Long-term reorganization of ocean nutrient distribution
Data files
Feb 10, 2026 version files 1.15 GB
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README.md
1.76 KB
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Scripts_publication_v2.zip
15.80 KB
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TwodN.csv
380.30 MB
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TwodP.csv
766.95 MB
Abstract
Oceans rely on nutrients like nitrate and phosphate to support the growth of phytoplankton and marine productivity. Using nearly a century of global ocean data, this study shows that nutrients are changing in different ways depending on location. This entry contains scripts needed for data analysis presented in this manuscript, as well as the nutrient data files. The data analysis consists of four parts: (i) preprocessing, (ii) estimating long-term surface nutrient trends, (iii) estimating vertical trends and their significance, and (iv) analysis of CMIP6 data. The data consists of the pre-processed World Ocean Database observations.
Dataset DOI: 10.5061/dryad.qrfj6q5vz
Description of the data and file structure
There are two data files, TwodN.csv and TwodP.csv, that contain all pre-processed observations from the World Ocean Database
Files and variables
File: Scripts_publication_v2.zip
Description:
Collection of scripts for each analysis:
- Preprocessing
- Estimate long-term surface trend
- Estimate vertical profile trends
- Estimate nutrient dynamics in CMIP6 models
File: TwodN.csv
Description:
Fields:
- LON (degrees E)
- LAT (degrees N)
- year (integer)
- month (integer, 1-12)
- Depthm (sampling depth, integer, unit:m)
- BotDepthm (Bottom depth, integer, unit:m)
- Nitratemmolkg (Nitrate conc. unit: mmol/kg)
- idx (unique 3D ocean grid cell)
- N0 (surface nitrate conc
- unit: mmol/kg)
- monthly_anomaly (Nitrate anomaly, unit: mmol/kg)
- region2 (region id, integer)
File: TwodP.csv
Description:
Fields
- LON (degrees E)
- LAT (degrees N)
- year (integer)
- month (integer, 1-12)
- Depthm (sampling depth, integer, unit:m)
- BotDepthm (Bottom depth, integer, unit:m)
- Phosphatemmolkg (Phosphate conc. unit: mmol/kg)
- idx (unique 3D ocean grid cell)
- N0 (surface nitrate conc, unit: mmol/kg)
- monthly_anomaly (Phosphate anomaly, unit: mmol/kg)
- region2 (region id, integer).
Code/software
You need Matlab (Mathworks) to run the analyses
Access information
Other publicly accessible locations of the data:
- NA
Data was derived from the following sources:
- World Ocean Database: https://www.ncei.noaa.gov/products/world-ocean-database
All analyses are done using Matlab (v2023a). There are four groups of scripts. Group 1 consists of scripts to preprocess World Ocean Database observations. These are not needed as the shared data files (TwodN.csv and TwodP.csv) have all needed information. Group 2 consists of scripts required to estimate the long-term surface nutrient trends using different approaches. The key script is ‘regression_anomaly_wRandom_function.m’ as it describes the various regression approaches. Group 3 consists of scripts required to estimate the vertical profile trends and associated significance. ‘random_longterm_depth_parallel.m’ randomizes the observation along the temporal axis and estimates the randomized trends. ‘regression_anomaly_depth_top30.m’ estimate the vertical trend profile for Figure 3. ‘AutoEnconder_LongTerm.m’ uses an AutoEncoder to ‘learn’ the unsupervised profile of the randomized profiles and compared to the observed. Group 4 consists of scripts needed for processing CMIP6 model outputs. It is assumed that you have already processed the raw data files into a standardized grid (lat, lon, depth, time). ‘CMIP_trend_analysis.m’ calculates and the trends required for Figure 4 and ‘plot_trends_combined.m’ is used for plotting.