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1. Introduction Study of the climate system through the use of high-resolution atmospheric general circulation models (GCMs) coupled with land surface, ocean, and sea ice represents a tremendous computational cost, which is out of reach for many research centers. Regional climate models (RCMs) are frequently employed to provide lower-cost climate simulations using high-resolution representations of the atmospheric dynamics and physics, as well as forcing at the interface between the atmosphere
1. Introduction Study of the climate system through the use of high-resolution atmospheric general circulation models (GCMs) coupled with land surface, ocean, and sea ice represents a tremendous computational cost, which is out of reach for many research centers. Regional climate models (RCMs) are frequently employed to provide lower-cost climate simulations using high-resolution representations of the atmospheric dynamics and physics, as well as forcing at the interface between the atmosphere
1. Introduction At the Canadian Centre for Climate Modelling and Analysis (CCCma), a new regional climate model, the CCCma Regional Climate Model (CanRCM4), has been developed. CanRCM4’s novelty does not arise from the method of solution in its dynamical core or the climate-based physics package it employs. Both of these are well known and currently operational for global model applications. The novelty of CanRCM4 stems from a new philosophy of coordinating the development and application of
1. Introduction At the Canadian Centre for Climate Modelling and Analysis (CCCma), a new regional climate model, the CCCma Regional Climate Model (CanRCM4), has been developed. CanRCM4’s novelty does not arise from the method of solution in its dynamical core or the climate-based physics package it employs. Both of these are well known and currently operational for global model applications. The novelty of CanRCM4 stems from a new philosophy of coordinating the development and application of
. It also provides a basis for understanding the ultimate response of the climate system in fully interactive GCM climate integrations. In this study we employ a combination of offline radiative transfer model calculations with integrations of the Canadian Centre for Climate Modelling and Analysis (CCCma) fourth-generation atmospheric general circulation model (AGCM4) to consider the impact of various OSA schemes on the climate system. In this “atmospheric” configuration AGCM4 is driven by
. It also provides a basis for understanding the ultimate response of the climate system in fully interactive GCM climate integrations. In this study we employ a combination of offline radiative transfer model calculations with integrations of the Canadian Centre for Climate Modelling and Analysis (CCCma) fourth-generation atmospheric general circulation model (AGCM4) to consider the impact of various OSA schemes on the climate system. In this “atmospheric” configuration AGCM4 is driven by
1. Introduction With both the increase in computer power and a more complete representation of the many interactions in the climate system, climate models have become increasingly complex. Consequently, understanding their responses can often be just as difficult as understanding climate change in the real world. Radiative forcing and climate sensitivity were key concepts developed in the early days of climate modeling to aid understanding of the global mean temperature response ( Houghton et
1. Introduction With both the increase in computer power and a more complete representation of the many interactions in the climate system, climate models have become increasingly complex. Consequently, understanding their responses can often be just as difficult as understanding climate change in the real world. Radiative forcing and climate sensitivity were key concepts developed in the early days of climate modeling to aid understanding of the global mean temperature response ( Houghton et
1. Introduction Regional climate models (RCMs) are one-way nested limited-area models that are used to downscale low-resolution atmospheric information, usually reanalyses or (general circulation model) GCM-simulated data (e.g., Giorgi 1990 ; Christensen et al. 2007 ). Although the application of lateral boundary conditions (LBC) constrains RCMs’ simulations, the dynamical formulation and physical parameterizations of RCMs are as nonlinear as those of any GCM, and thus, nested models may
1. Introduction Regional climate models (RCMs) are one-way nested limited-area models that are used to downscale low-resolution atmospheric information, usually reanalyses or (general circulation model) GCM-simulated data (e.g., Giorgi 1990 ; Christensen et al. 2007 ). Although the application of lateral boundary conditions (LBC) constrains RCMs’ simulations, the dynamical formulation and physical parameterizations of RCMs are as nonlinear as those of any GCM, and thus, nested models may
