantic Oceans. The study, particularly useful for coastal protection, was published in Scientific reports on nature.

All ocean basins have experienced significant warming and rising sea levels in recent decades, fueled by climate change. However, there are important regional differences arising from different processes at different time scales, such as those associated with temperature changes due to natural causes.

To better interpret observations of sea level variations in coastal regions at the local level, the team of Verónice Nieves, a distinguished GenT program researcher at the University of Valencia’s Image Processing Laboratory (IPL), developed a machine learning approach that uses sea temperature estimates to model sea level variability. sea ​​and associated uncertainties ranging in time ranges from months to several years.

The study is now published in the journal Scientific reports on nature also shows that the physical relationships between temperature variables in the upper layers of open sea regions and estimates of sea level anomalies in coastal areas of these regions can be used in combination with machine learning methods to make reasonably accurate short-term sea level predictions (one to several years).

They conclude that to date, short-term regional variations in coastal sea levels are still largely influenced by natural processes in large open ocean regions, such as the open ocean, temperature changes down the water column at 700 meters, which are closely related to internal natural climate variability. These processes are superimposed on the impact of other effects, such as tides, among others.

“Climate is an extremely complex and dynamic system that can change naturally in unexpected ways; and, in that sense, machine learning methods can provide useful insights for better interpretation of data showing complex nonlinear patterns and recognition of near future regional sea level changes,” she said. is Verónica Nieves, the first author of the article and leader of the AI4OCEANS group, at IPL, where this type of research continues. “Our models perform particularly well in coastal areas most affected by internal climate variability, but are widely applicable for estimating sea level rise and fall in many places around the world,” added Cristina Radín, a team member with whom Professor Gustau Camps-Valls he also collaborated.

This is the first study to use artificial intelligence techniques in the oceans to make this type of prediction. Modeling and forecasting sea level changes in the coming years is crucial for short-term decision-making and strategic planning of coastal protection measures.

The team also developed an interactive map, as a support tool that will enable the inspection of individual regions in which machine learning model prediction has been made.


Greater sea level variability is expected as the earth warms


More information:
Veronica Nieves et al. Predicting regional changes in coastal sea levels with machine learning, Scientific reports (2021). DOI: 10.1038 / s41598-021-87460-z

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Citation: Researchers predict sea level changes along many coasts around the world (2021, April 8) downloaded April 8, 2021 from https://phys.org/news/2021-04-sea-coasts-globe.html

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