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3046 JOURNAL OF CLIMATE VOLUME 26

Tropical Cloud Cluster Climatology, Variability, and Genesis Productivity

1 # @ CHRISTOPHER C. HENNON,* PHILIPPE P. PAPIN, CHRISTOPHER M. ZARZAR, JEREMY R. MICHAEL, J. ADAM CAUDILL,* CARSON R. DOUGLAS,* WESLEY C. GROETSEMA,* JOHN H. LACY,* & ZACHERY D. MAYE, JUSTIN L. REID,* MARK A. SCALES,* MELISSA D. TALLEY,* 1 AND CHARLES N. HELMS * University of North Carolina at Asheville, Asheville, North Carolina 1 University at Albany, State University of New York, Albany, New York # East Carolina University, Greenville, North Carolina @ National Oceanic and Atmospheric Administration/National Weather Service, Elko, Nevada & Air Force Combat Climatology Center, Asheville, North Carolina

(Manuscript received 13 June 2012, in final form 21 October 2012)

ABSTRACT

Tropical cloud clusters (TCCs) are traditionally defined as synoptic-scale areas of deep convection and associated cirrus outflow. They play a critical role in the energy balance of the tropics, releasing large amounts of latent heat high in the troposphere. If conditions are favorable, TCCs can develop into tropical (TCs), which put coastal populations at risk. Previous work, usually connected with large field campaigns, has investigated TCC characteristics over small areas and time periods. Recently, developments in satellite reanalysis and global best track assimilation have allowed for the creation of a much more extensive database of TCC activity. The authors use the TCC database to produce an extensive global analysis of TCCs, focusing on TCC climatology, variability, and genesis productivity (GP) over a 28-yr period (1982–2009). While global TCC frequency was fairly consistent over the time period, with relatively small interannual variability and no noticeable trend, regional analyses show a high degree of interannual variability with clear trends in some regions. Approximately 1600 TCCs develop around the globe each year; about 6.4% of those develop into TCs. The eastern North Pacific Ocean (EPAC) basin produces the highest number of TCCs (per unit area) in a given year, but the western North Pacific Ocean (WPAC) basin has the highest GP (;12%). Annual TCC frequency in some basins exhibits a strong correlation to sea surface temperatures (SSTs), particularly in the EPAC, North Atlantic Ocean, and WPAC. However, GP is not as sensitive to SST, supporting the hypothesis that the tropical process is most sensitive to atmospheric dynamical considerations such as vertical wind shear and large-scale vorticity.

1. Introduction appearance on infrared (IR) satellite imagery is normally quite obvious, since the expansive cirrus shield TCCs A tropical cloud cluster (TCC) has been defined as produce exhibits very cold cloud tops relative to the large, concentrated convection, similar to a mesoscale warmer low-level clouds and the ocean beneath (Mapes convective system (MCS), with a diameter of 250–2500 km and Houze 1993). (including cirrus outflow), lasting at least 6–24 h, and TCCs are important components of the tropical sys- forming over a tropical oceanic basin (e.g., Maddox 1981). tem and can engender thermal and dynamic changes at TCCs have also been traditionally viewed (as in this study) local and regional scales. Locally, TCCs transport sig- as synoptic-scale systems composed of multiple MCSs, nificant amounts of energy from the ocean and lower connected by a large cirrus shield, and lasting for a period sections of the atmosphere into the upper troposphere of days (e.g., McBride and Zehr 1981; Lee 1989). Their (Houze 1982). They also create upper-level clouds, which can contribute to upper-level heating through the absorption of longwave radiation, and reflect Corresponding author address: Christopher C. Hennon, Uni- versity of North Carolina at Asheville, 1 University Heights, CPO shortwave radiation back toward space. At regional #2450, Asheville, NC 28804. scales, Hartmann et al. (1984) showed that TCCs in- E-mail: [email protected] fluence the heating profile and dynamics of the Walker

