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On average 1-2 tropical cyclones form over the Arabian Sea each year, and few of these storms are intense enough to be classified as very severe or super cyclonic storms. As such, few studies have explicitly identified the seasonal to interannual changes in environmental conditions that are associated with Arabian Sea tropical cyclogenesis. However, over the last 30 yr several intense Arabian storms did form and make landfall, with large impacts, which motivates this new study of the basin. The conclusions of earlier studies are visited by utilizing modern observational and reanalysis data to identify the large-scale features associated with Arabian tropical cyclone variability on seasonal time scales. Then year-to-year changes in environmental conditions that are related to interannual variability in Arabian storms during the pre- and postmonsoon periods are elucidated. The analysis of the relationship between large-scale environmental variables and seasonal storm frequency supports conclusions from work completed more than 40 yr prior. Investigation of the year-to-year changes in premonsoon storm frequency suggests that May (June) storms are associated with an early (late) onset of the southwest monsoon. The findings also demonstrate that November cyclones (the month when the majority of postmonsoon cyclogenesis occurs) primarily form during periods when the Bay of Bengal experiences a broad region of high sea level pressure, implying that November storms form in either the Arabian Sea or the Bay of Bengal but not in both during the same year. Finally, the analysis of changes in a genesis potential index suggests that long-term variability in the potential for a storm to form is dictated by changes in midlevel moisture.
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A Climatology of Arabian Sea Cyclonic Storms
Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia
Lamont-Doherty Earth Observatory, The Earth Institute at Columbia University, Palisades, New York
(Manuscript received 8 January 2010, in final form 25 August 2010)
On average 1–2 tropical cyclones form over the Arabian Sea each year, and few of these storms are intense
enough to be classified as very severe or super cyclonic storms. As such, few studies have explicitly identified
the seasonal to interannual changes in environmental conditions that are associated with Arabian Sea tropical
cyclogenesis. However, over the last 30 yr several intense Arabian storms did form and make landfall, with
large impacts, which motivates this new study of the basin. The conclusions of earlier studies are visited by
utilizing modern observational and reanalysis data to identify the large-scale features associated with Arabian
tropical cyclone variability on seasonal time scales. Then year-to-year changes in environmental conditions
that are related to interannual variability in Arabian storms during the pre- and postmonsoon periods are
elucidated. The analysis of the relationship between large-scale environmental variables and seasonal storm
frequency supports conclusions from work completed more than 40 yr prior. Investigation of the year-to-year
changes in premonsoon storm frequency suggests that May (June) storms are associated with an early (late)
onset of the southwest monsoon. The findings also demonstrate that November cyclones (the month when the
majority of postmonsoon cyclogenesis occurs) primarily form during periods when the Bay of Bengal ex-
periences a broad region of high sea level pressure, implying that November storms form in either the Arabian
Sea or the Bay of Bengal but not in both during the same year. Finally, the analysis of changes in a genesis
potential index suggests that long-term variability in the potential for a storm to form is dictated by changes in
midlevel moisture.
1. Introduction
Although only a couple Arabian Sea cyclonic storms
form each year, occasionally a very powerful storm will
make landfall. The strongest storm in the recent record
was super cyclonic storm Gonu, which formed in early
June 2007 and made landfall in both Oman and Iran.
Gonu caused an estimated $4 billion (U.S. dollars) in
damages and collectively 100 deaths in Oman, United
Arab Emirates, and Iran (JTWC 2007). To date, Gonu is
the closest any cyclone on record had come to actually
entering the Persian Gulf. One of the most destructive
Arabian Sea storms, in terms of loss of human life, was
very severe cyclonic storm 03A, which made landfall
in the western Indian state of Gujarat in 1998 with a
maximum sustained wind speed of 105 kt (54 m s
More than 1100 deaths were attributed to this storm,
many of which were reported to be workers in salt mines
that were not evacuated prior to landfall (JTWC 1998).
Although the damage from Gonu and very severe cy-
clonic storm 03A was intense, landfall of seemingly in-
nocuous storms can present opportunity for devastating
losses. For example, in October 2008 deep depression
02A made landfall in Yemen with a sustained wind speed
of only 25 kt (13 m s
). Despite the weak nature of the
storm, there was report of widespread flooding, 22 000
displaced people, 180 deaths, and $1 billion in damages
(WHO 2009).
Because the northern Indian Ocean as a whole has
relatively few cyclonic storms in a given year (Lander
and Guard 1998), there are few publications related to
climate and tropical cyclones (TCs) here and even fewer
that explicitly consider storms that form over the Arabian
Sea. Gray (1968) performed a comprehensive summary
of global tropical cyclone activity and the conditions that
Corresponding author address: Amato T. Evan, Department of
Environmental Sciences, University of Virginia, 291 McCormick
Road, Charlottesville, VA 22904.
DOI: 10.1175/2010JCLI3611.1
Ó2011 American Meteorological Society
forced observed seasonality of cyclogenesis in every
basin. With regard to storms forming over the Arabian,
Gray (1968) identified the bimodal seasonal cycle of
Arabian storm frequency and attributed the depression
of July and August cyclogenesis to strong vertical shear
in the presence of intense upper-level easterlies and the
seasonal displacement of the monsoon trough over the
Indian subcontinent. Gray otherwise noted that Arabian
storms tended to form during the spring northward ad-
vancement or fall southward withdraw of the monsoon
trough, and that vertical shear values were favorable
poleward of the trough when the trough’s meridional
position was between 58and 108N.
Recently Camargo et al. (2010) summarized more re-
cent findings on tropical cyclones and climate globally,
and here we discuss the subset of Arabian Sea tropical
cyclones in slightly more detail. Lee et al. (1989) exam-
ined environmental characteristics surrounding severe
cyclonic storm 02A, which formed over the Arabian Sea
in June 1979. Lee et al. (1989) identified a series of east-
ward frontal systems that appear to strengthen the Somali
jet and extend its low-level flow eastward across the
Arabian. This intensification of low-level westerlies en-
hanced the cyclonic circulation of a preexisting distur-
bance that was just poleward of the jet axis, which, when
combined with the development of sufficient upper-level
outflow channels, provided the needed conditions for
storm organization and genesis. Camargo et al. (2009)
used fields ofthe so-called genesis potential (GP) index in
conjunction with the Wheeler and Hendon (2004) Madden–
Julian oscillation (MJO) index to determine the role of
the MJO in global cyclogenesis. Camargo et al. (2009)
demonstrated that enhanced values of the GP index
over the northern Indian Ocean during August–October
(ASO) for the active phases of the MJO over the Indian
Ocean were associated with increases in midlevel mois-
ture and low-level vorticity. However, given the bimodal
distribution of Arabian Sea storms, diagnosis of the re-
lationship between Arabian storms and the MJO would
be made more relevant by instead considering the periods
of May–June and October–November. Using cyclone
data from a storm atlas published by the India Meteo-
rological Department (IMD) for the period 1877–1989,
Singh et al. (2000) found neither long-term linear trends
in seasonal Arabian Sea cyclonic storm frequency nor
a statistically significant correlation with the Southern
Oscillation index. However, the quality of this historical
data is not discussed, and therefore it is impossible to
evaluate the robustness of the Singh et al. (2000) results
without further details on the data sources and short-
In this manuscript we use Joint Typhoon Warning
Center (JTWC) best-track data in conjunction with
observational records and reanalysis data to identify
coherent large-scale conditions associated with Arabian
Sea tropical cyclone frequency on seasonal to inter-
annual time scales. Our goal is to revisit earlier findings
regarding the seasonality of Arabian storms (i.e., Gray
1968) and to identify environmental conditions associ-
ated with observed year-to-year changes in Arabian
cyclogenesis. The remainder of this article is organized
as follows: Section 2 describes data used in this study;
section 3 presents a recent climatology of Arabian storm
frequency and intensity; section 4 discusses intraseasonal
cyclonic storm variability in terms of large-scale envi-
ronmental features; section 5 examines the environmen-
tal conditions associated with year-to-year changes in
Arabian cyclones; and section6 uses the genesis potential
index as another method to understand intra- and in-
terannual Arabian cyclonic storm variability. Section 7
a discussion of other factors not considered here that
may be important to understanding Arabian Sea cyclonic
storm frequency and intensity.
2. Data
Here we consider Arabian Sea tropical cyclones over
the period of 1979–2008 using position and intensity
estimates reported in the JTWC best-track dataset (Chu
et al. 2002). Although JTWC annual tropical cyclone
reports (ATCRs) began to include storm summaries for
the Arabian Sea in 1972, JTWC best-track data for the
Arabian are likely to be of good quality post-1978, since
it is after this date that the ATCRs and best-track re-
cords consistently agree on the number of tropical cy-
clones that formed in thebasin (Chu et al. 2002). Intensity
estimates are reported as the maximum 1-min sustained
wind speed at 10-m height in knots and are made using
the Dvorak model (Dvorak 1975, 1982, 1984, 1995). As
such the quality of these intensity estimates may be low
(Chu et al. 2002).
At the time of writing the International Best Track
Archive for Climate Stewardship (IBTrACS) (Knapp
et al. 2010) included best-track data from the IMD for
the Arabian over the period of 1991–2008. Agreement
in identification of a tropical cyclone for the IMD and
JTWC data (both from the IBTrACS) was high. In
general, disagreement only occurred for cases when the
JTWC identified very weak and short-lived storms whose
maximum wind speeds peaked just above 34 kt for one
or two 6-hourly periods, which were not identified in
the IMD analysis. Intensity estimates from the JTWC and
IMD did differ (both are also from IBTrACS), with the
IMD estimates being consistently 5–10 kt lower than
those from JTWC. However, this may be the result of
1JANUARY 2011 E V A N A N D C A M A R G O 141
comparing 1-min JTWC and 3-min IMD wind speeds
(Knapp et al. 2010). Because we are interested in long-
term changes in Arabian cyclone activity, here we only
use position and intensity data from the JTWC archive.
