Table of Contents

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

Data are presented in both the original Matlab data format (file "Xu_2021_model_output.mat") and a non-proprietary plain-text format "Xu_2021_model_output.zip" which contains a .txt file for each variable stored in Xu_2021_model_output.mat.  See file descriptions for more information about the structure of these files.

A model of the ocean aluminum (Al) cycle was developed in the MATLAB programming language, using the  Ocean Circulation Inverse Model v2 to resolve physical transport. The model resolves deposition and dissolution of dust at the sea surface, and internal cycling of Al in the ocean by reversible scavenging, biological uptake, and sediment resuspension. The model is solved for the steady-state aluminum distribution, which is then compared to observations from the GEOTRACES Intermediate Data Product 2017 to optimize uncertain parameters. 60 different configurations of the model are optimized, representing all combinations of 12 dust deposition fields and 5 configurations of the OCIM transport model. The model equations are described in Xu and Weber 2021 (https://doi.org/10.1029/2021GB007049) and full model code and inputs are available for download on Zenodo (https://zenodo.org/records/10139317). 

This dataset contains the main model results: the fully optimized model-predicted aluminum distribution, as well as estimates of Al supply to the surface ocean from dust deposition from the 60 model configurations described above.

Location:
Output from a global 3-dimensional model steady state model. Aluminum concentration is on a 3-dimensional grid, and dust deposition rates on a 2-dimensional grid. Grid information is found in the grid structure as follows:

grid.xt - Longitude
grid.yt - Latitude
grid.zt - Depth

The model was developed and run on the University of Rochester BlueHive computing cluster.

The model runs using custom code developed for this project and is available on the Zenodo repository (https://zenodo.org/records/10139317).

Data within Xu_2021_model_output.mat was loaded using Matlab online version r2023b. Each variable was exported as individual plain-text files and added as a non-propriety form of this dataset (see Data File "Xu_2021_model_output.zip"). Struct contents of grid and basin_masks which were added into subfolders "grid" and "basin_masks."

>> load("/MATLAB Drive/Xu_2021_model_output.mat")
>> whos
Name Size Bytes Class Attributes

Alconc 91x180x24 3144960 double
Aldep 91x180x60 7862400 double
basin_masks 1x1 524832 struct
cost 1x60 480 double
grid 1x1 3279200 struct
mod_names 1x60 7798 cell

>> writematrix(Alconc,'Xu_2021_model_output/Alconc.txt');
>> writematrix(Aldep,'Xu_2021_model_output/Aldep.txt');
>> writematrix(basin_masks.SOC,'Xu_2021_model_output/basin_masks/SOC.txt');
>> writematrix(basin_masks.ATL,'Xu_2021_model_output/basin_masks/ATL.txt');
>> writematrix(basin_masks.PAC,'Xu_2021_model_output/basin_masks/PAC.txt');
>> writematrix(basin_masks.IND,'Xu_2021_model_output/basin_masks/IND.txt');
>> writematrix(cost,'Xu_2021_model_output/cost.txt');
>> writematrix(grid.x,'Xu_2021_model_output/grid/x.txt');
>> writematrix(grid.y,'Xu_2021_model_output/grid/y.txt');
>> writematrix(grid.z,'Xu_2021_model_output/grid/z.txt');
>> writematrix(grid.AREA,'Xu_2021_model_output/grid/AREA.txt');
>> writematrix(grid.M3d,'Xu_2021_model_output/grid/M3d.txt');
>> writecell(mod_names,'Xu_2021_model_output/mod_names.txt');

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.