1. Introduction
In the United States, El Niño–Southern Oscillation (ENSO) is known to influence temperature and precipitation distributions (Kousky et al. 1984; Ropelewski and Halpert 1986; Halpert and Ropelewski 1992; Dai et al. 1997; Green et al. 1997; Smith et al. 1999; Higgins et al. 2002); frequency of the generation of cyclones in the Gulf of Mexico (Kunkel and Angel 1999); cloud cover (Angell and Korshover 1987); and a wide range of meteorological hazards including tornados (Agee and Zurn-Birkhimer 1998; Hagemeyer 1998; Schaefer and Tatom 1998), hurricanes (O’Brien et al. 1996; Pielke and Landsea 1999), and even snowpack (Cayan 1996) and wildfires (Harrison and Meindl 2001). A study conducted by Harrison and Larkin (1998) on the intense 1997–98 event established that about 90% of the United States has at least one statistically significant historical weather association with El Niño.
Lightning poses substantial threat to lives and property (Curran et al. 2000), yet little has been published on how it might be affected by ENSO. One of the few studies to address the impact of ENSO on lightning is by Goodman et al. (2000), in which they examined winter season (December–February) lightning data over the Southeast for 1989–99. They found a 100%–200% increase in the frequency of lightning along the Gulf Coast and adjacent waters during the intense 1997–98 ENSO event. Goodman et al. (2000) analyzed the number of days that the National Lightning Detection Network (NLDN) detected a flash within a 0.5° box. Hamid et al. (2001) examined lightning activity over Indonesia during the 1997–98 El Niño using data from the Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) and Lighting Imaging Sensor (LIS). They found that although precipitation decreased, average lightning activity increased by 57%, which is an indication of more intense convection. Durden et al. (2004) found that time series of the principal component anomalies of lightning and upper-level radar reflectivity are highly correlated with the Southern Oscillation index, and hence, El Niño. They investigated the global tropics using seasonally averaged, 5° gridded data from the LIS and TRMM PR for December 1997 to May 2001. Lajoie and Laing (2008, hereafter Part I), describe the spatial distribution of flash density at annual, monthly, and seasonal scales for the winter and summer. They showed patterns of lightning distribution on 2.5 km, which highlighted the influence of mesoscale coastal circulations, midlatitude cyclones, and fronts on flash density patterns. For a comprehensive, technical background on the physical mechanisms of lightning, the reader is referred to Uman (2000).
The overall aim of this study is to increase knowledge and understanding concerning the influence of the ENSO cycle on lightning activity along the Gulf Coast. This region has the highest lightning flash density in the nation (Orville and Huffines 2001; Zajac and Rutledge 2001) and is also known by the anomalous precipitation and temperature that occur in response to ENSO. Specific objectives are (i) to calculate the month-by-month correlations between lightning activity and ENSO for winter and summer seasons using the lightning data and a concurrent series of sea surface temperature anomaly values from the equatorial Pacific; (ii) to determine the significance of the correlations and their temporal and geographic variations for the Gulf Coast region.
a. ENSO cycle and its impact on the Gulf Coast
ENSO sea surface temperature anomalies (warm or cool) typically commence during late spring or summer, peaking and reaching their maximum areal extent over the tropical Pacific during the boreal winter. The event usually ends by the following summer. ENSO teleconnections (during either warm or cool events) become most evident over North America during this wintertime peak (Diaz and Markgraf 2000; Trenberth and Stepaniak 2001). The primary ENSO teleconnection with North America is a displacement or disruption of the jet stream (Ropelewski and Halpert 1986). During a warm episode winter, the subtropical jet stream shifts southward, becomes more zonal, and strengthens, displacing the typical storm track from the northern to southern United States and enhancing moisture flow from the Pacific. The consequence is a cooler, wetter, and stormier southern tier of the United States (Ropelewski and Halpert 1986; Green et al. 1997). Goodman et al. (2000) compared the winter 1997–98 lighting enhancement with regional climatology data and were able to conclusively ascribe it to increased synoptic-scale forcing, directly attributed to ENSO and the stronger-than-normal upper-level jet stream. Kunkel and Angel (1999) found an increase in the frequency of cyclones generated in the Gulf of Mexico during El Niño winters.