For our analysis of large-scale conditions associated
with Arabian cyclogenesis, we employ reanalysis fields
from the National Centers for Environmental Prediction–
National Center for Atmospheric Research (NCEP–
NCAR) Global Reanalysis 1 (Kalnay et al. 1996) and sea
surface temperature (SST) data from both the Met Office
Hadley Centre Sea Ice and Sea Surface Temperature
dataset (HadISST) (Rayner et al. 2003) and the Na-
tional Oceanic and Atmospheric Administration Opti-
mum Interpolation Sea Surface Temperature (NOAA
OISST) dataset for the genesis potential index calcula-
tions (Reynolds et al. 2002). Details of how the genesis
potential index was derived and can be calculated are
given in Camargo et al. (2007).
3. Recent cyclonic storm activity
We define an Arabian Sea cyclone as a storm whose
best-track genesis occurs in the northern Indian Ocean
and west of 778E. Most Arabian Sea cyclones form close
to the western coast of the Indian subcontinent and
follow a northerly or northeasterly track. Several storms
have formed more toward the center of the basin or
closer to the equator and have taken a more easterly
path (Fig. 1). Since the vast majority of recent publica-
tions on tropical cyclones and climate have focused on
understanding activity in the Atlantic basin, it is useful
to define the nomenclature used for Arabian Sea storms
in terms of the Atlantic storm intensity classification
system (Table 1).
Based on the JTWC best-track data for the period
1979–2008, 41 cyclonic storms formed in the Arabian
Sea, of which 23 made landfall with tropical depression
or stronger intensities. The average lifetime of these
storms was 3.4 days, and the range of lifetimes is 1–9 days.
In this record 8 storms were classified as severe cyclonic
storms, 7 were classified as very severe cyclonic storms,
and 1 super cyclonic storm was recorded.
One metric of gauging storm frequency and duration
is cyclonic storm days: the number of days that a tropical
cyclone, whose lifetime maximum best-track wind speed
is at least 35 kt (17 m s
), is present in the basin. The
annual distribution of monthly total cyclonic storm days
over the Arabian Sea is bimodal, with peaks in activ-
ity occurring May–June and then October–December
(MJOND), with decreased tropical cyclone activity
during the peak of the Indian monsoon (Fig. 2; Gray
1968). Over the period of 1979–2008, there has been an
average of 4.7 cyclonic storm days per year over the
Arabian Sea, with 1981, 1990, 1991, 2000, 2005, 2008
having 0 storms; and 1998 and 2004 having more than
15 cyclonic storm days (Fig. 3). There is an apparent
increase in cyclonic storm days over the period of 1992–
2008 when compared to 1979–91. The mean annual cy-
clonic storm days for the earlier period is 2.3, while the
mean for the latter is 6.4. The difference of 4.1 cyclonic
storm days between the two time spans is statistically
significant at the 97% level based on the tscore of a two-
tailed Student’s tstatistic on the two populations (as well
as when using the Tukey Honestly Significant Differ-
ence Test based on our post hoc determination of the
1991 separation between the two periods). There were
FIG. 1. Map of the Arabian Sea cyclonic storm tracks for the
period 1979–2008. Filled circles represent the genesis points for
each storm. Only storms with genesis in the Arabian Sea are
included here.
TABLE 1. Classification convention for TCs over the Arabian Sea
and the North Atlantic (and eastern Pacific). Winds for Arabian
and Atlantic storms are reported as 1-min averages. Note that the
Atlantic equivalent category for an Arabian storm is an approxi-
speed (kt)
Arabian Sea
Approx Atlantic
17–33 Tropical depression Tropical depression
34–47 Cyclonic storm Named tropical storm
48–63 Severe cyclonic storm Named tropical storm
64–119 Very severe
cyclonic storm
Hurricane categories 1–3
$120 Super cyclonic storm Hurricane categories 4 and 5
an average of 0.8 storms per year pre-1992 and 1.8 storms
per year post-1992; however, the average storm lifetime
remained more or less constant (3.0 and 3.7 days for the
pre- and post-1992 dates, respectively). Therefore, the
increase in cyclonic storm days is more reflective of an
increase in storm frequency than duration. It is possible
that this increase in cyclonic storm days is related to im-
provements in observational platforms or changes in op-
erational guidelines (e.g., Landsea et al. 2006), and it is
not possible to demonstrate that the increase in storm-
days is physical without further analysis of the best-track
data and its origin.
During the presouthwest monsoon period (May–June;
Fig. 2), from one year to the next, storms generally occur
either during May or June; however, they seldom occur
consecutively in both (Fig. 3). We hypothesize this pref-
erence for one month over the other is a function of the
southwest monsoon onset date, with an early onset fa-
voring May storms and a late onset favoring June storms,
a theory we develop further in section 3 that is consistent
with Gray (1968). During the active months following the
southwest monsoon withdrawal (September–December;
Fig. 2), the division in activity between the pre- and post-
1991 periods is clear, with only 5 months registering some
storm activity in the earlier epoch and 17 active months
in the later epoch (Fig. 3).
Another useful metric for quantifying tropical cyclone
activity is accumulated cyclone energy (ACE; Bell 2003),
which is the sum of the square of a storm’s maximum
sustained 1-min wind speed. Since ACE is a summation
over the lifetime over the storm, seasonal ACE values
are a function of storm frequency, duration, and intensity.
A monthly histogram of ACE for the period 1979–2008
shows that ACE for the premonsoon period is more than
double that of the postmonsoon period (Fig. 4). Clima-
tological total ACE for May and June is 40 and 47 kt
respectively, while the total ACE for November, the
busiest month in the postmonsoon period, is 25 kt
Similar to the time series of TC days, there are clearly
two periods of storm activity in the annual ACE time
series, although the division in the ACE series occurs
6 yr after the TC days separation: 1979–97 and 1998–
2008 (Fig. 5). Annual average ACE of the earlier period
(2.2 kt
) is roughly one-fourth the average annual ACE
value of the later period (8.2 kt
). While increases in
cyclonic storm days over the last 30 yr appear to be re-
lated to increases in storm-days during both the pre- and
postsouthwest monsoon period (Fig. 3), the dramatic
increase in annually averaged ACE is seemingly domi-
nated by four premonsoon cyclonic storms (Fig. 5): very
severe cyclonic storm TC 03A (June 1998), very severe
cyclonic storm TC 02A (May 1999), very severe cyclonic
storm TC 01A (May 2001), and super cyclonic storm
Gonu (June 2007). However, the fact that ACE appears
to dramatically increase at 1998 is suspect, since this is
also the period in time when a Meteosat geostationary
satellite that was positioned at 08E longitude was re-
positioned to 638E, establishing 3-hourly IR and visible
satellite observations directly over the Arabian Sea (e.g.,
Knapp and Kossin 2007). It is probable that an increase
in satellite observations would lead to increased and
earlier detection of weak storms, or it allow for changes
in the JTWC 6-hourly intensity estimates (Landsea et al.
2006). Independent satellite intensity analysis suggests
FIG. 2. Histogram of the number of cyclonic storm days, by
month, for the period 1979–2008. See text for the definition of
a cyclonic storm day. Only storms with genesis over the Arabian
Sea are considered.
FIG. 3. (top) Annual time series and (bottom) monthly scatter-
plot of cyclonic storm days (CS-days) over the Arabian Sea for the
period 1979–2008. In the (bottom) the number of days is indicated
by the fill color of each circle.
ANUARY 2011 E V A N A N D C A M A R G O 143
that seasonal intensity over the northern Indian Ocean is
decreasing over time (Kossin et al. 2007); however, up-
dates to the intensity estimate methodology that account
for dramatic temporal changes in regional satellite cov-
erage show an increase in the intensity of the strongest
storms over the last quarter century (Elsner et al. 2008).
Given the low confidence in the Arabian intensity esti-
mates (Chu et al. 2002) without further data analysis, it is
not yet possible to determine if any portionof the upward
trend in Arabian Sea cyclones, as seen in the JTWC best
track, are physical.
4. Seasonal variability
It is well known that the southwest monsoon circula-
tion dominates the large-scale features of the Arabian
Sea, and that no cyclonic storms develop over the Ara-
bian while the monsoon trough is at its most northerly
position and located over the Indian subcontinent (Fig. 2).
Gray (1968) explored the environmental controls es-
tablishing the bimodal seasonal cycle of Arabian cyclo-
genesis. As mentioned earlier Gray (1968) found that
pre- and postmonsoon storms occurred when the mon-
soon trough was located at 58–108N latitude; however, he
found that during the monsoon onset (July–August),
copious vertical shear and displacement of the trough
over the Indian subcontinent precluded intensification
of any convective system. Gray (1968) also speculated
that cool Arabian Sea surface temperatures associated
with the monsoon onset would not have an important
effect on storm genesis.
Here we revisit these 4-decade-old findings in more
detail by analyzing the seasonal evolution of large-scale
features relevant to cyclone genesis and intensification,
namely sea surface temperature, upper- and lower-level
convergence, wind speed and direction, vertical wind
shear, low-level vorticity, and sea level pressure (SLP).
While this is not an exhaustive list of environmental
conditions that are important to understanding cyclone
activity, at least for the Arabian Sea, these features do
seem to well describe large-scale conditions as they re-
late to cyclone activity in this basin.
