Elodie Martinez

The seasonal variability of phytoplankton vertical distribution is investigated in the South Pacific where observations are scarce and scattered. We used 13 BioGeoChemical‐Argo floats deployed across diverse oceanic environments. The seasonal latitudinal displacement of the Tasman front induces transitions from mesotrophic to oligotrophic conditions. This shift results in Chlorophyll‐a concentration vertical distribution changing from bloom types to Subsurface Chlorophyll Maxima (SCM) types, with intermediate hybrid types between these extremes. Such hybrid profiles frequently occur in the equatorial Pacific, highlighting a large‐scale pattern rather than local island mass effect. In oligotrophic regions, seasonal variations of light availability and stratification dynamics below the mixed layer likely relate SCM to an increase in carbon biomass or photoacclimation. A biomass increase is frequently observed, contrary to previous studies, suggesting that subsurface phytoplankton biomass may have been largely underestimated. This calls for further observations of the water column in these remote undersampled open ocean areas.

The seasonal variability of phytoplankton vertical distribution is investigated in the South Pacific where observations are scarce and scattered. We used 13 BioGeoChemical-Argo floats deployed across diverse oceanic environments. The seasonal latitudinal displacement of the Tasman front induces transitions from mesotrophic to oligotrophic conditions. This shift results in Chlorophyll-a concentration vertical distribution changing from bloom types to Subsurface Chlorophyll Maxima (SCM) types, with intermediate hybrid types between these extremes. Such hybrid profiles frequently occur in the equatorial Pacific, highlighting a large-scale pattern rather than local island mass effect. In oligotrophic regions, seasonal variations of light availability and stratification dynamics below the mixed layer likely relate SCMs to an increase in carbon biomass or photoacclimation. A biomass increase is frequently observed, contrary to previously reported, suggesting that subsurface phytoplankton production may have been largely underestimated. This calls for further observations of the water column in these remote undersampled open ocean areas.

Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐ a concentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. The results suggest that the merging algorithm of Globcolour‐GSM is not reliable for trend detection and that observed changes in Schl after 2012/2016 are not supported by changes of the physical ocean. These results emphasize the need for careful interpretation of satellite‐derived Schl trends and highlight the potential of machine learning in understanding the complex interactions in marine ecosystems.

Corrigendum: Reconstructing global chlorophyll-a variations using a non-linear statistical approach

Editorial: AI and remote sensing in ocean sciences

Time series of satellite-derived chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), continuously generated since 1997, are still too short to investigate the low-frequency variability of phytoplankton biomass (e.g. decadal variability). Machine learning models such as Support Vector Regression (SVR) or Multi-Layer Perceptron (MLP) have recently proven to be an alternative approach to mechanistic ones to reconstruct Chl synoptic past time-series before the satellite era from physical predictors. Nevertheless, the relationships between phytoplankton and its physical surrounding environment were implicitly considered homogeneous in space, and training such models on a global scale does not allow one to consider known regional mechanisms. Indeed, the global ocean is commonly partitioned into biogeochemical provinces (BGCPs) into which phytoplankton growth is supposed to be governed by regionally-”homogeneous” processes. The time-evolving nature of those provinces prevents imposing a priori spatially-fixed boundary constraints to restrict the learning phase. Here, we propose to use a multi-mode Convolutional Neural Network (CNN), which can spatially learn and combine different modes, to globally account for interregional variabilities. Each mode is associated with a CNN submodel, standing for a mode-specific response of phytoplankton biomass to the physical forcing. Beyond improving performance reconstruction, we show that the different modes appear regionally consistent with the ocean dynamics and that they may help to get new insights into physical-biogeochemical processes controlling phytoplankton spatio-temporal variability at global scale.

The authors wish to make the following corrections to the paper

Plastic debris has become an acute marine pollution concern worldwide in modern times. Indonesia is particularly impacted because of its high population density, heavy rainfall rate and numerous coastlines. A Lagrangian analysis was performed to simulate the fate of fictive marine debris drifting along surface currents, including tides and Stokes drift. The fictive particles were released according to the discharge rate of 21 Indonesian rivers and advected over 4 years. Most of the particles were stranded along Indonesian coasts (60%), before 6 months had elapsed (84%) and within a range of 1000 km (76%). The time variability exhibited two seasonal peaks, one centered on January-February and one on June-July, consistent with in situ observations. However, the results underline the complexity of performing direct comparisons between in situ observations and numerical simulations for stranded waste due to limited measurements and the heterogeneity of field methods and protocols.