1. Introduction
Every year, multiple meteorological organizations, such as NOAA; research groups, such as that at Colorado State University (http://hurricane.atmos.colostate.edu/forecasts/); and private firms release predictions of the number of tropical storms, hurricanes, and major hurricanes they expect to form that year, particularly within the North Atlantic basin. Other attributes of tropical cyclone (TC) activity, however, such as when the first TC will form or how long the season will last, are typically not forecast. Officially, the Atlantic TC season begins on 1 June and ends on 30 November. Over 97% of TC activity within the Atlantic basin occurs between these two dates (Hurricane Research Division 2015). However, as assessed utilizing the National Hurricane Center hurricane database (HURDAT2; Landsea and Franklin 2013), the average first TC formation for the period 1979–2014 occurred on 30 June and the average last TC formation occurred on 4 November. The question thus remains, what causes some seasons to be significantly shorter or longer than average?
It is worthwhile to consider whether there could be a relationship between the period over which the conditions necessary for TC development are present at one or more locations and the length of the TC season. Gray (1968, 1979) identified six necessary but not sufficient criteria for TC development: sea surface temperatures (SSTs) greater than or equal to 26.5°C, abundant lower- to midtropospheric relative humidity, conditional instability through a deep tropospheric layer, large lower-tropospheric cyclonic relative vorticity, low vertical wind shear of the horizontal winds (roughly less than 10 m s−1 between the surface and tropopause), and displacement by at least 5° latitude poleward from the equator. These six conditions may be condensed [e.g., as done by Frank (1987) and others] into four: sufficiently large cyclonic vertical vorticity, weak vertical wind shear of the horizontal winds, SSTs greater than or equal to 26°C, and abundant lower- to midtropospheric relative humidity. The SST criterion may alternatively be expressed in terms of upper-oceanic heat content and/or potential intensity, the latter of which is influenced by both SST and outflow-layer temperatures (e.g., Emanuel 1986). Likewise, the relative humidity and vertical wind shear criteria may alternatively be represented by ventilation, which reflects the extent to which environmental, midtropospheric low-entropy air may be imported into the circulation of a TC or predecessor disturbance (e.g., Tang and Emanuel 2012; Tang and Camargo 2014).
Subsequent investigators (e.g., Bruyère et al. 2012; Camargo et al. 2007; Emanuel 2010; Emanuel and Nolan 2004; McGauley and Nolan 2011; Schumacher et al. 2009; Tang and Camargo 2014; Tippett et al. 2011) have utilized these criteria to develop, apply, and evaluate genesis potential indices. Such indices are designed to highlight whether conditions favorable for TC development exist in observations or climate model outputs at one or more locations and times. For example, applying the ventilation index of Tang and Emanuel (2012) to eight model outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) representative concentration pathway 8.5 (RCP8.5) experiment, Tang and Camargo (2014) identified a potential decrease in the average annual number of days that the ventilation index is favorable for TC genesis in the North Atlantic Ocean of up to 2 weeks by the end of the twenty-first century. Alternatively, downscaling methods may be applied to climate model outputs to assess projected future changes in TC season length (e.g., Dwyer et al. 2015).
In the context of genesis potential indices, greater lower- to midtropospheric moisture content, higher upper-oceanic heat content and/or potential intensity, lower vertical wind shear, and sufficiently large cyclonic vertical vorticity indicate a greater potential for tropical cyclogenesis (e.g., McGauley and Nolan 2011). Presumably, TC seasons that start earlier or end later than normal are characterized by the presence of the necessary ingredients for TC formation at times when they are not typically present. The inverse is presumably true for seasons that begin later and/or end earlier than normal. Herein, we seek to examine the extent to which this is true on subseasonal time scales, particularly in the context of the genesis pathways that early- and late-season North Atlantic TCs typically follow (McTaggart-Cowan et al. 2008, 2013). Note that the genesis pathways identified by McTaggart-Cowan et al. (2008, 2013) describe the environments within which the physical processes believed to be responsible for tropical cyclogenesis (e.g., wind-induced surface heat exchange; Emanuel 1986) are triggered so as to lead to the formation of a tropical cyclone.
In addition to understanding the synoptic- to climate-scale factors that control the length of an individual TC season, it is worthwhile to consider whether the length of the Atlantic TC season has increased or decreased in recent decades. Focusing on an extended North Atlantic main development region (MDR) south of 30°N and east of 75°W where an increase of 0.5°–1.5°C in mean June–November SST was observed between 1950 and 2007, Kossin (2008) utilized quantile regression to identify changes in the annual distribution of Atlantic TC formation dates. Their findings (Fig. 4 of Kossin 2008) suggest that the Atlantic TC season has increased in length by 10 or more days per decade since 1950, reflective of both earlier starts and later ends to the season, with a level of confidence between 80% and 90%. This was attributed, albeit also with low confidence, to warmer May SSTs across the extended MDR, with an increase of the area-averaged SST by 1°C corresponding to as much as a 20-day shift in the start and/or end of the season.
The present research has multiple aims. First, the historical record indicates that Atlantic TC seasons since 2008 have not been associated with anomalously active early- and late-season TC activity (Table 1), in contrast to that in the preceding years referenced by Kossin (2008). Quantile regression is utilized to identify whether a statistically significant trend in Atlantic TC season length exists for the period 1979–2014 when considering only TCs that formed within Kossin’s (2008) extended MDR. Second, the historical record also indicates that when the entire Atlantic basin is considered, Atlantic TC seasons prior to 2008 appear to feature the same anomalously active early- and late-season TC activity as was found over the extended MDR subset thereof (Kossin 2008; Table 2). This is noteworthy given that many of the earliest- and latest-forming TCs undergo genesis outside of Kossin’s (2008) extended MDR (Figs. 1 and 2). Quantile regression is utilized to identify whether a statistically significant trend in Atlantic TC season length exists for the period 1979–2007 when considering TCs that formed anywhere within the Atlantic basin. This analysis is repeated for the period 1979–2014 given the apparent termination of anomalously active early- and late-season TC activity since 2008 (Table 2). In contrast to Kossin (2008), however, these analyses all are exclusively statistical; no physically based hypothesis is advanced or tested to attempt to explain the existence, or lack thereof, of a statistically significant trend in season length over any region or temporal extent considered. Third, we separately investigate how the synoptic- to planetary-scale distributions of proxies for the necessary conditions for tropical cyclogenesis vary on subseasonal to interannual time scales between early- and late-starting (ending) Atlantic TC seasons, particularly in light of the different genesis pathways followed by early- and late-season TCs.