The large-scale dynamics of the southwest monsoon
are described in numerous publications, and here we
refer the reader to Hastenrath (1991, chapter 6) for a
more detailed explanation of the circulation patterns
described here. The southwest summer monsoon is as-
sociated with a low level southwesterly jet off the east-
ern coast of northern equatorial Africa (the Somali or
East African jet), and a southerly cross-equatorial flow
along the western Indian Ocean (Fig. 6). Surface wind
stress associated with the jet forces an offshore Ekman
flow, upwelling along the coast of East Africa and the
Arabian Peninsula, and cooling of the Arabian Sea dur-
ing the height of the monsoon period (Fig. 6).
Based on Hadley SST data spanning the period of
1979–2008, the forcing of Arabian sea surface temper-
atures by low-level winds is strongest during July and
August and results in a bimodal annual distribution of
regional SST, with maximums in May (absolute) and
October (relative), and nearly equivalent minimums in
January and July (Fig. 7). Climatologically, the jet and
the southerly cross-equatorial flow begin to develop in
May–June; the maximum in the lower-jet-level wind
speeds and surface ocean cooling is during July–August;
FIG. 4. Monthly histogram of ACE for the period 1979–2008.
ACE is only calculated for storms having genesis points over
the Arabian Sea and a maximum sustained wind speeds .34 kt
(17 m s
FIG. 5. As in Fig. 3, but for ACE.
and by October, the low-level circulation over much of
the Arabian is northerly and the monsoon trough is
displaced to near the equator (Fig. 6). Despite the strong
wind-driven cooling along the western boundary of the
Arabian Sea, ocean surface temperatures over many
parts of the basin remain above 26.58C, an empirical
threshold for cyclogenesis (Gray 1968), throughout the
entire year (Fig. 6), and most storms form along the
eastern boundary of the Arabian (Fig. 1), where surface
temperatures are 18–48C warmer than those of the ba-
sin’s western sector (Fig. 6).
The Somali jet breaks eastward once it leaves the
coast of eastern Africa, and this westerly branch of the
jet has a latitudinal shift according to the season, with
the jet position slightly north of the equator during the
boreal spring, at a northerly maximum during July–
August, and then taking up a position near or south of
the equator during the fall (Fig. 8). Large-scale low-level
relative vorticity (here calculated as the 925–700-mb
layer-mean vorticity) is stratified about the westerly
branch of the Somali jet, with negative vorticity equa-
torward and positive vorticity poleward of the jet’s axis.
Cyclongenesis consistently occurs poleward of the jet’s
monthly climatological axis (Fig. 8) (Gray 1968). To
some degree the annual cycle of the position and in-
tensity of the jet, coupled with the cool ocean surface
temperatures that result from wind-driven upwelling
along the eastern coast of Africa, explain the pattern
of cyclogenesis from one month to the next over the
Arabian. For example, during the months of July and
August, the jet is at its most poleward position and the
only regions where climatological low-level vorticity
values are favorable for cyclogenesis (Fig. 8) are also
those areas where ocean temperatures are cool because
of wind-driven upwelling and temperature advection
(Fig. 6). During the pre- and postmonsoon periods of
May–June and October–November, respectively, the
FIG. 6. Maps of monthly-mean SST (shaded) and 10-m winds (arrows) for the months of (left) May, (middle) August, and (left) October.
The climatology period is 1979–2008.
FIG. 7. Annual cycle of monthly-mean Arabian Sea SST (8C;
7.58–208N, 508–808E) for the period 1979–2008.
ANUARY 2011 E V A N A N D C A M A R G O 145
equatorialposition of the jet and broad region of positive
vorticity values (Fig. 8) that are coupled with warmer
regional ocean temperatures (Fig. 6) provide a larger
favorable region for storm development.
The genesis points of Arabian Sea cyclones, position
of the Somali jet, and pattern of Arabian Sea SST are
clearly related (Fig. 8). However, the Arabian Sea also
exhibits an annual cycle of vertical wind shear that is
very relevant to understanding the seasonal pattern of
cyclone development (layer vertical shear is defined as
the difference between the 200–150- and 925–700-hPa
layers, calculated using 6-hourly reanalysis data over the
period of 1979–2008; Gray 1968). Over the Arabian Sea,
vertical shear largely follows the magnitude and di-
rection of the 200-hPa wind: during the late fall, winter
(not shown), and early spring months, the upper tropo-
spheric flow poleward of 108N is dominated by the
subtropical westerly jet; and upper-level flow during the
summer months is similarly controlled by the tropical
easterly jet, which develops in late spring and has a
maximum in magnitude during July–August (Fig. 9).
Climatological vertical shear values are between 5 and
10 m s
in the regions where cyclone genesis events have
been recorded for the months of May, October, and
November (Fig. 9), suggesting that during these months,
cyclone formations are not necessarily shear limited. As
FIG. 8. Maps of monthly-mean low-level relative vorticity (shaded) and 850-hPa vector winds (arrows). TC genesis locations for each
month are denoted by a cross, and storms that reached a maximum 1-min sustained wind speed of 64 kt (33 m s
) are indicated by a circle
at the genesis location. Climatology period for vorticity, winds, and cyclogenesis is 1979–2008.
discussed earlier, during these months the relatively low
shear values are also accompanied by positive low-level
vorticity (Fig. 8) and warm SST (Fig. 6). Cyclogenesis
during the months of June and September occurred in
regions where long-term mean shear values were 10–
25 m s
(Fig. 9), implying that storms forming during
the summer months, when the tropical easterly jet is
a maximum in magnitude, are likely to be shear limited.
5. Interannual variability
Although Arabian Sea cyclogenesis has been noted in
May–June and August–December, most storms (70%),
and nearly all (90%) of the very severe and super cy-
clonic storms, form during the months of May, June, and
November. Therefore, it is instructive to examine how
conditions during these months differed when cyclo-
genesis is active (with TCs) and inactive (without TCs)
seasons. To do this we analyze the reanalysis fields com-
posited on storm counts. We define anomalous conditions
associated with storm genesis to be the difference be-
tween the mean fields for months when tropical cyclo-
genesis did occur and the mean fields for months when no
storm genesis took place. Statistical significance is at-
tributed to the composite differences using a two-tailed
Student’s ttest and a 90% significance of difference
While it is unlikely that monthly-mean reanalysis fields
are influenced sufficiently by the short-lived Arabian
storms to bias our composites, as this possibility was
FIG. 9. As in Fig. 8, but for monthly-mean vertical wind shear (shaded) and 200-hPa vector winds (arrows).
ANUARY 2011 E V A N A N D C A M A R G O 147
discussed in Swanson (2008), we briefly examined this is-
sue. We considered the differences of June 2007 monthly-
mean fields of the reanalysis variables with another June
2007 mean constructed without including data from those
days when super cyclonic storm Gonu was present in the
basin. Not surprisingly, there was no noticeable change in
the monthly-mean fields that can be attributed to the
passage of this storm (not shown).
For the case of cyclonic storms in May, statistically
significant anomalous conditions include cooler ocean
surface temperatures off the Somali coast that extend
eastward at 108N, low SLP, an enhanced Somali jet and
positive vorticity north of the jet axis, a developing
tropical easterly jet with an upper-level anticyclone cen-
tered over northwestern India, increased vertical wind
shear south of 158N, increased midlevel moisture, and
broad upper-level divergence (Fig. 10). These anomalous
conditions are similar to those present during the south-
west summer monsoon; anomalous onshore low-level
flow along the western coast of the Indian subcontinent,
which forces cooler ocean surface temperatures, is co-
incident with a tropical easterly jet work together to in-
crease the local vertical shear (Figs. 6, 8, and 9). Thusly,
conditions are favorable for May storms to form when
there is an early monsoon onset. Interestingly, May cy-
clogenesis is more favorable when ocean temperatures
are anomalously cool and the magnitude of vertical shear
is anomalously large (Fig. 10).
In many ways anomalous conditions for June storm
genesis are opposite of those for May cyclogenesis. June
storms are associated with an enhanced Somali jet with
a more equatorial position and offshore low-level flow
along the Indian subcontinent, anomalous upper-level
westerlies, low SLP across much of the basin, and an
increase in vorticity in the region of genesis (Fig. 11).
These conditions are consistent with the patterns of
upper- and lower-level winds before the monsoon onset
(Figs. 8 and 9), thus implying that June genesis is asso-
ciated with a late onset of the southwest monsoon. Al-
though weaker vertical shear and increased midlevel
moisture are also present when June storms form, the
differences are not statistically significant, and lower and
upper-level convergence fields are not organized well
enough as to suggest important differences in large-scale
vertical motion.
During the month of November, enhanced cyclonic
storm activity is not as clearly associated with a typical
monsoon circulation. Low-level equatorial easterlies,
an upper-level ridge over the northern Indian subcon-
tinent, and high SLP over the Bay of Bengal conspire to
force regional low-level convergence and upper-level
divergence in the November genesis region (Fig. 12). In
addition, midlevel relative humidity is anomalously high
across much of the Arabian (Fig. 12), which is coincident
with negative anomalies of outgoing longwave radiation
(not shown). There is nearly no anomalous change in
vertical shear or surface temperature in the genesis re-
gion, and no statistically significant anomalies of low-
level vorticity and SLP are associated with November
cyclogenesis (Fig. 12).
As high SLP over the Bay of Bengal is coincident with
Arabian Sea cyclogenesis for the month of November
(Fig. 12), there is the interesting possibility that when
conditions are favorable over the Arabian Sea for
storms to form, they are not over the Bay of Bengal. This
is born out in composites of outgoing longwave radia-
tion, which show positive anomalies over the Bay of
Bengal when composited on Arabian Sea storms. From
1979 to 2008, 9 Arabian Sea and 14 Bay of Bengal
tropical cyclones formed. During 21 years of the 1979–
2008 period, 1 storm formed in either the Arabian Sea
or the Bay of Bengal, no storms formed at all during
8 of these years, and during only 1 yr (1986) did a storm
form in both basins (Table 2). This 1986 Bay of Bengal
storm formed far to the north and had a lifetime of
only 18 h.
