Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Data Files
• Supplemental Files
• Related Publications
• Related Datasets
• Parameters
• Project Information
• Funding

A model of the ocean thorium-230 and 232 cycle was developed in the MATLAB programming language, using the  Ocean Circulation Inverse Model v2 (Devries & Holzer, 2019 - https://doi.org/10.1029/2018JC014716) to resolve physical transport. The model resolves radiogenic and lithogenic sources of the thorium isotopes, and internal cycle due to scavenging onto biogenic, lithogenic, and hydrothermally-sourced particles. Discrete Sample Data from the GEOTRACES IDP2021v2 are assimilated to optimize uncertain parameters. The model is fully described and all equations are provided in Xu and Weber 2025 (https://doi.org/10.1029/2024GB008485) and the full model code and inputs are available for download at the Zenodo repository (https://doi.org/10.5281/zenodo.10139433). 

Codes developed to run the model and process output are available for download at the Zenodo repository (https://doi.org/10.5281/zenodo.10139433).  The model was developed and run on the University of Rochester BlueHive computing cluster.

Geolocation is the global ocean (output from a steady-state global ocean model - grid information is provided).

Parameter names for Xu_2025_model_output.nc file:

Grid information
x: Longitude of grid cell centers in degrees East of Prime meridian
y: Latitude of grid cell centers in degrees North of Equator
z: Depth of the grid cell (m)
M3d: Land/ocean masks (1 = ocean, 0 = land)
MSKS.ATL: Atlantic Ocean Mask (1 = Atlantic, otherwise 0)
MSKS.PAC: Pacific Ocean Mask (1 = Pacific, otherwise 0)
MSKS.IND: Indian Ocean Mask (1 = Indian Ocean, otherwise 0)
MSKS.SOUTH: Southern Ocean Mask (1 = Southern Ocean, otherwise 0)
VOL: Volume of each grid cell (m3)

Model output
Jdep: Depth-integrated soluble 232Th dust deposition source flux (mmol/m2/yr), stored as a two dimensional field. 
Jdep3D: Full three-dimensional 232Th source field from dust deposition (mmol/m3/yr).
Jsed: Depth-integrated soluble 232Th sediment dissolution source flux (mmol/m2/yr), stored as a two dimensional field. 
Jsed3D: Full three-dimensional 232Th source field from sediment dissolution (mmol/m3/yr).
Th232: Dissolved 232Th concentration distribution (pM) from our best-fitting model.
Th230: Dissolved 230Th concentration distribution (pM) from our best-fitting model.

* Header information was extracted from Xu_2025_model_output.nc and added to the file comment so parameters in the file are clearer in the metadata and are searchable.

* Xu_2025_model_output.nc attached as the primary data file
* plotting code plot_Th232_budget.m attached as a supplemental file.

NSF Award Abstract:
Tiny marine organisms called phytoplankton play a critical role in Earth's climate, by absorbing carbon dioxide from the atmosphere. In order to grow, these phytoplankton require nutrients that are dissolved in seawater. One of the rarest and most important of these nutrients is iron. Even though it is a critical life-sustaining nutrient, oceanographers still do not know much about how iron gets into the ocean, or how it is removed from seawater. In the past few years, scientists have made many thousands of measurements of the amount of dissolved iron in seawater, in environments ranging from the deep sea, to the Arctic, to the tropical oceans. They found that the amount of iron in seawater varies dramatically from place to place. Can this data tell us about how iron gets into the ocean, and how it is ultimately removed? Yes. In this project, scientists working on making measurements of iron in seawater will come together with scientists who are working on computer models of iron inputs and removal in the ocean. The goal is to work together to create a program that allows our computer models to "learn" from the data, much like an Artificial Intelligence program. This program will develop a "best estimate" of where and how much iron is coming into the ocean, how long it stays in the ocean, and ultimately how it gets removed. This will lead to a better understanding of how climate change will impact the delivery of iron to the ocean, and how phytoplankton will respond to climate change. With better climate models, society can make more informed decisions about how to respond to climate change. The study will also benefit a future generation of scientists, by training graduate students in a unique collaboration between scientists making seawater measurements, and those using computer models to interpret those measurements. Finally, the project aims to increase the participation of minority and low-income students in STEM (Science, Technology, Engineering, and Mathematics) research, through targeted outreach programs.

Iron (Fe) is an important micronutrient for marine phytoplankton that limits primary productivity over much of the ocean; however, the major fluxes in the marine Fe cycle remain poorly quantified. Ocean models that attempt to synthesize our understanding of Fe biogeochemistry predict widely different Fe inputs to the ocean, and are often unable to capture first-order features of the Fe distribution. The proposed work aims to resolve these problems using data assimilation (inverse) methods to "teach" the widely used Biogeochemical Elemental Cycling (BEC) model how to better represent Fe sources, sinks, and cycling processes. This will be achieved by implementing BEC in the efficient Ocean Circulation Inverse Model and expanding it to simulate the cycling of additional tracers that constrain unique aspects of the Fe cycle, including aluminum, thorium, helium and Fe isotopes. In this framework, the inverse model can rapidly explore alternative representations of Fe-cycling processes, guided by new high-quality observations made possible in large part by the GEOTRACES program. The work will be the most concerted effort to date to synthesize these rich datasets into a realistic and mechanistic model of the marine Fe cycle. In addition, it will lead to a stronger consensus on the magnitude of fluxes in the marine Fe budget, and their relative importance in controlling Fe limitation of marine ecosystems, which are areas of active debate. It will guide future observational efforts, by identifying factors that are still poorly constrained, or regions of the ocean where new data will dramatically reduce remaining uncertainties and allow new robust predictions of Fe cycling under future climate change scenarios to be made, ultimately improving climate change predictions. A broader impact of this work on the scientific community will be the development of a fast, portable, and flexible global model of trace element cycling, designed to allow non-modelers to test hypotheses and visualize the effects of different processes on trace metal distributions. The research will also support the training of graduate students, and outreach to low-income and minority students in local school districts.