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@ARTICLE{Tesch:1006966,
      author       = {Tesch, Tobias and Kollet, Stefan and Garcke, Jochen},
      title        = {{C}ausal deep learning models for studying the {E}arth
                      system},
      journal      = {Geoscientific model development},
      volume       = {16},
      number       = {8},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2023-01917},
      pages        = {2149 - 2166},
      year         = {2023},
      abstract     = {Earth is a complex non-linear dynamical system. Despite
                      decades of research and considerable scientific and
                      methodological progress, many processes and relations
                      between Earth system variables remain poorly understood.
                      Current approaches for studying relations in the Earth
                      system rely either on numerical simulations or statistical
                      approaches. However, there are several inherent limitations
                      to existing approaches, including high computational costs,
                      uncertainties in numerical models, strong assumptions about
                      linearity or locality, and the fallacy of correlation and
                      causality. Here, we propose a novel methodology combining
                      deep learning (DL) and principles of causality research in
                      an attempt to overcome these limitations. On the one hand,
                      we employ the recent idea of training and analyzing DL
                      models to gain new scientific insights into relations
                      between input and target variables. On the other hand, we
                      use the fact that a statistical model learns the causal
                      effect of an input variable on a target variable if suitable
                      additional input variables are included. As an illustrative
                      example, we apply the methodology to study
                      soil-moisture–precipitation coupling in ERA5 climate
                      reanalysis data across Europe. We demonstrate that,
                      harnessing the great power and flexibility of DL models, the
                      proposed methodology may yield new scientific insights into
                      complex non-linear and non-local coupling mechanisms in the
                      Earth system.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000972780500001},
      doi          = {10.5194/gmd-16-2149-2023},
      url          = {https://juser.fz-juelich.de/record/1006966},
}