To test if this shift of November cyclogenesis to either
the Arabian Sea or the Bay of Bengal observed in the
best-track data is statistically significant, we randomly
reassigned the years during which genesis occurred for
the 14 Bay of Bengal storms and the 9 Arabian Sea
storms and then counted the number of years an overlap
of storms (1 Arabian and 1 Bay of Bengal during the
same November) would occur over the 30-yr span. This
was repeated 10 000 times, and a distribution of the
number of years a storm formed in both basins was
FIG. 10. Anomalous large-scale features associated with May Arabian cyclonic storm genesis. Maps represent the differences between
means of months that did and did not contain a cyclonic storm. Statistical significance at the 90% level is based on a tscore from a two-
tailed ttest of the separation of the means from the two distributions at each grid cell and is indicated by a solid black contour. Features
considered here are (a) SST, (b) 850- and (c) 200-hPa winds, (d) SLP, (e) low-level layer-mean vorticity, (f) layer-mean vertical shear,
(g) 600-hPa relative humidity, and (h) 850- and (i) 200-hPa convergence. Cyclone genesis points are indicated by a cross, and storms that
reached a wind speed of 64 kt or greater during their lifetime are indicated by a circle. Period for the composites is 1979–2008.
ANUARY 2011 E V A N A N D C A M A R G O 149
constructed from the results. From the cumulative dis-
tribution function of this distribution, less than 0.4% of
the simulations resulted in a distribution analogous to
that in the observations, where a storm formed in both
basins during only one year (the average was 4.5 years
that had November storms occur in both basins). There-
fore, we suggest that the lack of overlap between No-
vember storms seen in the best-track data is statistically
significant at the 99% level and is consistent with our
composite analysis. Future work will be aimed at identi-
fying the physical mechanisms that force this preference
for November genesis in one basin or another.
Thus far our analysis has focused on various climato-
logical conditions as they relate to monthly storm ac-
tivity, and it has neglected explicit considerations of
stability. The following section addresses the role of
atmospheric stability in conjunction with vertical shear,
low-level vorticity, midlevel humidity, and ocean surface
temperatures by analysis of the so-called genesis poten-
tial index.
6. Analysis of the GP index
The GP index is an empirically derived index used to
characterize the suitability of local environmental con-
ditions for cyclone genesis (Emanuel and Nolan 2004),
and an in-depth description of the GP index and its
constituents can be found in Camargo et al. (2007).
Briefly, the GP index as used here is defined as
GP 5105h
(1 10.1Vshear)2, (1)
where his the absolute vorticity at 850 hPa (s
), fis
the relative humidity at 600 hPa (%), V
is the po-
tential intensity (PI, m s
) (Emanuel 1988; Bister and
Emanuel 2002a,b), and V
is the magnitude of the ver-
tical wind shear between 850 and 200 hPa (m s
). Daily
global GP fields are from the NCEP–NCAR Global
Reanalysis 1 (Kalnay et al. 1996) and the NOAA OISST
(Reynolds et al. 2002) dataset. The GP data have a
horizontal resolution of 2.58. We note that the absolute
magnitude of any GP value is arbitrary and has no direct
physical significance.
Regionally averaged monthly-mean GP index data
can reproduce the annual cycle of cyclonic storms over
the northern Indian Ocean (Camargo et al. 2007). In
addition,the spatial pattern of climatological monthly GP
index reflects the position of historical cyclone genesis
points during each month of the tropical cyclone season
(Fig. 13). Genesis generally occurs in regions where the
climatological-mean GP index has high values, and GP is
mostly low in those areas where no cyclone genesis has
been observed (Fig. 13). In contrast, we also find two May
and one October genesis points west of about 658Ein
regions of climatologically low GP (Fig. 13).
Monthly GP index data also reflect the year-to-year
changes in storm frequency. Differences in monthly GP
index fields composited around storm genesis (similar
to that described in more detail in section 4) all show
positive GP anomalies for years during which a storm
formed, and the spatial patterns of those anomalies well
describe the best-track genesis positions (Fig. 14). In
general genesis coincides with regions of positive GP
anomalies, except for one May storm and one October
storm that formed over the western sector of the Ara-
bian Sea, and for one November storm and one De-
cember storm that formed very close to the equator. It is
also interesting to note that November GP anomalies
are less than zero over the Bay of Bengal when com-
posited on Arabian Sea genesis (only the western part of
the Bay of Bengal is shown, Fig. 14), further supporting
the theory of an ‘‘either/or’’ basin preference for No-
vember storm formation.
We note that a similar composite analysis using PI
instead did not show broad regions of positive potential
intensity anomalies that were clearly associated with
genesis locations. Hart et al. (2007) found a memory of
40–60 days in PI fields after the passage of a storm in the
Atlantic and Pacific basins, and so we also examined the
effect of TCs on local PI to determine if the storms
themselves may be affecting our GP composite analysis.
We repeated the methods of Hart et al. (2007) for all
Arabian Sea storms during 1979–2008, but we instead
found that on average the PI anomalies decay on an
e-folding time scale of 5–7 days (not shown). Therefore,
the possibility of the storms strongly affecting the GP
composite analysis is very low.
One benefit of utilizing GP data is the ability to evaluate
the temporal and spatial characteristics of basinwide storm
activity independent of best-track data. We create a sea-
sonal time series of Arabian GP that is an average of
monthly GP values in the locations where historical cy-
clogenesis has occurred. This is in effect drawing a box
around the locations where storms have formed in the past
(this ‘‘box’’ changes from one month to the next) and then
averaging the GP values in this area to calculate seasonal
means. The time series of Arabian seasonal GP has a min-
imum in 1984 and a maximum in 1993 and exhibits a sta-
tistically significant upward trend (Fig. 15). From 1986 to
2003 seasonal GP varies with a very regular pattern of local
minima (1986, 1991, 1997) that are immediately followed
by local maxima (1987, 1993, 1999), then again minima
(with values larger than the previous minima: 1989, 1995,
2001), and lastly another maxima (with values that are
smaller than the previous maxima: 1990, 1996, 2002). This
FIG. 11. As in Fig. 10, but for anomalous large-scale features associated with June Arabian cyclonic storm genesis.
ANUARY 2011 E V A N A N D C A M A R G O 151
FIG. 12. As in Fig. 10, but for anomalous large-scale features associated with June Arabian cyclonic storm genesis.
3- and 6-yr periodicity is odd, but we have not yet identi-
fied an artificial source of the repetition and for now
should assume it is physical. A 1-4-6-4-1 low-pass filter of
the seasonal GP time series shows the low frequency
decrease in GP values from 1979 until 1985 and the
subsequent increase in GP through 2008, as well as the
5–6-yr periodicity in the time series that persists from at
least 1985 to 2008 (Fig. 15). The difference between
mean GP values pre- and post-1992 (1.0 and 1.4,
respectively) is statistically significant at the 99% level.
Therefore, also considering the statistically significant
upward trend, to some extent the GP data does support
a physical interpretation of the increase in cyclonic
storms days (Fig. 3), which we determined was largely
due to increases in storm frequency rather than duration.
It is well known that Indian monsoon dynamics can be
externally forced by ENSO (e.g., Shukla and Paolino
1983; Kumar et al. 2006), and it is possible that tropical
FIG. 13. Monthly-mean GP over the Arabian. GP climatology period is 1979–2008, and data are from daily reanalysis atmospheric and
weekly SST fields. Note that genesis points are only plotted for storms that formed over the Arabian Sea.
TABLE 2. Years during which November TCs formed over the Arabian Sea or the Bay of Bengal. The only year when a storm formed in
both basins was 1986.
Arabian Sea Bay of Bengal
Storm years 1979, 1980, 1982, 1986, 1993, 1994, 1997, 2003, 2004 1981, 1983–88, 1992, 1995, 1998, 2002, 2005, 2007, 2008
ANUARY 2011 E V A N A N D C A M A R G O 153
cyclone activity in the Arabian Sea is related to ENSO in
the same way that monsoon rainfall is coupled to ENSO
variability in the 2–7-yr time scales (Torrence and
Webster 1999). Perhaps more relevant is that strong
˜o events are often accompanied by a delayed mon-
soon onset (Joseph et al. 1994). However, we have not
found a robust connection between metrics for storm
activity and ENSO. For example, using a definition for
an ENSO event similar to that of Trenberth (1997),
mean seasonal (MJOND) cyclonic storm days and ACE
values are not statistically different when separated by
seasonal positive and negative ENSO events (95% sig-
nificance level). In addition, difference maps of mean
monthly GP for different ENSO phases, created in a
manner similar to that done in Fig. 14 and for the same
months, do not show large and coherent regions of posi-
tive or negative GP anomalies over the Arabian (not
shown). It is likely that the periodicity in the smoothed
GP time series (Fig. 15) is at some level forced by ENSO.
However, without a clear physical mechanism for de-
termining how this relationship evolves over time, it is
difficult to ascertain the relevance of ENSO to Arabian
tropical cyclone activity without a more in-depth in-
vestigation, which is beyond the scope of this work.
In theory, reanalysis data are consistent over time, and
therefore we are able to extend the Arabian Sea GP
time series in Fig. 15 back to 1950 (Fig. 16). Over the last
60 yr, seasonal Arabian GP has exhibited large decadal-
scale swings; GP is above the long-term mean for much
of the first half of the time series and below that mean
during the second half of the record. The maximum in
GP occurs in 1960 and the minimum in 1984, with near-
mean values at the beginning and end of the record.
Interestingly, the 30-yr period we consider here is the
minimum in GP, and therefore storm formation, over
the last 60 yr (Fig. 16). Spectral analysis on the 59-yr GP
time series (not shown) suggests that a 3–6-yr periodicity
in GP that exists from the mid-1980s through 2000 (Fig.
