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The Spatial and Temporal Distribution of Lightning Strikes and Their Relationship with Vegetation Type, Elevation, and Fire Scars in the Northern Territory MUSA KILINC
AND
JASON BERINGER
School of Geography and Environmental Science, Monash University, Victoria, Australia (Manuscript received 17 October 2005, in final form 3 July 2006) ABSTRACT In this paper the authors explore the spatial and temporal patterns of lightning strikes in northern Australia for the first time. In particular, the possible relationships between lightning strikes and elevation, vegetation type, and fire scars (burned areas) are examined. Lightning data provided by the Bureau of Meteorology were analyzed for a 6-yr period (1998–2003) over the northern, southern, and coastal regions of the Northern Territory (NT) through the use of Geographical Information Systems (GIS) to determine the spatial and temporal characteristics of lightning strikes. It was determined that the highest densities of lightning strikes occurred during the monsoon transitional period (dry to wet) and during the active monsoon periods, when atmospheric moisture is highest. For the period of this study, lightning was far more prevalent over the northern region (1.21 strikes per km2 yr⫺1) than over the southern (0.58 strikes per km2 yr⫺1) and coastal regions (0.71 strikes per km2 yr⫺1). Differences in vegetation cover were suggested to influence the lightning distribution over the northern region of the NT, but no relationship was found in the southern region. Lightning strikes in the southern region showed a positive relationship with elevations above 800 m, but no relationship was found in the northern region, which could be due to the low-lying topography of the area. A comparison of lightning densities between burned and unburned areas showed high variability; however, the authors suggest that, under ideal atmospheric conditions, large-scale fire scars (⬎500 m) could produce lightning strikes triggered by either enhanced free convection or mesoscale circulations.
1. Introduction Information concerning the characteristics of lightning strikes in different geographical regions is of interest and can augment research on the interaction between the radiative properties of the surface and the atmosphere. The surface energy balance, albedo, surface roughness, and the Bowen ratio are important factors in determining available energy and the partitioning of the energy fluxes over different surface types (Beringer and Tapper 2002), which are important characteristics in determining the microclimate and regional climate. The differential heating of two adjacent surfaces caused by radiative flux contrasts is likely to cause convective activity through uplift and generate mesoscale circulations (Pielke and Avissar 1990). If the heating contrast is large, then enhanced convection may occur, leading to the formation of thunderclouds and lightning (Dissing and Verbyla 2003). There has been a
Corresponding author address: Musa Kilinc, School of Geography and Environmental Science, Monash University, Victoria 3800, Australia. E-mail:
[email protected] DOI: 10.1175/JCLI4039.1 © 2007 American Meteorological Society
JCLI4039
great deal of research on the electrical structure of thunderstorms and the relationships between the polarities of the strikes (Latham 1991; Orville 1994; Vonnegut et al. 1995; Orville et al. 2002). However, there is very little understanding of the relationship between lightning strikes and surface characteristics of elevation, vegetation type, or other surface inhomogeneities, such as fire scars (burned areas). Lightning strikes are produced mainly from cumulonimbus, which are formed through four mechanisms: buoyant warm air rising due to intense surface heating, strong heating contrast between surfaces, frontal lifting, or by the uplift of air parcels due to orographic lifting (Sturman and Tapper 1996). All of these processes may trigger convection and, hence, lightning activity. Lightning originates around 3–4 km above sea level and is effectively caused by a charge separation that takes place within the negatively charged reservoir of the cloud and the positive electric field of the ground surface (Cooray 2003). The resulting discharge is negative, positive, or a cloud-to-cloud stroke. In the Northern Territory (NT) of Australia (Fig. 1), thunderstorms and lightning strikes are most common during convective periods, when there is a strong influ-
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FIG. 1. Map of the Northern Territory (Australia) illustrating the three study areas used; northern, southern, and coastal regions.
