Global and Planetary Change 56 (2007) 387 – 398
www.elsevier.com/locate/gloplacha

Cyclone activity associated with the interannual seesaw oscillation ofsummer precipitation over northern Eurasia
Yoshiki Fukutomi a,⁎, Kooiti Masuda a , Tetsuzo Yasunari a,bba
Frontier Research Center for Global Change, JAMSTEC, Yokohama, Japan
Hydrospheric Atmsopheric Research Center, Nagoya University, Nagoya, Japan

Received 25 April 2005; accepted 19 July 2006
Available online 24 October 2006

Abstract
This study describes surface cyclone activity associated with the interannual variability in summer precipitation in northernEurasia and how that activity may be connected to other climate signals. An east–west seesaw oscillation of precipitation acrossSiberia is the primary mode of interannual variability in the summer hydrological cycle over northern Eurasia. This variation occurs
at sub-decadal timescales of about 6–8 years. The spatial characteristics of cyclone frequency and cyclone tracks at the two poles invariability [eastern Siberia (ES)-wet–western Siberia (WS)-dry and WS-wet–ES-dry] were examined, and temporal variability inregional cyclone frequency was compared to basin-scale precipitation variability. The analysis period was from 1973 to 2002, when
the precipitation variability signal was predominant.
Cyclone behavior suggested that the regions of enhanced (reduced) cyclone activity coincided with regions of increased(decreased) precipitation in each phase of the oscillation. Such behavior reflects the zonal displacement of the track of frequent
storm activity that accompanies the changes in precipitation. Comparisons of the temporal characteristics confirmed the importanceof regional cyclone frequency on precipitation variability in both eastern and western Siberia. Low-frequency changes in regional
cyclone activity may produce the precipitation oscillation. We used various climate signals to explore connections between regional
precipitation and cyclone activity in Siberia. Results suggest that the North Atlantic Oscillation (NAO) from the preceding winter issignificantly and negatively correlated with summer surface cyclone frequency and precipitation over western Siberia. Enhanced(reduced) summer cyclone activity and precipitation in western Siberia follows low- (high-) winter NAO. However, the physicalmechanisms linking summer cyclone activity and precipitation over western Siberia with the preceding climate conditions
associated with the winter NAO remain unclear.
© 2006 Elsevier B.V. All rights reserved.
Keywords: cyclone activity; interannual variability of summer precipitation; Siberia; North Atlantic Oscillation

1. Introduction⁎ Corresponding author. Frontier Research Center for Global
Change, Yokohama Institute for Earth Sciences, Japan Agency
for Marine-Earth Science and Technology (JAMSTEC), 3173-25
Showamachi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.
Tel.: +81 45 778 5539; fax: +81 45 778 5706.
E-mail address: fukutomi@jamstec.go.jp (Y. Fukutomi).
0921-8181/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.gloplacha.2006.07.026

An east–west precipitation oscillation is a noteworthy characteristic of the interannual variability in thesummer (June–August: JJA) hydrological cycle overnorthern Eurasia (Fukutomi et al., 2003, 2004). An outof-phase relationship exists in the spatial and temporalvariation in large-river-basin-scale summer precipitation

