1. Introduction A mechanism for explaining low-frequency SST variability is that the ocean is forced stochastically by fluxes representing weather noise ( Hasselmann 1976 ). Weather noise is the part of the atmospheric variability that is not the response to the boundary or external forcing. In Hasselmann’s one-point slab ocean linear model, which is closely related to the theory of Brownian motion ( Einstein 1905 ), the weather noise forcing is taken to be random noise independent of the
1. Introduction A mechanism for explaining low-frequency SST variability is that the ocean is forced stochastically by fluxes representing weather noise ( Hasselmann 1976 ). Weather noise is the part of the atmospheric variability that is not the response to the boundary or external forcing. In Hasselmann’s one-point slab ocean linear model, which is closely related to the theory of Brownian motion ( Einstein 1905 ), the weather noise forcing is taken to be random noise independent of the
, a 4-km elevation map can capture much more detailed topography features over the URGB than can a 12-km map. So far, however, few attempts have been made to employ very fine spatial resolutions for (long term) hydroclimatology studies. One of the objectives of this research was to investigate whether, or to what extent, this modeling can improve the accuracy of hydroclimate studies. In addition to model spatial resolution, the choice of atmospheric forcing field exerts an important influence on
, a 4-km elevation map can capture much more detailed topography features over the URGB than can a 12-km map. So far, however, few attempts have been made to employ very fine spatial resolutions for (long term) hydroclimatology studies. One of the objectives of this research was to investigate whether, or to what extent, this modeling can improve the accuracy of hydroclimate studies. In addition to model spatial resolution, the choice of atmospheric forcing field exerts an important influence on
propagation theory is able to predict the wave train emanating from the tropical heating ( Egger 1977 ; Opsteegh and Van den Dool 1980 ; Hoskins and Karoly 1981 ). In the presence of a zonally asymmetric mean flow, the response to tropical heating takes the form of a preferred pattern that is similar to the PNA ( Simmons et al. 1983 ). The effect of transients is another factor that contributes to the extratropical atmospheric response to the tropical forcing. It is shown in many studies that the
propagation theory is able to predict the wave train emanating from the tropical heating ( Egger 1977 ; Opsteegh and Van den Dool 1980 ; Hoskins and Karoly 1981 ). In the presence of a zonally asymmetric mean flow, the response to tropical heating takes the form of a preferred pattern that is similar to the PNA ( Simmons et al. 1983 ). The effect of transients is another factor that contributes to the extratropical atmospheric response to the tropical forcing. It is shown in many studies that the
functions ( Yang et al. 2003 ). With increasing age, the shape and size of contrail particles may approach that of natural cirrus. All these properties make simple radiation estimates difficult. Several studies computed the changes in net downward irradiance (flux) or radiative forcing (RF) caused by additional thin cirrus and contrails ( Stephens and Webster 1981 ; Meerkötter et al. 1999 ; Chen et al. 2000 ; Myhre et al. 2009 ; Rap et al. 2010 ; Frömming et al. 2011 ; Markowicz and Witek 2011
functions ( Yang et al. 2003 ). With increasing age, the shape and size of contrail particles may approach that of natural cirrus. All these properties make simple radiation estimates difficult. Several studies computed the changes in net downward irradiance (flux) or radiative forcing (RF) caused by additional thin cirrus and contrails ( Stephens and Webster 1981 ; Meerkötter et al. 1999 ; Chen et al. 2000 ; Myhre et al. 2009 ; Rap et al. 2010 ; Frömming et al. 2011 ; Markowicz and Witek 2011
1. Introduction Quantitative understanding of climate change during the industrial era suffers from a highly uncertain effective radiative forcing (radiative forcing plus adjustments) due to anthropogenic aerosols, ( Boucher et al. 2013 ). Stevens (2015 , hereinafter S15) proposed that the temporal and spatial characteristics of the observed warming since preindustrial times provide a powerful constraint on the total anthropogenic aerosol forcing. We follow S15 and assume that warming
1. Introduction Quantitative understanding of climate change during the industrial era suffers from a highly uncertain effective radiative forcing (radiative forcing plus adjustments) due to anthropogenic aerosols, ( Boucher et al. 2013 ). Stevens (2015 , hereinafter S15) proposed that the temporal and spatial characteristics of the observed warming since preindustrial times provide a powerful constraint on the total anthropogenic aerosol forcing. We follow S15 and assume that warming
