Warmer, deeper, and greener mixed layers in the North Atlantic subpolar gyre over the last 50 years

• Main Authors: /Martinez, Elodie, Raitsos, D. E., Antoine, D.
• Format: Text
• Language: English
• Published: 2016
• Subjects: AMO; bottom-up processes and top-down control; chlorophyll; climate; cycles; Continuous Plankton Recorder; NAO; North Atlantic subpolar gyre; phytoplankton decadal variability; plankton climate links; North Atlantic; North Atlantic oscillation; Copepods
• Online Access: https://www.documentation.ird.fr/hor/fdi:010066185

Shifts in global climate resonate in plankton dynamics, biogeochemical cycles, and marine food webs. We studied these linkages in the North Atlantic subpolar gyre (NASG), which hosts extensive phytoplankton blooms. We show that phytoplankton abundance increased since the 1960s in parallel to a deepening of the mixed layer and a strengthening of winds and heat losses from the ocean, as driven by the low frequency of the North Atlantic Oscillation (NAO). In parallel to these bottom-up processes, the top-down control of phytoplankton by copepods decreased over the same time period in the western NASG, following sea surface temperature changes typical of the Atlantic Multi-decadal Oscillation (AMO). While previous studies have hypothesized that climate-driven warming would facilitate seasonal stratification of surface waters and long-term phytoplankton increase in subpolar regions, here we show that deeper mixed layers in the NASG can be warmer and host a higher phytoplankton biomass. These results emphasize that different modes of climate variability regulate bottom-up (NAO control) and top-down (AMO control) forcing on phytoplankton at decadal timescales. As a consequence, different relationships between phytoplankton, zooplankton, and their physical environment appear subject to the disparate temporal scale of the observations (seasonal, interannual, or decadal). The prediction of phytoplankton response to climate change should be built upon what is learnt from observations at the longest timescales.