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1. Introduction The approximate “phase locking” of El Niño events to the seasonal cycle (i.e., the tendency of El Niño events to peak during the second half of the calendar year) suggests that interactions between interannual and seasonal time scales are important contributors to tropical climate variability. This suggestion has motivated many studies, which have provided several hypotheses on the complex mechanisms at work for the existence of such interactions. In the present paper we focus
1. Introduction The approximate “phase locking” of El Niño events to the seasonal cycle (i.e., the tendency of El Niño events to peak during the second half of the calendar year) suggests that interactions between interannual and seasonal time scales are important contributors to tropical climate variability. This suggestion has motivated many studies, which have provided several hypotheses on the complex mechanisms at work for the existence of such interactions. In the present paper we focus
1. Introduction Over oceans, sea surface temperature is the most important surface state variable controlling fluxes of water and energy from the surface into the lower atmosphere. Over land, both surface temperature and surface soil moisture (SSM) are critical. Variability in both quantities is imprinted on near-surface weather and climate over land. The largest source of temporal variability in surface temperature is the seasonal cycle (also referred to as the “annual cycle”). Many
1. Introduction Over oceans, sea surface temperature is the most important surface state variable controlling fluxes of water and energy from the surface into the lower atmosphere. Over land, both surface temperature and surface soil moisture (SSM) are critical. Variability in both quantities is imprinted on near-surface weather and climate over land. The largest source of temporal variability in surface temperature is the seasonal cycle (also referred to as the “annual cycle”). Many
that these long time adjustment scales might prevent the troposphere from adjusting to the stratospheric cooling if a realistic seasonal cycle were added to the model. This might be the case, if, for example, the troposphere is sensitive to the peak strength of the winter polar vortex, but the influence of the polar vortex takes longer than the winter season to be felt in the troposphere. In this note, we start from the viewpoint that it is worthwhile to better understand the characteristics of
that these long time adjustment scales might prevent the troposphere from adjusting to the stratospheric cooling if a realistic seasonal cycle were added to the model. This might be the case, if, for example, the troposphere is sensitive to the peak strength of the winter polar vortex, but the influence of the polar vortex takes longer than the winter season to be felt in the troposphere. In this note, we start from the viewpoint that it is worthwhile to better understand the characteristics of
1. Introduction Hemispheric asymmetries in stratospheric wave driving suggest a seasonal cycle in tropical lower-stratospheric upwelling (e.g., Yulaeva et al. 1994 ; Rosenlof 1995 ; Randel et al. 2002a , b ; Ueyama and Wallace 2010 ; Fueglistaler et al. 2011 ). Whereas dynamical forcing in each hemisphere commonly maximizes in its respective winter season, Northern Hemisphere wave driving is large compared with Southern Hemisphere wave driving. Consequently, there is stronger tropical
1. Introduction Hemispheric asymmetries in stratospheric wave driving suggest a seasonal cycle in tropical lower-stratospheric upwelling (e.g., Yulaeva et al. 1994 ; Rosenlof 1995 ; Randel et al. 2002a , b ; Ueyama and Wallace 2010 ; Fueglistaler et al. 2011 ). Whereas dynamical forcing in each hemisphere commonly maximizes in its respective winter season, Northern Hemisphere wave driving is large compared with Southern Hemisphere wave driving. Consequently, there is stronger tropical
climatology, trends, interannual variability, and “superintensity” (e.g., Emanuel 2000 ; Persing and Montgomery 2003 ; Wing et al. 2007 ; Zeng et al. 2007 ; Holland and Bruyère 2014 ; Kossin 2015 ; Sobel et al. 2016 ). A recent study by the authors ( Gilford et al. 2017 , hereafter GSE17 ) showed that the seasonal cycle of western North Pacific (WNP) TC PI is relatively flat (seasonally damped) compared to the TC PI seasonalities of the North Atlantic (NA), eastern North Pacific (ENP), and
climatology, trends, interannual variability, and “superintensity” (e.g., Emanuel 2000 ; Persing and Montgomery 2003 ; Wing et al. 2007 ; Zeng et al. 2007 ; Holland and Bruyère 2014 ; Kossin 2015 ; Sobel et al. 2016 ). A recent study by the authors ( Gilford et al. 2017 , hereafter GSE17 ) showed that the seasonal cycle of western North Pacific (WNP) TC PI is relatively flat (seasonally damped) compared to the TC PI seasonalities of the North Atlantic (NA), eastern North Pacific (ENP), and
