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  • machine learning
  • English  (2)
  • Danish
  • 2020-2023  (2)
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  • English  (2)
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  • 1
    Publication Date: 2022-12-06
    Description: Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties.
    Description: Plain Language Summary: Deep neural nets are hard to interpret due to their hundred thousand or million trainable parameters without further postprocessing. We demonstrate in this paper the usefulness of a network type that is designed to drastically reduce this high dimensional information in a lower‐dimensional space to enhance the interpretability of predictions compared to regular deep neural nets. Our approach is, on the one hand, able to reproduce small‐scale cloud related processes in the atmosphere learned from a physical model that simulates these processes skillfully. On the other hand, our network allows us to identify key features of different cloud types in the lower‐dimensional space. Additionally, the lower‐order manifold separates tropical samples from polar ones with a remarkable skill. Overall, our approach has the potential to boost our understanding of various complex processes in Earth System science.
    Description: Key Points: A Variational Encoder Decoder (VED) can predict sub‐grid‐scale thermodynamics from the coarse‐scale climate state. The VED's latent space can distinguish convective regimes, including shallow/deep/no convection. The VED's latent space reveals the main sources of convective predictability at different latitudes.
    Description: EC ERC HORIZON EUROPE European Research Council http://dx.doi.org/10.13039/100019180
    Description: Columbia sub‐award 1
    Description: Advanced Research Projects Agency - Energy http://dx.doi.org/10.13039/100006133
    Description: Deutsches Klimarechenzentrum http://dx.doi.org/10.13039/100018730
    Description: National Science Foundation Science and Technology Center Learning the Earth with Artificial intelligence and Physics
    Keywords: ddc:551.5 ; machine learning ; generative deep learning ; convection ; parameterization ; explainable artificial intelligence ; dimensionality reduction
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2022-12-07
    Description: The radiogenic isotope heterogeneity of oceanic basalts is often assessed using 2D isotope ratio diagrams. But because the underlying data are at least six dimensional (87Sr/86Sr, 143Nd/144Nd, 176Hf/177Hf, and 208,207,206Pb/204Pb), it is important to examine isotopic affinities in multi‐dimensional data space. Here, we apply t‐distributed stochastic neighbor embedding (t‐SNE), a multi‐variate statistical data analysis technique, to a recent compilation of radiogenic isotope data of mid ocean ridge (MORB) and ocean island basalts (OIB). The t‐SNE results show that the apparent overlap of MORB‐OIB data trends in 2‐3D isotope ratios diagrams does not exist in multi‐dimensional isotope data space, revealing that there is no discrete “component” that is common to most MORB‐OIB mantle sources on a global scale. Rather, MORB‐OIB sample stochastically distributed small‐scale isotopic heterogeneities. Yet, oceanic basalts with the same isotopic affinity, as identified by t‐SNE, delineate several globally distributed regional domains. In the regional geodynamic context, the isotopic affinity of MORB and OIB is caused by capturing of actively upwelling mantle by adjacent ridges, and thus melting of mantle with similar origin in on, near, and off‐ridge settings. Moreover, within a given isotopic domain, subsidiary upwellings rising from a common deep mantle root often feed OIB volcanism over large surface areas. Overall, the t‐SNE results define a fundamentally new basis for relating isotopic variations in oceanic basalts to mantle geodynamics, and may launch a 21st century era of “chemical geodynamics.”
    Description: Plain Language Summary: The isotopic heterogeneity of basalts erupted at mid ocean ridges (MORB) and ocean islands (OIB) reflects the chemical evolution of Earth's mantle. The visual inspection of various 2D isotope ratio diagrams has fueled a four decade‐long discussion whether basalt heterogeneity reflects melting of only a small number of mantle components, and in particular, whether the apparent overlap of local data trends in global 2D isotope ratio diagrams indicates that melting of a common mantle component contributes to most MORB‐OIB. Here, we use multi‐variate statistical data analysis to show that the apparent overlap of MORB‐OIB data trends in 2D isotope ratio diagrams does not exist in multi‐dimensional isotope data space. Our finding invalidates any inference made for mantle compositional evolution based on the previously proposed existence of a common mantle component, its potential nature or distribution within the mantle. Rather, global MORB‐OIB sample small‐scale isotopic heterogeneities that are distributed stochastically in the Earth's mantle. Yet, MORB‐OIB with the same isotopic affinity, as identified by our multi‐variate data analysis, delineate several globally distributed regional domains. Within the regional geodynamic context, this discovery forms a fundamentally new basis for relating isotopic variations in MORB‐OIB to mantle geodynamics.
    Description: Key Points: Multi‐variate statistical data analysis (t‐distributed stochastic neighbor embedding) identifies global Sr‐Nd‐Hf‐Pb isotopic affinities of oceanic basalts. There is no “common mantle component;” rather, global mid ocean ridge‐ocean island basalts sample stochastically distributed small‐scale isotopic heterogeneities. Globally distributed regional domains of isotopically alike oceanic lavas define a new basis for relating isotopic variations to geodynamics.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung http://dx.doi.org/10.13039/501100001711
    Description: DAAD, German Academic Exchange Service
    Description: https://doi.org/10.25625/0SVW6S
    Description: https://doi.org/10.25625/BQENGN
    Keywords: ddc:551.9 ; mantle heterogeneity ; MORB ; OIB ; geodynamics ; t‐SNE ; radiogenic isotopes ; machine learning
    Language: English
    Type: doc-type:article
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