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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
anticipated in 2021 (at the time of writing). The human-made changes are raising concerns over dangerous levels of climate change leading to major impacts on ecosystems and society. Avoiding dangerous climate change has led to the adoption of the 2015 Paris Agreement with the objective to limit global warming to 2°C or even below, compared to preindustrial temperature ( UNFCCC 2015 ). Climate research and climate modeling can help address the issues around how to reach this objective, such as the
anticipated in 2021 (at the time of writing). The human-made changes are raising concerns over dangerous levels of climate change leading to major impacts on ecosystems and society. Avoiding dangerous climate change has led to the adoption of the 2015 Paris Agreement with the objective to limit global warming to 2°C or even below, compared to preindustrial temperature ( UNFCCC 2015 ). Climate research and climate modeling can help address the issues around how to reach this objective, such as the
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
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
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
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
suitable for addressing the above questions or projecting future climate in this region. The most suitable approach is to consistently estimate evaporation and all other heat fluxes through the use of a laterally constrained and vertically coupled Gulf–atmosphere model. The remaining part of this paper is organized as follows: Section 2 describes the data used in this study, the coupled ocean–atmosphere model, and the design of experiments. Section 3 presents the results of the coupled simulation
suitable for addressing the above questions or projecting future climate in this region. The most suitable approach is to consistently estimate evaporation and all other heat fluxes through the use of a laterally constrained and vertically coupled Gulf–atmosphere model. The remaining part of this paper is organized as follows: Section 2 describes the data used in this study, the coupled ocean–atmosphere model, and the design of experiments. Section 3 presents the results of the coupled simulation
. 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
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
). Owing to its proximity, such circulation changes could be consistent with shifts in the strength and location of the ABSL. In particular, Fogt et al. (2012b) showed that ABSL variability is significantly linked to the SAM, which over the last few decades has shown a significant trend toward a more positive phase during summer and autumn (e.g., Marshall 2003 ). Moreover, given this connection, the ability of coupled atmosphere–ocean climate models to represent the present climate of West
). Owing to its proximity, such circulation changes could be consistent with shifts in the strength and location of the ABSL. In particular, Fogt et al. (2012b) showed that ABSL variability is significantly linked to the SAM, which over the last few decades has shown a significant trend toward a more positive phase during summer and autumn (e.g., Marshall 2003 ). Moreover, given this connection, the ability of coupled atmosphere–ocean climate models to represent the present climate of West
