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Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections.
The National Center for Atmospheric Research community climate model: CCM3
Kiehl, J. T. et al · 1998
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A standard test for AGCMs including their physical parametrizations: I: The proposal
Neale, R. B. & Hoskins, B. J · 2000
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Some enhancements of decision tree bagging
Geurts, P · 2000
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Random forests
Breiman, L · 2001
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The elements of statistical learning (Springer, 2001), 2nd edn
Hastie, T., Tibshirani, R. & Friedman, J · 2001
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Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities
Khairoutdinov, M. F. & Randall, D. A · 2003
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Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities
Khairoutdinov, M. F. & Randall, D. A · 2003
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Time scales of climate response
Stouffer, R. J · 2004
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The cumulus parameterization problem: Past, present, and future
Arakawa, A · 2004
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A new approach for 3D cloud-resolving simulations of large-scale atmospheric circulation
Kuang, Z., Blossey, P. N. & Bretherton, C. S · 2005
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The frequency of extreme rain events in satellite rain-rate estimates and an atmospheric general circulation model
Wilcox, E. M. & Donner, L. J · 2007
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Resolving convection in a global hypohydrostatic model
Garner, S. T., Frierson, D. M. W., Held, I. M., Pauluis, O. & Vallis, G. K · 2007
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On the verification and comparison of extreme rainfall indices from climate models
Chen, C. T. & Knutson, T · 2008
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On the verification and comparison of extreme rainfall indices from climate models
Chen, C.-T. & Knutson, T · 2008
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On the selection of decision trees in random forests
Bernard, S., Heutte, L. & Adam, S · 2009
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Tree approximation of the long wave radiation parameterization in the NCAR CAM global climate model
Belochitski, A. et al · 2011
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Scikit-learn: Machine learning in python
Pedregosa, F. et al · 2011
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Sensitivity of tropical precipitation extremes to climate change
O’Gorman, P. A · 2012
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Factors controlling the position of the intertropical convergence zone on an aquaplanet
Möbis, B. & Stevens, B · 2012
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Origins of differences in climate sensitivity, forcing and feedback in climate models
Webb, M. J., Lambert, F. H. & Gregory, J. M · 2013
Cited alongside, same era.
Using ensemble of neural networks to learn stochastic convection parameterizations for climate and numerical weather prediction models from data simulated by a cloud resolving model
Krasnopolsky, V. M., Fox-Rabinovitz, M. S. & Belochitski, A. A · 2013
Cited alongside, same era.
Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events
O’Gorman, P. A. & Dwyer, J. G · 2018
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Prognostic validation of a neural network unified physics parameterization
Brenowitz, N. D. & Bretherton, C. S · 2018
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A practical approach to scale-adaptive deep convection in a GCM by controlling the cumulus base mass flux
Ahn, M.-S. & Kang, I.-S · 2018
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Lossless (and lossy) compression of random forests
Painsky, A. & Rosset, S · 2018
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Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events
O’Gorman, P. A. & Dwyer, J. G · 2018
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Arakawa, A. & Wu, C.-M · 2013
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Spread in model climate sensitivity traced to atmospheric convective mixing
Sherwood, S. C., Bony, S. & Dufresne, J · 2014
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Modelling the diurnal cycle of tropical convection across the ‘grey zone’
Pearson, K. et al · 2014
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A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling
Grell, G. A., Freitas, S. R. et al · 2014
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Effects of orography and surface heat fluxes on the south asian summer monsoon
Ma, D., Boos, W. & Kuang, Z · 2014
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Convective self-aggregation feedbacks in near-global cloud-resolving simulations of an aquaplanet
Bretherton, C. S. & Khairoutdinov, M. F · 2015
Cited alongside, same era.
Clouds and the atmospheric circulation response to warming
Ceppi, P. & Hartmann, D. L · 2016
Cited alongside, same era.
Prognostic validation of a neural network unified physics parameterization
Brenowitz, N. D. & Bretherton, C. S · 2018
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DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains
Stevens, B. et al · 2019
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Spatially extended tests of a neural network parametrization trained by coarse-graining
Brenowitz, N. D. & Bretherton, C. S · 2019
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Applications of deep learning to ocean data inference and subgrid parameterization
Bolton, T. & Zanna, L · 2019
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Climate models permit convection at much coarser resolutions than previously considered
Vergara-Temprado, J., Ban, N., Panosetti, D., Schlemmer, L. & Schär, C · 2019
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Achieving conservation of energy in neural network emulators for climate modeling
Beucler, T., Rasp, S., Pritchard, M. & Gentine, P · 2019
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Tropical cyclogenesis in warm climates simulated by a cloud-system resolving model
Fedorov, A. V., Muir, L., Boos, W. R. & Studholme, J · 2019
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Achieving conservation of energy in neural network emulators for climate modeling
Beucler, T., Rasp, S., Pritchard, M. & Gentine, P · 2019
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Spatially extended tests of a neural network parametrization trained by coarse-graining
Brenowitz, N. D. & Bretherton, C. S · 2019
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Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz96 case study (v1.0)
Rasp, S · 2020
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