15) is local to that period only and not a regular feature
of the entire dataset.
It is possible that the GP time series suffers from some
nonphysical artifact in the reanalysis data. One way to
estimate, to first order, the validity of the GP data over
these long time scales is to plot Arabian Sea storm
counts from the JTWC best track over this same period.
Keeping in mind that there is little confidence in the
best-track storm counts pre-1978, the level of agreement
between these two totally independent estimates of
FIG. 14. Anomalous monthly-mean GP composites. Difference maps created by subtracting monthly-mean GP for months without storm
activity from the mean for months that did contain at least one storm (1979–2008).
storm formation is surprising (Fig. 16). Besides the up-
swing in storm counts from 1970 to 1978, the two lines
follow the same decadal patterns, and the correlation
coefficient between the two is 0.78 (60% of the variance
in one time series can be explained by the other). In ad-
dition, the nonfiltered annual time series of Arabian GP
and JTWC storm counts is well correlated with an Rvalue
of 0.62 (39% of variance explained, not shown). Differ-
ences between the JTWC data and the GP for the 1970–
78 could be due to error in the storm counts, a breakdown
in the robustness of the GP formulation over this period,
biases in the NCEP data, or a mix of all three.
To identify the factors that are forcing observed var-
iability in the seasonal Arabian Sea GP time series (Fig.
16), and by association storm formation, we plot simi-
larly constructed time series of SST, low-level vorticity,
layer-mean vertical wind shear, and 600-hPa relative
humidity, as these are all input to the GP calculations
(Fig. 17). The time series of SST is increasing over this
period at a rate of 0.018Cyr
, similar to that reported
by Clark et al. (2000), and the time series of relative
humidity is decreasing at a rate of 0.13% yr
, while
relative vorticity and vertical shear remain generally flat
(Fig. 17). It is obvious that the smoothed relative hu-
midity time series (Fig. 17) is most similar to the GP
series (Fig. 16), and the two are correlated with an Rvalue
of 0.90 (81% variance explained). The smoothed pattern
of relative vorticity (Fig. 17) is also similar to that of GP
(Fig. 16), with a correlation Rvalue of 0.71 (51% var-
iance explained) for the smoothed series. The time se-
ries of vertical shear is not well correlated with GP, and
the SST time series is negatively correlated with the GP
data (Rvalue of 20.52). Thusly, changes in relative vor-
ticity and midlevel moisture seem to dominate the long-
term variability of Arabian GP.
A time series of 600-hPa relative humidity (Fig.17) and
specific humidity (notshown) are nearly indistinguishable
(Rvalue of 0.98 for the unsmoothed annual time series of
each), while 600-hPa temperature is not well correlated
with relative humidity. It is plausible that dynamically
forced reductions in midlevel moisture are driving the
decline in relative humidity and therefore GP over the
last 60 yr.
7. Discussion and concluding remarks
In this study we have attempted to understand the
relationship between Arabian Sea cyclonic storms and
their large-scale environment by analyzing observational
and reanalysis data. Analysis of monthly cyclonic storm
days and ACE indicate that starting in the early 1990s,
there has been an increase in the numbers, duration, and
intensity of Arabian Sea cyclones (Figs. 3 and5), although
data quality issues suggests these increases in storm
metrics are likely nonphysical. There is a well-known,
bimodal distribution to seasonal Arabian cyclonic storm
activity (Figs. 2 and 4). We demonstrated how the sea-
sonal cycle in activity is a function of the state of the
coupled low-level winds and surface ocean temperature
(Fig. 6) by presenting long-term monthly-mean maps of
low-level winds and relative vorticity, vertical wind shear,
and 200-hPa winds, showing how these environmental
features change together over the course of the cyclone
FIG. 15. Time series of seasonal Arabian Sea GP. The seasonal
time series (thin line) is an average of GP data for the months of the
Arabian cyclone season (May, June, and September–December)
in the regions where historical cyclogenesis has occurred. The
smoothed series (thick black line) is created by applying a 5-yr low-
pass filter (1-4-6-4-1) to the seasonal series.
FIG. 16. Time series of seasonal Arabian Sea GP (black line) and
storm counts (gray line) for the period 1950–2008. These smoothed
time series are created by filtering the annual time series of each
variable with a 5-yr low-pass filter. Annual time series is defined as
in Fig. 15.
ANUARY 2011 E V A N A N D C A M A R G O 155
season to establish regions over the Arabian Sea that are
favorable for storm development (Figs. 8 and 9), consis-
tent with earlier findings (Gray 1968).
We hypothesized that in addition to shaping the
pattern of seasonal variability, year-to-year changes in
large-scale features over the Arabian Sea also forced
interannual variability in storm frequency. To test this
we analyzed difference maps of SST, SLP, relative vor-
ticity, vertical wind shear, 850- and 200-hPa vector winds,
600-hPa relative humidity, and upper- and lower-level
convergence, composited around storm counts for the
three most active months (May, June, and November).
These composite maps all suggest that large-scale fea-
tures coherently organize themselves in such a way as
to create an anomalous environment that is conducive
to cyclonic storm activity. We found that May cyclo-
genesis events were associated with large-scale condi-
tions, consistent with an early southwest monsoon onset
(Fig. 10), while June storms were associated with condi-
tions consistent with a delayed monsoon onset (Fig. 11).
November storms were most clearly associated with sea
level pressure anomalies over the Bay of Bengal, implying
a preference for November storm formation in either the
time (Fig. 12; Table 2).
We used the genesis potential index as another way of
understanding the relationship between Arabian tropi-
cal cyclones and the large-scale environment. Long-term-
mean GP fields all show positive GP anomalies in the areas
of historical cyclogenesis (Fig. 13), supporting the hypoth-
esis that over the course of the season, environmental
features coherently organize themselves in such a way
as to create regions suitable for cyclogenesis. Monthly
GP difference maps, composited around cyclonic storm
frequency generally show positive GP anomalies in the
regions where storm genesis has taken place, supporting
our hypothesis that year-to-year changes in large-scale
environmental conditions force interannual variability
Arabian storms (Fig. 14).
Since monthly GP fields reproduce the seasonal pat-
terns of cyclogenesis, we analyzed a regional Arabian
GP time series to determine if the time evolution of the
GP is useful in understanding the time series of seasonal
Arabian cyclonic storm days and ACE. Over the last
30 yr, seasonal Arabian GP has increased (Fig. 15), sup-
porting a physical interpretation of the upward trends
in storm metrics from the best track. However, when
looking over the last 60 yr, seasonal Arabian GP was
much higher during the late 1950s through the early
1960s, and that recent values were below the mean. Sur-
prisingly, seasonalmean GPwas very well correlated with
annual Arabian Sea storm counts from the historical re-
cord (Fig. 16). We lookedat time series of GP constituent
variables and found that most of the variance in the GP
data (Fig. 15) can be explained by changes in relative
humidity, and to a lesser degree changes in relative vor-
ticity (Fig. 17).
GP values are large over the Persian Gulf from July
through September (Fig. 13). Clearly no storm genesis
could be expected over this relatively small body of water,
and entrainment of dry desert air from the surrounding
environment into any storm that may enter the gulf would
overwhelm the effect of very warm water temperatures
and climatologically low vertical shear (Fig. 9). However,
it is interesting to speculate on the fate of a storm that
would track into the Persian Gulf, especially since this
nearly occurred in 2007 with super cyclonic storm Gonu.
The large-scale dynamics of the Indian Ocean and the
southwest monsoon are complicated, owing to the cou-
pled response to external forcing, such as the Southern
Oscillation (e.g., Shukla and Paolino 1983), and the re-
gion’s internal dynamics (Saji et al. 1999; Webster et al.
1999). We have attempted to compartmentalize the dy-
namics of the Arabian to simplify the analysis of factors
FIG. 17. Seasonal Arabian time series of (from top to bottom)
SST, low-level vorticity, layer-mean vertical wind shear (note the
inverse yscale for vertical shear), and 600-hPa relative humidity.
Description for individual time series plots as in Fig. 15.
influencing cyclonic storm development here. However,
because of this simplification, we are neglecting the role
of coupled equatorial processes, such as the equatorial
Indian dipole (Saji et al. 1999; Webster et al. 1999),
variability of the Seychelles–Chagos thermocline ridge
(Vialard et al. 2009), and the influences of these pro-
cesses on the strength and timing of Arabian low-level
winds and coastal upwelling (Izumo et al. 2008). Here
we also do not account for the effect of the MJO on the
year-to-year variability of seasonal Arabian Sea tropical
cyclone activity (Camargo et al. 2009). It is possible that
considering the enhanced convective activity associated
with one or more phases of the MJO, in conjunction with
the large-scale ocean–atmosphere patterns associated
with cyclogenesis we describe here, will help to paint
a more complete picture of the environmental condi-
tions associated with Arabian Sea storm activity.
The Arabian Sea is also a region with intense dust
outbreaks that originate over either the Arabian Pen-
insula or the Pakistan–Afghanistan region (e.g., Evan
et al. 2006). It is interesting to speculate on the possi-
bility that dust outbreaks here may indirectly influence
tropical cyclone activity in a manner similar to what has
been proposed for African dust outbreaks and Atlantic
hurricanes (Evan et al. 2008). Initial efforts to determine
the importance of mineral aerosols on Arabian Sea SST,
performed in a manner similar to those in Evan et al.
(2009) for the Atlantic, imply that Arabian Sea SST is
not sensitive to mineral aerosols via the aerosol direct
effect (not shown). However, other work has suggested
the dust outbreaks may alter the state of the southwest
monsoon via the so-called elevated heat pump (Lau
et al. 2009). If springtime dust outbreaks deposit mineral
aerosols over snow- and ice-covered surfaces of the
Himalayan foothills, the resulting reduction in albedo
would provide an elevated heat source via the absorp-
tion of solar radiation by the aerosols, resulting in an
upper-level warm-core anticyclone over northern In-
dian and enhanced monsoon flow (Lau et al. 2009).
