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Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties.
Pollution and the planetary albedo
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Aerosols, cloud microphysics, and fractional cloudiness
B. A. Albrecht · 1989
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M7: An efficient size-resolved aerosol microphysics module for large-scale aerosol transport models
Elisabetta Vignati, Julian Wilson, and Philip Stier · 2004
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The aerosol-climate model echam5-ham
P. Stier, J. Feichter, S. Kinne, S. Kloster, E. Vignati, J. Wilson, and et al · 2005
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Salsa: a sectional aerosol module for large scale applications
H. Kokkola, H. Korhonen, K. E. J. Lehtinen, R. Makkonen, A. Asmi, S. Järvenoja, T. Anttila, A.-I. Partanen, M. Kulmala, H. Järvinen, A. Laaksonen, and V.-M. Kerminen · 2008
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Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
IPCC · 2013
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Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events
Paul A. O’Gorman and John G. Dwyer · 2018
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Deep learning to represent subgrid processes in climate models
Stephan Rasp, Michael S. Pritchard, and Pierre Gentine · 2018
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Could machine learning break the convection parameterization deadlock?
P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi, and G. Yacalis · 2018
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Bounding global aerosol radiative forcing of climate change
N. Bellouin, J. Quaas, E. Gryspeerdt, S. Kinne, P. Stier, D. Watson-Parris, O. Boucher, K. S. Carslaw, M. Christensen, A.-L. Daniau, and el al. Dufresne · 2020
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Towards physically-consistent, data-driven models of convection, 2020
Tom Beucler, Michael Pritchard, Pierre Gentine, and Stephan Rasp · 2020
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Physically regularized machine learning emulators of aerosol activation
S. J. Silva, P.-L. Ma, J. C. Hardin, and D. Rothenberg · 2020
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Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements
A. A. Nair and F. Yu · 2020
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Climatebench: A benchmark dataset for data-driven climate projections
Duncan Watson-Parris, Yuhan Rao, Dirk Olivié, Øyvind Seland, Peer J Nowack, Gustau Camps-Valls, Philip Stier, Shahine Bouabid, Maura Dewey, Emilie Fons, and et al · 2021
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ClimART: A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models
Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, and David Rolnick · 2021
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Physics-informed machine learning: Case studies for weather and climate modelling
Karthik Kashinath, M Mustafa, Adrian Albert, J.-L Wu, C Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, R Wang, and et al · 2021
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Dc3: A learning method for optimization with hard constraints
Priya Donti, David Rolnick, and J Zico Kolter · 2021
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Enforcing analytic constraints in neural networks emulating physical systems
Tom Beucler, Michael Pritchard, Stephan Rasp, Jordan Ott, Pierre Baldi, and Pierre Gentine · 2021
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Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and lorenz 96 case study (v1.0)
S. Rasp · 2020
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Machine learning the warm rain process
A. Gettelman, D. J. Gagne, C.-C. Chen, M. W. Christensen, Z. J. Lebo, H. Morrison, and G. Gantos · 2021
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Emulating aerosol microphysics with machine learning
Paula Harder, Duncan Watson-Parris, Dominik Strassel, Nicolas Gauger, Philip Stier, and Janis Keuper · 2021
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Aerosol microphysics emulation dataset, January 2022
Paula Harder and Duncan Watson-Parris · 2022
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