The 5th- and 95th-percentile TC formation dates for TCs forming within the extended MDR of Kossin (2008) for the years 2001–14. For reference, the 1979–2014 average 5th- and 95th-percentile TC formation dates are provided in the bottom row.
Formation location, as assessed utilizing National Hurricane Center “best track” data, of all Atlantic TCs forming on or prior to the 10th-percentile formation date for each Atlantic TC season. For TC formation events between 1979 and 2010, the highest-probability genesis pathway determined following McTaggart-Cowan et al. (2013) is illustrated by the appropriate symbol. The genesis date within 2-week bins is referenced by the color given to each symbol.
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
As in Fig. 1, but for Atlantic TCs forming on or after the 90th-percentile formation date for each Atlantic TC season.
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
The remainder of this work is structured as follows. Section 2 outlines the data utilized and methodology followed in this study. Section 3 presents results regarding long-term trends in Atlantic TC season length for the period 1979–2014. Section 4 presents results regarding the synoptic- to planetary-scale conditions that characterize early- and late-starting (ending) Atlantic TC seasons. A summary of the key findings of the work and a discussion of their implications are presented in section 5.
2. Data and methodology
In this study, the National Hurricane Center HURDAT2 database (Landsea and Franklin 2013) is used to identify Atlantic TC formations, excluding tropical depressions and subtropical cyclones, between 1979 and 2014. A total of 431 TC formation events are identified across the entire basin, with 228 of these being located within the extended MDR considered by Kossin (2008). The year 1979 is chosen as the start of our analysis period for several reasons. First, this is the first year for which ERA-Interim data (Dee et al. 2011), the use of which is described below, are available. Second, substantial improvements in the quantity and quality of satellite clear-sky radiance and cloud-track wind observations were realized beginning in 1979, resulting in more robust atmospheric reanalysis products from 1979 onward (e.g., Uppala et al. 2005). Third, so as to facilitate direct comparison between our results and those of Kossin (2008), we desire that the start of our period of record closely match that of Kossin (2008). We note that the sensitivity to a starting year of 1979 versus 1980 in our results is negligible (not shown). Finally, as routine geostationary satellite surveillance of TCs began in the mid-1960s (e.g., Neumann et al. 1999), it is unlikely that TCs were systematically missed over part or all of the period of record considered herein (e.g., Landsea 2007). Landsea (2007) also notes that advances in observational and analysis capabilities appear to be contributing to one additional Atlantic TC event per year since 2002, with many of their cited examples occurring at the start or end of the TC season. While it is possible that this could influence both the results presented herein as well as those of Kossin (2008), no attempt is made to account for this possible change in classification practices.
To quantify long-term trends in Atlantic TC season length, we apply the method of quantile regression. In contrast to linear (or least squares) regression, which provides a method by which the nature of an assumed linear relationship between the means of grouped data can be evaluated, quantile (or percentile) regression (Koenker and Bassett 1978; Koenker and Hallock 2001) provides a method for evaluating the nature of an assumed linear relationship for conditional quantile functions. One common application of quantile regression is median regression, though quantile regression need not be limited exclusively to the median (or 50th percentile) of a dataset. Quantile regression has been applied in the atmospheric sciences to examine long-term trends in Atlantic TC season length (Kossin 2008) and the intensity of the most-intense Atlantic TCs (Elsner et al. 2008). Quantile regression considers the topology of the full dataset of TC events rather than just the first and last TCs for each season, thereby increasing the degrees of freedom of the statistical analysis; furthermore, unlike linear regression, it is relatively insensitive to individual outliers within the data (Kossin 2008).
Herein, quantile regression is first utilized to replicate the findings of Kossin (2008), considering only TCs that formed within their extended MDR between 1980 and 2007. Quantiles between the 5th and 95th percentile, at 5% intervals, are considered. This analysis is then replicated for the period beginning in 1979; as noted earlier, the results are found to be insensitive to small differences in the start year. Next, to evaluate whether the positive but not statistically significant trend in Atlantic TC season length identified by Kossin (2008) is maintained when the period of record is extended to the present, the analysis is repeated for the period ending in 2014. Subsequently, to evaluate whether the positive but not statistically significant trend in Atlantic TC season length identified by Kossin (2008) is maintained when the set of TCs is extended to the full Atlantic basin, the analysis is repeated for the periods of 1979–2007 and 1979–2014 when considering all Atlantic basin TCs. Finally, the sensitivity in how changing the end of the period of record by one year at a time is examined by repeating the analysis, over both Kossin’s (2008) extended MDR and the full basin, for end years between 2001 and 2014. The 90th-percentile confidence interval is computed for each analysis utilizing bootstrapping (e.g., Efron and Tibshirani 1993) with 200 surrogates.
To assess how the synoptic- to planetary-scale distributions of the necessary conditions for TC formation vary on subseasonal to interannual time scales between early- and late-starting (ending) Atlantic TC seasons, linear regression is utilized. The 10%, 25%, 75%, and 90% TC formation date for each Atlantic TC season is first identified. These values are then linearly regressed against monthly mean fields of SST, 850-hPa relative vorticity, 600-hPa relative humidity, 500-hPa geopotential height, and 850–200-hPa vertical wind shear magnitude. Monthly mean SST data are obtained from the NOAA Extended Reconstructed Sea Surface Temperature, version 3b (ERSST.v3b), dataset on a 2° latitude × 2° longitude grid (Smith et al. 2008). It should be noted that this dataset differs slightly from that described by Smith et al. (2008) in that satellite-derived SST data are not used in its composition so as to remove a cold SST bias introduced by nonclear-sky satellite SST retrievals. All monthly mean atmospheric fields are obtained from ERA-Interim (Dee et al. 2011) on an approximate 0.7° latitude × 0.7° longitude grid. The results are insensitive to one-isobaric-level (50 hPa) shifts in the isobaric level(s) over which atmospheric fields are considered (not shown). Sensitivity in the results to the choice of SST or atmospheric reanalysis dataset is expected to be small but is not explicitly examined.