However, whether this proposed causal chain is robust is
debatable, and any consequences on seasonal Arabian
Sea tropical cyclone activity are not yet clear.
Acknowledgments. Partial support for this work was
provided by a grant from Risk Prediction Initiative. We
are grateful for correspondence with R. Murnane and
R. Pappert. We thank Christopher Landsea and two
anonymous reviewers for their comments on an ear-
lier version of this paper.
Bell, G. D., 2003: Atlantic hurricane season [in ‘‘State of the Cli-
mate in 2002’’]. Bull. Amer. Meteor. Soc., 84, S19–S26.
Bister, M., and K. A. Emanuel, 2002a: Low frequency variability of
tropical cyclone potential intensity 1. Interannual to inter-
decadal variability. J. Geophys. Res., 107, 4801, doi:10.1029/
——, and ——, 2002b: Low frequency variability of tropical cyclone
potential intensity 2. Climatology for 1982–1995. J. Geophys.
Res., 107, 4621, doi:10.1029/2001JD000780.
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.
——, M. C. Wheeler, and A. H. Sobel, 2009: Diagnosis of the MJO
modulation of tropical cyclogenesis using an empirical index.
J. Atmos. Sci., 66, 3061–3074.
——, A. H. Sobel, A. G. Barnston, and P. J. Klotzbach, 2010: The
influence of natural climate variability on tropical cyclones,
and seasonal forecasts of tropical cyclone activity. Global
Perspectives on Tropical Cyclones: From Science to Mitigation,
J. C. L. Chan and J. D. Kepert, Eds., World Scientific Series on
Asia-Pacific Weather and Climate, Vol. 4, World Scientific
Publishing, 325–360.
Chu, J.-H., C. R. Sampson, A. S. Levine, and E. Fukada, 2002: The
Joint Typhoon Warning Center tropical cyclone best-tracks,
1945–2000. U.S. Naval Research Laboratory Rep. NRL/MR/
7540-02-16, 22 pp.
Clark, C. O., J. E. Cole, and P. J. Webster, 2000: Indian Ocean SST
and Indian summer rainfall: Predictive relationships and their
decadal variability. J. Climate, 13, 2503–2519.
Dvorak, V. F., 1975: Tropical cyclone intensity analysis and fore-
casting from satellite imagery. Mon. Wea. Rev., 103, 420–430.
——, 1982: Tropical cyclone intensity analysis and forecasting from
satellite visible or enhanced infrared imagery. NOAA Na-
tional Environmental Satellite Service, Applications Labora-
tory Training Notes, 42 pp.
——, 1984: Tropical cyclone intensity analysis using satellite data.
NOAA Tech. Rep. 11, 45 pp.
——, 1995: Tropical clouds and cloud systems observed in satel-
lite imagery: Tropical cyclones. Workbook Vol. 2, 359 pp.
[Available from NOAA/NESDIS, 5200 Auth Rd., Washington,
DC 20333.]
Elsner, J. B., J. P. Kossin, and T. H. Jagger, 2008: The increasing
intensity of the strongest tropical cyclones. Nature, 455, 92–95.
Emanuel, K. A., 1988: The maximum intensity of hurricanes.
J. Atmos. Sci., 45, 1143–1155.
——, and D. S. Nolan, 2004: Tropical cyclone activity and global
climate. Proc. 26th Conf. on Hurricanes and Tropical Meteo-
rology, Miami, FL, Amer. Meteor. Soc., 10A.2. [Available
online at
of a new over-water advanced very high resolution radiometer
dust detection algorithm. Int. J. Remote Sens., 27, 3903–3924.
——, and Coauthors, 2008: Ocean temperature forcing by aero-
sols across the Atlantic tropical cyclone development region.
Geochem. Geophys. Geosyst., 9, Q05V04, doi:10.1029/
——, D. J. Vimont, A. K. Heidinger, J. P. Kossin, and R. Bennartz,
2009: The role of aerosols in the evolution of tropical North
Atlantic Ocean temperatures anomalies. Science, 324, 778–781.
Gray, W. M., 1968: Global view of the origin of tropical distur-
bances and storms. Mon. Wea. Rev., 96, 669–700.
Hart, R. E., R. N. Maue, and M. C. Watson, 2007: Estimating local
memory of tropical cyclones through MPI anomaly evolution.
Mon. Wea. Rev., 135, 3990–4005.
1JANUARY 2011 E V A N A N D C A M A R G O 157
Hastenrath, S., 1991: Climate Dynamics of the Tropics. Kluwer
Academic Publishers, 486 pp.
Izumo, T., C. B. Monte
´gut, J. J. Luo, S. K. Behera, S. Masson, and
T. Yamagata, 2008: The role of the western Arabian Sea up-
welling in Indian monsoon rainfall variability. J. Climate, 21,
Joseph, P. V., J. K. Eischeid, and R. J. Pyle, 1994: Interannual
variability of the onset of the Indian summer monsoon and its
association with atmospheric features, El Nin
˜o, and sea sur-
face temperature anomalies. J. Climate, 7, 81–105.
JTWC, 1998: Tropical Cyclone 03A. 1998 annual tropical cyclone
report, JTWC Rep., 111–113.
——, 2007: Section 3: Detailed cyclone reviews: TC 02A (Gonu).
2007 annual tropical cyclone report, JTWC Rep., 62–64.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re-
analysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.
Knapp, K. R., and J. P. Kossin, 2007: A new global tropical cyclone
data set from ISCCP B1 geostationary satellite observations.
J. Appl. Remote Sens., 1, 013505, doi:10.1117/1.2712816.
——, M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J.
Neumann, 2010: The International Best Track Archive for
Climate Stewardship (IBTrACS): Unifying tropical cyclone
data. Bull. Amer. Meteor. Soc., 91, 363–376.
Kossin, J. P., K. R. Knapp, D. J. Vimont, R. J. Murnane, and
B. A. Harper, 2007: A globally consistent reanalysis of hurri-
cane variability and trends. Geophys. Res. Lett., 34, L04815,
Kumar, K., B. Rajagopalan, M. Hoerling, G. Bates, and M. Cane,
2006: Unraveling the mystery of Indian monsoon failure dur-
ing El Nin
˜o. Science, 314, 115–119.
Lander, M. A., and C. P. Guard, 1998: A look at global tropical
cyclone activity during 1995: Contrasting high Atlantic activity
with low activity in other basins. Mon. Wea. Rev., 126, 1163–
Landsea, C. W., B. A. Harper, K. Hoarau, and J. A. Knaff, 2006:
Can we detect trends in extreme tropical cyclones? Science,
313, 452–454.
Lau, K. M., K. M. Kim, C. Hsu, and B. Holben, 2009: Possible in-
fluences of air pollution, dust and sandstorms on the Indian
monsoon. WMO Bull., 58, 22–30.
Lee, C. S., R. Edson, and W. M. Gray, 1989: Some large-scale
characteristics associated with tropical cyclone development
in the North Indian Ocean during FGGE. Mon. Wea. Rev.,
117, 407–426.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V.
Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003:
Global analyses of sea surface temperature, sea ice, and night
marine air temperature since the late nineteenth century.
J. Geophys. Res., 108, 4407, doi:10.1029/2002JD002670.
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and
W. Wang, 2002: An improved in situ and satellite SST analysis
for climate. J. Climate, 15, 1609–1625.
Saji, N. H.,B. N. Goswami, P. N. Vinayachandran, andT. Yamagata,
1999: A dipole mode in the tropical Indian Ocean. Nature, 401,
Shukla, J., and D. A. Paolino, 1983: The Southern Oscillation and
long-range forecasting of the summer monsoon rainfall over
India. Mon. Wea. Rev., 111, 1830–1837.
Singh, O. P., T. M. A. Khan, and M. S. Rahman, 2000: Changes
in the frequency of tropical cyclones over the North Indian
Ocean. Meteor. Atmos. Phys., 75, 11–20.
Swanson, K. L., 2008: False causality between Atlantic hurricane
activity fluctuations and seasonal lower atmospheric wind anom-
alies. Geophys. Res. Lett., 35, L18807, doi:10.1029/2008GL034469.
Torrence, C., and P. J. Webster, 1999: Interdecadal changes in the
ENSO–monsoon system. J. Climate, 12, 2679–2690.
Trenberth, K. E., 1997: The definition of El Nin
˜o. Bull. Amer.
Meteor. Soc., 78, 2771–2777.
Vialard, J., and Coauthors, 2009: Cirene: Air–sea interactions in
the Seychelles–Chagos thermocline ridge region. Bull. Amer.
Meteor. Soc., 90, 45–61.
Webster, P. J., A. M. Moore, J. P. Loschnig, and R. R. Leben, 1999:
Coupled ocean–atmosphere dynamics in the Indian Ocean
during 1997–98. Nature, 401, 356–360.
Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time
multivariate MJO index: Development of an index for moni-
toring and prediction. Mon. Wea. Rev., 132, 1917–1932.
WHO, 2009: WRO Yemen situation report No. 3: Heavy flooding in
Yemen, October 27, 2008, WHO Rep., 2 pp. [Available online
... Various factors can affect the surge and wave height, including TC characteristics, coastline shape, ocean bathymetry, and local features (Pattanayak et al., 2016). While it is demonstrated that TCs of the Arabian Sea are intensifying (Evan and Camargo, 2011), and the extreme waves and surge heights are remarkably dependent on the characteristics of TCs (Camelo et al., 2020), limited numbers of research that studied TCs generated waves and surges are available (e.g., Pattanayak et al., 2016). ...