DOI: 10.1175/JCLI-D-12-00387.1

Ó 2013 American Meteorological Society 15 MAY 2013 H E N N O N E T A L . 3047 circulation. Of course, TCCs can also develop into data, they successfully tracked TCCs that formed during tropical cyclones (TCs), which can have significant the Winter Monsoon Experiment (WMONEX). A more physical and societal impacts. recent tracker was developed by Kerns and Zipser TCCs form in a number of different environments (2009). One useful feature of their method is the merger throughout the world. In the North Atlantic Ocean of convection with European Centre for Medium-Range (NATL), easterly waves moving off the African coast Weather Forecasts (ECMWF) low-level (925–850 hPa) often spawn TCCs. Chen et al. (2008) showed that ap- and midlevel (700–600 hPa) vorticity maxima. proximately 60% of all tropical cyclones that form in the The development of a new global, gridded IR dataset Atlantic basin develop from these easterly waves. While [Gridded Satellite (GridSat; Knapp et al. 2011)] and easterly waves can also support TCCs in the Pacific a global tropical best track dataset [In- Ocean basin, the monsoonal trough is also a productive ternational Best Track Archive for Climate Stewardship area for producing TCCs (Lee 1989) as well as TCs. (IBTrACS; Knapp et al. 2010)] led to a new global TCC Chen et al. (2008) showed that 71% of TCs in the tracking algorithm (Hennon et al. 2011). The algorithm western North Pacific Ocean (WPAC) basin are pro- adopted a similar TCC definition used in Hennon and duced in the monsoonal trough. In the North Indian Hobgood (2003) alongside an added IR brightness Ocean (NIND), monsoonal circulations also contribute temperature T(b) threshold tailored for each basin. to the formation of TCCs. However, it is only during the This IR brightness temperature is similar to the cold IR transitional seasons of spring and autumn that these satellite temperature thresholds used in previous studies features are located far enough south into the Indian (Williams and Houze 1987; Mapes and Houze 1993). A Ocean to generate TCCs that have the potential to be- dataset was created from this single channel dataset come tropical cyclones (Lee 1989). Other atmospheric that cataloged TCCs from 1982 to 2009; it contains in- events, such as mixed Rossby–gravity waves, equatorial formation regarding a TCC’s size, location, convective Rossby waves, Kelvin waves, and the Madden–Julian intensity, cloud top height, and development status. oscillation (MJO) can help to enhance convection This paper will present an analysis of the Hennon et al. within monsoonal troughs near the intertropical con- (2011) TCC dataset. There have been numerous studies vergence zone (ITCZ; Frank and Roundy 2006), leading that have investigated the nature of satellite-detected to TCC development. Other relevant work includes tropical convection from a global perspective (e.g., Maloney and Hartmann (2000), who showed how con- Mohr and Zipser 1996; Nesbitt et al. 2000). This research vective activity in the eastern North Pacific Ocean goes beyond these earlier efforts by substantially in- (EPAC) basin is influenced by the MJO phase there, creasing the temporal scale of the survey (28 years versus and Leroy and Wheeler (2008), who demonstrated the one season), expanding the scope by investigating in- importance of the MJO phase in predicting tropical cy- terannual and interdecadal variability, and investigating clogenesis in the Southern Hemisphere. the global and regional tropical cyclogenesis production. The nature of individual TCCs has been well docu- To our knowledge, this is the most comprehensive mented. In general, TCCs tend to exhibit a strong global survey of TCCs to date. diurnal cycle, with a peak in deep convection before The following section will briefly describe the TCC dawn. They have very cold cloud top temperatures; dataset, including how a TCC is defined. The results previous studies (e.g., Williams and Houze 1987; Mapes begin in section 3, which presents a global perspective of and Houze 1993; Hennon et al. 2011) have used tem- TCC activity. Section 4 focuses on regional TCC char- perature thresholds of 205–235 K to identify TCCs. acteristics in several ocean basins. A summary and ideas Similar to TCs, TCCs tend to move in the direction of for future work are presented in section 5. the larger-scale planetary circulation. Past studies have also viewed TCCs from a mesoscale perspective (e.g., 2. Cloud cluster dataset Mapes and Houze 1993; Chen et al. 1996; Kerns and Chen 2013). Although they focus on the smaller-scale We use the global TCC data, version v01r01, described convective features of TCCs and are not climatologies in Hennon et al. (2011) for this work. TCCs were objec- per se, these papers are complimentary to this work and tively identified in GridSat-calibrated IR data (8-km extend our understanding of the nature of TCCs. spatial resolution; see Knapp et al. 2011) based on the A number of automated tracking algorithms have characteristics of atmospheric convection, including been developed to produce TCC datasets. Williams and size, shape, and persistence. Table 1 shows the IR Houze (1987) developed a tracker to investigate TCCs brightness temperature thresholds used in each ocean off the coast of Borneo. Using Geostationary Meteoro- basin by the algorithm. ‘‘Developing’’ TCCs, or those logical Satellite-1 (GMS-1) IR brightness temperature clusters that formed into a , are identified 3048 JOURNAL OF CLIMATE VOLUME 26

TABLE 1. Brightness temperature T(b) thresholds applied by the 2011 for details). To provide a complete picture on global algorithm. TCC activity, all results presented here that reference Basin (Acronym) Basin (Expansion) Threshold (K) developing TCCs (unless noted otherwise) were identi- fied from the IBTrACS data. NATL North Atlantic Ocean 224 SATL South Atlantic Ocean 227 Other important considerations of the TCC dataset EPAC Eastern North Pacific Ocean 228 must be noted. There are no TCCs initiated poleward of SPAC South Pacific Ocean 221 308 latitude. The algorithm used to create the data WPAC Western North Pacific Ocean 219 eliminated those cases in order to filter out possible NIND North Indian Ocean 218 midlatitude systems (TCCs that move poleward of 308 SIND South Indian Ocean 221 are tracked). A survey of the data showed that about 1.3% (1.2%) TCCs in the Northern (Southern) Hemi- by comparing their locations with the IBTrACS global sphere developed into tropical cyclones poleward of 308 best track data (Knapp et al. 2010). TCCs are tracked latitude, a small contribution to the ;25% missed through time by an automated tracker that searches developers mentioned above. back in the 3-hourly GridSat data for previous TCC Second, there is a small portion of the EPAC (bounded locations. Numerous variables are calculated for each by 808W to the west and the Isthmus of Panama to the ‘‘fix’’ and recorded in the dataset, including T(b). The north) that the algorithm incorrectly labels as TCCs of data contain 28 years (1982–2009) of global TCC activity, NATL rather than EPAC. In some years, there could derived from numerous geostationary satellites. As with be as many as 10–20 (3%–4%) clusters misclassified. the GridSat data, the TCC data are provided in 3-hourly Thus, the NATL and EPAC TCC frequency should be time increments. Mature TCs are removed from the TCC viewed with some caution, though we do not believe this dataset. Hennon et al. (2011) contains technical details of error significantly changes the conclusions presented the tracking algorithm, including thresholds, searching here. routines, and the application of IBTrACS to determine Sea surface temperature (SST) data are used to identify development. relationships between the ocean and TCC activity. The Two important shortcomings of the TCC data have National Oceanic and Atmospheric Administration made it necessary to adjust the way in which the data are (NOAA) Optimum Interpolation SST Analysis, version used and presented. First, the TCC algorithm does not 2 (OIv2), dataset was selected for use (Reynolds et al. detect all developing clusters (those that form into 2002). The OIv2 data are created from a blend of in situ a tropical cyclone and are tracked by a forecast center). and satellite data and are mapped to a 18 grid. The About 25% of TCs listed in IBTrACS are not tracked monthly data were accessed from NOAA Operational prior to genesis. Hennon et al. (2011) speculate on the Model Archive Distribution System (NOMADS) de- reasons for these ‘‘missed’’ , which include TCs velopment group website. that form close to land, develop rapidly (such that they Finally, we define genesis productivity GP as the did not qualify as a ‘‘cluster’’ before genesis), or are percentage of TCCs that develop into a TC. The GP is missed from incomplete global satellite coverage prior calculated as to 1998. Prior to July 1998, there was a large north–south   swath spanning 558–858E longitude over the Indian Ocean TC(IBTrACS) GP 5 100%, (1) that was not covered by any geostationary satellite. This TCC(total) was resolved when Meteosat-5 was moved to a longitude of 638E. On average, approximately 170–200 TCCs where TC(IBTrACS) is the number of TCs in the per year were missed in the Indian Ocean prior to the IBTrACS dataset for the period and area of interest and satellite’s repositioning. Therefore, many of the results TCC(total) is the number of TCCs for the same pa- presented here are partitioned into ‘‘incomplete’’ rameters. If a TC was never identified as a TCC (i.e., if (1982–97) and ‘‘complete’’ (1998–2009) coverage eras. the cluster did not satisfy the tracking requirements at We also note that there are some inconsistencies that any time prior to being declared a TC), it is included in have been found between reporting agencies that make the numerator but not the denominator of Eq. (1). This up the IBTrACS record (Schreck et al. 2012) when occurred for about 25% of all developing cases and a is tracked by more than one forecast office, but may artificially inflate the GP calculations by ,1%, this happens infrequently and the differences are small. depending on the area and time period. However, it should be noted that if there is more than Figure 1 shows how the basin boundaries are defined one IBTrACS track for a storm, they were averaged in the TCC data (light shaded areas); they were slightly together to produce a single track (see Hennon et al. modified from the boundaries used in the IBTrACS 15 MAY 2013 H E N N O N E T A L . 3049