Four sets of linear regression analyses between monthly mean values of each field and nth percentile formation date are conducted: 1) June monthly mean to 10th-percentile formation date, 2) June and July monthly means to 25th-percentile formation date, 3) October and November monthly means to 75th-percentile formation date, and 4) November monthly mean to 90th-percentile formation date. The results are found to be qualitatively similar between linear regressions conducted against the 10th- and 25th-percentile formation dates and between linear regressions conducted against the 75th- and 90th-percentile formation dates (not shown). Consequently, the results presented in section 4 consider only linear regression computed between June monthly mean fields and the 10th-percentile formation date and between November monthly mean fields and the 90th-percentile formation date. As presented herein, positive values of (1) indicate an earlier start (as measured by the 10th-percentile formation date) or later end (as measured by the 90th-percentile formation date) of the TC season as a given oceanic or atmospheric field increases in value. All linear regression analyses are conducted globally with particular focus given to describing the results across the North Atlantic Ocean, North America, and equatorial Pacific Ocean. This area extends into the equatorial Pacific Ocean so as to reflect the potential influence of El Niño–Southern Oscillation (ENSO; e.g., Bjerknes 1969) upon Atlantic basin TC activity, particularly late in the Atlantic TC season (Dunion 2011; Klotzbach 2011a,b).
To complement the spatial linear regression analyses described above, we compute the linear correlation coefficient between the 10th- (90th-) percentile formation date and June (November) monthly mean values of selected teleconnection indices. Teleconnection indices considered include the Arctic Oscillation (AO; e.g., Thompson and Wallace 1998), Atlantic meridional mode (AMM; e.g., Moura and Shukla 1981), Atlantic multidecadal oscillation (AMO; e.g., Kerr 2000), North Atlantic Oscillation (NAO; e.g., Barnston and Livezey 1987), oceanic Niño (ONI; e.g., L’Heureux et al. 2013), Pacific decadal oscillation (PDO; e.g., Mantua et al. 1997), Pacific–North American (PNA; e.g., Wallace and Gutzler 1981), quasi-biennial oscillation (QBO; e.g., Baldwin et al. 2001), and Sahel precipitation (SPI; e.g., Haywood et al. 2013) indices. Many of these teleconnection indices have been shown to exert a control on North Atlantic TC activity on subseasonal to interannual scales, particularly for that which occurs at lower latitudes (e.g., Gray 1984; Kossin and Vimont 2007; Kossin et al. 2010; Villarini et al. 2010; Klotzbach 2011a; Villarini et al. 2012; to cite but a few examples). Only linear relationships that are statistically significant to ≥90% confidence, as assessed utilizing a Student’s t test, are reported upon in the results that follow.
3. Results: Long-term trends in season length
Over the period 1979–2007, when considering only TCs that formed within the extended MDR of Kossin (2008), the Atlantic TC season increased in length by approximately 1.5 days per year, as primarily associated with a later end to the season (Fig. 3a). This trend is not statistically significant to ≥90% confidence, however. These results are nearly identical to those obtained for the period 1980–2007 considered by Kossin (2008, their Fig. 4c), with subtle differences resulting from the inclusion of 1979 within our analysis. For the periods 1979–2001 and 1979–2002, no trend—statistically significant or otherwise—in Atlantic TC season length can be identified (Figs. 3f,g). There is some indication of a broadening of the peak of the season—here defined as formation events occurring between the 25th and 75th percentiles—but this result is also not statistically significant to ≥90% confidence. Introducing subsequent seasons into the analysis (Figs. 3b–e), particularly the active 2005 season (Beven et al. 2008), results in the gradual development of the positive but not statistically significant trend identified over the period 1979–2007 (Fig. 3a). This trend primarily results from four seasons (2001, 2003, 2005, and 2007) with TC season end dates—here represented by the 95th-percentile TC formation date to increase the degrees of freedom of the analysis—from 2 to 7 weeks later than the 1979–2014 average (Table 1).
Trend, in days per year, in the 5th–95th-percentile Atlantic TC formation dates between (a) 1979 and 2007, (b) 1979 and 2006, (c) 1979 and 2005, (d) 1979 and 2004, (e) 1979 and 2003, (f) 1979 and 2002, (g) 1979 and 2001, and (h) 1979 and 2014 for TCs that formed within the Kossin (2008) subset of the full Atlantic basin. The blue line indicates the 90% confidence interval.
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
Extending the end of the analysis through 2014 eliminates any trend in Atlantic TC season length (Fig. 3h). The below-average 2009 season (Berg and Avila 2011) contributes particularly strongly to the elimination of this trend, as manifest by a season end date approximately 2.5 weeks earlier than the long-term average (Table 2). This implies that the positive trend identified by Kossin (2008) to between 80% and 90% confidence is primarily a function of the several abnormally long TC seasons seen during the early to mid-2000s. It should be noted that extending the analysis through 2014 also decreases uncertainty in the results, as manifest by a narrower 90% confidence interval at both the start and end of the season.
When considering TC formation events across the entire Atlantic basin, no statistically significant trend in season length is identified for the period 1979–2007 (Fig. 4a) despite numerous late-ending TC seasons in the early to mid-2000s (Table 2). For the periods 1979–2001 and 1979–2002, a not statistically significant trend toward seasons that are shorter by one day per year is identified (not shown). The comparatively longer seasons of 2003– (Table 2) act to eliminate this trend but are not sufficient to result in an identifiable trend toward longer seasons. Furthermore, no trend—statistically significant or otherwise—in Atlantic TC season length is identified for the period 1979–2014 (Fig. 4b). As for the Kossin (2008) extended MDR, the uncertainty in this result is somewhat less than that associated with the period 1979–2007 (cf. Figs. 4a,b). Consequently, it is argued that the positive but not statistically significant trend toward longer Atlantic TC seasons identified by Kossin (2008) is not indicative of a robust long-term trend but is primarily the result of both the subset of TC formation events considered and the end date of the analysis. Fluctuations in season length therefore appear to be primarily controlled by subseasonal to interannual variability in the necessary conditions for TC formation, particularly early and late in the season.
As in Fig. 3, but for TCs that formed anywhere within the North Atlantic basin for (a) 1979–2007 and (b) 1979–2014.
