The Gap between Simulation and Understanding in Climate Modeling

By I. Held. 2005

528 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/bfebd900c7f82b170bce65ff96f2fe1fbfebf6fd

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In Atmospheric Science

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Citations in the corpus

Two-Layer Dynamic Recycling Model (2L-DRM): Learning from Moisture Tracking Models of Different Complexity (2020, background): “The model results will change when key sources of complexity are added or subtracted, and our understanding increases when we can identify the interrelationships between the models of different levels of complexity (Held 2005).”
Climate and climate change in a radiative‐convective equilibrium version of ECHAM6 (2013, background): “Rather, understanding develops by working through a hierarchy of models, and model configurations, to connect robust behavior of the most comprehensive models to basic physical principles, laws, or facts [e.g., Held, 2005; Bony et al., 2011].”
The physics of climate variability and climate change (2019, background): “role of such a hierarchy in the understanding and prediction of climate variability (Dijkstra and Ghil, 2005; Ghil and Robertson, 2000; Held, 2005; Lucarini, 2002; Saltzman, 2001).”
Predicting Climate Change Using Response Theory: Global Averages and Spatial Patterns (2015, methodology): “Therefore, we are talking of a modelling strategy that differs from the conventional approach aiming at achieving the highest possible resolution in the fluid fields and the highest precision in the parametrization of the highest possible variety of subgrid scale processes [12], but rather focuses o”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, background): “In climate modeling, low-fidelity models consider only a limited number of atmospheric effects, whereas high-fidelity models are fully coupled atmospheric and oceanic simulation models [99, 132].”
Stochastic climate dynamics: Random attractors and time-dependent invariant measures (2011, background): “These concepts have been applied to climate dynamics — across a full hierarchy of models, from conceptual “toy” models via so-called intermediate models and all the way to high-resolution general circulation models (GCMs) — as well as to the related uncertainties [9, 10, 11].”
Atmospheric circulation of tidally locked exoplanets: a suite of benchmark tests for dynamical solvers (2010, methodology): “The complexity of atmospheric modelling and its inherent non-linearity motivate clean comparisons between studies that either utilize different methods of solution or even implement the same methods differently (Held 2005).”
Climate model genealogy (2011, background): “Third, models are rarely built with lasting value as a primary goal [Held, 2005] and are super-”
Climate dynamics and fluid mechanics: Natural variability and related uncertainties (2008, methodology): “net exercise [6–8] – that there is enormous scatter in this space; see also [84,85].”
The Tropical Response to Extratropical Thermal Forcing in an Idealized GCM: The Importance of Radiative Feedbacks and Convective Parameterization (2009, unknown): “For sufficiently complex systems such as comprehensive GCMs, it is necessary to understand how the dynamics change as key sources of complexity are added or subtracted ( Held 2005 ).”
The end of model democracy? (2010, background): “In fact the beauty of a model often is its simplicity, and the fact that we can understand it and relate it to the behavior of more complex models (Held 2005).”