ence from the monsoon. The monsoonal influence during the wet season has a significant role in distributing moisture throughout the northern region; however the southern regions are very dry and are influenced by subtropical cold fronts (Beringer and Tapper 2000). Thunderstorms develop in homogeneous air masses that are associated with convergence zones and instability caused by monsoon onset and the active monsoon period (wet season) (Sturman and Tapper 1996). The contrast between the heating properties of the ocean and land is a trigger for strong sea-breeze development, which can stretch several hundred kilometers inland (Simpson 1994). Sea breezes are a form of mesoscale circulation and aid in the development of convective activity via frontal uplift and cause instability, and hence thunderstorms and lightning. Previous studies investigating the contrast of lightning strike density be-
tween land and ocean have shown that there is high variability between the distribution of lightning strikes over the ocean and land (Boccippio et al. 2000; Williams and Stanfill 2002; Williams et al. 2002). Forced convection, or orographic lifting, triggers convection by transporting sensible and latent heat vertically into the atmosphere via uplift. The convection may then intensify the instability of an area by stronger updrafts and cause lightning discharges. For example, the valley/mountain winds produced from differential heating between two sloping surfaces, combined with orographic lifting, can be an instigator of lightning strikes and therefore produces a relationship between elevation and lightning strike density (López and Holle 1986; Lericos et al. 2002; Orville et al. 2002; Dissing and Verbyla 2003). Dissing and Verbyla (2003) found that a positive correlation between lightning strike density
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and elevation existed up to a maximum elevation of 1100–1200 m. Mesoscale circulations can aid in the development of convective activity via uplift and cause convergence and instability over the affected area, which may then trigger thunderclouds and hence lightning. The relationship between lightning strike distribution and vegetation was also studied by Dissing and Verbyla (2003), who found that mesoscale circulations triggered by the differential heating between two contrasting vegetation types were likely to produce lightning strikes. Surface inhomogeneities provide a heating contrast between two adjacent surfaces, which could produce mesoscale circulation patterns similar to a sea breeze (Segal et al. 1988), though other examples have also been previously studied: snow breeze (Segal et al. 1991), salt lake breeze (Tapper 1991), and lake breeze (Laird et al. 2003). Heating contrasts between surfaces arise due to differences in albedo, surface roughness, and the way in which energy is partitioned into sensible, latent, and ground heat flux (Beringer and Tapper 2002). For example, Pielke and Vidale (1995) found that a larger sensible heat flux over particular vegetation types increased the air temperature and, as a result, triggered convection. In the Maritime Continent Thunderstorm Experiment (MCTEX), Beringer and Tapper (2002) showed that the sensible heat flux among various vegetation types (savannah, grasslands, forest, and shallow tidal strait) was an important factor in the production of a buoyant boundary layer that could produce convergence as an uplift mechanism. Almost one-third of Australia’s savanna region, including the NT, is burned each year by pastoralists, Aboriginal landholders, and conservationists (RussellSmith et al. 2000). Resultant fire scars provide largescale inhomogeneities that could potentially provide a trigger for lightning. Lightning itself, is not a significant source of fire ignition; however during the transition period from dry to wet, when fuel moisture is still relatively low, lightning strikes may ignite fires (Rorig and Ferguson 2002). A study of the impact of savanna fires on the surface energy balance was recently completed by Beringer et al. (2003), who found that the surface energy properties varied considerably before and after a fire event. Post fire albedo values were shown to decrease by half, from 0.12 to 0.06, while the latent heat flux over the surface decreased by 30%–75% (Beringer et al. 2003). Consequently, fire scars were more intensely heated and therefore the sensible heat flux increased by ⬃ 36% when compared to the surrounding unburnt surfaces. This has the potential to trigger convection and lightning due to the development of a mesoscale circulation and associated convergence and up-
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lift over a fire scar. An increased heat source over burned areas may also lead to buoyancy and result in local and regional instability. In a model used by Knowles (1993), a halving of the albedo over burned vegetation areas increased convection and resulted in the formation of a mesoscale circulation system. In this paper we explore the spatial and temporal patterns of lightning strikes in northern Australia, for the first time, and the possible relationships between lightning strikes and elevation, vegetation type, and fire scars. Through the use of Geographical Information Systems (GIS), we analyzed lightning data provided by the Bureau of Meteorology for a 6-yr period (1998– 2003) over the northern, southern, and coastal regions of the NT.
2. Study area The Northern Territory (Australia) was selected as the study area for this project since Christian et al. (2003) estimated that, on average, 44 ⫾ 5 lightning flashes occur around the globe every second, which strike between the geographic regions of 30°N and 30°S and account for approximately 75% of the global lightning count (Torancita et al. 2002). In addition, the NT has a variety of vegetation types from grassland to rainforest and moderate terrain variability ranging from ⬃400 m in the northern region to ⬃1500 m in the southern region. A high degree of burning, especially during the dry season, occurs and previous studies suggest that up to 5.5% of the NT can undergo burning (Beringer et al. 1995). The study area encompassed the NT coastal waters and islands, including Groote Eylandt and the Tiwi Islands. Therefore, the study area was divided into three geographic zones: the northern region, the southern region, and the coastal waters (Fig. 1). The northern region (⫺10° to ⫺16°S) has a distinct monsoonal climate with both a wet and dry season. The wet summer months, from December to March, are hot and humid with high rainfall, which is roughly 80% of the annual rainfall (Sturman and Tapper 1996). The dry season lasts from May to September and is dominated by fair sky conditions with little rainfall and low humidity from the southeasterly winds (Bureau of Meteorology 1998). These two contrasting seasons are separated by two periods of transition that occur around March and October. On the other hand, the southern region is not affected by the monsoonal flows since the intertropical convergence zone (ITCZ) does not extend that far south, but instead is influenced by the southeasterly flows emanating from the subtropical continental air mass in central Australia.
