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.
Holder, Christopher and Gnanadesikan, Anand. "How Well do Earth System Models Capture Apparent Relationships Between Phytoplankton Biomass and Environmental Variables?." Global Biogeochemical Cycles, vol. 37, no. 7, Jul. 2023. https://doi.org/10.1029/2023gb007701
Holder, Christopher, & Gnanadesikan, Anand (2023). How Well do Earth System Models Capture Apparent Relationships Between Phytoplankton Biomass and Environmental Variables?. Global Biogeochemical Cycles, 37(7). https://doi.org/10.1029/2023gb007701
Holder, Christopher, and Gnanadesikan, Anand, "How Well do Earth System Models Capture Apparent Relationships Between Phytoplankton Biomass and Environmental Variables?," Global Biogeochemical Cycles 37, no. 7 (2023), https://doi.org/10.1029/2023gb007701
@article{osti_2421172,
author = {Holder, Christopher and Gnanadesikan, Anand},
title = {How Well do Earth System Models Capture Apparent Relationships Between Phytoplankton Biomass and Environmental Variables?},
annote = {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.},
doi = {10.1029/2023gb007701},
url = {https://www.osti.gov/biblio/2421172},
journal = {Global Biogeochemical Cycles},
issn = {ISSN 0886-6236},
number = {7},
volume = {37},
place = {United States},
publisher = {American Geophysical Union (AGU)},
year = {2023},
month = {07}}
Institution, Physical Oceanography Department/Woods Hole Oceanographic; Center/NASA, Goddard Institute For Space Studies/Earth Sciences Division/Science And Exploration Directorate/Goddard Space Flight
Institution, Physical Oceanography Department/Woods Hole Oceanographic; Center/NASA, Goddard Institute For Space Studies/Earth Sciences Division/Science And Exploration Directorate/Goddard Space Flight