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
4. Results: Seasonal influences upon season length
a. Early-starting seasons
Tropical cyclones that form before the 10th-percentile date for each season preferentially do so in the Gulf of Mexico, the northwestern Caribbean Sea, and near the southeastern United States coastline (Fig. 1). Note that the TCs within this composite that form in the MDR are almost exclusively associated with later-starting Atlantic TC seasons. The preferred genesis pathway for TCs that form in the preferred regions for early-season development is weak tropical transition, as evaluated utilizing the genesis pathway classification database of McTaggart-Cowan et al. (2013). For reference, tropical transition (TT; Davis and Bosart 2003, 2004) occurs in environments in which organized deep, moist convection upshear of an extratropical cyclone, through the horizontal and vertical redistribution of potential vorticity, acts to weaken initially strong vertical wind shear atop the surface low. If the cyclone is located over sufficiently warm waters (SST ≥ 24°–26°C) for a sufficiently long period of time (≥24 h) and is of sufficient intensity so as to foster wind-induced surface heat exchange (e.g., Emanuel 1986), an initially cold-core extratropical cyclone may transform into a warm-core TC. In the preferred genesis locations for early-season Atlantic TCs, June monthly mean SST is between 24° and 28°C (Fig. 5a), 600-hPa relative humidity is between 40% and 60% (Fig. 5b), 850-hPa relative vorticity is approximately zero (Fig. 5d), and the 850–200-hPa vertical wind shear magnitude is less than 10 m s−1 (Fig. 5e). In the June monthly mean, as compared to known TC-genesis-supporting environments in the deep tropics (e.g., Gray 1968, 1979; Frank 1987) and subtropical to midlatitudes (e.g., Mauk and Hobgood 2012), the SST and deep-layer vertical wind shear criteria for tropical cyclogenesis generally are met, whereas the precursor disturbance and midtropospheric relative humidity criteria for tropical cyclogenesis generally are not met.
Slope of the best-fit regression line between the 10th-percentile Atlantic TC formation date and June monthly mean (a) SST (°C), (b) 600-hPa relative humidity (%), (c) 500-hPa geopotential height (m), (d) 850-hPa relative vorticity (×10−5 s−1), and (e) 850–200-hPa vertical wind shear magnitude (m s−1) over the domain 10°S–70°N, 150°W–0°. A one standard deviation increase in a given monthly mean field is associated with an n-day change (shaded; positive = earlier) in the 10th-percentile Atlantic TC formation date. The June monthly mean field is contoured in each panel. Cross-hatching is utilized to identify regions where the slope of the best-fit regression line is nonzero to at least 90% (light gray), 95% (dark gray), and 99% (black) confidence.
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
Earlier-starting Atlantic TC seasons are associated with a statistically significant (to ≥90% confidence, as assessed utilizing a Student’s t test) increase in June monthly mean 850-hPa relative vorticity in the eastern Gulf of Mexico (Fig. 5d). A one standard deviation increase in 850-hPa relative vorticity (0.5 × 10−5 s−1; Fig. 6d) is associated with a 6–9-day earlier start to the Atlantic TC season relative to its mean. As the June climatological mean 850-hPa relative vorticity is anticyclonic across the Gulf of Mexico (Fig. 5d), the presence of increased 850-hPa relative vorticity in the eastern Gulf of Mexico in June indicates a greater likelihood that a seedling disturbance for TC formation exists in the Gulf of Mexico in earlier-starting Atlantic TC seasons. Given that most early-forming Atlantic TCs do so via tropical transition (Fig. 1), it is believed that a stationary frontal boundary along and ahead of the June climatological mean 500-hPa trough in the far western Atlantic (Fig. 5c) is the likely source of such a disturbance. However, it is not immediately clear what the driving force is behind increased 850-hPa relative vorticity in the eastern Gulf of Mexico during earlier-starting Atlantic TC seasons. Though 500-hPa geopotential height is typically lower across the Gulf of Mexico and northwestern Caribbean Sea during earlier-starting Atlantic TC seasons, this correlation is not statistically significant to ≥90% confidence (Fig. 5c). It is possible that this result is a reflection of the earlier-developing TCs themselves. However, this is unlikely given a typically short duration of TCs near land compared to the duration of June and the lack of a similar relationship between increased 600-hPa relative humidity and earlier-starting Atlantic TC seasons (Fig. 5b).
Standard deviation of the June monthly mean (a) SST (°C), (b) 600-hPa relative humidity (%), (c) 500-hPa geopotential height (m), (d) 850-hPa relative vorticity (×10−5 s−1), and (e) 850–200-hPa vertical wind shear magnitude (m s−1).
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
Though few coherent, statistically significant (to ≥90% confidence) linear correlations between subseasonal variability and the 10th-percentile Atlantic TC formation date exist within the North Atlantic basin, the 10th-percentile Atlantic TC formation date is associated with statistically significant linear correlations to large-scale variability in the Southern Hemisphere (Fig. 7). This is particularly manifest by variability in the 500-hPa longwave pattern (Fig. 7c) and its impacts upon and relationship with atmospheric and oceanic variability in the remaining fields considered (Figs. 7a,b,d,e). It is unclear, however, as to whether the relationship between such variability, whether in whole or in part, and the 10th-percentile Atlantic TC formation date is causal or merely associative in nature. The cause of this variability is also uncertain, as it does not closely resemble that associated with any predominant planetary-scale modes of climatic variability known to the authors. Therefore, further research is necessary to identify what is responsible for the synoptic- to planetary-scale variability associated with early-starting Atlantic TC seasons.
b. Late-ending seasons
Tropical cyclones that form after the 90th-percentile formation date for each season preferentially do so in the western Caribbean Sea and subtropical western Atlantic (Fig. 2). Late-season TCs that form in the subtropical western North Atlantic almost exclusively are the result of tropical transition, whereas TCs that form in the western Caribbean Sea either result from tropical transition or form in nonbaroclinic environments. In the preferred genesis locations for late-season Atlantic TCs, November monthly mean SST is between 24° and 28°C (Fig. 8a), 600-hPa relative humidity is between 30% and 60% (Fig. 8b), 850-hPa relative vorticity is approximately zero (Fig. 8d), and the 850–200-hPa vertical wind shear magnitude is between 10 and 20 m s−1 (Fig. 8e). In the November monthly mean, as compared to known TC-genesis-supporting environments, SST generally is sufficiently warm, deep-layer vertical wind shear is marginally favorable, and both midtropospheric relative humidity and lower-tropospheric relative vorticity are insufficiently large to promote tropical cyclogenesis.