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The vegetative landscape of the NT is diverse and supports a range of vegetation types such as tropical open woodland, eucalypt (Eucalyptus Tetrodonta and E. miniata) and acacia woodland (Acacia aneura), shrubland (A. georginae), and tussock and hummock grasslands (Williams et al. 1997). The distribution and composition of vegetation is strongly associated with the monsoonal climate and consequently rainfall (Hutley et al. 2001). For example, the eucalypt and acacia woodlands are found in the north of the NT, where annual rainfall is ⬃1700 mm, while much of the shrublands and grasslands lie in the southern regions, which are subject to only ⬃250 mm. The NT has a moderately variable landscape with large, flat sand plains and disconnected ranges. Much of the northern region is flat with a maximum elevation of 400 m, with the main geological feature of a sandstone plateau located in Western Arnhem Land. There are extensive ranges in the southern regions; the MacDonnell Range runs west and east of Alice Springs; the Davenport and Murchison Ranges lie north; and the Harts and Dulcie are northeast. There are also numerous ranges that are scattered around the region, in excess of 1000 m, and the highest peak is Mount Zeil at 1531 m.
3. Method To examine the spatial and temporal variation of lightning strikes we used a ground-based lightning detection network for the Australasian region that was made operational in 1998 by Global Positioning and Tracking Systems (GPATS). The system uses the series III Lightning Positioning and Tracking System (LPATS). The basic principle of operation is to use the time of arrival of the lightning discharge at three or more receivers. The three sensor system can effectively monitor large areas, 1 million km2. The detection efficiency of the LPATS sensor is greater than 90% with a spatial accuracy of 200 m (Global Positioning and Tracking Systems, 2004). The GPATS sensors are able to detect strokes as close as 0.5 ms apart and correctly identify them into three stroke types: positive, negative, and cloud-to-cloud strokes. The lightning data acquired spanned from October 1998 to December 2003; however some months of lightning data were missing, specifically January 2002, March 2003, April 2000, May 1999 and 2000, June 2000 and 2003, July 2003, August 2003, September 2003, October 2003, and November 2003. The gaps within the dataset effectively make this investigation a 4-yr study period, which may not be representative of the broad patterns of lightning strike density over the NT, and hence the conclusions presented in this paper are only preliminary. However, the
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year-to-year variability is small, suggesting consistent patterns over that period. The dataset was incorporated into a GIS to allow for the exploration of relationships of lightning strikes with surface characteristics, such as elevation, vegetation, and fire scars. The project was undertaken in the GIS laboratory at Monash University (Clayton) using ArcMap (ESRI, Inc., 1999–2002, version 8.3; http:// www.esri.com). For the analysis of lightning strike distribution with vegetation and elevation, only the northern and southern regions were used. The vegetation dataset used in this project was the National Vegetation information System (NVIS) 2001—Major Vegetation Groups— Version 1.0 (Department of the Environment and Heritage 2004). The vegetation classes were merged from the original 27 classes to 4 classes: tropical open woodland, eucalypt and acacia woodland, shrubland, and grassland. The dataset had a cell resolution of 1 km and was rescaled to a cell resolution of 250 m to be consistent with the elevation dataset. Elevation data were obtained from the GEODATA TOPO 250K Series 2 project created by Geoscience Australia. The original 50-m contour, vector tile map sheets at a scale of 1:250 000 were rasterized to a 250-m cell resolution with 100-m contour intervals. The lightning strike dataset was rasterized to a 250-m cell size and grouped into two spatial datasets (northern and southern regions) and four temporal (seasonal) datasets: early wet (October–December), late wet (January–March), early dry (April–June), and late dry (July–September), so that lightning strikes in relation to elevation and vegetation could be analyzed. As a result of rasterizing the data from a feature dataset with a spatial accuracy of 200 m to grid cells of 250 m, the Modifiable Area Unit Problem (MAUP) is created since analyzing different sized cells gives different results. However, this was unavoidable owing to the processing power of the computer and is likely to slightly underestimate lightning strike density. A possible model that would allow for a thorough investigation of MAUP and other sources of error would be in the form of 关strike density兴 ⫽ 关elevation term兴 ⫹ 关vegetation term兴
⫹ 关spatial autocorrelation兴 ⫹ 关error兴. Only the northern and southern seasonal data for the early dry, early wet, and late wet seasons were used. The late dry season was omitted because of the lack of strikes. The hypothesis that fire scars could initiate mesoscale circulations producing lightning strikes was in-
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TABLE 1. Summary of lightning strikes in the northern, southern, and coastal regions of the Northern Territory. Stroke type
Total strikes
Percentage of strikes
Northern region strikes
Percentage of strikes
Southern region strikes
Percentage of strikes
Coastal region strikes
Percentage of strikes
Positive Negative CC Total
5 736 356 1 043 877 769 353 7 549 586
76 14 10
1 258 492 187 458 232 945 1 678 895
75 11 14
580 637 189 340 58 565 828 542
70 23 7
527 953 79 144 125 015 732 112
72 11 17
Density*
1.10
1.21 ⫺2
* Mean annual strikes km
0.58
0.71
⫺1
yr .
