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README.MD

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 ## Project Description
 
-In this study, we demonstrate that machine learning techniques can predict abyssal MOC strength using only satellite-observable variables. We train a suite of models for this task using the ``Estimating the Circulation and Climate of the Ocean'' (ECCO) state estimate, obtaining state-of-the-art performance. We incorporate the ``Australian Community Climate and Earth System Simulator Ocean Model'' (ACCESS), a high-resolution numerical ocean circulation model; and observational ``Rapid Climate Change-Meridional Overturning Circulation and Heatflux Array'' (RAPID) data, a cross-basin sensor array that directly measures the Atlantic MOC strength. Our experiments indicate an approximately linear relationship between satellite-observable variables and abyssal MOC strength. We additionally demonstrate the utility of observational data for predicting long-range oceanic dependencies through the integration of RAPID, and show that a deep learning model is able to accurately capture latitude-invariant features for MOC strength prediction. Through these experiments, we present a methodology for predicting abyssal circulation, which will be instrumental in informing climate policy and empowering further oceanographic research.
+In this study, we demonstrate that machine learning techniques can predict abyssal MOC strength using only satellite-observable variables. We train a suite of models for this task using the "Estimating the Circulation and Climate of the Ocean" (ECCO) state estimate, obtaining state-of-the-art performance. We incorporate the "Australian Community Climate and Earth System Simulator Ocean Model" (ACCESS), a high-resolution numerical ocean circulation model; and observational "Rapid Climate Change-Meridional Overturning Circulation and Heatflux Array" (RAPID) data, a cross-basin sensor array that directly measures the Atlantic MOC strength. Our experiments indicate an approximately linear relationship between satellite-observable variables and abyssal MOC strength. We additionally demonstrate the utility of observational data for predicting long-range oceanic dependencies through the integration of RAPID, and show that a deep learning model is able to accurately capture latitude-invariant features for MOC strength prediction. Through these experiments, we present a methodology for predicting abyssal circulation, which will be instrumental in informing climate policy and empowering further oceanographic research.
 
 This work was carried out as part of the [Artificial Intelligence for Environmental Risks](https://ai4er-cdt.esc.cam.ac.uk/) (AI4ER) Centre for Doctoral Training Guided Team Challenge (GTC), which ran from November, 2023 to March, 2024.