FIG. 1. Basin boundaries used for this study. The light shaded areas correspond to the areas used in the identification of the TCCs. The dark shaded areas are the basin subsets used to determine relationships between sea surface temperature and TCC frequency. TCCs over land areas are not tracked and are not considered in this study. data. The SST data presented later are calculated from evident that the highest concentrations of TCCs occur in a subset of each basin (dark shaded areas); this was done the permanent convergent zones [ITCZ and the South to make it more convenient to work with the SST data. Pacific convergence zone (SPCZ)] and in the eastern All SST domains, with the exception of the NATL, ex- NIND in association with the Asian monsoon circula- tend over the vast majority of the basins. However, since tion. Minima are most clearly seen in the southeast Pa- there is a disconnect between the SST and TCC domains, cific and southern Atlantic, where cool SSTs and the correlations presented here should be interpreted a relatively stable atmosphere limit convective activity. with some caution.1 Recall that TCCs that develop poleward of 308 latitude are excluded from the data, although a few do move into those regions after their formation. 3. Global perspective of tropical cloud clusters Outgoing longwave radiation (OLR) has traditionally We now present the analysis in two sections, global been used as a proxy for convective activity on many and basin characteristics. The global section presents time scales. Although not used for this particular study, an overview of TCC frequency, GP, lifespan, and in- Fig. 2b shows the average OLR over the same 1997–2008 tensity. This section is followed by a more focused period for comparison purposes. Note that the relative analysis on interesting patterns of TCC activity in minima in OLR generally correspond to active areas of selected individual basins, specifically the NATL, TCC activity, specifically in the ITCZ, SPCZ, eastern EPAC, WPAC, and NIND. Other basins are not Atlantic, and eastern NIND regions. further investigated here because of a dearth of activity Machado and Rossow (1993) studied land and ocean [South Atlantic Ocean (SATL)] or as a matter of TCCs (defined using a different IR threshold temperature brevity [South Pacific Ocean (SPAC) and South Indian than in this dataset) over an 8-month period (January– Ocean (SIND)]. February and July–August 1987–88) and found a similar spatial distribution pattern. In this study, we consider a. Spatial distribution oceanic TCCs only, masking out any TCCs that form or Figure 2a (from Hennon et al. 2011) shows the density move over land, including what Machado and Rossow of global TCC activity from 1998 to 2007. It is clearly showed as especially strong areas of TCC activity over the South American and African continents, particu- larly in the austral summer months. 1 We also recognize that the basins as defined here do not nec- essarily correspond to traditional boundaries used by tropical b. Annual frequency cyclone forecast centers. For example, the National Hurricane Center (NHC) area of responsibility for the EPAC extends west- Numbers of TCCs were counted for each calendar ward to 1408W; the EPAC boundary in this study is 1808 (north- year and are shown in Fig. 3. The GP, shown as a solid central Pacific). The north-central Pacific is climatologically very line in each figure, varies from year to year but has an different than the NHC EPAC, with far fewer genesis cases. Therefore, the GP data presented here is lower than one might average value of 7.7% (6.4%) for the incomplete normally expect for the NHC EPAC, which is traditionally much (complete) data period. Certain basins are more favor- more active in the eastern half. able for TC development than others; GP values range 3050 JOURNAL OF CLIMATE VOLUME 26