vestigated by utilizing the fire scar datasets derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) (Department of Land Information 2004). Twelve fire scars that burned during October or November were selected for the years 2000–02. We selected fire scars to include a range of sizes from small (⬍200 km2) to large fires (⬎500 km2). When choosing the fire scar samples, sites that were near the coast were rejected because dynamical processes near coastal environments differ from the dynamical processes occurring in the interior regions. Similarly, sites that were in close proximity to other fire scars were also rejected because fires surrounding the sample site may have effected the lightning distribution of the general region, through the production of a larger circulation system. Following Latham (1991), who investigated lightning strikes during an actively burning fire, we created a 50-km buffer zone around each fire scar with 10-km intervals. Control areas (same attributes as the fire scar sample, i.e., area, vegetation, and elevation) were created to provide a measure of lightning strike density in an area, free of burning that could be compared to the fire scar area itself during the same time period, since lightning activity increases rapidly during the transition from the dry to the wet season. These areas were created roughly 100 km away from the fire scar in order to negate the response of seasonality. The changes in albedo and sensible and latent heating caused by a savanna fire can persist up to several weeks to months (Beringer et al. 2003): therefore, mesoscale circulations are most likely to develop most strongly soon after the fire event when there is the greatest contrast in the surface energy balance (Knowles 1993). We analyzed lightning strikes two weeks prior and two weeks after the fire event to observe whether or not there was a change in lightning strike density over the fire scar during the specified time period. We then compared these results with the control site to find if seasonality was a factor in influencing the lightning strike density over the fire scar sample.
4. Results Between October 1998 and December 2003, 7 549 586 positive cloud-to-ground, negative, and cloud-to-cloud (CC) stroke types were recorded in the study area. Of those, 76% of total lightning strikes were of positive polarity and 14% negative polarity while 10% were a CC stroke (Table 1). We are uncertain as to why our study has such high rates of positive strikes when compared to other studies around the world (see, e.g., Pinto et al. 1996; Orville et al. 1987); this may require further examination. A possible explanation for the high percentage of positive strikes is the GPATS lightning sensors’ ability to differentiate between negatively and positively charged strikes, as has been documented with magnetic direction-finding networks (Stolzenburg 1994).
a. Land–ocean lightning contrast For the period of this study, lightning was far more prevalent over the northern region (1.21 strikes per km2 yr⫺1) than over the southern (0.58 strikes per km2 yr⫺1) and coastal regions (0.71 strikes per km2 yr⫺1). The early and late dry periods were characterized by very few strikes for the three regions (Fig. 2). The northern
FIG. 2. Monthly mean distribution (1998–2003) of lightning strikes for the northern, southern, and coastal regions.
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and southern regions both had maximum lightning strike density during November, which coincides with the southerly movement of the ITCZ. With the establishment of the ITCZ, the frequency of lightning strikes begins to slowly decrease through the late wet season. However, the coastal region appears to not be grossly affected by the southerly shift and establishment of the ITCZ compared to the other two regions since there is little variability between November, December, and January. This study showed that the coastal region of the NT contained a greater concentration of lightning strikes over the coast (55%) than the southern region (45%), which differs from the studies of Christian (1999) who found that there was a considerable difference between the lightning strikes detected between the land (82%) and the ocean (18%). However, this difference can probably be attributed to the scale of study. Christian investigated the lightning distribution on a global scale, whereas this study was based specifically on the Northern Territory, and its coastal waters. Boccippio et al. (2000) focused their study on the tropical zone and found that the land–ocean lightning contrast varied by a factor of 2, which compares well with the northern region of our study, but not so well with the southern region. There are a number of factors explaining the land– ocean contrast. The most common explanation relates to the differential heating between the ocean and the land. The land surface is asymmetrically heated faster than the ocean because of the lower thermal inertia and higher sensible heat flux over the land, illustrated by the higher Bowen ratio for land (0.2–1) compared to ocean (0.1) (Williams et al. 2002). This heating difference causes the lower atmosphere to become vertically unstable and may cause deep convection and therefore trigger thunderstorm activity (Williams and Stanfill 2002). One measure of the potential convective intensity is the convective available potential energy (CAPE). A thunderstorm with a larger CAPE is likely to produce a stronger updraft within the cloud, where the vertical motions are likely to affect the mixed phase region of the cloud and the charge separation, allowing for the development of a more vigorous and electrically intensified storm (Cooray 2003). A larger CAPE can either result from strong surface heating and hot boundary layer air or by the presence of cold air aloft (Williams and Stanfill 2002). CAPE values for oceans and the northern region of the NT are quite similar— both experience values between 0 and 3000 J kg⫺1 (Williams and Stanfil 2002). Furthermore, when there is a greater CAPE over the ocean, the lightning activity is still low since CAPE only represents the potential en-
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FIG. 3. Storm height distribution observed by the TRMM precipitation radar in January 1999 for northern Australia. Dark patches around Darwin, Groote Eylandt, and the Gulf of Joseph Bonaparte indicate areas of elevated storm height (http:// www.eorc.jaxa.jp).
