the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations
Paul R. Field
Gregory S. Elsaesser
Alejandro Bodas-Salcedo
Brian H. Kahn
Mark D. Zelinka
Chihiro Kodama
Thorsten Mauritsen
Benoit Vanniere
Malcolm Roberts
Pier L. Vidale
David Saint-Martin
Aurore Voldoire
Rein Haarsma
Adrian Hill
Ben Shipway
Jonathan Wilkinson
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Whether increased aerosol increases or decreases liquid cloud mass has been a longstanding question. Observed correlations suggest that aerosols thin liquid cloud, but we are able to show that observations were consistent with an increase in liquid cloud in response to aerosols by leveraging a model where causality could be traced.
Whether increased aerosol increases or decreases liquid cloud mass has been a longstanding question. Observed correlations suggest that aerosols thin liquid cloud, but we are able to show that observations were consistent with an increase in liquid cloud in response to aerosols by leveraging a model where causality could be traced.
tuning). Tuning uses degrees of freedom allowed by uncertainties in model approximations to modify parameters to make the simulation better align with some selected observed target(s). We describe how these tuning targets, parameters, and philosophy vary across six US modeling centers in order to increase the transparency of the practice.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
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Uncertainty with respect to cloud phases over the Southern Ocean and Arctic marine regions leads to large uncertainties in the radiation budget of weather and climate models. This study investigates the phases of low-base and mid-base clouds using satellite-based remote sensing data. A comprehensive analysis of the correlation of cloud phase with various parameters, such as temperature, aerosols, sea ice, vertical and horizontal cloud extent, and cloud radiative effect, is presented.