FIG. 2. Comparison of TCC density and OLR. (a) The number of TCCs per year (for the 10-yr period of 1998–2007) that tracked within 55 km of a grid point (from Hennon et al. 2011). 2 (b) The OLR averaged over the same time period (W m 2). from ;12% in the WPAC to ;4% in the South Pacific d. Lifespan Ocean (the SATL basin had only one developer in the record). In each period, there appears to be little if any The data show that approximately 7% of TCCs are trend in the global numbers of TCCs or in the proportion able to transition into a TC and suggest that there is that develop into TCs. For the full coverage record a finite period of time whereby a TCC has the op- (Fig. 3, 1998–2009), an average of 1604 TCCs formed portunitytomakethistransition.Figure4shows each year, with a standard deviation of 40.3. Tables 2 and the distribution of the lifetimes of both developing 3 show the global TCC counts for the incomplete and (Fig. 4a) and nondeveloping (Fig. 4b) TCCs. The dark full area coverage eras. line shows the cumulative distribution of the number of TCCs for each time period. Note that the de- c. Genesis productivity veloping cases in this instance are from the algorithm, The consistency of TCC numbers and TC genesis not IBTrACS. cases produces an arguably stable GP each year. In the For the developing cases (Fig. 4a), about 60% of full satellite coverage period (Table 3), the GP ranged TCCs form into TCs within 24 h of being identified. from a low of 5.6% (2007) to a high of 7.5% (2005), with Furthermore, 95% of TCCs develop within 60 h of the an average development rate of 6.4% (or about 100 first identification: only 5% of TCCs develop into TCs developing versus 1600 nondeveloping TCCs per year). after 60 h. These results are reflected in the distribution There is a small but perceivable downward trend in GP of the nondeveloping TCCs (Fig. 4b). Just less than 95% 2 values from 1998 to 2009 (;0.05% yr 1); a longer data of all nondevelopers persist for 60 h or less. Note that record is required to say with any confidence if this is TCCs as defined in the dataset must persist for at least significant. Noticeably higher GP values are seen in the 24 h before being recorded; therefore, there are zero incomplete coverage era (Fig. 3, Table 2) since 188 nondeveloping TCCs that had a lifetime of ,24 h. TCCs per year, on average, were missed in the Indian However, developing TCCs (Fig. 4a) are allowed to Ocean prior to 1998. form less than 24 h prior to genesis. 15 MAY 2013 H E N N O N E T A L . 3051

FIG. 3. Time series of global TCC activity for the incomplete coverage era (left of vertical line) and the complete coverage era (right). White bars are the total number of TCCs; black bars are the number of developing TCCs. The black line indicates the genesis productivity.

These preliminary results on TCC lifespan, though pressure are commonly used. These variables are not exploratory, suggest that there may be critical genesis available for TCCs, so another way of quantifying time thresholds. Further investigation in this area is intensity is desired. For this study, we define intensity warranted but is beyond the scope of the current as the minimum 3-hourly T(b) of the cluster; each study. 3-hourly minimum is then averaged over its entire non-TC lifespan. The minimum T(b)istheTCCpixel e. Intensity with the lowest cloud top temperature. Note that our Intensity is more easily defined for TCs, where the intensity definition is probably more indicative of a maximum sustained wind speed and minimum sea level persistent upward mass flux rather than other harder- to-measure indicators like updraft vertical velocity or TABLE 2. Global annual totals of nondeveloping TCCs, tropical high radar reflectivity at high altitudes. Other poten- cyclones, and the genesis productivity for the incomplete coverage tial intensity data, such as Tropical Rainfall Measur- era (1982–97). ing Mission (TRMM; Simpson et al. 1988) reflectivity, Genesis Nondeveloping Tropical cyclones productivity Year TCCs (IBTrACS) (%) TABLE 3. As in Table 2, but for the complete coverage era 1982 1416 107 7.6 (1998–2009). 1983 1382 93 6.7 Nondeveloping Tropical cyclones Genesis 1984 1448 115 7.9 Year TCCs (IBTrACS) productivity (%) 1985 1453 119 8.2 1986 1418 109 7.7 1998 1537 107 7.0 1987 1502 102 6.8 1999 1674 110 6.6 1988 1342 90 6.7 2000 1608 109 6.8 1989 1273 128 10.1 2001 1728 99 5.7 1990 1368 120 8.8 2002 1639 95 5.8 1991 1356 96 7.1 2003 1613 106 6.6 1992 1437 127 8.8 2004 1572 98 6.2 1993 1505 102 6.8 2005 1565 117 7.5 1994 1473 112 7.6 2006 1587 95 6.0 1995 1402 98 7.0 2007 1546 87 5.6 1996 1421 116 8.2 2008 1596 101 6.3 1997 1464 112 7.7 2009 1594 97 6.1 Total 22 660 1746 7.7 Total 17 665 1124 6.4 3052 JOURNAL OF CLIMATE VOLUME 26

FIG. 4. Cumulative frequency distribution of global TCCs for (a) developing and b) nondeveloping cases. do not provide enough coverage for a meaningful produce higher (colder) cloud tops; the difference is analysis. about 2 K. Figure 5 shows the minimum cloud top temperature The nonparametric Mann–Whitney test (Mann and for nondeveloping and developing TCCs (from the Whitney 1947), commonly used to determine if differ- algorithm, not IBTrACS). Figure 5a (Fig. 5b) covers the ences in medians between two nonnormally distributed, time period without (with) full coverage of the Indian independent datasets are significant, was performed for Ocean. The linear trend (shown by the dotted line) in all of the basins. Tests were performed for both the the early record (Fig. 5a) shows virtually no slope, difference between the mean T(b) and the minimum indicating that the global average TCC intensity did not T(b) of developing versus nondeveloping TCCs. Results change significantly from 1982 to 1997. There is a notice- showed there were statistically significant differences (at able separation between the intensity of nondeveloping the 95% confidence level) in all basins between developers versus developing TCCs. In general, developing TCCs and nondevelopers in the minimum T(b). For the mean 15 MAY 2013 H E N N O N E T A L . 3053

FIG. 5. Infrared minimum cloud top brightness temperature [T(b); K] time series for non- developing (solid lines) and developing (long dashed lines) TCCs for the (a) incomplete and (b) complete coverage era. The linear trends are shown as short dashed lines.