The northeastern Gulf precipitation is above normal during El Niño summer and drier elsewhere in the Gulf [for more information see the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) Web site online at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/lanina]. In this period, teleconnections are strongest in the Caribbean and Central America where precipitation is below normal. Atlantic hurricane activity is suppressed during El Niño summers (Gray 1984; O’Brien et al. 1996.)
The cool phase of ENSO, La Niña, has a reverse effect on North America compared with El Niño, although some variability is evident (Glantz 2001; Trenberth and Stepaniak 2001). During La Niña years, the upper-level flow becomes more meridional; the jet stream shifts north (entering the continent over the Pacific Northwest) and becomes more variable in intensity. The southern tier of the United States has warmer-than-normal temperatures, as well as decreased storminess and precipitation during winter. During the summer, La Niña teleconnections are strongest in the Caribbean and southern Gulf (more information available online at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/lanina). In summer, the extreme South is cooler than normal, with enhanced precipitation over the eastern Gulf and dry conditions in Texas and Louisiana (Green et al. 1997). La Niña years are marked by a greater frequency of tropical cyclones and more damage per cyclone (Pielke and Landsea 1999).
In addition to detecting the major features of the winter and summer anomalies, Green et al. (1997) found enhanced precipitation in regions along the eastern seaboard, including Florida, during springs that followed warm events. Portions of south Texas were found to be cooler than normal. North Texas and northern Alabama were found to be drier during El Niño springs than in neutral years. For cool events, Green et al. (1997) established that the anomalies are sometimes reversed from those associated with warm events, but not everywhere. In spring, warm anomalies are found in northern Florida, Georgia, and South Carolina; the Gulf Coast regions may exhibit increased precipitation. For the fall season of El Niño, the northern Gulf is anomalously wet. During La Niña fall seasons, the Texas coastal region is very dry, the northern Gulf is mostly wet, and Florida is mostly dry.
b. Quantifying and categorizing ENSO events
The amplitude of the ENSO anomaly can vary greatly (Philander 1990). Variables routinely monitored include SST fluctuations, surface pressure shifts, trade wind intensity variations, and many other elements, using direct observations as well as remote sensing techniques (Magnun et al. 1998). The ENSO index used for this study is derived from SST measurements described by Smith and Reynolds (2004). The Niño-3.4 region (Fig. 1) is considered to be most suitable for monitoring climate variability on global scales, as SST variability here signals the strongest effect on shifting precipitation patterns from the west to the central Pacific [Barnston et al. (1997); for more information see the International Research Institute for Climate Prediction Web site online at http://iri.columbia.edu/climate/ENSO/index.html]. It has also been observed that prognostic numerical weather models exhibit the highest skill when initializing with data from this region (see online at http://iri.columbia.edu/climate/ENSO/index.html). The oceanic Niño index (ONI) was formally defined as the “three-month average of sea surface temperature departures from normal” in the Niño-3.4 region of the Pacific (Department of Commerce 2003). Table 1 in Part I lists the ONI for 1995–2002.
In this study the ENSO–lightning relationship is examined through the use of monthly Niño-3.4 SST anomalies and their correlation with flash density variations. Spatial correlation patterns are also compared with the flash density maps described in Part I.
2. Data and methodology
The study area is the Southeast and the adjacent waters of the Gulf of Mexico bordered by 33°–24°N, 79°–99°W (Part I, their Fig. 1). Two datasets are utilized. The first is the total set of cloud-to-ground (CG) lightning flash records detected in the study area by the NLDN for January 1995–December 2002. See Part I for more information on the lightning data records. The second dataset is the concurrent 96-month time series of the Niño-3.4 region SST anomaly (the ONI) of the equatorial Pacific, which was downloaded from the NOAA CPC Web site (http://www.cpc.ncep.noaa.gov/data/indices/).