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The present study aims to address the future projection of waves and storm surges of Tropical cyclones over the Arabian Sea, using the atmosphere-ocean numerical models and CMIP5 climate models. A Pseudo Global Warming (PGW) approach was utilised to implement the future climate and a WRF-FVCOM-SWAN framework was utilized to estimate the changes of a historical event in the future climate. The uncertainties included in different parts of the framework can lead to remarkable changes in this future estimation and are required to be addressed and quantified for a more appropriate estimation. Different factors such as forcing, boundary condition, and physics play a significant role in the uncertainties of wave and surge models. The study revealed that the wind forcing provided by the WRF model is the governing factor with the highest importance.
... Based on the aforementioned criteria, a MOV was identified during the following years : 1983: -1985: , 1987: -1989: , 1992: , 1994: , 1996: , 1998: , 2001: -2011: , 2015: . Thus, over the period 1982: -2021, the MOV formed in ∼58% of the years. ...
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During certain years, a synoptic scale vortex called the monsoon onset vortex (MOV) forms within the northward advancing zone of precipitating convection over the Arabian Sea. The MOV does not form each year and the reason is unclear. Since the Madden‐Julian Oscillation (MJO) is known to modulate convection and tropical cyclones in the tropics, we examined its role in the formation of the MOV. While the convective and transition phases of the MJO do not always lead to MOV formation, the suppressed phase of the MJO hinders the formation of the MOV more consistently. This asymmetric relationship between the MJO and MOV can be partially explained by the modulation of the large‐scale environment, measured by a tropical cyclone genesis index. It also suggests that the Arabian Sea is generally near a critical state that is favorable for MOV formation during the monsoon onset period.
... The increase in the formation of extremely severe cyclones over the AS basin during the post-monsoon season is associated with high SSTs and weak VWS (Murakami et al. 2017). It is argued that the recent increase in the intensity of TC over the AS during the premonsoon season (May-June) is associated with a decrease in the VWS due to the simultaneous rise in the black carbon and sulphate emission (Evan and Camargo 2011). ...
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Tropical cyclone ‘Tauktae’, in May 2021, was the strongest pre-monsoon cyclone that formed in the Arabian Sea after Kandla in 1998. It turned into an extremely severe cyclonic storm undergoing rapid intensification under favourable conditions. The current study is an effort to understand the role of warm ocean conditions favourable for the genesis and intensification ofextremely severe cyclonic storm ‘Tauktae’. Very high sea surface temperature anomaly (0.8–1.6°C) and high tropical cyclone heat potential (120–140 kJ/cm2) over the tropical cyclone genesis point and along the tropical cyclone track provided theconditions for the rapid intensification of the tropical cyclone ‘Tauktae’. High sea surface temperature and tropical cyclone heat potential enhanced the accumulated cyclone energy of tropical cyclone ‘Tauktae’, which is very high when compared to the climatological mean. The presence of warm core eddies was seen in the area, where the tropical cyclone had rapidly intensified from a very severe cyclone to an extremely severe cyclonic storm from 16 to 17 May 2021. High sea surface temperature, tropical cyclone heat potential, and warm-core eddies create warm ocean conditions that provided continuous energy in the form of sensible and latent heat flux from the ocean surface to the atmosphere. Our analysis shows that along with the favourable atmospheric conditions, the excessively warm ocean led to the genesis and intensification of tropical cyclone ‘Tauktae’. As the Arabian Sea continues to warm, it is inevitable to monitor and understand its effect on tropical cyclone genesis and intensification, which can open the way to predict and mitigate the catastrophic effect of such extreme weather events over India.
... Except from 1981 to 1990, most TCs with extreme rainfall originated from the BoB. The low frequency of TCs with extreme precipitation in the AS may be primarily due to the lower frequency of cyclogenesis (Singh et al 2001, Evan andCamargo 2011). Since the AS's average sea surface temperature (SST) is less than that of the BoB (Jaswal et al 2012), the moisture uptake during TCs may not be large enough to cause significant rainfall over land (Knutson et al 2010). ...
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Tropical cyclones (TC) cause compound extremes of rainfall and wind gust. However, their occurrence and impacts on India still need to be better understood. Using ERA5 reanalysis and cyclone eAtlas, we examine the compound extremes of precipitation and wind gust driven by TCs that made landfall over India during 1981-2021. Based on the joint return period of compound extremes, the five worst TCs occurred in May 1990, May 1999, May 2010 (Laila), October 2014 (Hudhud), and May 2020 (Amphan). A majority of TCs during 1981-2021 originated from the Bay of Bengal (BoB) and only a few from the Arabian Sea (AS). While the frequency of all the TCs has either declined or remained stable in the North Indian Ocean (NIO, BoB, AS) during 1981-2021, the frequency of TCs with compound extremes has increased by about three-fold during the most recent decade (2011-2021). Compound extremes driven by TCs affect large regions along the coast and risk infrastructure and human lives. The frequency of TCs with large area of impact (greater than 200,000 km2) compound wind and precipitation extreme extent exhibits a three-fold rise during 1981-2021, indicating an increase in the hazard associated with the compound extremes driven by TCs in India.
... The most significant of these is the Indian summer monsoon season which occurs during the summer months (JJAS), starting from June and lasting till September, and causes heavy rainfall over Central and Southern India due to the northward shift of the Tropical Convergence Zone and formation of a synoptic scale heat low over northwestern India (Francis and Gadgil, 2006;Krishnamurthy and Ajayamohan, 2010;Romatschke et al., 2010). In the pre-monsoon (MAM or March, April and May), and post-monsoon (OND or October, November and December) seasons, tropical cyclonic storms with strong torrential winds and are a primary source of heavy precipitation (Evan and Camargo, 2011;Hamada et al., 2014;Mohanty et al., 2012;Tyagi et al., 2011;Vissa et al., 2013), while thunderstorms and mesoscale convective systems over land and ocean, also cause cloudbursts leading to debris flow and flash flooding (Dimri, 2013;Kikuchi and Wang, 2010;Romatschke et al., 2010;Virts and Houze, 2016). Thus, an understanding of how moderate and extreme precipitation, in the Indian subcontinent, scale with changing temperatures is imperative and can give insight for betterment of the regional climate projections of extreme events in the study domain. ...
Intensity and frequency of short duration precipitation extremes are expected to increase under a warming climate at ~7%/K, following the Clausius-Claypeyron scaling relationship. Recent studies have analysed global and regional scaling rates for precipitation extremes against temperature over an annual period, however assessment of seasonal variations of precipitation-temperature scaling, is largely unexplored, especially over the Indian subcontinent. Satellite derived and reanalysis based precipitation data sets can function as a suitable alternative to rain-gauge based data sets over data-sparse regions. In the present study, the performance of three high resolution data sets - GPM-IMERG satellite derived, ERA5 and IMDAA reanalysis precipitation - in determining the seasonal variations in precipitation-temperature scaling rates are investigated. When compared with the IMD data, IMERG and IMDAA capture the spatial variations and magnitude of scaling rates of daily precipitation extremes much better than ERA5. Two calibrated datasets - AIMERG and AERA5 are also analyzed and compared to IMD scaling rates. Following this, we further probe into the performance of the IMERG, ERA5 and IMDAA data sets over the entire Indian subcontinent, by including the results of scaling rates over oceans. The scaling rates of all three are found to be comparable in terms of magnitude and spatial distribution when analyzed over the entire Indian subcontinent. Significant seasonal variations in scaling rates are found over the Indian subcontinent, with the highest scaling rates in the post-monsoon and pre-monsoon seasons, and weaker rates in the monsoon season. Land–ocean contrast in scaling rates is evident across all seasons, but weak ocean scaling rates are found in the monsoon season for all three data sets.
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The Arabian Sea accounts for a small fraction of Tropical Cyclones—about 2% of the annual global mean. However, the damage they might inflict there and along its coastlines, which are thickly populated, is considerable. This study explores the influence of the changes in the vertical profiles of atmosphere and oceanic environment throughout the seasons of March–June (MAMJ) and October–December (OND) in clustering the cyclogenesis over the Eastern Arabian Sea (EAS) next to the Indian West coast in recent decades. Further investigation has been done into the precise contribution of atmospheric and oceanic factors to fluctuations in cyclone intensity throughout the MAMJ and OND seasons separately. Two seasons have been studied independently in order to better understand the distinct influences of the vertical fluctuation of atmospheric factors and the thermal structure of the oceanic subsurface on cyclogenesis. More severe cyclones are caused by high tropical cyclone heat potential, and ocean subsurface warming present in this sea region influences the genesis of storms mostly during MAMJ. On the other hand, mid tropospheric relative humidity and thermal instability influences more on increasing cyclogenesis and its clustering over EAS during OND season. The findings suggest that large-scale oceanic subsurface conditions have a crucial influence on cyclogenesis over EAS through oceanic sensitivity to atmospheric forcing. This cyclone tendency and its clustering over EAS needs attention in terms of forecasting, catastrophe risk reduction, and climate change adaptation due to the security of coastal urban and rural habitats, livelihoods, and essential infrastructure along the coasts.