T(b), all basins showed statistically significant differences density, genesis productivity, relationship to SST, and between developing and nondeveloping TCCs at the 95% seasonal/interannual variability for TCCs for the NATL, confidence level, except for the SIND basin. EPAC, WPAC, and NIND basins. Analyses were done The differences between developing and nondevel- for all other basins, but we focus on these four because of oping minimum T(b) remain in the full coverage period space constraints. (Fig. 5b), but now downward linear trends are detected. a. TCC density A nonparametric Mann–Kendall trend significance test produced p values of 0.021 (0.545) for the nondevel- The total number of TCCs generated by each basin in oping (developing) time series. Thus, the cooling trend a year were counted and normalized by the basin area. in the nondeveloping TCCs is statistically significant at Fig. 6 shows the TCC density for the four basins. In the 95% confidence level, but not significant for the comparison to all basins, the three most prolific in terms 2 developing cases. It is unknown if the cooling trends are of average density are the EPAC (11.47 3 10 6 TCC 2 2 physical or an artifact from the inclusion of the missing km 2 yr 1), WPAC (10.95), and NATL (9.02). TCC Indian Ocean satellite data (or perhaps a mixture of both). generation in the EPAC is driven primarily by the A more focused examination of the NIND TCCs will be equatorial trough, with enhancement likely from east- presented within the following section. erly waves (Serra et al. 2008), the Madden–Julian os- cillation (Maloney and Hartmann 2000), and Kelvin waves (Schreck and Molinari 2011). Some mechanisms 4. Basin comparisons in the WPAC for creating TCs (and, assumedly, most TCCs form throughout the tropics in all ocean basins, TCCs) were discussed in Ritchie and Holland (1999); though there are differences in atmospheric and oceanic the identified patterns include the monsoon shear line, forcing mechanisms in each. This section will present the monsoon confluence region, monsoon gyre, and easterly 3054 JOURNAL OF CLIMATE VOLUME 26

FIG. 6. Time series of TCC density stratified by basin. waves. NATL TCC generation is related to the ITCZ for it to happen (e.g., Gray 1968; Simpson et al. 1997; (these clusters typically do not develop into TCs), east- DeMaria et al. 2001), including warm SSTs, deep co- erly waves (e.g., Thorncroft and Hodges 2001), and in- lumnar moisture, and favorable upper-level winds. stabilities jettisoned from midlatitude systems. Table 4 Table 4 shows a comparison of the genesis pro- shows the annual averages for the rest of the basins as ductivity for individual ocean basins. The WPAC is the 2 2 2 well as the global average (7.69 3 10 6 TCC km 2 yr 1). most productive basin in the world with just over 12% of Although there is no discernible trend in TCC activity TCCs developing into TCs. The SIND basin is also globally (Fig. 3), there are some notable trends in in- productive (;8.5%), although TCCs are less numerous dividual basins. For example, TCC activity in the NATL there. TCCs in the EPAC and the NATL develop, on has increased by about 25% over the 28-yr data period. average, 6% of the time. The Atlantic Multidecadal Oscillation (AMO), a mode The annual variability of genesis productivity is shown of variability that describes long-term changes in the in Fig. 7. There is a higher (lower) degree of variance in Atlantic SST, has been positively connected to Atlantic the data in most basins prior to (since) 1999. Also note Ocean TC activity (Goldenberg et al. 2001) and is the sudden drop in productivity in the WPAC around a plausible explanation for the increase in TCC activity. 1999; this is a result of a decrease in genesis events A similar increase in TCC frequency over the period (;14% drop between the 1982–97 and the 1998–2009 of record is seen in the WPAC (;20%). Whereas the periods; see Cooper and Falvey 2010) and the increase in NATL TCC numbers appear to gradually increase be- TCC activity noted in the previous section. ginning in the mid-1990s, the WPAC exhibit two distinct There are no obvious differences in NATL variability periods of activity: less active from 1982 to 1998 and more in the time series. The 1989, 1995, and 2005 NATL active from 1999 to 2009. As we will see in the following seasons stand out as very favorable ones for de- section, this dramatic increase in TCC activity did not velopment, with productivity values around 10%. Those lead to a corresponding increase in TC development. Other basins, with the exception of the Indian Ocean basins, show little or no trend. There is little doubt that TABLE 4. Average TCC density and genesis productivity by increases in TCCs for the Indian Ocean are at least ocean basin (1982–2009). Values for the Indian Ocean basin (i.e., North Indian and South Indian regions) are only for the 1998–2009 partially caused by the satellite coverage discontinuity in period. The boldface indicates a higher than global value in Table 2 the late 1990s. However, there is evidence that part of because the 1982–97 period is included. this increase arises from natural interdecadal variability. Density Genesis This will be discussed in section 4d. 2 2 2 Region (TCC 3 10 6 km 2 yr 1) productivity (%) b. Genesis productivity North Atlantic 9.02 6.0 The conditions that allow for TCC generation are South Atlantic 2.57 0.1 Western Pacific 10.95 12.4 usually not sufficient to support the development of Eastern Pacific 11.47 6.0 TCs. Globally, only 6.4% of TCCs develop into TCs South Pacific 6.59 3.8 (Table 3). Although skillfully predicting tropical cyclo- North Indian 10.51 7.0 genesis remains elusive, there has been a plethora of re- South Indian 5.93 8.5 search that established the necessary conditions required Global 7.69 7.1 15 MAY 2013 H E N N O N E T A L . 3055

FIG. 7. TCC genesis productivity (number of developing TCCs/total number of TCCs).