The flash density mapping and corrections for network detection efficiency changes are described in Part I. Simple Pearson’s correlations were computed between concurrent monthly pairings of the ONI and CG lightning flash deviation values from the study area. By finding SST anomalies and clusters of lightning variability that are highly correlated, we can identify potential teleconnections (i.e., recurring and persistent climate patterns that span large geographical areas). The correlation procedure is performed initially for the entire domain, then on progressively smaller bin sizes for each month in the period of record. The domain grid is partitioned into approximately equal P × Q subdomains. For example, if the user specifies P = 2 and Q = 2 (or 2 × 2 subdomains), the lightning data are aggregated over four bins of dimension 408 × 209 in the original grid. Progressively smaller bin sizes of 10 × 5, 20 × 10, 40 × 20, and 80 × 40 (roughly 204 × 209, 102 × 105, 51 × 52, and 25 × 26 km bins, respectively) were explored to establish an optimal bin size that would reveal positive and negative correlations on scales that would be useful for exploring variability related to mesoscale and synoptic-scale patterns. The finest grid (80 × 40 bins) was chosen because it facilitated identification of mesoscale features, such as frontal bands and sea-breeze convection.
For each resultant bin, the lightning flash deviation from the monthly mean is calculated, and each 8-month time series of values is compared with the corresponding, 8-member series of Niño-3.4 SST anomaly values. Thus, SST anomalies are compared to above- or below-normal monthly lightning values in any given bin. The correlation values computed by the script for each user-specified domain were then written out to monthly text files and the text files imported into ArcMap GIS [Environmental Systems Research Institute, Inc. (ESRI)] and visualized on a base map of the study area. Correlations in the extreme southern Gulf are discounted, as network detection is drastically reduced in that area.
Given the occurrence of a strong El Niño event during the period of study, there are concerns that the correlation analysis will be highly sensitive to that period. The variability of the lightning data is tested to determine the sensitivity to any periods of extreme lightning activity. A time series of the monthly departures from the mean as a percentage of the standard deviation was constructed and compared with a time series of the ONI SST anomalies. Figure 2 shows that although monthly lightning extremes exist throughout the period of study; there is no particular bias of extreme monthly values during the strong 1997–98 El Niño. The moving average of lightning departures shows that the ONI correlates well with monthly mean lightning anomalies for most of the study period. Exceptions are early 1997 and mid-2000 when the trends were opposite and late 1995 when the lightning anomaly line is nearly flat.
3. ENSO–lightning correlations
a. Winter
In December, maximum ENSO–lightning correlations occur in a south-southwest–east-northeast-banded pattern, mostly over the eastern Gulf, across central Florida, and along the coast of Georgia and South Carolina (Fig. 3). A broken line of high correlation values occurs over central Texas. Except for some areas offshore west-central Florida, most areas of moderate–high correlations are not coincident with December lightning maxima (Fig. 3). The strongest response to ENSO fluctuations during December occurs in eastern Gulf Coast areas that have low flash densities. It is interesting to compare the December pattern with results from Durden et al. (2004). They found that, although average flash rates and upper-level reflectivities had similar empirical orthogonal functions (EOFs) the anomalies of both had different spatial patterns. However, both have principal component time series that are correlated with the Southern Oscillation index and, hence, El Niño.
During January, positive correlation maxima shift to the western Gulf, Texas, and the central Gulf (Fig. 4). Unlike December, maximum flash densities are coincident with positive SST correlations over the western Gulf (Fig. 4). Flash densities are higher in January than December and maxima occur in several southwest–northeast bands emanating southeastward from central Texas. The moderate–high correlations values display a similar banded formation except over the water and in central Texas. Over the western Gulf, where winter lightning is relatively common, activity is enhanced during the warm ENSO phase and diminished during the cool ENSO phase.