Slope of the best-fit regression line between the 90th-percentile Atlantic TC formation date and November monthly mean (a) SST (°C), (b) 600-hPa relative humidity (%), (c) 500-hPa geopotential height (m), (d) 850-hPa relative vorticity (×10−5 s−1), and (e) 850–200 hPa vertical wind shear magnitude (m s−1) over the domain 10°S–70°N, 150°W–0°. A one standard deviation increase in a given monthly mean field is associated with an n-day change (shaded; positive = later) in the 90th-percentile Atlantic TC formation date. The November monthly mean field is contoured in each panel. Cross-hatching is utilized to identify regions where the slope of the best-fit regression line is nonzero to at least 90% (light gray), 95% (dark gray), and 99% (black) confidence.
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
To first order, late-ending Atlantic TC seasons preferentially occur during cool-phase (La Niña) ENSO events. This is manifest by a strengthened Walker circulation, as reflected in both November monthly mean SST (Figs. 8a and 9a) and 600-hPa relative humidity (Figs. 8b and 9b) fields across the equatorial Pacific. This is also reflected by a statistically significant (to ≥90% confidence, as assessed utilizing a Student’s t test) inverse linear relationship between the 90th-percentile formation date and the November mean value of the ONI (R = −0.33). In the context of the linear regression analyses presented in Figs. 8 and 9, a one standard deviation reduction in SST (1°–2°C; Fig. 10a) and 600-hPa relative humidity (7.5%–12.5%; Fig. 10b) in the central to eastern equatorial Pacific is associated with a 6–9-day extension of the Atlantic TC season relative to its mean.
As in Fig. 6, but for November monthly mean fields.
Citation: Journal of Climate 29, 1; 10.1175/JCLI-D-15-0324.1
The increased likelihood of late-season TC formation in the western Caribbean Sea during La Niña events is consistent with Dunion (2011) and Klotzbach (2011a,b). Together, these studies found that atmospheric conditions known to be hostile to TC development—namely, increased vertical wind shear, increased static stability, and decreased midtropospheric relative humidity—occur in the western Caribbean Sea during October and November more (less) frequently during El Niño (La Niña) events. To that end, increased November monthly mean 600-hPa relative humidity (Fig. 8b), increased November monthly mean 850-hPa relative vorticity (Fig. 8d), and decreased November monthly mean 850–200-hPa vertical wind shear (Fig. 8e), relative to their respective climatologies, in the Caribbean Sea during later-ending Atlantic TC seasons are consistent with the influence of La Niña. It is uncertain, however, the extent to which an increased (decreased) likelihood of late-season TC formation at higher latitudes is directly attributable to La Niña (El Niño) and its influence upon the global circulation during boreal fall (e.g., Horel and Wallace 1981).
Late-ending Atlantic TC seasons also preferentially occur during negative-phase PDO events. This is manifest by abnormally cold November monthly mean SST along the west coast of North America and abnormally warm November monthly mean SST in the central North Pacific Ocean (Figs. 8a and 9a). This is also reflected by a statistically significant (to ≥90% confidence, as assessed utilizing a Student’s t test) inverse linear relationship between the 90th-percentile formation date and the November mean value of the PDO index (R = −0.34). In the context of the linear regression analyses presented herein, a one standard deviation increase in SST across the central North Pacific (0.5°–1°C; Fig. 10a) and decrease in SST along the west coast of the United States (~0.5°C; Fig. 10a) are associated with an approximate 6-day increase in the length of the Atlantic TC season relative to its mean. The relationship between late-season Atlantic TC activity and the PDO has, to the authors’ knowledge, not yet been documented within the refereed literature, and further identification of the physical underpinnings behind this relationship is planned for further study. It should be noted, however, that there exists a statistically significant (to ≥95% confidence, as assessed utilizing a Student’s t test) direct linear relationship between the November ONI and PDO indices (R = 0.38), such that negative-phase PDO events during boreal fall have a tendency to occur during La Niña events.
In closer proximity to where late-season Atlantic TCs form, late-ending Atlantic TC seasons are associated with an eastward shift of the November monthly mean 500-hPa longwave pattern across the United States and subtropical western North Atlantic Ocean (Fig. 8c). A one standard deviation (30–40 m; Fig. 10c) increase (decrease) in 500-hPa geopotential height across the central United States (subtropical western North Atlantic Ocean) is associated with a 3–9-day extension of the Atlantic TC season relative to its mean. Though not statistically significant to ≥90% confidence, late-ending Atlantic TC seasons are also associated with increased 500-hPa geopotential heights near Greenland (Fig. 8c), wherein a one standard deviation (≥50 m; Fig. 10c) increase in 500-hPa geopotential height is associated with an approximate 3-day extension of the Atlantic TC season relative to its mean. The occurrence of below-normal 500-hPa geopotential heights in the subtropical western North Atlantic Ocean with above-normal 500-hPa geopotential heights near Greenland is consistent with an elevated likelihood of cutoff extratropical cyclones in the subtropical western North Atlantic. Further, given sufficiently warm SSTs, this is consistent with an increased likelihood of late-season TT occurrence in the subtropical western Atlantic so long as deep, moist convection associated with a given extratropical cyclone is able to locally reduce the 850–200-hPa vertical wind shear from meso- to synoptic time scales (Davis and Bosart 2003, 2004).
c. Representativeness of linear regression analyses
Spatial linear correlation is used to quantify the extent to which the linear regression analyses presented in Figs. 5, 7, 8, and 9 are representative of the variability in monthly mean fields observed with both early- and late-starting (ending) seasons. Herein, spatial linear correlation is computed over the region 10°S–70°N, 150°–0°W, consistent with the region depicted in Figs. 5 and 8. The statistically significant linear correlations between the 90th-percentile Atlantic TC formation date and boreal autumn atmospheric and oceanic variability associated with both the ENSO and PDO (section 4b) motivate the selection of the southern and western extents of this domain. The genesis locations for many of the earliest- and latest-forming Atlantic TCs (Figs. 1 and 2), many of which originate from precursor disturbances of midlatitude origin, motivate the selection of the northern extent of this domain. Similar qualitative insight to that described below is obtained when spatial linear correlation is computed over only the tropical North Atlantic (0°–30°N, 95°–10°W) or over a global domain (not shown). As a result, only the results for the region 10°S–70°N, 150°–0°W are presented below.
The results of the spatial linear correlation analyses are presented in Tables 3 and 4. To first order, the linear regression analyses are to some extent representative of variability in monthly mean fields observed with both early- and late-starting (ending) seasons. Anomalies in the monthly mean fields for the five earliest-starting and latest-ending Atlantic TC seasons are generally positively correlated with the linear regression fields, while anomalies in the monthly mean fields for the five latest-starting and earliest-ending Atlantic TC seasons are generally negatively correlated with the linear regression fields. However, correlations are generally weak, highly variable between individual Atlantic TC seasons within a given five-member composite, and stronger for some variables than for others. This implies a limit upon the predictive ability of the linear regression analyses.
