Combined dataset of observations and neural network predictions of downward vertical turbulent heat flux at 0°N, 140°W used in "The equatorial Pacific's cold tongue as a primary thermal conduit to the deep ocean"
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This contains the hybrid observation-neural network predicted dataset of downward vertical turbulent heat flux (Jq) at 0N, 140W used in the following manuscript. Also included is the neural network used to make predictions from state variables, vertical gradients, and surface parameters. Please see the main manuscript for a detailed description: Iyer, S. and J.N. Moum. The equatorial Pacific’s cold tongue as a primary thermal conduit to the deep ocean. Submitted to Nature. Contents: "Jq_combined.nc": This contains the hybrid observational-neural network predicted dataset of Jq. Coverage is from May 1990 to March 2020 and 30 to 90 m depth with 4 hour temporal resolution and 5 m vertical resolution. The variable "Jq" contains time-depth gridded values of Jq (W m^{-2}; positive downward). The variable "Jq_flag" denotes whether data are from observations or neural network predictions. Observations are smoothed from higher-resolution data available on the NOAA GTMBA website "https://www.pmel.noaa.gov/tao/drupal/disdel/". Please cite that source for use of the observational data. The authors recommend these data should only be used in the context of analyzing longer-term variability, as point-to-point predictability is weaker and the neural network predictions include some peculiarities regarding the prediction of low values of Jq on shorter timescales (please see the Supplementary Information of the manuscript for details). "neural_network.nc": This contains the neural network specifications (weights, bias terms, mean/standard deviations of inputs used to normalize) used to calculate the predictions provided in "Jq_combined.nc". "trained_neural_network.mat": This contains the trained neural network (weights, biases, other specifications) as output automatically during training by MATLAB. This can be used to make predictions of Jq using the same neural network with MATLAB with minimal additional coding. The specifications are also included in the netCDF file "neural_network.nc" so that predictions can be made with other software as desired. Contact: Suneil IyerOregon State Universityiyersu@oregonstate.edu Date updated: 14 August 2025 DOI: 10.5281/zenodo.16877995
