Search Results

You are looking at 1 - 10 of 30 items for :

  • Middle atmosphere x
  • CCSM4/CESM1 x
  • All content x
Clear All
Richard B. Neale, Jadwiga Richter, Sungsu Park, Peter H. Lauritzen, Stephen J. Vavrus, Philip J. Rasch, and Minghua Zhang

1. Introduction The Community Atmosphere Model, version 4 (CAM4), is the seventh generation atmospheric general circulation model (AGCM) developed with significant community collaboration at the National Center for Atmospheric Research (NCAR). CAM4 comprises the atmosphere component of the Community Climate System Model, version 4 (CCSM4; Gent et al. 2011 ). For the first time CAM is fully integrated into the coupled CCSM4 system using either data or thermodynamic components of surface models

Full access
Clara Deser, Adam S. Phillips, Robert A. Tomas, Yuko M. Okumura, Michael A. Alexander, Antonietta Capotondi, James D. Scott, Young-Oh Kwon, and Masamichi Ohba

counterparts (e.g., Harrison and Larkin 1998 ; Okumura and Deser 2010 ). Driven primarily by coupled ocean–atmosphere processes within the tropical Indo-Pacific basin, the effects of ENSO are transmitted worldwide via atmospheric teleconnections, affecting precipitation and temperature in many societally vulnerable areas ( Trenberth et al. 1998 ; Alexander et al. 2002 ). Although the basic physical mechanisms of thermal and dynamical air–sea coupling that give rise to ENSO are well studied (see reviews

Full access
S. J. Ghan, X. Liu, R. C. Easter, R. Zaveri, P. J. Rasch, J.-H. Yoon, and B. Eaton

Boucher 2000 ; Myhre 2009 ), indirect effects ( Lohmann and Feichter 2005 ), and semidirect effects ( Hansen et al. 1997 ; Koch and Del Genio 2010 ). The term aerosol direct effects refers to the direct impact of anthropogenic aerosol particles on the planetary energy balance through scattering, absorption, and emission of radiation in the atmosphere, without consideration of the aerosol effects of the radiative heating on clouds. Aerosol indirect effects refer to the impact through the

Full access
Synte Peacock

The general circulation model used in this study is the most recent version of CCSM4, which consists of active atmosphere, ocean, land, and sea ice components. A preindustrial control simulation with fixed CO 2 (284.7 ppm), fixed incoming solar radiation at the top of the atmosphere (1360.9 W m −2 ), and prescribed aerosols (black and organic carbon, sulfate, dust, and sea salt) was run for 1300 years to achieve an equilibrium climate and initial conditions for the twentieth-century simulations

Full access
Christine A. Shields, David A. Bailey, Gokhan Danabasoglu, Markus Jochum, Jeffrey T. Kiehl, Samuel Levis, and Sungsu Park

low-resolution CCSM4 (henceforth called T31x3) uses a T31 spectral dynamical core for the atmospheric and land components (horizontal grid of 3.75° × 3.75°) with 26 atmospheric layers in the vertical. The ocean and ice components employ a nominal 3° irregular horizontal grid (referred to as x3) with 60 ocean layers in the vertical. The intermediate-resolution CCSM4 utilizes the finite-volume (FV) dynamical core ( Lin 2004 ) with a nominal 2° atmosphere and land horizontal grid (1.9° × 2

Full access
A. Gettelman, J. E. Kay, and K. M. Shell

, and conclusions are in section 7 . 2. Methodology We apply radiative kernels calculated offline to the climate response in doubled CO 2 experiments with atmospheric GCMs coupled to slab ocean models (SOMs). In CESM, SOM experiments yield results very similar to atmospheric models coupled to a full dynamic ocean ( Bitz et al. 2012 ). For feedbacks attributed to atmospheric physical parameterizations, the same feedbacks found in the SOM runs can be diagnosed with stand-alone atmosphere model SST

Full access
Laura Landrum, Marika M. Holland, David P. Schneider, and Elizabeth Hunke

1. Introduction The Antarctic sea ice cover undergoes a large seasonal range from a climatological maximum of approximately 19 million km 2 in extent in September to a minimum of 3 million km 2 in February (e.g., Cavalieri and Parkinson 2008 ) ( Fig. 1 ). The seasonal cycle of ice advance and retreat is influenced by the dominant seasonality in the atmosphere and the semiannual oscillation (SAO)—a biannual (spring and autumn) strengthening and poleward migration of the circumpolar trough (e

Full access
C. M. Bitz, K. M. Shell, P. R. Gent, D. A. Bailey, G. Danabasoglu, K. C. Armour, M. M. Holland, and J. T. Kiehl

1. Introduction Equilibrium climate sensitivity (ECS) is an often used metric to evaluate the climate response to a perturbation in the radiative forcing. It is specifically defined as the equilibrium change in global mean surface air temperature that results from doubling the concentration of carbon dioxide (CO 2 ) in the atmosphere ( IPCC 1990 ). In this study we investigate how the new Community Climate System Model, version 4 (CCSM4) responds to doubling CO 2 compared to the previous

Full access
K. J. Evans, P. H. Lauritzen, S. K. Mishra, R. B. Neale, M. A. Taylor, and J. J. Tribbia

1. Introduction Here we report on the climate produced by a spectral element atmospheric dynamical core option within the Community Climate System Model (CCSM), version 4. The CCSM is a state-of-the-art climate model with atmosphere, ocean, land, and ice component models that exchange information through a flux coupler ( Gent et al. 2011 ). The spectral element dynamical core comes from the High-Order Method Modeling Environment (HOMME; Dennis et al. 2005 , 2012 ), which has been integrated

Full access
J. E. Kay, B. R. Hillman, S. A. Klein, Y. Zhang, B. Medeiros, R. Pincus, A. Gettelman, B. Eaton, J. Boyle, R. Marchand, and T. P. Ackerman

diagnostic that is currently available in COSP. b. Study goals The primary goal of this study is to evaluate mean state clouds in the Community Atmosphere Model (CAM) using multiple independent satellite datasets and their corresponding instrument simulators in COSP. The main findings expose large, and at times compensating, model cloud biases both globally and in key climatic regions. The presented model cloud biases could result from both model circulation (dynamics) and cloud parameterization (physics

Full access