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-00353.1 Guèye , A. K. , S. Janicot , A. Niang , S. Sawadogo , B. Sultan , A. Diongue-Niang , and S. Thiria , 2012 : Weather regimes over Senegal during the summer monsoon season using self-organizing maps and hierarchical ascendant classification. Part II: Interannual time scale . Climate Dyn. , 39 , 2251 – 2272 , https://doi.org/10.1007/s00382-012-1346-8 . 10.1007/s00382-012-1346-8 Haarsma , R. J. , F. Selten , B. van den Hurk , W. Hazeleger , and X. Wang , 2009
-00353.1 Guèye , A. K. , S. Janicot , A. Niang , S. Sawadogo , B. Sultan , A. Diongue-Niang , and S. Thiria , 2012 : Weather regimes over Senegal during the summer monsoon season using self-organizing maps and hierarchical ascendant classification. Part II: Interannual time scale . Climate Dyn. , 39 , 2251 – 2272 , https://doi.org/10.1007/s00382-012-1346-8 . 10.1007/s00382-012-1346-8 Haarsma , R. J. , F. Selten , B. van den Hurk , W. Hazeleger , and X. Wang , 2009
DPM are supported by the Ocean and Climate Dynamics program of CSIRO. TJO is supported by an Australian Research Council Future Fellowship. CF is supported by the Deutsche Forschungsgemeinschaft through the CliSAP Cluster of Excellence. We are grateful for the thorough and constructive reviews of the manuscript. REFERENCES Barnston , A. G. , and R. E. Livezey , 1987 : Classification, seasonality, and persistence of low-frequency atmospheric circulation patterns . Mon. Wea. Rev. , 115
DPM are supported by the Ocean and Climate Dynamics program of CSIRO. TJO is supported by an Australian Research Council Future Fellowship. CF is supported by the Deutsche Forschungsgemeinschaft through the CliSAP Cluster of Excellence. We are grateful for the thorough and constructive reviews of the manuscript. REFERENCES Barnston , A. G. , and R. E. Livezey , 1987 : Classification, seasonality, and persistence of low-frequency atmospheric circulation patterns . Mon. Wea. Rev. , 115
-flow regimes over the Sacramento Valley in 1991. They classified the wind regime by observing the wind direction in Davis, California, which sits just north of the San Francisco Bay Delta. Over their study period (May–September 1991), 72% of the days were classified as having some of the features of marine air intrusion (southerly wind at Davis). Sources that have analyzed projected temperature and wind fields suggest that stronger sea-breeze events will occur in coastal California in response to climate
-flow regimes over the Sacramento Valley in 1991. They classified the wind regime by observing the wind direction in Davis, California, which sits just north of the San Francisco Bay Delta. Over their study period (May–September 1991), 72% of the days were classified as having some of the features of marine air intrusion (southerly wind at Davis). Sources that have analyzed projected temperature and wind fields suggest that stronger sea-breeze events will occur in coastal California in response to climate
influence of North Atlantic SSTs on summer European climate is at the decadal time scale ( Sutton and Hodson 2005 ; Knight et al. 2006 ; Sutton and Dong 2012 ), whereby variability of the AMO causes shifts toward wetter or drier regimes, although these studies do not invoke robust dynamical changes behind these shifts. Arctic sea ice variability has been shown to drive shifts in the North Atlantic jet stream and associated circulation patterns in the summer season ( Screen et al. 2013 ; Petrie et al
influence of North Atlantic SSTs on summer European climate is at the decadal time scale ( Sutton and Hodson 2005 ; Knight et al. 2006 ; Sutton and Dong 2012 ), whereby variability of the AMO causes shifts toward wetter or drier regimes, although these studies do not invoke robust dynamical changes behind these shifts. Arctic sea ice variability has been shown to drive shifts in the North Atlantic jet stream and associated circulation patterns in the summer season ( Screen et al. 2013 ; Petrie et al
atmosphere associated with higher humidity profiles than the other easterly regimes. The ME regime corresponds to break monsoon conditions and occurs on about half of the days during the north Australian wet season. To illustrate the variability and length of each regime, the temporal evolution of the regime classification is shown in Fig. 3 . One can see that the active monsoon regime (DW) and the break periods (ME) are the only two regimes that, once established, tend to last several days. Except
atmosphere associated with higher humidity profiles than the other easterly regimes. The ME regime corresponds to break monsoon conditions and occurs on about half of the days during the north Australian wet season. To illustrate the variability and length of each regime, the temporal evolution of the regime classification is shown in Fig. 3 . One can see that the active monsoon regime (DW) and the break periods (ME) are the only two regimes that, once established, tend to last several days. Except
