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Home » Can Earth System Models Capture Intricate Links between Phytoplankton, Environmental Factors, and Global Carbon?

Can Earth System Models Capture Intricate Links between Phytoplankton, Environmental Factors, and Global Carbon?

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A partnership between CPO’s Climate Observations and Monitoring (COM) Program, Climate Variability and Predictability (CVP) Program, and NOAA’s Global Ocean Monitoring and Observing (GOMO) Program supported a new study in Global Biogeochemical Cycles. Christopher Holder and NOAA-funded scientist Anand Gnanadesikan of Johns Hopkins University wrote this new article with an interest in understanding how changes in ocean circulation impact the way marine ecosystem processes cycle and store carbon. The goal was to find out how well Earth System Models (ESMs) capture these relationships when considering multiple environmental factors and climate change trends.

Small, freely drifting marine organisms called phytoplankton are the main source of energy for food chains in marine ecosystems. The amount of phytoplankton and how they are distributed in the ocean has a strong influence on ocean carbon cycling and larger interactions with global climate. The researchers used a machine learning technique to investigate links between the way models and satellite observations represent global phytoplankton biomass and environmental variables like light, temperature, salinity, and solar radiation. Results showed that up to ninety-eight percent of the changes in phytoplankton biomass can be explained by the environmental variables, with solar radiation as the most important factor. The work in this study opens the door to analyze upcoming satellite datasets more effectively as well as identify and compare individual phytoplankton groups. The machine learning model the researchers used can also be applied as a tool to evaluate projections from other models. This project was funded by a collaboration between COM, CVP, and GOMO to increase the use and value of ocean observations, advance our understanding of climate variability and change, and enhance NOAA’s ability to model and predict the Earth System. 

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