ergy available but lacks a trigger mechanism to produce lifting and lightning strikes (Williams and Stanfill 2002). CAPE alone may not influence lightning strike density but may also be dependant on cloud height. The work presented by Ushio et al. (2001) and Mushtak et al. (2003) on storm height and lightning density suggests that the cloud height (low over ocean and high over land) influences the conversion of CAPE to updraft kinetic energy. A larger cloud height would allow for wider and stronger updrafts within the cloud and thus would lead to a larger main negative charge region, hence higher strike rates over the areas (Mushtak et al. 2003). The Gulf of Joseph Bonaparte, the Darwin area, and Groote Eylandt all experience the highest cloud-base storm height as seen from the Tropical Rainfall Measuring Mission (TRMM) radar image (Fig. 3).
b. The spatial and temporal distribution of lightning strikes On a temporal basis, there was a large seasonal contrast across all regions with peak densities occurring in the early and late wet seasons. Seasonally averaged lightning strike densities for the study period are presented.
1) EARLY
WET SEASON
During the early wet season (Fig. 4a), lightning was uniformly distributed across the whole region, though the southern region had less frequent strikes (0.00–0.50 strikes km⫺2). This is expected since the southern region is associated with southeasterly wind flow, while
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FIG. 4. Mean (1998–2003) lightning strike density (per km2) showing the spatial and temporal variability of lightning strikes in the Northern Territory during the (a) early wet season, (b) late wet season, (c) early dry season, and (d) late wet season. Note that scales are not constant.
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the northern region is associated with northwesterly monsoonal flow. The density values were much higher along the base of the northern region, with some areas experiencing densities greater than 7.00 strikes km⫺2 (e.g., Groote Eylandt and the Gulf of Joseph Bonaparte). The high densities over Limmen Bight may be caused by local convective systems and by the interaction between the northwest monsoon flow and a sea breeze influenced by the strong horizontal winds from the Gulf of Carpenteria. This area is also characterized by a regular convergence line that produces morning glories as well as squall lines (Smith and Noonan 1998). Interaction of these atmospheric disturbances with monsoon flow may enhance deep convection and produce an increase in lightning density. A similar process is noted for the Gulf of Joseph Bonaparte. However, the main difference between the two is that the lightning density over Limmen Bight appears to be localized while the density over the Gulf of Joseph Bonaparte is strengthened by the monsoonal westerlies and thus affects a broader area.
2) LATE
WET SEASON
The late wet season is characterized by a welldeveloped active monsoon, which produces a band of highly concentrated strikes south and west of the northern region (Fig. 4b). The southern region of the NT has low strike rates since the monsoonal flow does not reach that far south. An interesting feature of the late wet season is the low frequency of lightning strikes (0.00–1.07 strikes km⫺2) that occurred over Arnhem Land and the Gove Peninsula, compared to the west coast of the NT. There may be a number of possible explanations for this phenomenon. The most likely explanation is that thunderstorms tend to be favored over the western half of the northern region because of lowlevel convergence affecting the area, whereas the eastern part is under the influence of a divergent ridge pushing in from Queensland (J. Arthurs 2004, personal communication). Also, sea-breeze convergence is expected to be stronger in the west than in the east, especially if there are low-level easterly flows, which may trigger storms that penetrate a long way inland (J. Arthurs 2004, personal communication).
3) EARLY
DRY SEASON
The early dry season is characterized by the end of the transition from the wet to the dry season and had fewer strikes because the dry southeasterly wind flow begins to dominate the landscape (Fig. 4c). Remnants of moisture from the late wet season over the northern region were still evident, which affected the stability of the area and hence lightning strike density (⬎0.49
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strikes km⫺2). However, lightning strikes in the northern region were located around the coastal regions and did not transgress inland, which may be associated with the retreating ITCZ. There was also a strong concentration of lightning strikes in the southeastern region, which may have arisen as a result of cold front incursions.
4) LATE
DRY SEASON
The late dry season is characterized by little atmospheric moisture, and thus fewer lightning strikes were observed when compared to other seasons (Fig. 4d). A majority of the strikes were located in the central interior region with a slight bias toward the west (0.16 strikes km⫺2). As in the case of the early dry season, many of these strikes can be attributed to cold front incursions emanating from central Australia. The northern region was associated with very few to no strikes at all (0.00–0.054 strikes km⫺2), which was likely caused by the limited moisture availability that restricted cloud formation and lightning strikes.
