Search Results
analyzed in section 3 . The mechanisms governing sea surface warming are studied in section 4 . Cause of the enhanced surface warming along the band of high SSHA is examined in section 5 , and the coupling between SST and high SSHA is delineated in section 6 . In section 7 , the salinity effects on sea level variability are investigated using a hydrostatic relationship established from a 1½-layer model. Discussion and summary are given in section 8 . 2. IndOOS and satellite observations Ocean
analyzed in section 3 . The mechanisms governing sea surface warming are studied in section 4 . Cause of the enhanced surface warming along the band of high SSHA is examined in section 5 , and the coupling between SST and high SSHA is delineated in section 6 . In section 7 , the salinity effects on sea level variability are investigated using a hydrostatic relationship established from a 1½-layer model. Discussion and summary are given in section 8 . 2. IndOOS and satellite observations Ocean
mixing in the ocean and coastal upwelling, intensifying the warming in the SEIO ( Murtugudde et al. 2000 ). Consequently, the southeastern TIO SSTA shows an abrupt change from negative to positive during winter. The persistence barrier of the western TIO SSTA is locked to spring, while the barriers of the South China Sea and southeastern TIO SSTA are locked to fall and winter, respectively. The present results show that the seasonal cycle of the prevailing surface winds has an important influence on
mixing in the ocean and coastal upwelling, intensifying the warming in the SEIO ( Murtugudde et al. 2000 ). Consequently, the southeastern TIO SSTA shows an abrupt change from negative to positive during winter. The persistence barrier of the western TIO SSTA is locked to spring, while the barriers of the South China Sea and southeastern TIO SSTA are locked to fall and winter, respectively. The present results show that the seasonal cycle of the prevailing surface winds has an important influence on
1. Introduction Over the past decades, advancements in satellite technology have revolutionized oceanography by providing global-scale measurements of sea surface variables. The future Surface Water and Ocean Topography (SWOT) satellite mission aims at capturing, on a global average, sea surface height (SSH) signals with wavelengths ≥ 15 km ( Durand et al. 2010 ; Fu and Ubelmann 2014 ; Wang et al. 2019 ) and is expected to further improve our understanding of the oceanic mesoscale (from 300
1. Introduction Over the past decades, advancements in satellite technology have revolutionized oceanography by providing global-scale measurements of sea surface variables. The future Surface Water and Ocean Topography (SWOT) satellite mission aims at capturing, on a global average, sea surface height (SSH) signals with wavelengths ≥ 15 km ( Durand et al. 2010 ; Fu and Ubelmann 2014 ; Wang et al. 2019 ) and is expected to further improve our understanding of the oceanic mesoscale (from 300
1. Introduction The basic pattern of sea surface temperature (SST) in the tropical Pacific Ocean is characterized by a vast warm pool in the west (∼29°C annual mean SST), and a relatively narrow tongue of cold surface water extending westward from the coast of South America (∼25°C annual mean SST). The result is an approximately linear zonal gradient of SST between the western and eastern equatorial Pacific corresponding to a zonal SST difference (Δ x SST) of ∼4°C. A persistent change of Δ x
1. Introduction The basic pattern of sea surface temperature (SST) in the tropical Pacific Ocean is characterized by a vast warm pool in the west (∼29°C annual mean SST), and a relatively narrow tongue of cold surface water extending westward from the coast of South America (∼25°C annual mean SST). The result is an approximately linear zonal gradient of SST between the western and eastern equatorial Pacific corresponding to a zonal SST difference (Δ x SST) of ∼4°C. A persistent change of Δ x
layer is proportional to the wind stress, and the wind stress curl due to SST gradients can be very large in the vicinity of ocean fronts ( Chelton et al. 2004 ). This implies a relationship between surface temperature and vertical motions at the base of the Ekman layer, and suggests that the growth of baroclinic waves may be affected by the air–sea coupling, analogous to the effect found for a sloping bottom. The purpose of this note is to explore this coupling in its simplest context, and to
layer is proportional to the wind stress, and the wind stress curl due to SST gradients can be very large in the vicinity of ocean fronts ( Chelton et al. 2004 ). This implies a relationship between surface temperature and vertical motions at the base of the Ekman layer, and suggests that the growth of baroclinic waves may be affected by the air–sea coupling, analogous to the effect found for a sloping bottom. The purpose of this note is to explore this coupling in its simplest context, and to
