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Xu J, Dai W, Goldberg J, Shah P, Hu I, Chen C, deFilippi C, Sun J. Explainable Machine Learning to Improve Donor-Recipient Matching at Time of Heart Transplant. J Heart Lung Transplant 2023. [DOI: 10.1016/j.healun.2023.02.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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Wing AA, Stauffer CL, Becker T, Reed KA, Ahn M, Arnold NP, Bony S, Branson M, Bryan GH, Chaboureau J, De Roode SR, Gayatri K, Hohenegger C, Hu I, Jansson F, Jones TR, Khairoutdinov M, Kim D, Martin ZK, Matsugishi S, Medeiros B, Miura H, Moon Y, Müller SK, Ohno T, Popp M, Prabhakaran T, Randall D, Rios‐Berrios R, Rochetin N, Roehrig R, Romps DM, Ruppert JH, Satoh M, Silvers LG, Singh MS, Stevens B, Tomassini L, van Heerwaarden CC, Wang S, Zhao M. Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations. J Adv Model Earth Syst 2020; 12:e2020MS002138. [PMID: 33042391 PMCID: PMC7539986 DOI: 10.1029/2020ms002138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 06/11/2023]
Abstract
The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.
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Affiliation(s)
- Allison A. Wing
- Department of Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeFLUSA
| | - Catherine L. Stauffer
- Department of Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeFLUSA
| | | | - Kevin A. Reed
- School of Marine and Atmospheric SciencesStony Brook UniversityStony BrookNYUSA
| | - Min‐Seop Ahn
- Department of Atmospheric SciencesUniversity of WashingtonSeattleWAUSA
| | - Nathan P. Arnold
- Global Modeling and Assimilation OfficeNASA Goddard Space Flight CenterGreenbeltMDUSA
| | - Sandrine Bony
- Laboratoire de Météorologie Dynamique (LMD)/IPSL/Sorbonne Université/CNRSParisFrance
| | - Mark Branson
- Department of Atmospheric ScienceColorado State UniversityFort CollinsCOUSA
| | | | | | - Stephan R. De Roode
- Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote SensingDelft University of TechnologyDelftNetherlands
| | | | | | - I‐Kuan Hu
- Rosenstiel School of Marine and Atmospheric ScienceUniversity of MiamiMiamiFLUSA
| | - Fredrik Jansson
- Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote SensingDelft University of TechnologyDelftNetherlands
- Centrum Wiskunde and InformaticaAmsterdamNetherlands
| | - Todd R. Jones
- Department of MeteorologyUniversity of ReadingReadingUK
| | - Marat Khairoutdinov
- School of Marine and Atmospheric Sciences, and Institute for Advanced Computational Science, Stony Brook UniversityState University of New YorkStony BrookNYUSA
| | - Daehyun Kim
- Department of Atmospheric SciencesUniversity of WashingtonSeattleWAUSA
| | - Zane K. Martin
- Department of Applied Physics and Applied MathematicsColumbia UniversityNew YorkNYUSA
| | - Shuhei Matsugishi
- Atmosphere and Ocean Research InstituteThe University of TokyoKashiwaJapan
| | | | - Hiroaki Miura
- Department of Earth and Planetary Science, Graduate School of ScienceThe University of TokyoTokyoJapan
| | - Yumin Moon
- Department of Atmospheric SciencesUniversity of WashingtonSeattleWAUSA
| | | | - Tomoki Ohno
- Japan Agency for Marine‐Earth Science and TechnologyYokohamaJapan
| | - Max Popp
- Laboratoire de Météorologie Dynamique (LMD)/IPSL/Sorbonne Université/CNRS/École Polytechnique/École Normale SupérieureParisFrance
| | | | - David Randall
- Department of Atmospheric ScienceColorado State UniversityFort CollinsCOUSA
| | | | - Nicolas Rochetin
- Max Planck Institute for MeteorologyHamburgGermany
- Laboratoire de Météorologie Dynamique (LMD)/IPSL/Sorbonne Université/CNRS/École Polytechnique/École Normale SupérieureParisFrance
| | - Romain Roehrig
- CNRM, Université de Toulouse, Météo‐France, CNRSToulouseFrance
| | - David M. Romps
- Department of Earth and Planetary ScienceUniversity of CaliforniaBerkeleyCAUSA
- Climate and Ecosystem Sciences DivisionLawrence Berkeley National LaboratoryBerkeleyCAUSA
| | - James H. Ruppert
- Department of Meteorology and Atmospheric Science and Center for Advanced Data Assimilation and Predictability TechniquesPennsylvania State UniversityUniversity ParkPAUSA
| | - Masaki Satoh
- Atmosphere and Ocean Research InstituteThe University of TokyoKashiwaJapan
| | - Levi G. Silvers
- School of Marine and Atmospheric SciencesStony Brook UniversityStony BrookNYUSA
| | - Martin S. Singh
- School of Earth, Atmosphere, and EnvironmentMonash UniversityClaytonVictoriaAustralia
| | | | | | | | - Shuguang Wang
- Department of Applied Physics and Applied MathematicsColumbia UniversityNew YorkNYUSA
| | - Ming Zhao
- NOAA/Geophysical Fluid Dynamics LaboratoryPrincetonNJUSA
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Yoon H, Kim S, Yang J, Chen H, Hu I, Kim B, Bilfinger T, Matthews R, Franceschi D, Moore W, Stessin A, Ryu S. Prognostic Factors and Outcome After SBRT for Stage I Non-Small Cell Lung Cancer (NSCLC) Using Different Fractionation Regimens From 2007 to 2013. Int J Radiat Oncol Biol Phys 2015. [DOI: 10.1016/j.ijrobp.2015.07.1616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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