1. Introduction Temperature inversions in the lower troposphere are a common feature of the Arctic climate ( Serreze et al. 1992 ; Liu et al. 2006 ; Zhang et al. 2011 ), and these surface-based inversions are more frequent and more stable during the winter months compared to summer months ( Tjernstrom and Graversen 2009 ; Zhang et al. 2011 ). Regional and global climate models (GCMs) have difficulties representing Arctic inversions ( Dethloff et al. 2001 ; Tjernstrom et al. 2008 ; Boé et
1. Introduction Temperature inversions in the lower troposphere are a common feature of the Arctic climate ( Serreze et al. 1992 ; Liu et al. 2006 ; Zhang et al. 2011 ), and these surface-based inversions are more frequent and more stable during the winter months compared to summer months ( Tjernstrom and Graversen 2009 ; Zhang et al. 2011 ). Regional and global climate models (GCMs) have difficulties representing Arctic inversions ( Dethloff et al. 2001 ; Tjernstrom et al. 2008 ; Boé et
1. Introduction When coupled atmosphere–mixed layer ocean models respond to imposed perturbations in atmospheric concentrations of “greenhouse gases” (most commonly CO 2 ), they predict changes in equilibrium global mean surface temperature that can differ by as much as a factor of 2 or more ( Cubasch et al. 2001 ). Likewise, models used to simulate the climate of the Last Glacial Maximum produce a range of responses ( Masson et al. 2006 ). The surface temperature changes are, of course, only
1. Introduction When coupled atmosphere–mixed layer ocean models respond to imposed perturbations in atmospheric concentrations of “greenhouse gases” (most commonly CO 2 ), they predict changes in equilibrium global mean surface temperature that can differ by as much as a factor of 2 or more ( Cubasch et al. 2001 ). Likewise, models used to simulate the climate of the Last Glacial Maximum produce a range of responses ( Masson et al. 2006 ). The surface temperature changes are, of course, only
resolution and domain (18 vertical levels extending to 38-km altitude, comparable to modern climate-oriented GCMs). At the same time, however, Lindzen et al. (1968) pointed out that spurious resonances will occur in a model atmosphere with an artificial “top.” Much later, Zwiers and Hamilton (1986) examined their GCM’s output for tides and concluded that compensating errors were at work: tidal amplitude was diminished by the model’s omission of much of the ozone heating but enhanced by the model’s
resolution and domain (18 vertical levels extending to 38-km altitude, comparable to modern climate-oriented GCMs). At the same time, however, Lindzen et al. (1968) pointed out that spurious resonances will occur in a model atmosphere with an artificial “top.” Much later, Zwiers and Hamilton (1986) examined their GCM’s output for tides and concluded that compensating errors were at work: tidal amplitude was diminished by the model’s omission of much of the ozone heating but enhanced by the model’s
1. Introduction This paper updates our earlier study of atmospheric tides in climate-oriented general circulation models ( Covey et al. 2011 , hereafter CDML ). The tides interact with surface and higher-altitude processes that play important roles in atmospheric dynamics and climate. CDML found a surprising consistency of model simulations with each other and with observations. The tides are driven in large part by solar heating of the ozone layer, which occurs in the altitude range 30
1. Introduction This paper updates our earlier study of atmospheric tides in climate-oriented general circulation models ( Covey et al. 2011 , hereafter CDML ). The tides interact with surface and higher-altitude processes that play important roles in atmospheric dynamics and climate. CDML found a surprising consistency of model simulations with each other and with observations. The tides are driven in large part by solar heating of the ozone layer, which occurs in the altitude range 30
1. Introduction Since publication of the first assessment report of the Intergovernmental Panel on Climate Change (IPCC) in 1990, there have been major improvements in our ability to model the climate system ( Randall et al. 2007 ; Trenberth et al. 2007 ; Flato et al. 2013 ; Hartmann et al. 2013 ). Thirty years ago, the climate science community performed single simulations with a small number of pioneering atmosphere–ocean models. Today, more complex Earth system models (ESMs) are used to
1. Introduction Since publication of the first assessment report of the Intergovernmental Panel on Climate Change (IPCC) in 1990, there have been major improvements in our ability to model the climate system ( Randall et al. 2007 ; Trenberth et al. 2007 ; Flato et al. 2013 ; Hartmann et al. 2013 ). Thirty years ago, the climate science community performed single simulations with a small number of pioneering atmosphere–ocean models. Today, more complex Earth system models (ESMs) are used to