The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …

Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

The future response of marine ecosystem diversity to continued anthropogenic forcing is
poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the …

Abstract Machine learning covers a large set of algorithms that can be trained to identify
patterns in data. Thanks to the increase in the amount of data and computing power …

Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …

Pelagic fauna is expected to be impacted under climate change according to ecosystem
simulations. However, the direction and magnitude of the impact is still uncertain and still not …

Rapid advances in hardware and software, accompanied by public-and private-sector
investment, have led to a new generation of data-driven computational tools. Recently, there …

This paper begins with a review of the history of remote sensing algorithms for the
determination of particulate inorganic carbon (PIC; aka calcium carbonate), primarily …

Prochlorococcus is both the smallest and numerically most abundant photosynthesizing
organism on the planet. While thriving in the warm oligotrophic gyres, Prochlorococcus …

Marine microbial communities sustain ocean food webs and mediate global elemental
cycles. These communities will change with climate; these changes can be gradual or …