c. Vegetation, elevation, and fire scar analysis Previous studies have found that vegetation affects the distribution of lightning (Dissing and Verbyla 2003). Likewise, orographic lifting caused by elevated areas was shown to influence lightning strike density (López and Holle 1986; Lericos et al. 2002; Orville et al. 2002; Dissing and Verbyla 2003). To investigate the relationship between lightning strikes, vegetation, and elevation an exploratory data analysis was undertaken. Box plots were used to explore the data in this section because they show the variability within the data and also provide an indication of the symmetry and skewness of the data. A statistical analysis of the data, namely analysis of variance (ANOVA) and a Chisquared test, was attempted, but the results are not presented in this discussion because some datasets could not be normalized or showed inhomogeneity of variance. The highly variable nature of the data, both spatially and temporally, made it difficult to apply standard statistical techniques. To use box plots, the data were first normalized by taking the logarithm of the density so that the data would better fit a normal distribution curve to decrease the affects of outliers and extremes. The southern region vegetation analysis showed no relationship between vegetation type and the distribution of lightning strikes (Fig. 5a). The mean for the three vegetation types lies within ⫺3.65 and ⫺3.80 (log annual strikes km⫺2), though eucalypt/acacia woodland and shrubland show greater variability from the mean, probably because of the smaller area of these vegeta-
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FIG. 5. Mean lightning strike density divided into vegetation classes for the (a) southern region and (b) northern region. Graphs show box plots with mean ⫾ standard error. Note that the less negative numbers translate to higher lightning strike density.
tion types. In contrast, the northern region vegetation results showed that lightning strikes were significantly higher for grasslands (⫺2.8 log annual strikes km⫺2), followed by shrublands, tropical woodland, and eucalypt/acacia woodland (Fig. 5b). However, the standard error was quite variable. Shrublands had the greatest variability, while eucalypt/acacia woodland had the least. Annual lightning strikes in the southern region, in relation to elevation, showed that lightning strikes were consistently low below a threshold elevation of 800 m (Fig. 6a), after which lightning strike density increased almost linearly with elevation. The highest lightning strike density was at an elevation of 1200 m (⫺1.8 log annual strikes km⫺2), but was quite variable owing to the small extent of high elevation areas. In the northern region, the annual lightning strike density relationship
with elevation (Fig. 6b) showed no significant relationship with lightning density. However, elevation for the northern region extends only to 400 m, and therefore this is consistent with the southern region. We mapped lightning strikes two weeks prior and two weeks after 12 fire events (7 cases for the November months and 5 cases for the October months) between 1999 and 2003 to investigate the relationship between fire-scarred areas and lightning activity. Out of the 12 cases, 5 showed higher densities over the 50-km buffer zone than the control after samples (Table 2), which would suggest that under ideal atmospheric conditions an increase in lightning density can occur. Three of the five cases that showed an increase in lightning strike density toward the fire scar were large-scale fires (⬃500 km2). When considering the spatial pattern of lightning strikes over the fire scar, the wind flow and
FIG. 6. As in Fig. 5 but for mean lightning strike density divided into elevation intervals of 100 m.
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TABLE 2. Effects of fire scar area on lightning strike density two weeks before and after a fire. Case study
Area of fire scar (km2)
Comparison of lightning strikes before and after a fire scar
1 2 3 4 5 6 7 8 9 10 11 12
354 1135 496 198 452 341 1857 1197 1213 665 163 276
No effect No effect Increase Increase No effect No effect Increase No effect Increase No effect No effect Increase
wind direction must also be accounted for. A possible mesoscale-induced thunderstorm can be conceived as convergence from all sides of the fire scar, and a case of this is illustrated in Fig. 7a. Large clusters of strikes north, northwest, and southwest of the fire scar can be seen, with the highest densities of strikes occurring within 20 km. The other fire scars did not show consistent patterns, but some showed a plume formation upwind or downwind of the fire scar (Fig. 7b).
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5. Discussion Differences in surface properties, such as roughness and the partitioning of energy (Bowen ratio) into sensible and latent heat flux, may influence lightning strike density. The results from the box plots indicate that vegetation does not influence the density of lightning strikes in the southern region. A likely explanation is the small variation in heating rates over the sparse vegetation types since the southern region is homogeneously dry for most of the year. The main influence on lighting activity over the southern region is likely to be caused by cold front incursions rather than a variation in vegetation. Conversely, the northern region showed a significant difference in lightning density between vegetation types with grassland having the highest rates. These grasslands are surrounded by woodlands that may cause mesoscale interactions to arise. However, it is more than likely that the grasslands have a much higher sensible heat flux because they have senesced during the dry season. Woodlands continue to extract deep soil water and have much lower Bowen ratios compared with grasslands (Beringer et al. 2003). Therefore increased sensible heat flux may drive thermal convection and increase lightning strike density. In addition, grasslands are located partly on the fringes of
FIG. 7. Spatial distribution of lightning strikes over a fire scar two weeks after a fire event, with a 50-km buffer zone: (a) possible mesocale-induced circulation as the lightning strikes are clustered around the fire scar (b) showing a plume formation northwest of the fire scar.
