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1. Introduction A primary objective of the current climate community and its sponsors is to create accurate predictions of future climate on decadal to centennial time scales and a broad spectrum of space scales by improving model-component performance and accuracy, by implementing efficient strategies to coupled model components, and by maximizing throughput on state-of-the-art computers capable of exceptional peak speeds. To assist in this endeavor, we have developed a climate model entitled
1. Introduction A primary objective of the current climate community and its sponsors is to create accurate predictions of future climate on decadal to centennial time scales and a broad spectrum of space scales by improving model-component performance and accuracy, by implementing efficient strategies to coupled model components, and by maximizing throughput on state-of-the-art computers capable of exceptional peak speeds. To assist in this endeavor, we have developed a climate model entitled
1. Introduction Estimating the expected impact of climate change on hydrometeorological risk, ecosystem functioning, permafrost thawing, snow and glacier melt, and water availability requires precipitation scenarios with high spatial and temporal resolution ( Giorgi 2006 ; Wilby and Fowler 2010 ). However, current global climate models (GCMs) have spatial resolutions that are usually no higher than 70–120 km ( Washington and Parkinson 2005 ; Solomon et al. 2007 ). The current trend of
1. Introduction Estimating the expected impact of climate change on hydrometeorological risk, ecosystem functioning, permafrost thawing, snow and glacier melt, and water availability requires precipitation scenarios with high spatial and temporal resolution ( Giorgi 2006 ; Wilby and Fowler 2010 ). However, current global climate models (GCMs) have spatial resolutions that are usually no higher than 70–120 km ( Washington and Parkinson 2005 ; Solomon et al. 2007 ). The current trend of
the lower troposphere and stably stratified PBL ( Serreze et al. 2009 ; Screen and Simmonds 2010 ). As Arctic sea ice and high-latitude terrestrial snow cover diminish in response to increasing GHG, the inversion is expected to weaken, with consequences for the rate of surface warming, cloud type and amount, and other effects ( Pavelsky et al. 2010 ; Deser et al. 2010 ; Alexander et al. 2010 ; Kay and Gettelman 2009 ). Boé et al. (2009) point out that climate models tend to overestimate the
the lower troposphere and stably stratified PBL ( Serreze et al. 2009 ; Screen and Simmonds 2010 ). As Arctic sea ice and high-latitude terrestrial snow cover diminish in response to increasing GHG, the inversion is expected to weaken, with consequences for the rate of surface warming, cloud type and amount, and other effects ( Pavelsky et al. 2010 ; Deser et al. 2010 ; Alexander et al. 2010 ; Kay and Gettelman 2009 ). Boé et al. (2009) point out that climate models tend to overestimate the
’s radiation balance; hence, the adequate representation of the radiative fluxes in the climate system is a prerequisite for any climate model. Local-to-global-scale observations of the radiative fluxes include surface (SFC) radiation measurements, for example, the Baseline Surface Radiation Network (BSRN; Ohmura et al. 1998 ) and the Global Energy Balance Archive (GEBA; Ohmura and Gilgen 1991 ) as well as satellite-derived data products such as ERBE (refer to Table 2 for expansion of dataset names
’s radiation balance; hence, the adequate representation of the radiative fluxes in the climate system is a prerequisite for any climate model. Local-to-global-scale observations of the radiative fluxes include surface (SFC) radiation measurements, for example, the Baseline Surface Radiation Network (BSRN; Ohmura et al. 1998 ) and the Global Energy Balance Archive (GEBA; Ohmura and Gilgen 1991 ) as well as satellite-derived data products such as ERBE (refer to Table 2 for expansion of dataset names
the influence of atmospheric GHG concentration on the climate system is a must and has been delved in by several research groups worldwide. Such scrutiny is presently applied to matters ranging from the development of state-of-the-art coupled climate models up to complex earth system models (ESM) that incorporate the complexity of the many components of the earth system. Examples of such complex system models are those developed by the largest climate research centers in the world, such as the