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between eastern and western Siberia (hereafter ES and
WS). The spatial precipitation anomaly acquires aneast–west dipole pattern at the extreme poles of theoscillation. The variation shows sub-decadal timescalesof about 6–8 years and has been especially pronounced
since the early 1970s. This oscillation is referred to as aprecipitation seesaw. Large-scale circulation anomalies
during the extreme phases show a quasi-stationary wave
pattern across northern Eurasia.
Fukutomi et al. (2004) showed that interannualchanges in storm-track activity aligned with the Arctic
frontal zone over northern Eurasia play a dynamic rolein maintaining the mean circulation pattern at the extreme ends of the oscillation. They used variance statistics for synoptic-scale (b 10 days) eddy components tohighlight the zonal displacement of high storm activity.
Such displacement is reflected in geographical changes
in the primary paths of traveling synoptic-scale weather
systems. The dynamic effects of synoptic processes werediagnosed in terms of transient eddy statistics (Fukutomi
et al., 2004), but the actual behavior of continental cyclones associated with the precipitation oscillation remains unclear.
Interannual increases (decreases) in annual discharge
from major Siberian rivers are controlled by enhanced(reduced) activity of high-latitude summer cyclones andtheir attendant precipitation (Semiletov et al., 2000).
Summer cyclone activity controls the strength of thehydrological cycle. Serreze and Etringer (2003) recently
established a seasonal climatology that relates daily precipitation events in Siberian river basins with synoptic
circulation types. They classified 500-hPa troughs (cyclonic flow) associated with heavy precipitation events in
the river basins and revealed that both 500-hPa troughfeatures and surface cyclogenesis counts are good proxies
for day-to-day precipitation events (storminess) inindividual basins. These previous studies have suggested
that large-scale changes in the frequency of cyclones help
to control basin-scale precipitation variability. Therefore,
it is reasonable to expect that northern Eurasian cyclone
activity is a major influence on the precipitation oscillation and the associated hydroclimate. However, little
work has elucidated the substantial interannual relationships between basin-scale precipitation and cyclone
activity that occurs over northern Eurasia in summer. It
is necessary to investigate the statistical features ofcyclones as main precipitating systems to understand theunderlying mechanisms influencing long-term changes inthe summer hydrological cycle over Siberia.
This study extends previous work (Fukutomi et al.,2004) to storm statistics using a Lagrangian-type approach. We sought to identify where extratropical

cyclones that are associated with the precipitation
oscillation form and move, and to confirm that interannualchanges in large-scale cyclone activity play an important
forcing role in the precipitation oscillation. Spatial
characteristics of cyclone frequency and tracks that arecharacteristic of the two opposite extremes of the
precipitation oscillation (ES-wet–WS-dry and WS-wet–ES-dry) were examined. Temporal variations in regional
cyclone frequency were then compared to changes inbasin-scale precipitation. The analysis period was 1973–
2002, when the precipitation seesaw signal was predominant. This paper describes results that support evidence
from previous work and discusses possible links to otherclimate factors.
2. Data
We used storm-track statistics for the Northern
Hemisphere (NH) obtained from the National Oceanicand Atmospheric Administration (NOAA)/Climate
Diagnostics Center (CDC). The surface cyclone statistics were derived from sea level pressure (SLP) datafrom the National Centers for Environmental Prediction(NCEP)–National Center for Atmospheric Research(NCAR) reanalysis. Detection of and tracking of surface
cyclones in this dataset followed an algorithm originallydeveloped by Serreze (1995) and Serreze et al. (1997).
Cyclone statistics were consistent with those used inother studies (e.g., Clark et al., 1999; Serreze et al.,
2001; McCabe et al., 2001; Serreze and Etringer, 2003).
The cyclone detection algorithm was applied to 6-hourly
SLP fields interpolated from a 2.5° × 2.5° latitude–
longitude grid to a 250-km version of the NH EASEgrid (Armstrong and Brodzik, 1995). The statistics forthis study were for the period 1948 to present.
A Global Cyclone Climatology derived from the
NCEP–NCAR reanalysis geopotential height fields
between 1957 and 1998 (Key and Chan, 1999) was also
used. This dataset was provided by the Cooperative
Institute for Meteorological Satellite Studies (CIMSS) at
the University of Wisconsin; it includes 12-hourly positions of cyclone centers and cyclone tracks at the 1000-hPa
and 500-hPa levels at a grid resolution of 2.5° × 5°
latitude–longitude. In this analysis, 500-hPa cyclone
centers and tracks for individual years in NH summer
were extracted from the original record. Konard (2001)
used the 500-hPa cyclone statistics from this dataset toinvestigate relationships between mid-tropospheric
cyclones and extreme precipitation events.
As in previous studies, monthly global land precipitation analyses [PREC/L (Chen et al., 2002)] captured
the spatial and temporal patterns of interannual summer

