Journal: Scientific Reports

Article Title: Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation as an initiator of El Niño/Southern Oscillation events

doi: 10.1038/s41598-017-09580-9

Figure Lengend Snippet: Intraseasonal extratropical atmospheric variability prior to El Nin∼o/Southern Oscillation events. ( a ) Shading: Correlation between boreal winter (Nov.-Mar.) standard deviation of daily-mean SLP and the seasonal-mean NINO3.4 index 12 months later (Dec.-Feb) for the period 1958-59 to 2000-01. Shading interval given by color bar at the bottom of the panel. Contours: regression of Dec.-Feb. sea surface temperatures (SST) against daily SLP variability in the Central North Pacific (CNP—designated by the box) during the prior extended boreal winter. Units - (K). Contour interval as labeled. ( b ) Normalized time-series of Dec.-Feb NINO3.4 index (solid line) and daily SLP variability in the CNP during the prior extended boreal winter (dotted line). Correlation between the two time-series given in the legend; the correlation value is statistically significant at the p < 0.01 level, based upon a two-tailed t-test. SLP data taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40) for 1959–2002. SST data taken from ORA-S3 ECMWF ocean reanalysis. Daily SLP variations at a given grid-point derived by first removing from the daily values the long-term climatological mean for that day, then calculating the standard deviation of the anomalous daily SLP values about the seasonal-mean anomaly for the given year. Daily SLP variability in the CNP derived by calculating the standard deviation of the anomalous daily SLP values about the seasonal-mean anomalies for the given year and then area averaging the grid-point values within the region 20-40 N; 155-180 W. The map in this figure is generated by MATLAB R2014a using routines found in the standard Mapping Toolbox ( http://www.mathworks.com/ ).

Article Snippet: Daily SLP variability in the CNP derived by calculating the standard deviation of the anomalous daily SLP values about the seasonal-mean anomalies for the given year and then area averaging the grid-point values within the region 20-40 N; 155-180 W. The map in this figure is generated by MATLAB R2014a using routines found in the standard Mapping Toolbox ( http://www.mathworks.com/ ).

Techniques: Standard Deviation, Labeling, Two Tailed Test, Derivative Assay, Generated

Journal: Scientific Reports

Article Title: Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation as an initiator of El Niño/Southern Oscillation events

doi: 10.1038/s41598-017-09580-9

Figure Lengend Snippet: Daily central North Pacific circulation patterns during low and high variance years. (a) Shading: Difference in composite-mean accumulated daily sea level pressure (SLP) anomalies during extended boreal winter (Nov.-Mar.) for days in which SLP averaged over the central North Pacific (CNP) is smaller than its climatological value (see Methods). Composite-means calculated for 10 years with highest day-to-day variance in CNP SLP and 10 years with lowest day-to-day variance in CNP SLP. Composite-mean accumulated values are normalized by the number of days in the season (151). Units - (hPa). Shading interval given by color bar at the right of the figure. Hatching: Areas where the two composite-mean accumulated daily SLP anomalies are statistically significantly different from one another at the p < 0.1 level, based upon a two-tailed t-test. Vectors: Same as shading except for difference in composite-mean accumulated 10 m wind anomalies. Only shown are vectors in which at least one component of the composite-mean accumulated daily 10 m wind anomalies are statistically significantly different from one another at the p < 0.1 level, based upon a two-tailed t-test. ( b ) Same as ( a ) except for days in which SLP averaged over the CNP is greater than its climatological value. ( c ) The sum of the values in ( a , b ) which by construction represents (exactly) the seasonal mean difference in SLP and 10 m winds during the 10 years with highest day-to-day variance in CNP SLP and the 10 years with lowest day-to-day variance in CNP SLP. These panels indicate that years with enhanced day-to-day variance in CNP SLP experience greater accumulation of lower-than-normal SLP than years with reduced day-to-day variance in CNP SLP (panel a ). Further, they experience less accumulation of higher-than-normal SLP (panel b), although the magnitude of this reduced accumulation is much less than in (panel a ). As a consequence, the main contributor to the seasonal-mean reduction in CNP SLP during high-variance years (panel c ) is increased frequency and magnitude of days with lower-than-normal SLP. The maps in this figure are generated by MATLAB R2014a using routines found in the standard Mapping Toolbox ( http://www.mathworks.com/ ).

Article Snippet: Daily SLP variability in the CNP derived by calculating the standard deviation of the anomalous daily SLP values about the seasonal-mean anomalies for the given year and then area averaging the grid-point values within the region 20-40 N; 155-180 W. The map in this figure is generated by MATLAB R2014a using routines found in the standard Mapping Toolbox ( http://www.mathworks.com/ ).

Techniques: Two Tailed Test, Generated

Journal: Scientific Reports

Article Title: Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation as an initiator of El Niño/Southern Oscillation events

doi: 10.1038/s41598-017-09580-9

Figure Lengend Snippet: Persistent extratropical anomalies (PEAs) during years with enhanced variance in daily sea level pressures over the central North Pacific. ( a ) Shading: Number of days experiencing high-pressure persistent (>5 day) extratropical anomalies (HPEAs) during extended boreal winter (Nov.-Mar.) regressed against daily sea level pressure (SLP) variability over the central North Pacific (CNP), as represented by the time-series in Fig. . Units - (days). Shading interval given by color bar at the bottom of the figure. Grey contour: Areas where the HPEA-day regression values are statistically significant at the p < 0.1 level, based upon a two-tailed t-test. Black contours: The seasonal mean difference in SLP during the 10 years with highest day-to-day variance in CNP SLP and the 10 years with lowest day-to-day variance in CNP SLP, as shown in Fig. . Contour interval is 1hPa; positive (negative) values shown as solid (dashed) lines; the 0-contour is omitted. ( b ) Shading, Grey contour: same as ( a ) except for number of days experiencing low-pressure PEAs (LPEAs) during extended boreal winter (Nov.-Mar.). Black contours: same as ( a ). The HPEA (LPEA) statistics are calculated using a two-dimensional index that is adopted from a blocking index , : first the seasonal cycle and interannual variability are removed from the daily 500hPa geopotential height field and then all grid points are searched for stationary positive (negative) anomalies that are larger (smaller) than one standard deviation for at least 5 days and satisfy a minimum spatial scale . The maps in this figure are generated by MATLABs R2014a using routines found in the standard Mapping Toolbox ( http://www.mathworks.com/ ).

Article Snippet: Daily SLP variability in the CNP derived by calculating the standard deviation of the anomalous daily SLP values about the seasonal-mean anomalies for the given year and then area averaging the grid-point values within the region 20-40 N; 155-180 W. The map in this figure is generated by MATLAB R2014a using routines found in the standard Mapping Toolbox ( http://www.mathworks.com/ ).

Techniques: Two Tailed Test, Blocking Assay, Standard Deviation, Generated