Earth Institute Research Projects

Collaborative Research: Uncertainty in Predictions of 21st Century Ocean Biogeochemical Change

Lead PI: Dr. Galen McKinley

Unit Affiliation: Geochemistry, Lamont-Doherty Earth Observatory (LDEO)

December 2017 - March 2020
Global ; Boulder, CO ; New York City, NY
Project Type: Research

DESCRIPTION: The biogeochemistry of the oceans is undergoing large-scale changes due to anthropogenic climate change. Recent research suggests these changes are occurring significantly on regional scales, but due to model uncertainties, it is difficult to constrain the difference between anthropogenic and natural influences. In studying climate change and its effect on ocean biogeochemistry in the future, it is crucial to be able to distinguish between these influences; therefore, it is critical to identify and quantify the uncertainty in Earth System Models (ESMs). The researchers will use output from Community Earth System Model (CESM) and models participating in the Fifth Coupled Model Intercomparison Project (CMIP5) to isolate prediction uncertainty due to 1) internal variability, 2) model structure, and 3) emission scenario. This research will bridge an existing gap between Earth System Models and observational studies to assess how climate change will influence ocean biogeochemistry. Additionally, this project will support an early-career scientist and a graduate student, and the researchers are dedicated to mentoring undergraduate students through various programs at Colorado University - Boulder, National Center for Atmospheric Research, and the University of Wisconsin.  Earth System Model (ESM) simulations used to predict future changes in ocean biogeochemistry attributed to either natural or anthropogenic influences suffer from uncertainties, particularly on regional scales. This is problematic because, as the ocean continues to undergo large-scale change under the current climate, it is crucial to have an accurate predictor of the future and to be able to delineate between natural and anthropogenic forcing. This research aims to quantify the uncertainty on three levels: uncertainty due to internal variability, model structure, and emission scenario. Using output from the Community Earth System Model (CESM) and models in the Fifth Coupled Model Intercomparison Project (CMIP5), this study will evaluate the degree to which uncertainty has changed with newer models. Additionally, observations from global databases, satellites, and time-series sites will be used to compare models and assess the varying levels of skill in predicting the biogeochemistry of a region. The researchers also plan to break down the various components of the driving mechanisms behind prediction uncertainty, so that future models can begin to take these factors into account.


National Science Foundation (NSF)





Lovenduski, N., G.A. McKinley, A.R. Fay, K. Lindsay, M.C. Long (2016) Partitioning uncertainty in ocean carbon uptake projections, Global Biogeochem. Cycles, 30, 1276–1287, doi:10.1002/2016GB005426.

McKinley, G.A., A.R. Fay, N. Lovenduski, and D.J. Pilcher (2017) Natural variability and anthropogenic trends in the ocean carbon sink, Ann. Rev. Mar. Sci. 9: 125-150, doi:10.1146/annurev-marine-010816-060529.

Peters, G.P., C. LeQuere, R.M. Andrew, J.G. Canadell, P. Friedlingstein, T. Ilyina, R.B. Jackson, F. Joos, J.I. Korsbakken, G.A. McKinley, S. Sitch, and P. Tans (2017) Towards real-time verification of CO2 emissions, Nature Climate Change, doi:10.1038/s41558-017-0013-9

Fay, A.R., N.S. Lovenduski, G.A. McKinley, D.R. Munro, C. Sweeney, A.R. Gray, P. Landschutzer, B. Stephens, T. Takahashi, N. Williams (2018) Utilizing the Drake Passage Time-series to understand variability and change in subpolar Southern Ocean pCO2, Biogeosciences 15, 3841-3855, doi:10.5194/bg-15-3841-2018.

Ridge, S.M. and G.A. McKinley: Expanded context of the Nutrient Stream: Advective controls on the North Atlantic anthropogenic carbon sink, Geophys. Res. Lett., in review 2019.


uncertainty earth system model biogeochemistry oceans climate change


Modeling and Adapting to Future Climate