Linear correlation coefficient, computed over the region 10°S–70°N, 150°W–0°, between the departure in the June monthly mean field from the 1979–2014 mean June monthly mean field and the slope of the best-fit linear regressions between the 10th-percentile Atlantic TC formation date and June monthly mean fields for the Atlantic TC seasons with the five earliest and five latest 10th-percentile formation dates. Positive (negative) linear correlation coefficients indicate that the anomaly field in question is of like (opposite) sense to the regression field.
As in Table 3, but for November monthly mean fields and the 90th-percentile Atlantic TC formation date.
For example, consider the Atlantic TC seasons with the five latest 90th-percentile formation dates, as listed in Table 4. Previously, La Niña and negative-phase PDO events were demonstrated to be associated with later-ending Atlantic TC seasons. Of the five latest-ending Atlantic TC seasons, as classified using the ONI, four were associated with neutral ENSO conditions and one (1994) was associated with El Niño conditions. Likewise, as classified by the PDO index, two of the five latest-ending Atlantic TC seasons were associated with weak positive-phase PDO conditions. Thus, while La Niña and negative-phase PDO events appear to increase the likelihood of a later-than-normal end to the Atlantic TC season, they do not represent the only pathways to a later-than-normal Atlantic TC season end. Further, slower-evolving fields with significant synoptic-scale structure in the linear regression analyses—particularly SST, but also 600-hPa relative humidity, 500-hPa geopotential height, and 850–200-hPa vertical wind shear magnitude—are generally associated with stronger correlations. The more rapidly evolving 850-hPa relative vorticity field, with primarily mesoscale structure in its linear regression analyses, is generally associated with weaker correlations. This implies that meso- to synoptic-scale variability not captured by the linear regression analyses, particularly with respect to whether or not a predecessor disturbance from which a TC can originate exists, contributes to variability in Atlantic TC season length.
5. Summary and discussion
The length of the Atlantic TC season varies from year to year with some seasons significantly shorter or longer than the average. Albeit only to 80%–90% confidence, Kossin (2008) suggested that the duration of the year over which TCs formed within an extended main development region of the Atlantic basin has increased by 1–2 days per year over the course of the last several decades. Herein, it was first determined whether this result is maintained when TC genesis events occurring over the full Atlantic basin are considered or when the period of record is extended to the present. Subsequently, monthly mean synoptic- to planetary-scale conditions associated with early- and late-starting (ending) Atlantic TC seasons were examined.
Quantile regression was applied to National Hurricane Center HURDAT2 data (Landsea and Franklin 2013) for the 36-yr period between 1979 and 2014 to first replicate and subsequently expand upon Kossin’s (2008) findings. Extending the end of the analysis through 2014 eliminated the positive but not statistically significant trend toward longer Atlantic TC seasons identified by Kossin (2008). Rather, this trend appears to be a function of several abnormally long Atlantic TC seasons—2001, 2003, 2005, and 2007—that occurred during the early to mid-2000s (Tables 1 and 2). Of these particular seasons, all but 2003 were associated with weak to moderate La Niña and negative-phase PDO conditions, each of which were subsequently identified to be associated with later ends to the Atlantic TC season. However, the precise reasons as to why these seasons were considerably longer than normal remains to some extent unclear. Furthermore, when TC formation events across the entire Atlantic basin were considered, no statistically significant trend in Atlantic TC season length was identified for the period 1979–2007 and no trend whatsoever in Atlantic TC season length was identified for the period 1979–2014. Variations in season length therefore appear to be primarily controlled by interannual variability in the necessary conditions for TC formation, particularly early and late in the Atlantic TC season.
Utilizing linear regression applied to June and November monthly mean reanalysis data, synoptic- to planetary-scale variability associated with early- and late-starting (ending) Atlantic TC seasons was identified. Earlier-starting Atlantic TC seasons are associated with increased June monthly mean 850-hPa relative vorticity across the eastern Gulf of Mexico, indicative of a greater likelihood that a precursor disturbance for TC formation exists in the Gulf of Mexico in June in earlier-starting Atlantic TC seasons. Given that most early-forming Atlantic TCs do so via tropical transition, it is believed that a stationary frontal boundary along and ahead of the June climatological mean 500-hPa trough in the far western Atlantic is the likely source of such a disturbance. There also exist statistically significant correlations between the 10th-percentile Atlantic TC formation date and large-scale variability within the Southern Hemisphere. However, whether these correlations are causal or merely associative is uncertain, as are the causes of such variability.
Late-ending Atlantic TC seasons primarily occur during La Niña and negative-phase PDO events. The former is consistent with several previous studies (e.g., Dunion 2011; Klotzbach 2011a,b) and is believed to be the primary influence upon late-forming TCs (independent of genesis pathway) in the western Caribbean Sea. Further investigation into the physical connection(s) associated with the latter is planned for further study. A later end to the Atlantic TC season is also associated with an eastward shift in the November monthly mean 500-hPa longwave pattern across the United States and subtropical western North Atlantic Ocean. It is this modulation of the atmospheric pattern that is hypothesized to increase the likelihood of late-season TT events across the subtropical Atlantic Ocean, although it is uncertain whether this result can also be attributed to modulation of the midlatitude atmospheric pattern by the ENSO and/or PDO.
While the linear regression analyses are to some extent representative of variability in monthly mean fields observed with both early- and late-starting (ending) seasons, there exist multiple pathways to an early- or late-starting and/or -ending Atlantic TC season. Consequently, while associated with statistically significant (to ≥90% confidence) linear relationships, these results explain only a small portion of the total variation in Atlantic TC season length. For instance, in order for TCs to form, environmental conditions must locally be favorable on shorter time scales from several hours to a few days, such as may be associated with a transient Madden–Julian oscillation event (e.g., Klotzbach 2010) and/or a convectively coupled atmospheric Kelvin wave (e.g., Schreck 2015). Thus, while the results elucidate variability in the large-scale conditions necessary to support TC formation associated with early- and late-starting (ending) Atlantic TC seasons, variability on smaller spatiotemporal scales must be known in order to more completely quantify variability in Atlantic TC season length.