February has the second-lowest number of lightning flashes for the 8-yr period (Part I). However, the area of positive correlations is much larger than in the other winter months. Moderate correlations, embedded with small clusters of high correlations, dominate all oceanic regions and Florida (Fig. 5). Clusters of high correlation values occur over southern Florida, southern Texas, and isolated areas over the Gulf. Correlations in the extreme southern Gulf should be discounted, as this is beyond the range of the network. However, even after discarding these data points, significant correlations are evident. When compared with the flash density map for February, the areas of highest correlation over Florida and Texas correspond with areas of low flash densities. It should be noted that during February 1998, the strong El Niño episode, there were almost 4 times as many lightning flashes as the February mean for the 8 yr. This is the most extreme difference in monthly total response for the study period and could be the reason for the widespread moderate correlations.
b. Summer
The summer is marked by high flash densities over Florida and moderate flash densities across the northern Gulf Coast (Part I). The summer lightning distribution is predominantly modulated by mesoscale, topographically induced circulations. The June correlation pattern shows that most of the Gulf region does not respond to changes in ENSO SST anomalies (Fig. 6a). Areas of moderate–high positive correlations are scattered across most of Texas and hugging the western Gulf Coast. The west Florida coast and coastal South Carolina are scattered with moderate negative correlation, indicating more lightning during La Niña summers. Negative correlation values are also scattered over the central Gulf.
July is, on average, the month of maximum lighting and flash densities (Part I). A large cluster of high positive correlations is centered over Louisiana and southern Mississippi while most of the oceanic regions show little response (Fig. 6b). A tiny cluster of high correlations occurs over south-central Florida. Moderate positive correlations are scattered along the Florida coastline. Negative correlations are scattered over Georgia and Alabama indicating that these areas have higher lightning flash densities during La Niña summers. The total flash counts for July 1997 and July 2002, El Niño years, are the highest of the 8-yr period (Part I, their Table 1). Based on the spatial correlation pattern, most of that increase is due to enhanced lightning clustered over Louisiana and nearby ocean.
August is the only month in which negative correlations are the dominant mode across the domain (Fig. 6c). While these negative correlations do not appear to be associated with a significant change in lightning flash density between July and August, it is possible that the negative correlations are due to a decrease in convective precipitation across the domain. El Niño summers are associated with a decrease in precipitation across Florida and the Caribbean. Fewer hurricanes form during El Niño years, which could have an impact on lightning distribution although flash rates in hurricanes are more than an order of magnitude less than continental thunderstorms (Molinari et al. 1999; Cecil et al. 2002). It should be noted that the months of June, July, and August have comparatively small areas of positive correlation compared with the winter months. During the warm season, large portions of the domain are only weakly correlated to the ENSO anomalies. As the warm season progresses the correlations become mostly negative or neutral by August.
c. Spring
In comparison with the winter months, spring months have weak correlations across much of the region. For the western Gulf and Texas, March shows a switch to negative correlations (Fig. 7a). Although positive correlations are over the Florida peninsula and nearby waters, the values are lower. This month also shows a small area of high positive values over southern Louisiana and Mississippi, a region that had little response to ENSO in the winter. For April, flash density deviations are mostly unaffected by the ENSO signal with a few areas of moderately negative correlations over Florida, Texas, and Arkansas, and a small cluster of positive flashes over the central Gulf and the northeastern parts of the domain (Fig. 7b). May has similar overall characteristics except over the southeastern Gulf where positive maximum are located (Fig. 7c). Areas across Georgia and Alabama show a moderately positive correlation. Climatologically, the spring is marked by an increase in thunderstorm days and a maximum is usually observed across the southern Great Plains. The correlation pattern indicates that an increase in Niño-3.4 SST anomaly is associated with decreasing flash densities in a region that is typically plagued by intense springtime thunderstorms and tornadoes.
d. Fall
Most of the domain has little or no response to variations in equatorial SSTs during September (Fig. 8a) except for scattered regions of the western Gulf with positive correlations and a few areas along the Florida coast with negative correlations. October correlations show a marked shift from the preceding two seasons. Most of the domain is dominated by high positive correlations, especially across the eastern half of the domain (Fig. 8b). Maximum values are north of a northeast–southwest line through central Florida. In both October and November, there is little response to SST anomalies over the western half of the domain. By November, the flash deviation correlations are maximized over the southeastern Gulf and southern Florida with smaller clusters of positive correlations along the northern border of the domain (Fig. 8c). The positive correlations over the southeastern Gulf are aligned in a banded structure indicating a return of frontal precipitation.