seasons produced 15, 20, and 28 sub-308 latitude tropical coefficient R values generally between 20.3 and 0.3 depressions, well above the climatological mean of (variance explained R2 between 20.09 and 0.09). How- about 11 named storms in a season. ever, there were stronger relationships on an annual basis in the NATL (R2 5 0.26), WPAC (R2 5 0.35), and c. Relationship to sea surface temperature EPAC (R2 5 0.46), all significant at the 95% confidence level. The data suggest a measureable (and not un- Although past research has shown that warm SSTs are expected) connection to ENSO variability in the Pacific a necessary ingredient for deep tropical convection (e.g., basins; therefore, we investigated this relationship further. Zhang 1993), it is thought that the adjustment of the tropical moist adiabatic profile makes convective in- d. Seasonal and interannual variability tensity insensitive to SST changes. Tompkins (2001) nicely summarizes recent developments in the connec- In all four of the basins more closely examined here, as tion between SST and tropical convection. Graham and expected, the seasonal variability is closely related to Barnett (1987) established a critical SST value of 27.58C where deep tropical convection occurs, although they 2 note that convective intensity is insensitive to SST above TABLE 5. Variance explained R between the annual mean SST this value. Subsequent modeling work supported these for each region and the annual TCC frequency for that region. The observations. For example, Tompkins and Craig (1999) NIND basin is split into the Arabian Sea (west North Indian) and used a 3D cloud resolving model to investigate the Bay of Bengal (east North Indian), as shown in Fig. 1. All corre- lations are significant at the 95% confidence level, except for the response of convection to the SST in the absence of South Atlantic. large-scale flow. They found that convection was ‘‘very insensitive’’ to SST, except for an upward shift in alti- Region Variance explained R2 tude of the convection as the SST increases. Thus, if T(b) North Atlantic 0.26 is used to measure ‘‘intensity’’ of convection, there South Atlantic 0.03 should be a measureable relationship to SST, since West Pacific 0.35 higher altitude convection will yield lower T(b) values. East Pacific 0.46 South Pacific 0.03 We investigated the relationship of TCC frequency to East North Indian 20.07 SST by comparing annual averages of each by region West North Indian 20.02 (Table 5). Globally, there is no correlation between South Indian 0.09 TCCs and SST. The majority of basins also show a weak Global (1982–97) 0.00 connection, with Pearson’s product moment correlation Global (1998–2009) 0.00 3056 JOURNAL OF CLIMATE VOLUME 26

FIG. 8. Average number of TCCs each month for the (a) NATL and (b) EPAC basins (1982–2009). The dotted line is the monthly averaged SST over the same period. changes in the SST (although atmospheric dynamical shown in Ramage (1974). Curiously, the NATL contains considerations should not be neglected). Figs. 8 and 9 a dual peak in TCC frequency, with maxima in May show the average number of TCCs that form in a given and then October; this could be a manifestation of the month for the NATL, EPAC, WPAC, and NIND midsummer drought (MSD), a bimodal precipitation basins. The EPAC and WPAC resemble a lognormal pattern centered in the Caribbean Sea (e.g., Chen and distribution, with the TCC peak activity coinciding Taylor 2002). with the warmest SSTs in July–August. The NIND also In general, the data suggest that interannual vari- appears to be lognormal but experiences a much ability in several basins is closely controlled by atmo- sharper peak in June, coinciding with the normal spheric variability arising from large-scale climatic Southeast Asian monsoon onset in May. Note the oscillations such as ENSO. In the following subsections, cooling of the sea surface in the NIND during mid-to- we will present findings related to interannual and sea- late summer; this can be attributed to increased mixing sonal variability for the NATL, EPAC, WPAC, and from the strengthening monsoonal southwesterlies, as NIND basins. 15 MAY 2013 H E N N O N E T A L . 3057

FIG. 9. As in Fig. 8, but for the (a) WPAC and (b) NIND basins.

1) NORTH ATLANTIC be in place for TCs to form. These conditions are controlled by a large degree by large-scale interannual The annual frequency of developing and non- oscillations, and, in fact, seasonal predictions of TC developing TCCs for the NATL is shown in Fig. 10. We frequency are heavily based on them. For example, it is highlight two points derived from this figure. First, the well known that ENSO phase is a strong driver of total number of TCCs in a given year is related to the annual TC counts in the North Atlantic (e.g., Landsea number of developing TCCs in that year (R2 5 0.30), 2000). However, this relationship is not as robust with although there are exceptions (e.g., 1989, 1997, and regards to TCC frequency; we find that the variance 2006). Although more TCCs provide more opportuni- explained between the Southern Oscillation index ties for genesis, other critical factors such as favorable (SOI; a measure of ENSO phase) (Ropelewski and atmospheric dynamics (e.g., low wind shear and positive Jones 1987) and TCC numbers in the North Atlantic to low-level relative vorticity) and sufficient moisture must be R2 5 0.12. 3058 JOURNAL OF CLIMATE VOLUME 26

FIG. 10. Time series of the total number of TCCs (bars), number of developing TCCs (black bars), and genesis productivity (developing/total; black line) for the North Atlantic basin.

Second, there is a noticeable upward trend in the spatial reach of TCC formation can be seen during numbers of TCCs generated in the North Atlantic (but the active seasons, especially in the north-central North no such trend in the long-term ‘‘productivity’’ of the Atlantic near 458W. Also note the higher frequencies in basin). The basin has produced at least 200 TCCs each the active seasons along the easterly wave track from year since 1995 (with the exception of 1996). This result 408W northwestward to around 658W. is consistent with the hypothesized connection between The changes in SST discussed above may be driven in the AMO and North Atlantic tropical activity part by the variability of the North Atlantic Oscillation (Goldenberg et al. 2001). A shift in the AMO occurred (NAO). The NAO is an atmospheric pressure oscilla- during the mid-1990s, producing relatively favorable tion with focused on the conditions for TCC and TC development since. and high (Wallace and Gutzler 1981). The The variability of annual tropical cyclone numbers ‘‘positive’’ NAO phase corresponds to strong zonal has been associated with SST anomalies for some time. flow over much of the NATL, which would in turn lead A threshold value of 268C has long been used as a nec- to increased turbulent mixing, surface evaporation, essary requirement for TC formation (e.g., Palme´n 1948; and cooling of the ocean surface. Molinari and Mestas- Gray 1968); note that this value is lower than the 27.58C Nun˜ ez (2003) note that observational and modeling threshold for deep convection determined by Graham studies have revealed a more nuanced tripole pattern in and Barnett (1987). Subtropical storms with shallower NATL SST forced by the NAO. Although these effects convection do form over relatively cool SSTs, especially are most noticeable in the NATL winter, they can carry in the NATL. During the NATL hurricane season (early through the following NATL hurricane season. There is June to late November), the critical SST threshold nor- a notable negative correlation between TCC frequency mally bisects the basin in a northwest-to-southeast-oriented and the NAO index (R2 520.29). Some of the most axis, extending from about 148N, 208W northwestward active NATL seasons in recent years (in terms of TCCs) to 308N, 708W. During active TCC seasons, the 268C occurred during the negative phase of the NAO (2005 isotherm is farther north and east during the TC season and 2008), although it should be noted that the second- (not shown), allowing for more TCC development in highest year in terms of TCC frequency (265) occurred that part of the NATL basin. After examining active during a positive NAO phase in 1999. Clearly, further versus inactive TCC seasons, we found that annual dif- investigation on the interplay between the SOI, NAO, ferences in TCC numbers arise from variations in the SST, and TCC variability in the NATL is warranted. density of TCCs in other parts of the basin as well. 2) EAST PACIFIC Fig. 11 shows a measure of TCC density, composited for three ‘‘inactive’’ (1986–88) and three ‘‘active’’ (2003–05) As expected, TCC activity in the Pacific basin is much TCC seasons for the North Atlantic. The increase in the more sensitive to the ENSO cycle. During an El Nin˜ o 15 MAY 2013 H E N N O N E T A L . 3059

