Fennel, Katja, Long, Matthew C., Algar, Christopher, Carter, Brendan, Keller, David P. , Laurent, Arnaud, Mattern, Jann Paul, Musgrave, Ruth, Oschlies, Andreas , Ostiguy, Josiane, Palter, Jaime B. and Whitt, Daniel B. (2023) Modelling considerations for research on ocean alkalinity enhancement (OAE). State Planet : SP, 2-oae2023 (9). pp. 1-29. DOI 10.5194/sp-2-oae2023-9-2023.

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Abstract

The deliberate increase in ocean alkalinity (referred to as ocean alkalinity enhancement, or OAE) has
been proposed as a method for removing CO2 from the atmosphere. Before OAE can be implemented safely,
efficiently, and at scale several research questions have to be addressed, including (1) which alkaline feedstocks
are best suited and the doses in which they can be added safely, (2) how net carbon uptake can be measured
and verified, and (3) what the potential ecosystem impacts are. These research questions cannot be addressed by
direct observation alone but will require skilful and fit-for-purpose models. This article provides an overview of
the most relevant modelling tools, including turbulence-, regional-, and global-scale biogeochemical models and
techniques including approaches for model validation, data assimilation, and uncertainty estimation. Typical bio-
geochemical model assumptions and their limitations are discussed in the context of OAE research, which leads
to an identification of further development needs to make models more applicable to OAE research questions. A
description of typical steps in model validation is followed by proposed minimum criteria for what constitutes
a model that is fit for its intended purpose. After providing an overview of approaches for sound integration
of models and observations via data assimilation, the application of observing system simulation experiments
(OSSEs) for observing system design is described within the context of OAE research. Criteria for model val-
idation and intercomparison studies are presented. The article concludes with a summary of recommendations
and potential pitfalls to be avoided.

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