4. Summary and concluding remarks
Cloud-to-ground lightning flashes for 1995–2002 and equatorial SSTs were analyzed to determine if the ENSO cycle has an influence on lighting activity along the Gulf Coast region. Simple Pearson’s correlations were computed between concurrent monthly pairings of Niño-3.4 SST and CG lightning flash deviation values from the study area, mapped using a GIS, and analyzed.
From a climatological perspective, an 8-yr period of record is likely too short to be highly reliable. This abbreviated temporal domain is largely unavoidable as the period of record with networked lightning data with high detection efficiency began in 1995. Therefore, these correlations should not be used in isolation but should be used as valuable input with other information as they help to identify the domains of influence of the Niño-3.4 SST anomalies.
Correlation results indicate an ENSO–lightning relationship, especially during winter months. More lightning occurs across the Gulf Coast during El Niño winters. Correlation values greater than 0.8 were noted over large swaths of the study area during winter. For January and February, in particular, areas of high correlation were also spatially coincident with areas of enhanced flash density. Both the enhanced CG flash regions and high correlation value banded patterns are indicative of frontal passages, the frequency and tracks of which are influenced by ENSO indirectly through its impact on the large-scale circulation patterns. A southward displacement of the jet stream and storm tracks results in more precipitation and lightning during winter.
Strong correlations exist in June and July for isolated pockets of the study area. El Niño has little influence over the Gulf Coast during the summer except for widespread negative correlations during August. Indications are that August lightning increases during La Niña summers. Summertime lightning across the Gulf Coast region is from airmass thunderstorms, thunderstorms caused by mesoscale processes, such as sea- and land-breeze circulations, and thunderstorms in the rainbands of tropical cyclones.
These findings can be applied to hazard mitigation and seasonal planning for lightning-related hazards such as wildfire outbreaks. Although this study was limited to ENSO impacts in the Gulf Coast region, there are well-documented ENSO teleconnections with much of the rest of the United States. Given additional data, similar studies could be conducted across the United States.
Acknowledgments
Lightning data was obtained by the Air Force Combat Climatology Center from Vaisala, Inc., owner and operator of the National Lightning Data Network. Special thanks to Dr. Kenneth Cummins for providing the relative detection efficiency correction factors and to Jennifer Boehnert for her assistance with GIS analysis. Dr. Olga Wilhelmi and two anonymous reviewers provided suggestions that improved the manuscript.
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Graphical depiction of Niño regions (see online at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions.shtml)
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Graphical depiction of Niño regions (see online at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions.shtml)
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Graphical depiction of Niño regions (see online at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions.shtml)
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Time series of the Niño-3.4 SST anomalies (bold black line), the lightning anomaly (pink dashed lines), and the moving average of the monthly lightning anomaly.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Time series of the Niño-3.4 SST anomalies (bold black line), the lightning anomaly (pink dashed lines), and the moving average of the monthly lightning anomaly.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Time series of the Niño-3.4 SST anomalies (bold black line), the lightning anomaly (pink dashed lines), and the moving average of the monthly lightning anomaly.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
(a) Mean CG flash density and (b) SST–lightning correlation for December 1995–2002.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
(a) Mean CG flash density and (b) SST–lightning correlation for December 1995–2002.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
(a) Mean CG flash density and (b) SST–lightning correlation for December 1995–2002.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 3, but for January.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 3, but for January.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 3, but for January.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 3, but for February.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 3, but for February.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 3, but for February.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
SST–lightning correlation for (a) June, (b) July, and (c) August 1995–2002.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
SST–lightning correlation for (a) June, (b) July, and (c) August 1995–2002.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
SST–lightning correlation for (a) June, (b) July, and (c) August 1995–2002.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 6, but for (a) March, (b) April, and (c) May.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 6, but for (a) March, (b) April, and (c) May.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 6, but for (a) March, (b) April, and (c) May.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 6, but for (a) September, (b) October, and (c) November.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 6, but for (a) September, (b) October, and (c) November.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
Same as in Fig. 6, but for (a) September, (b) October, and (c) November.
Citation: Monthly Weather Review 136, 7; 10.1175/2007MWR2228.1
