AOML_ET: Partial pressure of CO2 (pCO2) and sea-air CO2 fluxes for the global ocean, along with the predictor variables from 1998-01-01 to 2023-12-30, using an Extra Trees (extremely randomized trees) machine learning (NCEI Accession 0298989)

This dataset contains global surface water partial pressure of CO2 data, pCO2w data, and sea-air CO2 fluxes based on the extremely randomized trees or extra trees, ET machine learning approach. The products are labeled as AOML_ET. The outputs are monthly pCO2 and sea-air CO2 flux fields at 1°x1° resolution covering the global ocean from 1998 to 2023. The results of several different permutations of AOML-ET are provided. They all use the following predictor variables at monthly and at 1°x1° resolution. The variables are time, location, sea surface temperature, SST, sea surface salinity, SSS, mixed layer depth, MLD, and chlorophyll-a, chl-a. The training is performed on the v2020 and v2023 releases of the Surface Ocean CO2 Atlas (www.SOCATinfo). The sea-air CO2 fluxes are computed from the air-sea CO2 partial pressure difference, ∆pCO2, and a bulk gas transfer formulation with windspeed. This data holding includes files of the monthly 1°x1° predictor variables. Several AOML_ET products are provided in this accession. SOCATv2020 and SOCATv2023 are used for training; pCO2w and ∆pCO2 are target variables; two different datasets for air CO2 are used for the calculation of sea-air CO2 fluxes, and three different gas exchange parameterizations are applied to determine the sea-air CO2 fluxes. Details are found in the readme file.