How Well do Earth System Models Capture Apparent Relationships Between Phytoplankton Biomass and Environmental Variables?

Journal Article · 01 July 2023 · Global Biogeochemical Cycles

DOI: https://doi.org/10.1029/2023gb007701 · OSTI ID:2421172

Holder, Christopher ^[1]; Gnanadesikan, Anand  ^[2]

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1. Morton K. Blaustein Department of Earth and Planetary Sciences Johns Hopkins University Baltimore MD USA; OSTI
2. Morton K. Blaustein Department of Earth and Planetary Sciences Johns Hopkins University Baltimore MD USA

Abstract As phytoplankton form the base of the marine food web, understanding the controls on their abundance is fundamental to understanding marine ecology and its sensitivity to global climate change. While many Earth System Models (ESMs) predict phytoplankton biomass, it is unclear whether they properly capture the mechanistic relationships that control this quantity in the real ocean. We used Random Forest analysis to analyze the output of 13 ESMs as well as two observational data sets. The target variable was phytoplankton carbon and the predictors included environmental parameters known to influence phytoplankton, including nutrients, light, mixed layer depth, salinity, temperature, and upwelling. We examined the following: (a) What fractions of variability in ESMs and observations can be linked to the large‐scale environmental variables simulated by ESMs? (b) What are the dominant predictors and relationships affecting phytoplankton biomass? (c) How well do ESMs simulate phytoplankton carbon and do they simulate the relationships we see in observations? About 88%–96% of the variability in observational data sets and greater than 98% in the ESMs was accounted for by environmental variables known to influence phytoplankton biomass. The dominant predictors in the observational data sets were shortwave radiation and dissolved iron, with temperature and ammonium also relatively important. All the ESMs show that shortwave radiation is the most important variable and most of them predict the right sign of sensitivity to most variables. However, the models predict that biomass reaches maximum levels at unrealistically low levels of iron and unrealistically high levels of light.

• Research Organization: Johns Hopkins Univ., Baltimore, MD (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: SC0019344
• OSTI ID: 2421172
• Journal Information: Global Biogeochemical Cycles, Journal Name: Global Biogeochemical Cycles Journal Issue: 7 Vol. 37; ISSN 0886-6236
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English