The results presented in this manuscript motivate a number of future studies aimed both at better understanding variability in TC season length and at quantifying large-scale conditions that promote early-starting and late-ending TC seasons. For example, what results in multiple successive short or long Atlantic TC seasons, as was observed in the early 2000s? Given that late- (early-) ending Atlantic TC seasons primarily occur during La Niña (El Niño) and negative- (positive-) phase PDO events, the persistence of such conditions between seasons may increase the likelihood of successive Atlantic TC seasons of atypical length. Further, the 90th-minus-10th-percentile formation date (R = 0.43) is strongly linearly correlated to annual Atlantic TC count. Thus, to some extent, more (less) active Atlantic TC seasons are also longer (shorter) Atlantic TC seasons, consistent with the hypothesis of Dwyer et al. (2015). This is also consistent with the influence of ENSO upon Atlantic TC activity (e.g., Gray 1984). Further research is planned to study the viability of these inferences. The methods utilized in this study can also be readily adapted to other oceanic basins that feature well-defined TC seasons or to subsets of cyclones (e.g., only TCs with maximum sustained surface winds of ≥33 m s−1) for any basin in which TCs occur. Finally, given appropriate downscaling and statistical sampling methods, these methods may be applied to climate model outputs to evaluate potential future changes in both TC season length and early- and late-season genesis pathways under a wide range of emissions scenarios.
The results presented herein also motivate an investigation into the predictability—or lack thereof—of the large-scale conditions that promote early- or late-starting and -ending TC seasons, whether in the Atlantic basin or elsewhere. In other words, are shorter- and longer-than-normal TC seasons primarily driven by synoptic-scale variability? Or are such events primarily driven by subseasonal to climate-scale variability that evolves more slowly (and is thus more predictable) than that on the synoptic scale? The strong linear correlations of Atlantic TC season length with the ENSO, PDO, and seasonal Atlantic basin TC count argue in favor of subseasonal to climate-scale controls upon Atlantic TC season length. Likewise, we note that linear regression analyses conducted between the 10th-percentile formation date and May monthly mean fields and between the 90th-percentile formation date and October monthly mean fields bear some resemblance to those presented in Figs. 5 and 7 and Figs. 8 and 9, respectively, particularly for the slower-evolving SST and 600-hPa relative humidity fields (not shown). However, the limited overall extent to which the linear regression analyses are representative of observed variability in both early- and late-starting (ending) Atlantic TC seasons argues against dominant climate-scale controls, though it remains possible that such relationships may exist if combinations of multiple modes of climate variability are considered. Further investigation is planned to address the intrinsic predictability of comparatively short and long Atlantic TC seasons.
Acknowledgments
We acknowledge fruitful discussions with Rob Hodges, Sergey Kravtsov, and Kyle Swanson during the course of this research. We are indebted to Ron McTaggart-Cowan for making available to us the genesis pathway classification database described in McTaggart-Cowan et al. (2013). This manuscript benefitted greatly from constructive review comments provided by Phil Klotzbach and two anonymous reviewers. Monthly mean ERA-Interim data were obtained from the ECMWF. NOAA ERSST.v3b SST data were obtained from the NOAA/OAR/ESRL Physical Sciences Division. Teleconnection index data were obtained from NOAA/NCEP/Climate Prediction Center (ONI, NAO, AO, PNA, and QBO), the University of Washington (PDO and SPI), and NOAA/OAR/ESRL Physical Sciences Division (AMM and AMO). Quantile regression computations were carried out utilizing MATLAB code provided by Aslak Grinsted.
REFERENCES
Baldwin, M. P., and Coauthors, 2001: The quasi-biennial oscillation. Rev. Geophys., 39, 179–229, doi:10.1029/1999RG000073.
Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 1083–1126, doi:10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2.
Berg, R. J., and L. A. Avila, 2011: Atlantic hurricane season of 2009. Mon. Wea. Rev., 139, 1049–1069, doi:10.1175/2010MWR3476.1.
Beven, J. L., II, and Coauthors, 2008: Atlantic hurricane season of 2005. Mon. Wea. Rev., 136, 1109–1173, doi:10.1175/2007MWR2074.1.
Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163–172, doi:10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.
Bruyère, C. L., G. J. Holland, and E. Towler, 2012: Investigating the use of a genesis potential index for tropical cyclones in the North Atlantic basin. J. Climate, 25, 8611–8626, doi:10.1175/JCLI-D-11-00619.1.
Camargo, S. J., K. A. Emanuel, and A. H. Sobel, 2007: Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J. Climate, 20, 4819–4834, doi:10.1175/JCLI4282.1.
Davis, C. A., and L. F. Bosart, 2003: Baroclinically induced tropical cyclogenesis. Mon. Wea. Rev., 131, 2730–2747, doi:10.1175/1520-0493(2003)131<2730:BITC>2.0.CO;2.
Davis, C. A., and L. F. Bosart, 2004: The TT problem: Forecasting the tropical transition of cyclones. Bull. Amer. Meteor. Soc., 85, 1657–1662, doi:10.1175/BAMS-85-11-1657.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
Dunion, J. P., 2011: Rewriting the climatology of the tropical North Atlantic and Caribbean Sea atmosphere. J. Climate, 24, 893–908, doi:10.1175/2010JCLI3496.1.
Dwyer, J. G., S. J. Camargo, A. H. Sobel, M. Biasutti, K. A. Emanuel, G. A. Vecchi, M. Zhao, and M. K. Tippett, 2015: Projected twenty-first-century changes in the length of the tropical cyclone season. J. Climate, 28, 6181–6192, doi:10.1175/JCLI-D-14-00686.1.
Efron, B., and R. Tibshirani, 1993: An Introduction to the Bootstrap. Chapman and Hall, 436 pp.
Elsner, J. B., J. P. Kossin, and T. H. Jagger, 2008: The increasing intensity of the strongest tropical cyclones. Nature, 455, 92–95, doi:10.1038/nature07234.
Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585–605, doi:10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2.
Emanuel, K. A., 2010: Tropical cyclone activity downscaled from NOAA-CIRES reanalysis, 1908–1958. J. Adv. Model. Earth Syst., 2, 1, doi:10.3894/JAMES.2010.2.1.
Emanuel, K. A., and D. S. Nolan, 2004: Tropical cyclone activity and global climate. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 10A.2. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75463.htm.]
Frank, W. M., 1987: Tropical cyclone formation. A Global View of Tropical Cyclones, R. L. Elsberry, Ed., Office of Naval Research, 53–90.
Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669–700, doi:10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.
Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 155–218.