FIG. 11. The mean number of TCCs passing within 55 km of a grid point over a 3-yr period in the NATL for (a) inactive years (1986–88) and (b) active years (2003–05). The 1-, 5-, and 10-count isolines are drawn.

event, the warm SST anomalies in the eastern and Differences in interannual activity can be clearly seen central Pacific provide a favorable environment for deep in Fig. 13, a density map of TCC frequency for active and convective activity (R2 520.44 between TCCs and the inactive seasons. Unlike the NATL, there is no signifi- SOI for the EPAC). Furthermore, the low-level con- cant expansion of the areal extent of TCC convection. vergent arm of the Walker circulation shifts eastward, Rather, the ITCZ area produces nearly twice as many producing more favorable conditions for convection. TCCs during active seasons. Fig. 12 shows TCC and genesis productivity for the Compared to the NATL (R2 5 0.30), there is a much EPAC. Note that many of the peaks shown in the TCC weaker relationship (R2 5 0.08) in the EPAC between frequency correspond to El Nin˜ o seasons (e.g., 1982, the total number of TCCs and the number that develop 1987, early 1990s, 1997, and 2002). Conversely, the in a given year, suggesting that tropical cyclogenesis in quietest seasons (1988, 1989, and 1999) occurred during the EPAC is more dependent on regional dynamical strong La Nin˜ a events. Note that the 1989 season was considerations such as vertical wind shear and upper- very favorable for genesis despite the low numbers of level divergence; the 1989 season is a prime example of genesis candidates. this. There has been recent work connecting the strength 3060 JOURNAL OF CLIMATE VOLUME 26

FIG. 12. As in Fig. 10, but for the EPAC basin. and phase of the Madden–Julian oscillation to EPAC summer precipitation over the main TCC genesis region tropical cyclogenesis (e.g., Maloney and Hartmann in the WPAC from 1989 to 2008, with a notable upward 2000). Also, Camargo et al. (2007) showed that vertical shift in the late 1990s. Wang et al. (2012) attribute the wind shear was the largest contributor to ENSO-related increasing precipitation intensity to enhanced trade genesis anomalies in the EPAC. winds forced by significant trends of eastern (western) Pacific SST cooling (warming). The trades advect 3) WEST PACIFIC moisture and increase convergence in the WPAC. They attribute the trends to natural interdecadal variability in Interannual variability of TCC frequency in the the Pacific; this is certainly an interesting area for further WPAC is also controlled by ENSO variability (R2 5 0.45 investigation. between WPAC TCCs and the SOI). Fig. 14 is a time The correlation of annual TCC frequency and SST in series of TCC activity in the WPAC. TCC minima are the WPAC arises from adjustments from the ENSO clearly seen in the same El Nin˜ o years that maxima were cycle. Fig. 15 shows the TCC frequency for three active observed in the EPAC (1982, 1987, 1992, and 1997). We years (1999, 2001, and 2009) and three inactive years attribute this not to any significant changes in the SSTs (1982, 1987, and 1992). All three inactive (active) years of the region (the warm pool remains well above any correspond to the El Nin˜ o (La Nin˜ a) phase of the ENSO critical SST threshold) but to a general shift of the large- cycle. Fig. 15 clearly shows an eastward shift of TCC scale, low-level convergent region of the Walker circu- frequency, corresponding to the eastward shift of warm lation from the WPAC toward the central Pacific during SSTs and Walker circulation. Note the decrease in fre- El Nin˜ o events (e.g., Rasmusson and Wallace 1983). quency in the western portions of the basin, which is also As discussed previously, there are indications of a de- consistent with the Walker circulation shift. Although cadal time scale shift in the TCC frequency beginning in SSTs remain very high in this area, broad low-level 1999. From 1982 to 1998, the WPAC produced an annual convergence will be weaker during a warm event. average of about 252 TCCs; the mean increased more than 20% in the following decade, producing about 308 4) NORTH INDIAN TCCs annually. There was not, however, a corresponding increase in tropical cyclogenesis events (in fact, WPAC TCC activity in the North Indian basin is primarily 2 TCs were ;14% yr 1 less frequent on average from 1999 controlled by the Asian monsoon circulation, with to 2009). Thus, the genesis productivity dropped sharply a distinct frequency maximum during the summer in 1999 and remained low through 2009. months and minimum in winter (see Fig. 9b). However, The shift in WPAC TCC frequency in the late 1990s is there is a weaker-than-expected relationship between echoed in the recent work of Wang et al. (2012). They the ‘‘strength’’ of the monsoon circulation and the TCC show a statistically significant upward trend in early frequency. 15 MAY 2013 H E N N O N E T A L . 3061

FIG. 13. As in Fig. 11, but for the EPAC domain and (a) active years (1982, 1987, and 1997) and (b) inactive years (1988, 1989, and 1999).