-state classification performed on daily New Caledonia rainfall, Moron et al. (2016) have even managed to explain the relationships between the local precipitation and the large-scale atmospheric dynamics at various time scales. However, the link between the subseasonal variability and the diurnal cycle of precipitation in New Caledonia has not been investigated so far and the present study intends to fill this gap. The following sections document the influence of the MJO and wind regimes on the diurnal cycle of
-state classification performed on daily New Caledonia rainfall, Moron et al. (2016) have even managed to explain the relationships between the local precipitation and the large-scale atmospheric dynamics at various time scales. However, the link between the subseasonal variability and the diurnal cycle of precipitation in New Caledonia has not been investigated so far and the present study intends to fill this gap. The following sections document the influence of the MJO and wind regimes on the diurnal cycle of
events, we considered the objective classification of clusters as optimal and treated positive-neutral and negative-neutral as separate events. Fig . 3. Composites of the mean NDJ SST anomalies for years associated with (a) canonical El Niño, (b) canonical La Niña, (c) positive-neutral, and (d) negative-neutral regimes. It is important to emphasize here that some questions have been raised in the past on the existence of different types of ENSO events. For example, Ashok et al. (2007) indicated
events, we considered the objective classification of clusters as optimal and treated positive-neutral and negative-neutral as separate events. Fig . 3. Composites of the mean NDJ SST anomalies for years associated with (a) canonical El Niño, (b) canonical La Niña, (c) positive-neutral, and (d) negative-neutral regimes. It is important to emphasize here that some questions have been raised in the past on the existence of different types of ENSO events. For example, Ashok et al. (2007) indicated
employed for synoptic and climate classification ( Cavazos 2000 ; Reusch et al. 2007 ; Bailey et al. 2011 ; Kennedy et al. 2016 ), cloud classification ( Ambroise et al. 2000 ), and extreme weather ( Cassano et al. 2006 ). The SOM analysis produces a continuous distribution of synoptic regimes ranging from trough to ridge ( Hewitson and Crane 2002 ). Decomposing synoptic patterns into a continuum of regimes using SOMs has an advantage over linear methods like EOF decomposition, which produces
employed for synoptic and climate classification ( Cavazos 2000 ; Reusch et al. 2007 ; Bailey et al. 2011 ; Kennedy et al. 2016 ), cloud classification ( Ambroise et al. 2000 ), and extreme weather ( Cassano et al. 2006 ). The SOM analysis produces a continuous distribution of synoptic regimes ranging from trough to ridge ( Hewitson and Crane 2002 ). Decomposing synoptic patterns into a continuum of regimes using SOMs has an advantage over linear methods like EOF decomposition, which produces
),(d) 1200 UTC 28 Jan 1991. As for the sample cases shown in Figs. 2 and 3 , the weighted PCs at all analysis times during 1979–2014 excluding the summer months are plotted on the NPJ phase diagram in order to classify each analysis time into one of the four NPJ regimes, or to identify analysis times during which the NPJ lies within the unit circle ( Fig. 4 ). For this classification scheme, the analysis times are classified based on, first, whether the position of the NPJ within the NPJ phase diagram
),(d) 1200 UTC 28 Jan 1991. As for the sample cases shown in Figs. 2 and 3 , the weighted PCs at all analysis times during 1979–2014 excluding the summer months are plotted on the NPJ phase diagram in order to classify each analysis time into one of the four NPJ regimes, or to identify analysis times during which the NPJ lies within the unit circle ( Fig. 4 ). For this classification scheme, the analysis times are classified based on, first, whether the position of the NPJ within the NPJ phase diagram
. Nevertheless, given that the NPJ phase diagram is constructed from the two leading modes of 250-hPa zonal wind variability over the North Pacific, plotting the weighted PCs in the NPJ phase diagram and tracking their evolution over time encompasses many important aspects of the NPJ evolution. Fig . 5. Schematic illustrating the NPJ phase diagram and the classification scheme used to determine the NPJ regime prior to ETE initiation. The values plotted on the axes of the NPJ phase diagram correspond to the
. Nevertheless, given that the NPJ phase diagram is constructed from the two leading modes of 250-hPa zonal wind variability over the North Pacific, plotting the weighted PCs in the NPJ phase diagram and tracking their evolution over time encompasses many important aspects of the NPJ evolution. Fig . 5. Schematic illustrating the NPJ phase diagram and the classification scheme used to determine the NPJ regime prior to ETE initiation. The values plotted on the axes of the NPJ phase diagram correspond to the