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precipitation variability in Siberia. The precipitation
data were on a 2.5° latitude–longitude grid. Data wereavailable from 1948 to the present at the NOAA/Climate
Prediction Center (CPC).
North Atlantic Oscillation (NAO) index data werefrom http://www.cgd.ucar.edu/~jhurrell/nao.html. TheseNAO indices were calculated as the difference innormalized sea level pressure between Ponta Delgada
in the Azores and Reykjavik, Iceland. This definition of
the NAO index differs from that used by Hurrell (1996).
NAO data were used to determine the relationshipbetween regional cyclone occurrences and the NAO.
Note Hurrell (2003) describes basic characteristics of
the NAO.

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3. Results
3.1. Siberian precipitation seesaw
An interannual oscillation in precipitation occurs
over Siberia that assumes a seesaw pattern. This section
emphasizes that the precipitation seesaw is the leadinginterannual pattern of summer precipitation variability
over northern Eurasia, as noted in earlier work
(Fukutomi et al., 2003, 2004).
ES and WS precipitation indices, as well as a unified
index that combines them, were derived to identify seesaw extremes used to construct composites of precipitation, cyclone frequency, and cyclone tracks. Fig. 1a

Fig. 1. (a) Time series of summer (JJA) mean precipitation (mm month− 1) for the ES domain (blue thin line: 110°–135°E, 50°–75°N) and the WS
domain (red thick line: 60°–85°E, 50°–75°N). (b) Same as (a) except for anomalies. Six ES-wet–WS-dry summers (blue inverted triangles) and six
WS-wet–ES-dry summers (red triangles) are selected. (c) Precipitation seesaw index (see text). The selected six ES-wet–WS-dry summers (six WSwet–ES-dry summers) are indicated by blue inverted triangles (red triangles).

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shows time series of average summer precipitation indices for ES and WS for 1973–2003. The precipitationindex for ES (PES) is defined as the spatial average of JJA
mean precipitation over the ES region (110°–135°E,
50°–70°N). The precipitation index for WS (PWS) issimilarly defined for (60°–85°E, 50°–70°N). These

regions were defined following Serreze et al. (2003) andcovered roughly the Lena (ES) and Ob (WS) River basins.
Fig. 1b shows time series of index anomaly, which wascomputed as a deviation from the 30-yr climatology.
The precipitation seesaw index (hereafter PSI) was defined as half the difference between them: ((PES − PWS) / 2;

Fig. 2. (a) Composite of the summer mean precipitation (mm month–1) defined for the six ES-wet–WS-dry summers. (b) Same as (a) except for thesix WS-wet–ES-dry summers. (c) Composite difference between the two phases. The ES and WS regions that are used to define the precipitation
indices are indicated by black boxes. Positive (negative) values are indicated by solid (dashed) lines.

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Fig. 1c). Positive extremes of PSI represent ES-wet–WSdry summers, and negative extremes of PSI represent WSwet–ES-dry summers.
The two opposite poles of the seesaw were detectedusing PES, PWS, and PSI. The extremes were the six
highest ES-wet–WS-dry summers (1974, 1977, 1981,
1982, 1988, and 1989) and the six highest WS-wet–391

ES-dry summers (1979, 1986, 1992, 1996, 2001, and2002). When the ES region was very wet (above-normal precipitation), the WS region was very dry (belownormal precipitation), and vice versa. The following
conditions were considered in choosing the extremes:
1) ES-wet–WS-dry (WS-wet–ES-dry) summers are
the highest (lowest) phases of the PSI (Fig. 1c). 2) Pairs

Fig. 3. (a) Composite of cyclone frequency (cyclone/250 × 250 km2/season) for the six ES-wet–WS-dry summers. (b) Same as (a) except for the six
WS-wet–ES-dry summers. (c) Composite difference between the two phases. Positive (negative) values are indicated by solid (dashed) lines. Boxes E(100°–135°E, 50°–75°N) and W (55°–90°E, 50°–75°N) are outlined by heavy black lines (see text). Artifacts in the area 85°–90°N are masked out.