Fig. 16 shows the annual time series of TCC activity is not related to the monsoon strength at all (R2 5 and genesis productivity of the North Indian Ocean. As 0.00). The work of Fu and Hsu (2011) suggest that the mentioned previously, the jump in annual TCC numbers frequency and intensity of near-equatorial westerly after 1997 can be partially attributed to the addition of wind bursts associated with MJO events is responsible geostationary satellite coverage in July 1998. However, for creating a large area of cyclonic vorticity in the Bay the aforementioned work of Wang et al. (2012) shows of Bengal, which makes genesis more likely. that the NIND basin has been consistently ‘‘wetter’’ Interannual variability of TCCs is, however, related to since the late 1990s, suggesting that TCCs may have the ENSO cycle (R2 5 0.34). Typically, a developing El become more frequent; without observations prior to Nin˜ o (La Nin˜ a) leads to drier (wetter) conditions during 1999, it is not possible to know for sure. the monsoon season (e.g., Rasmusson and Carpenter We find that the correlation between TCC frequency 1983). However, there are complicating factors, in par- and the South Asian summer monsoon index (a measure ticular, the interplay between the Indian Ocean dipole of the seasonal variation of the magnitude of the area (IOD), ENSO, and the Indian monsoon/TCC activity. wind field; see Li and Zeng 2005) is fairly weak (R2 5 The IOD describes the west Indian/east Indian dipole in 0.14). Furthermore, genesis productivity in the basin SST; the positive phase has been shown to offset the 3062 JOURNAL OF CLIMATE VOLUME 26

FIG. 14. As in Fig. 10, but for the WPAC basin. negative impact that El Nin˜ o has on Indian rainfall A closer investigation of basin TCC characteristics (Ashok et al. 2004). was presented. The EPAC produced the highest number It should be noted that 2005 was a hyperactive season of TCCs per unit area, but the WPAC was the most in the North Indian basin, particularly in genesis pro- productive basin for developing TCs (.12%). Some ductivity (as was the case in the North Atlantic). Al- basins had a noticeable trend in TCC frequency with though TCC activity in the NIND is most strongly time. In particular, TCCs in the NATL basin have been connected to WPAC TCC frequency (R2 5 0.37), there on the rise, especially since the mid-1990s. Two distinct is a weaker correlation to NATL TCC activity as well eras in TCC frequency are found in the WPAC time (R2 5 0.20). This is another area that deserves further series, with a noticeable active period beginning in 1999. investigation. However, there has not been a corresponding increase in tropical cyclogenesis events, so the overall genesis productivity in the WPAC has, on average, been lower 5. Conclusions since that time. It has been long known that ENSO is a strong driver of a. Summary atmospheric convective variability in the Pacific (e.g., This paper presented an analysis of TCCs as tracked Ropelewski and Halpert 1987; Kiladis and Diaz 1989; in a 28-yr global dataset. The data were from an objec- Dai and Wigley 2000). The results presented here sug- tive TCC tracking algorithm that identified clusters gest that TCCs, a specially defined subset of convective according to a threshold of IR brightness temperature, features analyzed by these papers, are similarly affected. size, shape, and persistence. TCCs occur in nearly all Anomalous TCC patterns are believed to be driven by portions of the tropical oceans but are most frequent in zonal shifts of the low-level convergent branch of the regions of enhanced low-level convergence, such as the Walker circulation, as suggested by Dai and Wigley’s ITCZ, the SPCZ, and monsoonal troughs. In an average (2000) study of ENSO precipitation patterns. El Nin˜ o year, the tropics produce about 1600 TCCs, of which less events suppress TCC frequency in the WPAC; the than 7% develop into TCs. The development rate varies EPAC sees a corresponding increase in TCC activity. considerably by basin. In the NATL, ENSO does not play as strong a role Most developing TCCs develop within a short period in TCC generation as it does in genesis productivity. of time after identification: 60% (95%) develop within However, there is a notable correlation between the 24 (60) hours of their identification. The developers phase of the NAO and NATL TCC variability. It is also show a colder average IR brightness temperature plausible that the enhanced zonal winds that arise (;2–3 K), on average, than the nondevelopers, which from the positive phase of the NAO suppress SSTs suggests that the convective characteristics of TCCs over portions of the basin through turbulent mixing of may be an important factor in tropical cyclogenesis. the upper ocean. 15 MAY 2013 H E N N O N E T A L . 3063

FIG. 15. As in Fig. 11, but for the WPAC domain and (a) active years (1999, 2001, and 2009) and (b) inactive years (1982, 1987, and 1992). b. Future work if the length of the TCC time series is updated in future releases. With over 40 000 TCCs in the dataset, there are There have been several scientific questions that have a number of avenues for future applied research in this been raised here that may lead to new theoretical or area. Statistical prediction schemes for tropical cyclo- observational investigations. In particular, the noticeable genesis will benefit from the vast amount of new data. shift in WPAC TCC activity beginning in the late 1990s Investigations into variability on shorter time scales, is an interesting area of further research. The upturn in along with the inclusion of atmospheric observations, TCC frequency is coincident with a drop in TC fre- may yield new paradigms on the relationships between quency, producing a GP well below the 1982–98 period. tropical convection and their forcing mechanisms. Vari- Matsuura et al. (2003) provide an explanation on the large- ability on longer time scales will become more evident scale conditions that can lead to WPAC interdecadal 3064 JOURNAL OF CLIMATE VOLUME 26

FIG. 16. As in Fig. 10, but for the NIND basin.

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