Gray, W. M., 1984: Atlantic seasonal hurricane frequency. Part I: El Niño and 30 mb quasi-biennial oscillation influences. Mon. Wea. Rev., 112, 1649–1668, doi:10.1175/1520-0493(1984)112<1649:ASHFPI>2.0.CO;2.
Haywood, J. M., A. Jones, N. Bellouin, and D. Stephenson, 2013: Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nat. Climate Change, 3, 660–665, doi:10.1038/nclimate1857.
Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev., 109, 813–829, doi:10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.
Hurricane Research Division, 2015: Tropical cyclone FAQ: When is hurricane season? [Available online at http://www.aoml.noaa.gov/hrd/tcfaq/G1.html.]
Kerr, R. A., 2000: A North Atlantic climate pacemaker for the centuries. Science, 288, 1984–1985, doi:10.1126/science.288.5473.1984.
Klotzbach, P. J., 2010: On the Madden-Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282–293, doi:10.1175/2009JCLI2978.1.
Klotzbach, P. J., 2011a: The influence of El Niño–Southern Oscillation and the Atlantic multidecadal oscillation on Caribbean tropical cyclone activity. J. Climate, 24, 721–731, doi:10.1175/2010JCLI3705.1.
Klotzbach, P. J., 2011b: Forecasting October–November Caribbean hurricane days. J. Geophys. Res., 116, D18117, doi:10.1029/2011JD016146.
Koenker, R., and G. Bassett, 1978: Regression quantiles. Econometrica, 46, 33–50, doi:10.2307/1913643.
Koenker, R., and K. F. Hallock, 2001: Quantile regression. J. Econ. Perspect., 15, 143–156, doi:10.1257/jep.15.4.143.
Kossin, J. P., 2008: Is the North Atlantic hurricane season getting longer? Geophys. Res. Lett., 35, L23705, doi:10.1029/2008GL036012.
Kossin, J. P., and D. J. Vimont, 2007: A more general framework for understanding Atlantic hurricane variability and trends. Bull. Amer. Meteor. Soc., 88, 1767–1781, doi:10.1175/BAMS-88-11-1767.
Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Atlantic hurricane tracks. J. Climate, 23, 3057–3076, doi:10.1175/2010JCLI3497.1.
Landsea, C. W., 2007: Counting Atlantic tropical cyclones back to 1900. Eos, Trans. Amer. Geophys. Union, 88, 197–203, doi:10.1029/2007EO180001.
Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 3576–3591, doi:10.1175/MWR-D-12-00254.1.
L’Heureux, M. L., D. C. Collins, and Z.-Z. Hu, 2013: Linear trends in sea surface temperature of the tropical Pacific Ocean and implications for the El Niño-Southern Oscillation. Climate Dyn., 40, 1223–1236, doi:10.1007/s00382-012-1331-2.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1079, doi:10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
Mauk, R. G., and J. S. Hobgood, 2012: Tropical cyclone formation in environments with cool SST and high wind shear over the northeastern Atlantic Ocean. Wea. Forecasting, 27, 1433–1448, doi:10.1175/WAF-D-11-00048.1.
McGauley, M. G., and D. S. Nolan, 2011: Measuring environmental favorability for tropical cyclogenesis by statistical analysis of threshold parameters. J. Climate, 24, 5968–5997, doi:10.1175/2011JCLI4176.1.
McTaggart-Cowan, R., G. D. Deane, L. F. Bosart, C. A. Davis, and T. J. Galarneau Jr., 2008: Climatology of tropical cyclogenesis in the North Atlantic (1948–2004). Mon. Wea. Rev., 136, 1284–1304, doi:10.1175/2007MWR2245.1.
McTaggart-Cowan, R., T. J. Galarneau Jr., L. F. Bosart, R. W. Moore, and O. Martius, 2013: A global climatology of baroclinically influenced tropical cyclogenesis. Mon. Wea. Rev., 141, 1963–1989, doi:10.1175/MWR-D-12-00186.1.
Moura, A. D., and J. Shukla, 1981: On the dynamics of droughts in northeast Brazil: Observations, theory, and numerical experiments with a general circulation model. J. Atmos. Sci., 38, 2653–2675, doi:10.1175/1520-0469(1981)038<2653:OTDODI>2.0.CO;2.
Neumann, C. J., B. R. Jarvinen, C. J. McAdie, and G. R. Hammer, 1999: Tropical cyclones of the North Atlantic Ocean, 1871-1998. NOAA Tech. Rep., 206 pp.
Schreck, C. J., 2015: Kelvin waves and tropical cyclogenesis: A global survey. Mon. Wea. Rev., 143, 3996–4011, doi:10.1175/MWR-D-15-0111.1.
Schumacher, A. B., M. DeMaria, and J. A. Knaff, 2009: Objective estimation of the 24-h probability of tropical cyclone formation. Wea. Forecasting, 24, 456–471, doi:10.1175/2008WAF2007109.1.
Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate, 21, 2283–2296, doi:10.1175/2007JCLI2100.1.
Tang, B., and K. Emanuel, 2012: A ventilation index for tropical cyclones. Bull. Amer. Meteor. Soc., 93, 1901–1912, doi:10.1175/BAMS-D-11-00165.1.
Tang, B., and S. J. Camargo, 2014: Environmental control of tropical cyclones in CMIP5: A ventilation perspective. J. Adv. Model. Earth Syst., 6, 115–128, doi:10.1002/2013MS000294.
Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1297–1300, doi:10.1029/98GL00950.
Tippett, M. K., S. J. Camargo, and A. H. Sobel, 2011: A Poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis. J. Climate, 24, 2335–2357, doi:10.1175/2010JCLI3811.1.
Torn, R. D., and G. J. Hakim, 2008: Ensemble-based sensitivity analysis. Mon. Wea. Rev., 136, 663–677, doi:10.1175/2007MWR2132.1.
Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/qj.04.176.
Villarini, G., G. A. Vecchi, and J. A. Smith, 2010: Modeling the dependence of tropical storm counts in the North Atlantic basin on climate indices. Mon. Wea. Rev., 138, 2681–2705, doi:10.1175/2010MWR3315.1.
Villarini, G., G. A. Vecchi, and J. A. Smith, 2012: U.S. landfalling and North Atlantic hurricanes: Statistical modeling of their frequencies and ratios. Mon. Wea. Rev., 140, 44–65, doi:10.1175/MWR-D-11-00063.1.
Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784–812, doi:10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.