1. Introduction Much of the precipitation in the tropics occurs within a narrow zonal band of high rainfall known as the intertropical convergence zone (ITCZ). Because small changes in the position of the ITCZ can greatly perturb local precipitation, it is important to understand how the ITCZ may respond to external thermal forcing. While the ITCZ is often thought to be controlled by tropical mechanisms (e.g., Xie 2004 ), recent studies that have demonstrated that the ITCZ can respond to
1. Introduction Much of the precipitation in the tropics occurs within a narrow zonal band of high rainfall known as the intertropical convergence zone (ITCZ). Because small changes in the position of the ITCZ can greatly perturb local precipitation, it is important to understand how the ITCZ may respond to external thermal forcing. While the ITCZ is often thought to be controlled by tropical mechanisms (e.g., Xie 2004 ), recent studies that have demonstrated that the ITCZ can respond to
Polvani 2004 ; Song and Robinson 2004 ; Thompson et al. 2005 , 2006 ). Thus, the patterns appear both as unforced natural variability and as a forced response to perturbations of the climate system. In a previous study ( Ring and Plumb 2007 , hereafter RP07 ), using a simple atmospheric general circulation model, we investigated the extent to which the annular modes constitute such a preferred response of the climate system to forcing by prescribed torques [modeled on those used by Song and
Polvani 2004 ; Song and Robinson 2004 ; Thompson et al. 2005 , 2006 ). Thus, the patterns appear both as unforced natural variability and as a forced response to perturbations of the climate system. In a previous study ( Ring and Plumb 2007 , hereafter RP07 ), using a simple atmospheric general circulation model, we investigated the extent to which the annular modes constitute such a preferred response of the climate system to forcing by prescribed torques [modeled on those used by Song and
1. Introduction In a companion paper ( Scott and Polvani 2006 , hereafter Part I ) it was demonstrated that a realistic stratosphere (considered in isolation) possesses its own natural or internal variability, in the sense that, in the absence of any time dependence in the external forcing, the stratospheric flow evolves into a time-dependent regime consisting of quasi-periodic vacillations resembling stratospheric sudden warmings. By external forcing, we refer to forcing by processes external
1. Introduction In a companion paper ( Scott and Polvani 2006 , hereafter Part I ) it was demonstrated that a realistic stratosphere (considered in isolation) possesses its own natural or internal variability, in the sense that, in the absence of any time dependence in the external forcing, the stratospheric flow evolves into a time-dependent regime consisting of quasi-periodic vacillations resembling stratospheric sudden warmings. By external forcing, we refer to forcing by processes external
quantify the sea ice response to changes in atmospheric and oceanic forcing on interannual and decadal time scales. We have developed a coupled ice–ocean model, which we refer to as the Bering Ecosystem Study Ice–Ocean Modeling and Assimilation System (BESTMAS). In this paper, we use this model to quantify the interannual and decadal variability of the Bering Sea ice cover over the period 1970–2008. Specifically, we seek to quantify the following: 1) the mechanisms controlling the observed variability
quantify the sea ice response to changes in atmospheric and oceanic forcing on interannual and decadal time scales. We have developed a coupled ice–ocean model, which we refer to as the Bering Ecosystem Study Ice–Ocean Modeling and Assimilation System (BESTMAS). In this paper, we use this model to quantify the interannual and decadal variability of the Bering Sea ice cover over the period 1970–2008. Specifically, we seek to quantify the following: 1) the mechanisms controlling the observed variability
the net radiative flux at the surface ( Walsh and Chapman 1998 ), thereby impacting the surface energy budget. The shortwave and longwave radiative effect of clouds, or cloud radiative forcing (CRF), can be quantified by comparing the actual surface radiative flux to the flux during an equivalent clear-sky scene. In general, Arctic clouds have a warming effect on the surface, except for a period in the summer when the sun is highest and surface albedo is lowest ( Curry and Ebert 1992 ; Intrieri
the net radiative flux at the surface ( Walsh and Chapman 1998 ), thereby impacting the surface energy budget. The shortwave and longwave radiative effect of clouds, or cloud radiative forcing (CRF), can be quantified by comparing the actual surface radiative flux to the flux during an equivalent clear-sky scene. In general, Arctic clouds have a warming effect on the surface, except for a period in the summer when the sun is highest and surface albedo is lowest ( Curry and Ebert 1992 ; Intrieri
