RFROM (Random Forest Regression Ocean Maps; Lyman and Johnson, 2023) is a set of 1/4° x 1/4° x weekly resolution maps of ocean heat content (in 11 pressure layers) starting in January 1993 that use in situ temperature and salinity profiles to train machine learning models (Random Forests) that include satellite sea-surface height data as a predictor. Ocean temperature and salinity maps (for 58 pressure layers) are forthcoming.
GOBAI-O2 (Gridded Ocean Biogeochemistry from Artificial Intelligence - Oxygen; Sharp et al., 2022) is a gridded observation-based data product constructed via machine learning that provides three-dimensional monthly fields of dissolved oxygen in the global ocean from 2004 through 2021.
GOSML (Johnson and Lyman, 2022) is a global ocean surface mixed layer statistical monthly climatology of depth, temperature, and salinity that includes means; variances; values at depth-sorted 95th, 50th, and 5th percentiles; as well as skewness and kurtosis.
MIMOC (Monthly Isopycnal / Mixed-layer Ocean Climatology; Schmidtko et al., 2013) is a trio of global monthly ocean property maps from 80°S to 90°N at 0.5° lateral resolution, all available for download. Products include mixed layer and select interior ocean isopycnal maps.
These Equatorial Pacific Mean CTD/ADCP Sections in NetCDF from Johnson et al. (2002) have been widely used for ocean and climate model evaluation and validation, as well as observational studies.
GOBOP members have been serving as authors and editors for the Global Oceans chapter of the annual State of the Climate reports since 2005, with a focus on ocean heat content and sea-surface salinity anomalies.
OneArgo-Mat and OneArgo-R are MATLAB and R toolboxes that contain a variety of functions for accessing, processing, and visualizing Argo float data from the three Argo program missions (i.e. Core, Biogeochemical, and Deep). The functions are designed to be maximally efficient, to provide access to the most up-to-date data available, and to allow downloading and plotting of these data (e.g., trajectories, vertical profiles, time series) based on numerous user-defined conditions (e.g., geographic location, parameters, time window). These toolboxes are upgraded versions of the BGC-Argo-Mat and BGC-Argo-R toolboxes that were designed for Biogeochemical Argo.
Empirical Seawater Property Estimation Routines (ESPERs) from Carter et al. (2021) are regularly used in Biogeochemical Argo float data quality assessment and control, with numerous additional applications possible. The ESPERs are capable of predicting seawater phosphate, nitrate, silicate, oxygen, total titration seawater alkalinity, total hydrogen scale pH, and total dissolved inorganic carbon from up to 16 combinations of seawater property measurements.