The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations

By Jeffrey L Anderson, V. B. Alaji, A. Broccoli, William F. C Ooke, W. D. Ixon, L. Donner, K. Dunne, S. Freidenreich, T. Arner, R. Gudgel and 21 more. 2004

867 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ad91dfa21a1a3676fd0624dcf0f677f18bf72127

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In Atmospheric Science

1. Gang Chen
2. D. Frierson
3. P. O’Gorman
4. I. Held
5. L. Harris
6. Shian‐Jiann Lin
7. Gabriel Vecchi
8. Andrew T. Wittenberg

In Global and Planetary Change

1. P. O’Gorman
2. Gang Chen
3. D. Frierson
4. Shian‐Jiann Lin
5. L. Harris
6. I. Held
7. Gabriel Vecchi
8. Tapio Schneider

Citations in the corpus

• Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events (2018, methodology): “For the convection scheme, we use the version of RAS that was implemented in the GFDL AM2 model [ Anderson et al. , 2004].”
• Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. (2009, methodology): “1 by guest on 17 Septem ber 2020 frequency and intensity are effectively decoupled, so that reliable simulations of frequency can be generated in models with an unrealistic distribution of intensities.”