1. Introduction The Indian Ocean (IO) sea surface temperature (SST) profoundly impacts the Asian monsoon ( Kulkarni et al. 2007 ; Schott et al. 2009 ; Yang et al. 2010 ). In recent decades, it has been reported that the tropical IO SST has experienced rapid warming, with more statistically significant and faster warming in autumn since the late 1990s ( Levitus et al. 2005 ; Du et al. 2013 ). Previous studies have revealed that boreal autumn IO SST anomalies have a significant influence on
1. Introduction The Indian Ocean (IO) sea surface temperature (SST) profoundly impacts the Asian monsoon ( Kulkarni et al. 2007 ; Schott et al. 2009 ; Yang et al. 2010 ). In recent decades, it has been reported that the tropical IO SST has experienced rapid warming, with more statistically significant and faster warming in autumn since the late 1990s ( Levitus et al. 2005 ; Du et al. 2013 ). Previous studies have revealed that boreal autumn IO SST anomalies have a significant influence on
the importance of the tropical Indian Ocean in modulating rainfall variability in observations (e.g., Black et al. 2003 ; Clark et al. 2003 ) and in climate model simulations (e.g., Goddard and Graham 1999 ; Latif et al. 1999 ). Sea surface temperature (SST) patterns associated with the zonal mode of variability, the Indian Ocean Dipole (IOD; Saji et al. 1999 ; Webster et al. 1999 ), and associated changes in the basinwide atmospheric circulation are implicated in several flooding events (e
the importance of the tropical Indian Ocean in modulating rainfall variability in observations (e.g., Black et al. 2003 ; Clark et al. 2003 ) and in climate model simulations (e.g., Goddard and Graham 1999 ; Latif et al. 1999 ). Sea surface temperature (SST) patterns associated with the zonal mode of variability, the Indian Ocean Dipole (IOD; Saji et al. 1999 ; Webster et al. 1999 ), and associated changes in the basinwide atmospheric circulation are implicated in several flooding events (e
represents the thermal surface boundary condition for ocean-only model integrations. The latter routinely employ a constant α . However, as shown from observations by Frankignoul et al. (1998) , Frankignoul and Kestenare (2002 , hereinafter FK02) and Park et al. (2005) , α is not constant, but instead varies in space and time as well as with spatial scale. Midlatitude air–sea feedbacks are observed to feature a systematic seasonal dependence, being typically larger in fall and winter. Moreover
represents the thermal surface boundary condition for ocean-only model integrations. The latter routinely employ a constant α . However, as shown from observations by Frankignoul et al. (1998) , Frankignoul and Kestenare (2002 , hereinafter FK02) and Park et al. (2005) , α is not constant, but instead varies in space and time as well as with spatial scale. Midlatitude air–sea feedbacks are observed to feature a systematic seasonal dependence, being typically larger in fall and winter. Moreover
atmosphere, in conjunction with a more thorough understanding of the links between Indian Ocean sea surface temperature (SST) and regional climate, can help improve seasonal rainfall predictions and thus, ultimately, water and agricultural management. This study follows previous work linking a characteristic pattern of Indian Ocean SST anomalies, and an associated reorganization of the basinwide atmospheric circulation, to Australian rainfall anomalies in observations and climate model experiments
atmosphere, in conjunction with a more thorough understanding of the links between Indian Ocean sea surface temperature (SST) and regional climate, can help improve seasonal rainfall predictions and thus, ultimately, water and agricultural management. This study follows previous work linking a characteristic pattern of Indian Ocean SST anomalies, and an associated reorganization of the basinwide atmospheric circulation, to Australian rainfall anomalies in observations and climate model experiments
addition to a direct thermal response in the atmospheric boundary layer to sea surface temperature (SST) anomalies, there is also evidence for a significant modulation of the large-scale atmospheric circulation. However, relative to the atmosphere’s internal variability the ocean-induced changes are small. Nevertheless, a wealth of studies have been inspired by the possibility of utilizing the longer persistence of anomalies in the ocean, which in turn might modulate extratropical atmospheric
addition to a direct thermal response in the atmospheric boundary layer to sea surface temperature (SST) anomalies, there is also evidence for a significant modulation of the large-scale atmospheric circulation. However, relative to the atmosphere’s internal variability the ocean-induced changes are small. Nevertheless, a wealth of studies have been inspired by the possibility of utilizing the longer persistence of anomalies in the ocean, which in turn might modulate extratropical atmospheric