water masses within the ACBC. c. Seasonal cycle in the eastern EB The seasonal cycle has long been recognized as one of the dominant modes of variability in the Arctic Ocean (e.g., Polyakov 1999 ). Historical data from averaged profiles taken during the 1950s–80s spanning the eastern EB region show a distinct seasonal signal exhibited by a warmer and fresher SML during summer and colder and saltier SML in winter, whereas in the lower halocline and upper AW layer, temperatures are lower in summer
water masses within the ACBC. c. Seasonal cycle in the eastern EB The seasonal cycle has long been recognized as one of the dominant modes of variability in the Arctic Ocean (e.g., Polyakov 1999 ). Historical data from averaged profiles taken during the 1950s–80s spanning the eastern EB region show a distinct seasonal signal exhibited by a warmer and fresher SML during summer and colder and saltier SML in winter, whereas in the lower halocline and upper AW layer, temperatures are lower in summer
the seasonal cycle of EDW on the scale of the basin and its underlying mechanisms. In section 2 , we present the methods used to synthesize and analyze the observations, and characterize the seasonal cycle of the EDW layer. Our reference estimate of the full EDW volume budget is presented and analyzed in section 3 . To place the full budget estimate in context and associate it with an uncertainty estimate, stand-alone estimates of a volume census based on Argo profiles and of surface formation
the seasonal cycle of EDW on the scale of the basin and its underlying mechanisms. In section 2 , we present the methods used to synthesize and analyze the observations, and characterize the seasonal cycle of the EDW layer. Our reference estimate of the full EDW volume budget is presented and analyzed in section 3 . To place the full budget estimate in context and associate it with an uncertainty estimate, stand-alone estimates of a volume census based on Argo profiles and of surface formation
1. Introduction Under anthropogenic forcing, comprehensive climate models have projected a significant change in the sea surface temperature (SST) seasonal cycle ( Timmermann et al. 2004 ; Biasutti and Sobel 2009 ; Dwyer et al. 2012 ; Stine and Huybers 2012 ; Sobel and Camargo 2011 ; Carton et al. 2015 ; Liu et al. 2017 ; Thomas et al. 2018 ; Alexander et al. 2018 ), including an amplitude increase in SST seasonal cycle in the tropical Pacific, a phase delay and amplitude decrease in
1. Introduction Under anthropogenic forcing, comprehensive climate models have projected a significant change in the sea surface temperature (SST) seasonal cycle ( Timmermann et al. 2004 ; Biasutti and Sobel 2009 ; Dwyer et al. 2012 ; Stine and Huybers 2012 ; Sobel and Camargo 2011 ; Carton et al. 2015 ; Liu et al. 2017 ; Thomas et al. 2018 ; Alexander et al. 2018 ), including an amplitude increase in SST seasonal cycle in the tropical Pacific, a phase delay and amplitude decrease in
given to interannual variability in TC PI and its connections with the middle atmosphere ( Wing et al. 2015 ), and only a few studies have considered the seasonality of potential intensity ( Free et al. 2004 ; Tonkin et al. 2000 ). In this study, we calculate the seasonal cycles of tropical cyclone potential intensity in the main TC development regions with 34 years of reanalysis data and use a decomposition method to determine the main factors that drive TC PI seasonality in these regions. Tonkin
given to interannual variability in TC PI and its connections with the middle atmosphere ( Wing et al. 2015 ), and only a few studies have considered the seasonality of potential intensity ( Free et al. 2004 ; Tonkin et al. 2000 ). In this study, we calculate the seasonal cycles of tropical cyclone potential intensity in the main TC development regions with 34 years of reanalysis data and use a decomposition method to determine the main factors that drive TC PI seasonality in these regions. Tonkin
1. Introduction The annual cycle of surface temperature has changed over the last half century with systematic shifts toward earlier seasonal phasing on land, later seasonal phasing over the ocean, and smaller annual amplitude over land ( Thomson 1995 ; Thompson 1995 ; Mann and Park 1996 ; Wallace and Osborn 2002 ; Stine et al. 2009 ). Many mechanisms have been suggested to explain variability in the phase of the annual cycle of surface temperature, and we briefly review four of these and
1. Introduction The annual cycle of surface temperature has changed over the last half century with systematic shifts toward earlier seasonal phasing on land, later seasonal phasing over the ocean, and smaller annual amplitude over land ( Thomson 1995 ; Thompson 1995 ; Mann and Park 1996 ; Wallace and Osborn 2002 ; Stine et al. 2009 ). Many mechanisms have been suggested to explain variability in the phase of the annual cycle of surface temperature, and we briefly review four of these and
