Phytoplankton response to climate change (PRIME)

Phytoplankton are at the heart of the Earth system, connecting light to life in the ocean. Considered the most dynamic primary producer on Earth, regenerating every two to five days, they respond rapidly to changes in their environment, modulate the planetary cycling of major elements and compounds, and supply energy to the marine food-web. Understanding the response of phytoplankton to climate change is critical to predicting future changes in the Earth system.

Approaching 25-years of continuous data acquisition, and with an unparalleled capability to view the entire planet within a few days, satellite remote sensing of ocean colour is our principal means of monitoring phytoplankton abundance at global scale, through estimates of the chlorophyll-a pigment concentration, a measure of phytoplankton abundance. However, there are two major challenges to using satellite remote sensing of ocean colour for monitoring trends in phytoplankton abundance.

Firstly, converting the ocean-colour signal into estimates of phytoplankton abundance demands consideration of the phytoplankton type present and other optically active constituents in the water. Standard phytoplankton abundance algorithms in use by space agencies assume these constituents co-vary, which is unsuitable for detecting long-term trends as climate change may alter these constituents in different ways.

Secondly, the signal retrieved by satellite is only representative of the surface layer of the ocean (top 40 m at most). For many regions, below the surface layer (40-200 m), there exists a forest of phytoplankton hidden from the eyes of the satellite. At present, ocean-colour trends in surface phytoplankton abundance using standard algorithms cannot be trusted, and we cannot be sure that, if any such trends exist, they are representative of changes in the forest hidden below.

In this project, my team will develop the first ocean-colour algorithm designed specifically for monitoring climate change, that separates signals from water constituents using a theoretical and ecological framework, that harnesses the principles of compound remote sensing (thermal and visible satellite remote sensing). It will be run on climate-quality ocean satellite data records, recently available through the European Space Agency, to produce a multi-decadal record of surface phytoplankton abundance.

Furthermore, by mining vertical profiles of phytoplankton abundance from the past 30+ years of oceanographic sampling and emerging autonomous ocean robotics, my team will also produce a global, multi-decadal record of subsurface phytoplankton abundance. Bringing these data records together, we will quantify the effect of anthropogenic climate change on phytoplankton abundance, at least in parts of the ocean where the record is long-enough.

These data records will serve as a benchmark to compare and improve climate models, that simulate future predictions of the marine carbon cycle, marine biodiversity and fish stocks. The data records will be used as input to study the impact of climate change on oceanic primary production, phytoplankton carbon and ocean biogeography.