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the coast where they are probably influenced by mesoscale sea-breeze development, which is also a mechanism for convection and, hence, lightning strikes. However, these speculative causes cannot be confirmed using the current data. Atmospheric conditions are generally unsaturated through the atmospheric profile in the southern region due to low atmospheric moisture availability. For lightning strikes to occur, cloud formation is necessary via a trigger mechanism (Sturman and Tapper 1996). Cloud formation in the southern region is most likely to occur via orographic uplift of moist air incursions from southern cold fronts, which occur infrequently (Beringer and Tapper 2000). The results from the analysis show that there is little variation in lightning strike density to 700 m, which may be around the point of the lifting condensation level (LCL) during cold front incursions. Lightning strike density then increased consistently with higher elevation. This is due to the fact that the higher elevation more often causes air to be lifted to the LCL under a larger range of conditions compared to the 800-m elevation, which only caused the LCL to be reached under the most optimal conditions. The 1200-m contour had the highest strike density, consistent with the results achieved by Dissing and Verbyla (2003). Other studies investigating the spatial distribution of lightning strikes have not quantified their data in relation to elevation but have, however, observed positive relationships (López and Holle 1986; Lericos et al. 2002; Orville et al. 2002). In the northern region it is evident that elevation is not an important control factor. This is partly due to the shallow elevation range (0–400 m); therefore convection via uplift is not necessarily expected. Additionally, the highest elevation zones in the north are located in Arnhem Land where there is generally a low distribution of lighting strikes and thunderstorm days. The fire scar analysis showed that the differential heating between unburned and burned areas could be a possible trigger mechanism for convection, cloud cover, and lightning activity. We suggest that buoyant warm air rising as a result of intense surface heating and a strong heating contrast between two surfaces may lead to the generation of a mesoscale circulation. However, the intensity of these convective processes is likely to depend on the area of the surface: therefore, a larger effect on the atmosphere is expected over larger fire scars because it will provide sufficient horizontal gradients for flow to occur. In addition, as air moves across larger-sized scars the integrated heat input over the larger fetch will be much greater, which may then trigger the uplift of buoyant air and enhance convection and lighting. A small fire scar will show an increase in
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heating, but its impact on the heating of the boundary layer will be minimal. This hypothesis is consistent with our results, which showed increased lightning strike density over larger fire scars. The heating contrast over the large area is also likely to affect the atmospheric pressure over the fire scar, perhaps causing an inflow from all sides of the scar to converge and uplift air over the fire scar. Larger fire scars have also been associated with an increase in vertical air velocity causing an increase in updrafts and a larger circulation center over or around the fire scar (Knowles 1993). Our results supplement Knowles, who also hypothesized that a larger burn area would lead to a more intense mesoscale circulation around the fire scar. Our “plume” strikes are most likely associated with convection induced by buoyancy and by a shifting of the wind. The plume strikes may be associated with or influenced by other local convection systems and squall lines in the region. It may also be possible that lightning strikes may be associated with pyrocumulus clouds from the smoke plumes caused by the fire itself. Pollutants from bushfires, on occasions, are able to generate increased vertical updrafts by the heat from the fire; when these interact with the atmosphere under ideal conditions a pyrocumulus cloud may develop (T. Bannister 2003, personal communication). However, studies by Latham (1991) and Vonnegut et al. (1995) showed that fireinduced clouds alone could not generate lightning strikes, though under the influences of other atmospheric circulations occurring in the region an increase in lightning density may occur. We have suggested some plausible mechanisms and data to explain the interaction between fire scars and lightning strike density; however there is scope for further work. For example, an analysis of high temporal resolution dynamics using LPATS and meteorological radars could be undertaken. Field observations across fire scars using a network of automatic weather stations and profiling equipment could also elucidate mesoscale circulations.
