Estimation of Heat Content and Mean Temperature of Different Ocean Layers

Abstract

Oceans are reservoirs of heat energy represented by the heat content or the mean temperature, and are the source of energy for the atmospheric processes. Which process of the atmosphere interacts with the energy of which layer of the ocean is not clear, primarily, because of the nonavailability of oceanic heat energy of different layers on a required temporal and spatial scales. Realizing this requirement, we compute the ocean heat content (OHC) and the ocean mean temperature (OMT) from surface to 50, 100, 150, 200, 300, 500, 700 m and upto 26 • C isotherm depth. Thus, we computed altogether 16 variables from satellite observations of sea surface height anomaly (SSHA), sea surface temperature (SST), and the climatological values of the above 16 variables through an artificial neural network (ANN). The model is developed using 11 472 in situ and satellite collocated observations and is validated using 2479 independent values that are not used for developing the model. These estimations have a strong Pearson correlation coefficient, r, of more than 0.90 (at 99% confidence level) between the estimated and in situ values. These parameters are provided on near real time daily basis at a spatial resolution of 0.25 • at the Bhuvan website of National Remote Sensing Centre, Indian Space Research Organisation, which can be downloaded by a researcher for further ocean-atmosphere interaction investigations. Index Terms-Artificial neural network (ANN), ocean heat content (OHC), ocean mean temperature (OMT), sea surface height anomaly (SSHA), sea surface temperature (SST).

Dominant Modes of Upper Ocean Heat Content in the North Indian Ocean

“…as one unit. The National Remote Sensing Centre (NRSC) computed the OHC and ocean mean temperature from surface to 50 m, 100 m, 150 m, 200 m, 300 m, 500 m, 700 m and up to 26 • C isotherm depth (i.e., TCHP) following Jagadeesh et al [15]; the data isavailable on the NRSC website, Bhuvan (http://bhuvan.nrsc.gov.in). They used the artificial neural network (ANN) technique to estimate the above parameters from SST, sea surface height anomaly, and climatological OHC values at the respective depths.…”

OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

“…Particularly in recent years, machine learning has proven to be a cutting-edge application of empirical statistical methods to this field [18]. Of these techniques, neural networks (NN), as one of the most fundamental models of machine learning, has a proven ability to estimate various ocean parameters and subsurface information [19][20][21][22][23].…”

“…Our recent studies have demonstrated that, using a variety of machine-learning approaches including artificial neural networks (ANN), surface remote sensing data can be successfully applied to retrieving STA. These studies were inspired by the works of Jagadeesh et al [23] and Guinehut et al [24], which demonstrated that the subsurface thermal structure is dynamically linked to SSH and therefore, can be retrieved from remote sensing data. Su et al [14] verified the credibility of this theory by combining remote sensing data with machine-learning techniques such as support vector machines and random forests.…”

“…Accordingly, OHC as a variable may be closer related to the surface, which could lead to a more robust application of DORS. So far, using surface data to retrieve OHC has been rarely done [23,29]. In a pioneering work, Jagadeesh et al [23] trained an ANN to estimate discrete in-situ OHC in the Indian Ocean.…”

“…So far, using surface data to retrieve OHC has been rarely done [23,29]. In a pioneering work, Jagadeesh et al [23] trained an ANN to estimate discrete in-situ OHC in the Indian Ocean. Whether this approach can be further applied to the global ocean from gridded products is unclear.…”