the influence of atmospheric GHG concentration on the climate system is a must and has been delved in by several research groups worldwide. Such scrutiny is presently applied to matters ranging from the development of state-of-the-art coupled climate models up to complex earth system models (ESM) that incorporate the complexity of the many components of the earth system. Examples of such complex system models are those developed by the largest climate research centers in the world, such as the
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
inversely proportional to the climate feedback parameter. Rind et al. (1995) found the climate feedback parameter, defined as the initial tropopause energy imbalance divided by the equilibrium temperature response, to equal 0.95 W m −2 K −1 with sea ice response and 1.51 W m −2 K −1 without sea ice response. Thus, the sea ice response accounts for 37% of the temperature response to CO 2 doubling in Goddard Institute for Space Studies model ( Rind et al. 1995 ). Using the Met Office (UKMO) model
inversely proportional to the climate feedback parameter. Rind et al. (1995) found the climate feedback parameter, defined as the initial tropopause energy imbalance divided by the equilibrium temperature response, to equal 0.95 W m −2 K −1 with sea ice response and 1.51 W m −2 K −1 without sea ice response. Thus, the sea ice response accounts for 37% of the temperature response to CO 2 doubling in Goddard Institute for Space Studies model ( Rind et al. 1995 ). Using the Met Office (UKMO) model
. This is the reason why ECS has remained stubbornly in the range 1.5°–4.5°C ( Bony et al. 2006 ; Held and Soden 2000 ; Solomon et al. 2007 ) for three decades, with a factor of almost 3 difference among various Intergovernmental Panel on Climate Change (IPCC) models, and the model uncertainty could actually be even higher ( Huybers 2010 ). Fortunately, with respect to predictions of future warming within a century—which is far from equilibrium—it is the transient climate sensitivity that is more
. This is the reason why ECS has remained stubbornly in the range 1.5°–4.5°C ( Bony et al. 2006 ; Held and Soden 2000 ; Solomon et al. 2007 ) for three decades, with a factor of almost 3 difference among various Intergovernmental Panel on Climate Change (IPCC) models, and the model uncertainty could actually be even higher ( Huybers 2010 ). Fortunately, with respect to predictions of future warming within a century—which is far from equilibrium—it is the transient climate sensitivity that is more
1. Introduction Over two dozen different climate models contribute to the ongoing mission of the Intergovernmental Panel on Climate Change (IPCC), whose aim is to provide reliable estimates of future climate change to the public. The projections in the Fourth Assessment Report (AR4), the IPCC’s most recent, were mostly based on simple multimodel averages over the different participating models ( Meehl et al. 2007a ). The underlying assumption here and in similar studies is that models are more
1. Introduction Over two dozen different climate models contribute to the ongoing mission of the Intergovernmental Panel on Climate Change (IPCC), whose aim is to provide reliable estimates of future climate change to the public. The projections in the Fourth Assessment Report (AR4), the IPCC’s most recent, were mostly based on simple multimodel averages over the different participating models ( Meehl et al. 2007a ). The underlying assumption here and in similar studies is that models are more
1. Introduction Thanks to the development of highly scalable dynamical cores (e.g., Putman et al. 2005 ; Satoh et al. 2008 ; Dennis et al. 2012 ) that can exploit massively parallel computer architectures, we expect that global climate models in the next decade will run routinely at horizontal resolutions of 25 km or finer. Early results from climate simulations at these resolutions are promising in some respects. The models begin to explicitly capture important mesoscale convective
1. Introduction Thanks to the development of highly scalable dynamical cores (e.g., Putman et al. 2005 ; Satoh et al. 2008 ; Dennis et al. 2012 ) that can exploit massively parallel computer architectures, we expect that global climate models in the next decade will run routinely at horizontal resolutions of 25 km or finer. Early results from climate simulations at these resolutions are promising in some respects. The models begin to explicitly capture important mesoscale convective