6. Conclusions This preliminary study attempted to identify the effects of vegetation and elevation on lightning density by selecting two contrasting climatic regions. GIS allowed for data integration between the vegetation, elevation, fire scar, and lighting strike datasets as well as quantifying the relationship between them. In the northern region there was a distinct increase in lightning strike density from woodland to shrub to grassland. We suggest that the cause of this may be due to greater surface heating of grasslands than the other vegetation types,
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which would then allow for a heating differential to arise between the two surface types and may act as a mechanism. In the southern region, vegetation was shown to have no significant effect on lightning strikes because it is generally sparse and moisture is limited. However, elevation in the southern region, which ranges from 0 to 1200 m, was found to play an important role in initiating convection via orographic uplift, especially for elevations above 800 m. The influence of elevation in the northern region did not show a relationship with lightning density, which may be due to the small range of elevation (0–400 m). The study of lightning density over fire scar affected areas in the NT was largely based on the modeling research conducted by Knowles (1993), who hypothesized that the differential heating caused by the fire scar and the adjacent vegetative area may produce a mesoscale circulation that could generate lightning strikes. Our results suggest that the spatial distribution of lightning strikes over fire scars may be induced by mesoscale circulations or buoyancy due to intense heating. The distribution of lightning strikes over fire scars was greater over larger fire scars. This work illustrates the importance of fire in influencing spatial heterogeneities in surface properties and heating. Given the enormous extent of annual burning in northern Australia it has been suggested that this could feed back to effect regional climate through changes in monsoon strength (Beringer et al. 2003; Görgen et al. 2006). Acknowledgments. We thank Jim Arthur, Rob Porteous, Andrew Edwards, and the Bureau of Meteorology for providing data and support. This research was supported through Australian Research Council (ARC) Grant (DP0344744). REFERENCES Beringer, J., and N. J. Tapper, 2000: The influence of subtropical cold fronts on the surface energy balance of a semi-arid site. J. Arid Environ., 44, 437–450. ——, and ——, 2002: Surface energy exchanges and interactions with thunderstorms during the Maritime Continent Thunderstorm Experiment (MCTEX). J. Geophys. Res., 107, 4552, doi:10.1029/2001JD001431. ——, D. Packham, and N. J. Tapper, 1995: Biomass burning and resulting emissions in the Northern Territory, Australia. Int. J. Wildland Fire, 5, 229–235. ——, L. B. Hutley, N. J. Tapper, A. Coutts, A. Kerley, and A. P. O’Grady, 2003: Fire impacts on surface heat, moisture and carbon fluxes from a tropical savanna in north Australia. Int. J. Wildland Fire, 12, 333–340. Boccippio, D. J., S. J. Goodman, and S. Heckman, 2000: Regional differences in tropical lightning distributions. J. Appl. Meteor., 39, 2231–2248.
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Bureau of Meteorology, 1998: Climate of the Northern Territory. National Capital Printing, 38 pp. Christian, H. J., 1999: Optical detection of lightning from space. Proc. 11th Int. Conf. on Atmospheric Electricity, Guntersville, AL, International Commission on Atmospheric Electricity, 715–718. ——, and Coauthors, 2003: Global frequency and distribution of lightning as observed from space by the optical transient detector. J. Geophys. Res., 108, 4005, doi:10.1029/ 2002JD002347. Cooray, V., Ed., 2003: The Lightning Flash. The Institution of Electrical Engineers, 574 pp. Department of the Environment and Heritage, cited 2004: National Vegetation Information System. Australian Government. [Available online at http://www.deh.gov.au/erin/nvis/ index.html.] Department of Land Information, cited 2004: Fire scar mapping. Government of Western Australia. [Available online at http://www.rss.dola.wa.gov.au/newsite/apps/firescarmap. html.] Dissing, D., and D. L. Verbyla, 2003: Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation. Can. J. For. Res., 33, 770–782. Görgen, K., A. H. Lynch, A. G. Marshall, and J. Beringer, 2006: The impact of abrupt land cover changes by savanna fire on northern Australian climate. J. Geophys. Res., 111, D19106, doi:10.1029/2005JD006860. Hutley, L. B., A. P. O’Grady, and D. Eamus, 2001: Monsoonal influences on evapotranspiration of savanna vegetation of northern Australia. Oecologia, 126, 434–443. Knowles, J., 1993: The influence of forest fire induced albedo differences on the generation of mesoscale circulations. M.S. thesis, Dept. of Atmospheric Science, Colorado State University, 84 pp. Laird, N. F., D. A. R. Kristovich, and J. E. Walsh, 2003: Idealized model simulations examining the mesoscale structure of winter lake-effect circulations. Mon. Wea. Rev., 131, 206–222. Latham, D. J., 1991: Lightning flashes from a prescribed fireinduced cloud. J. Geophys. Res., 96, 17 151–17 157. Lericos, T. P., H. E. Fuelberg, A. I. Watson, and R. L. Holle, 2002: Warm season lightning distributions over the Florida peninsula as related to synoptic patterns. Wea. Forecasting, 17, 83–99. López, R. E., and R. L. Holle, 1986: Diurnal and spatial variability of lightning activity in northeastern Colorado and Central Florida during the summer. Mon. Wea. Rev., 114, 1288–1312. Mushtak, V., E. Williams, and D. Boccippio, 2003: Latitudinal variation of cloud base height and lightning parameters in the Tropics. Proc. 12th Int. Conf. on Atmospheric Electricity, Versailles, France, International Commission on Atmospheric Electricity, 9–13. Orville, R. E., 1994: Cloud-to-ground lightning flash characteristics in the contiguous United States: 1989–1991. J. Geophys. Res., 99, 10 833–10 841. ——, R. A. Weisman, R. B. Pyle, R. W. Henderson, and R. E. Orville Jr., 1987: Cloud-to-ground lightning flash characteristics from June 1984 through May 1985. J. Geophys. Res., 92, 5640–5644. ——, G. R. Huffines, W. R. Burrows, R. L. Holle, and K. L. Cummins, 2002: The North American Lightning Detection Network (NALDN)—First results: 1998–2000. Mon. Wea. Rev., 130, 2098–2109. Pielke, R. A., and R. Avissar, 1990: Influence of landscape struc-
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