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During the pre- and post-monsoon season, the eastern and western coasts are highly vulnerable to cyclones. The tropical cyclone "Tauktae" formed in the Arabian Sea on 14 May 2021 and moved along the western coast of India, and landfall occurred on 17 May 2021. During the cyclone, the maximum wind speed was 220 km/hr with a pressure of 935 millibars. This cyclone influenced the meteorological and atmospheric parameters and weather conditions of western, northern, and central India and caused devastating damage. A detailed satellite, Argo, and ground data analysis have been carried out to study the changes in the ocean, atmospheric and meteorological parameters during the cyclone formation until the landfall and beyond. During cyclone generation, the air temperature (AT) was maximum (30.51 o C), and winds (220 km/h) was strong with negative omega values (0.3). RH and RF were higher near the origin and landfall location of the cyclone, with an average of 81.28% and 21.45 mm/day, respectively. The concentration of traces gases (NO 2 , SO 2 , CH 4 , TCO, COVMR, and H 2 OMMR) and aerosols (AOD, AE and PMs) loading were increased over land along the cyclone track that degraded the quality of air. The detailed analysis shows pronounced changes in the land, ocean, meteorological and atmospheric parameters. The strong wind associated with the cyclone amalgamated the surrounding airmass degraded the air quality, and severely threatened the people living in the landfall areas. The results discussed in the present study show a pronounced change in the ocean, land, meteorological and atmospheric parameters and a strong coupling between the land-ocean-atmosphere associated with the cyclone.
Tropical cyclones (TCs) over the North Indian Ocean (NIO) are closely related to Asian summer monsoon activities and have a great impact on the precipitation in the Tibetan Plateau, southwestern China, and even the middle and lower reaches of the Yangtze River. In this paper, the research progress on the impacting mechanisms of NIO TCs on the weather in China and associated forecasting techniques is synthesized and reviewed, including characteristics of the NIO TC activity, its variability under climate change, related precipitation mechanism, and associated forecasting techniques. On this basis, the limitations and deficiencies in previous research on the physical mechanisms and forecasting techniques of NIO TCs affecting the weather in China are elucidated and the directions for future investigations are discussed.
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The formation of mid-tropospheric cyclones (MTCs), responsible for a large portion of annual precipitation and extreme rainfall events over western India, is studied using an unsupervised machine learning algorithm and cyclone tracking. Both approaches reveal four dominant weather patterns that lead to the genesis of these synoptic systems. Specifically, re-intensification of westward-moving synoptic systems from the Bay of Bengal (type 1, 51%), in-situ formation with a coexisting cyclonic system over the Bay of Bengal that precedes (type 2a, 31%) or follows (type 2b, 10%) genesis in the Arabian Sea, and finally in-situ genesis within a northwestward-propagating cyclonic anomaly from the south Bay of Bengal (type 2c, 8%). Thus, a large fraction of this region's rainy middle tropospheric synoptic systems form in association with cyclonic activity in the Bay of Bengal. The four variants identified also show a marked dependence on large-scale environmental features. In particular, type 1 and type 2a MTC formation primarily occurs in phases 4 and 5, and type 2b and type 2c MTCs form mainly in phases 3 and 4 of the boreal summer intraseasonal oscillation. Further, though in-situ formation with a Bay of Bengal cyclonic anomaly (types 2a and 2b) mostly occurs in June, downstream development is more likely in the core of the monsoon season. Out of all categories, type 2a is associated with the highest composite rain rate (60 mm day ) over western India and points towards the dynamic interaction between a low-pressure system over the Bay of Bengal and the development of MTCs over western India and the northeast Arabian Sea. This classification, identification of precursors, connection with cyclonic activity over the Bay of Bengal, and dependence on a large-scale environment provide an avenue for a better understanding of rain-bearing MTCs over western India.
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In the first part of this chapter, we give a review of the relationship of climate and tropical cyclones on various time scales, from intra-seasonal to decadal. The response of tropical cyclone activity to natural modes of variability, such as El Niño-Southern Oscillation and the Madden Julian Oscillation in various regions of the world are discussed. Genesis location, track types and intensity of tropical cyclones are influenced by these modes of variability. In the second part, a review of the state of the art of seasonal tropical cyclone forecasting is discussed. The two main techniques currently used to produce tropical cyclone seasonal forecasts (statistical and dynamical) are discussed, with a focus on operational forecasts.
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A seasonally independent index for monitoring the Madden-Julian oscillation (MJO) is described. It is based on a pair of empirical orthogonal functions (EOFs) of the combined fields of near-equatorially averaged 850- hPa zonal wind, 200-hPa zonal wind, and satellite-observed outgoing longwave radiation (OLR) data. Projection of the daily observed data onto the multiple-variable EOFs, with the annual cycle and components of interannual variability removed, yields principal component (PC) time series that vary mostly on the intraseasonal time scale of the MJO only. This projection thus serves as an effective filter for the MJO without the need for conventional time filtering, making the PC time series an effective index for real-time use. The pair of PC time series that form the index are called the Real-time Multivariate MJO series 1 (RMM1) and 2 (RMM2). The properties of the RMM series and the spatial patterns of atmospheric variability they capture are explored. Despite the fact that RMM1 and RMM2 describe evolution of the MJO along the equator that is independent of season, the coherent off-equatorial behavior exhibits strong seasonality. In particular, the north- ward, propagating behavior in the Indian monsoon and the southward extreme of convection into the Australian monsoon are captured by monitoring the seasonally independent eastward propagation in the equatorial belt. The previously described interannual modulation of the global variance of the MJO is also well captured. Applications of the RMM series are investigated. One application is through their relationship with the onset dates of the monsoons in Australia and India; while the onsets can occur at any time during the convectively enhanced half of the MJO cycle, they rarely occur during the suppressed half. Another application is the modulation of the probability of extreme weekly rainfall; in the ''Top End'' region around Darwin, Australia, the swings in probability represent more than a tripling in the likelihood of an upper-quintile weekly rainfall event from the dry to wet MJO phase.
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Recently documented trends in the existing records of hurricane intensity and their relationship to increasing sea surface temperatures suggest that hurricane intensity may be increasing due to global warming. However, it is presently being argued that the existing global hurricane records are too inconsistent to accurately measure trends. As a first step in addressing this debate, we constructed a more homogeneous global record of hurricane intensity and found that previously documented trends in some ocean basins are well supported, but in others the existing records contain trends that may be inflated or spurious.
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The authors examine relationships between Indian Ocean sea surface temperature (SST) variability and the variability of the: Indian monsoon, including analysis of potential long-lend predictions of Indian rainfall by regional SST and the influence of ENSO and decadal variability on the stability of the relationships. Using monthly gridded (4 degrees X 4 degrees) SST data from the Global Sea-Ice and Sea Surface Temperature (GISST) dataset that spans 1945-94, the correlation fields between the All-India Rainfall Index (AIRI) and SST fields over the tropical Indian Ocean are calculated. In the boreal fall and winter preceding the summer Indian monsoon, SST throughout the tropical Indian Ocean correlates positively with subsequent monsoon rainfall. Negative correlation occurs between SST and the AIRI in the subsequent autumn in the northern Indian Ocean only. A strong correlation (0.53) is found between the summer AIRI and the preceding December-February Arabian Sea SST. The correlation between the AIRI and the SST to the northwest of Australia for the same period is 0.58, The highest correlation (0.87) for the years following 1977 is found between the AIRI and the central Indian Ocean SST in the preceding September-November, but this relationship is much weaker in earlier years. Based upon these correlations, the authors define Arabian Sea (AS1), northwest Australia (NWA1), and central Indian Ocean (CIO1) SST indexes. The relationships of these indexes to the AIRI and ENSO are examined. The authors find that the high correlation of the AS1 and NWA1 SST indexes with the Indian summer rainfall is largely unaffected by the removal of the ENSO signal, whereas the correlation of the CIO1 index with the AIRI is reduced. The authors examine the interdecadal variability of the relationships between SST and the AIRI and show that the Indian Ocean has undergone significant secular variation associated with a climate shift in 1976. The possible mechanisms underlying the correlation patterns and The implications of the relationship to the biennial nature of the monsoon and predictability are discussed.
This study examines the local memory of atmospheric and oceanic changes associated with a tropical cyclone (TC). The memory is quantified through anomalous maximum potential intensity (MPI) evolution for 20 days prior to the arrival of a TC through 60 days after the TC passage. The local MPI weakens and is not restored to the evolving climatology until well after the TC has departed. Stabilization occurs through warming of the atmosphere and cooling of the ocean surface on different time scales. The time scale of MPI stabilization following TC passage is approximately 30-35 days for a tropical storm to 50-60 days for a category 3-5 hurricane, with significant storm-specific and basin-specific variability. The atmospheric stabilization (warming with respect to SST) begins with TC arrival and continues for approximately 7-10 days after passage, when the troposphere cools below normal. The rewarming of SST and the subsequent rewarming of the atmosphere occurs within approximately 35 days for all intensities, despite a positive (weakened) MPI anomaly through two months. This suggests that the atmosphere retains anomalous warmth beyond what can be attributable to sensible heating from the rewarmed SST. The maintenance of a positive MPI anomaly beyond 35 days is thus attributed to a feedback on larger scales that requires considerable further research. A TC's passage through a region does not always lead to a weakening of the MPI. In regions poleward of the sharp SST gradient, the MPI one month after TC passage is often several millibars stronger than climatology. There are also mesoscale regions of destabilization one month after TC passage that may result partially from salinity changes driven by oceanic mixing as well as changes in precipitation and evaporation.
The long-term mean date of the monsoon onset over Kerala (MOK) varies between 30 May and 2 June according to different estimates, with a standard deviation of 8-9 days. The earliest date of MOK, and the most delayed one, during the last 100 years differ by 46 days (7 May and 22 June, respectively). MOK switches on a spatially large and intense convective heat source over south Asia, lasting from June to September, whose moisture supply is made available through the cross-equatorial low-level jet stream. Analysis of the SST field has shown that delayed MOK is associated with warm SST anomalies at and south of the equator in the Indian and Pacific oceans and cold SST anomalies in the tropical and subtropical oceans to the north during the season prior to the monsoon onset (i.e., March to May). It is hypothesized that such SST anomalies over the Indian and Pacific oceans (generally found associated with El Nino, either in year 0 or year +1 or in both) cause the interannual variability of the MOK through their action in affecting the timing of the northwestward movement of the ECCM. -from Authors