tmm4py: Global Ocean Biogeochemical Modeling in Python With the Transport Matrix Method

Abstract Marine biogeochemical models are important tools in the quest to understand the cycling of chemical and biological tracers such as nutrients, carbon and oxygen, as well as key components of the Earth System Models used to project climate change. Historically, given the need for speed, global scale modeling has been performed in compiled languages like Fortran. However, as high level scripting languages such as Python and Julia gain popularity, the need for models and tools accessible from them has become imperative. This paper introduces tmm4py , a Python interface to a redesigned version of the Transport Matrix Method (TMM) software, a computationally efficient numerical scheme for “offline” simulation of marine geochemical and biogeochemical tracers. The TMM provides a convenient framework for developing and testing new biogeochemical parameterizations, as well as running existing complex models driven by circulations derived from state‐of‐the‐art physical models. tmm4py exposes all of the TMM library's functionality in Python, including transparent parallelization, allowing users to not only interactively use models written in compiled languages, but also develop complex models in pure Python with performance similar to compiled code. tmm4py enables users to exploit the large Python‐based scientific software ecosystem, including libraries for machine learning and deploying models on Graphics Processing Units. The various features of tmm4py are described and illustrated through practical examples, including a full‐fledged biogeochemical model written entirely in Python.

Authors

Institutions

Publication Details

Journal
Journal of Advances in Modeling Earth Systems
Published
2025-08-01
DOI
https://doi.org/10.1029/2025ms005028
Primary Topic
Oceanographic and Atmospheric Processes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

tmm4py: Global Ocean Biogeochemical Modeling in Python With the Transport Matrix Method

Samar Khatiwala
Journal of Advances in Modeling Earth Systems
Oceanographic and Atmospheric Processes
article

tmm4py: Global Ocean Biogeochemical Modeling in Python With the Transport Matrix Method

Samar Khatiwala
article en

Abstract

Abstract Marine biogeochemical models are important tools in the quest to understand the cycling of chemical and biological tracers such as nutrients, carbon and oxygen, as well as key components of the Earth System Models used to project climate change. Historically, given the need for speed, global scale modeling has been performed in compiled languages like Fortran. However, as high level scripting languages such as Python and Julia gain popularity, the need for models and tools accessible from them has become imperative. This paper introduces tmm4py , a Python interface to a redesigned version of the Transport Matrix Method (TMM) software, a computationally efficient numerical scheme for “offline” simulation of marine geochemical and biogeochemical tracers. The TMM provides a convenient framework for developing and testing new biogeochemical parameterizations, as well as running existing complex models driven by circulations derived from state‐of‐the‐art physical models. tmm4py exposes all of the TMM library's functionality in Python, including transparent parallelization, allowing users to not only interactively use models written in compiled languages, but also develop complex models in pure Python with performance similar to compiled code. tmm4py enables users to exploit the large Python‐based scientific software ecosystem, including libraries for machine learning and deploying models on Graphics Processing Units. The various features of tmm4py are described and illustrated through practical examples, including a full‐fledged biogeochemical model written entirely in Python.

Journal of Advances in Modeling Earth SystemsVol. 17(8)
Waseda University (JP)
Life below water
Openalex Percentile: Top 78%
Oceanographic and Atmospheric Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.