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of positive PES and negative PWS anomalies for the
ES-wet–WS-dry summers (positive PWS and negative PES anomalies for the WS-wet–ES-dry summers)(Fig. 1b). Composites were constructed based on thesecriteria.
The Siberian precipitation seesaw follows a characteristic pattern. Fig. 1a and b shows that the two precipitation
indices give rise to a clear out-of-phase behavior, as notedby Fukutomi et al. (2003, 2004). We found a significant
negative correlation (r = −0.64) between these two indicesthat exceeds the 99.9% confidence level (r = −0.57) according to a standard t-test assuming 28 degrees of freedom. The precipitation composite for ES-wet–WS-drysummers shows a striking increase in central-ES and adecrease in WS (Fig. 2a). In contrast, the composite for the
WS-wet–ES-dry summers shows more precipitation overWS than ES (Fig. 2b). The precipitation anomaly pattern,

defined as the difference between the two composite
precipitation fields (Fig. 2c), shows a clear east–west
dipole pattern across Siberia. Additionally, one can noticethat another center of comparatively weaker anomalies islocated over Eastern Europe. The relationship between
this upstream signal and the Siberian dipole pattern isworth investigating, however, that is not our present
concern. The whole pattern is basically similar to that
presented by Fukutomi et al. (2004, Fig. 3d).
3.2. Large-scale changes in cyclone activity associatedwith the precipitation seesaw
The frequency of surface cyclones over Siberia as afunction of the phase of the precipitation seesaw was
examined using the CDC storm-track data. Composites ofcyclone frequency (Fig. 3) were constructed with methods

Fig. 4. (a) Cyclone tracks passing through box E and their genesis points (dots) for the six ES-wet–WS-dry summers. (b) Same as (a) except for box
W for the six WS-wet–ES-dry summers.

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used for Fig. 2. Cyclone frequency was defined as thenumber of cyclone centers per summer at each EASE grid
point. Weak systems were ignored; only cyclones withcentral pressures at or below 1000 hPa were included.
This central pressure cut-off reduced the number of nonprecipitating weather systems in the total counts.

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Accordingly, the resulting statistics reflect precipitationbearing cyclone activity.
Fig. 3a shows the geographical distribution of
cyclone frequency for the ES-wet–WS-dry summers.
There were several local maxima in central-ES (east of90°E). These maxima corresponded closely to regions

Fig. 5. (a) Composite of cyclone track density (tracks/555 km circle/season) for the six ES-wet–WS-dry summers. (b) Same as (a) except for the six
WS-wet–ES-dry summers. (c) Composite difference between the two phases. Positive (negative) values are indicated by solid (dashed) lines.
Artifacts in the area 85°–90°N are masked out.

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of increased precipitation in central and eastern Siberia(Fig. 2a). In contrast, a pronounced maximum prevailed
over much of WS during WS-wet–ES-dry summers(Fig. 3b), a frequency maximum that overlapped a bandof heavier precipitation across WS (Fig. 2b). In
summary, cyclone occurrence increased (decreased)close to or within regions of above- (below-) normal
precipitation. Locations of relatively high cyclone
frequency matched those in a climatology obtained byChen et al. (1991) and Chen and Zhang (1996). Composite difference fields between the two contrasting
extremes (ES-wet–WS-dry summers minus WS-wet–ES-dry summers; Fig. 3c) show a dipole pattern very
similar to that of precipitation (Fig. 2c).
Fig. 3c includes two domains outlined by heavy solidlines, which characterize the spatial behavior of cyclone
tracks and temporal variation in regional cyclone frequency. One domain roughly covers ES and is defined asbox E (100°–135°E, 50°–75°N); a second domain covers
WS and is defined as box W (55°–90°E, 50°–75°N).
These domains encompass the regions of high and low
cyclone activity associated with the precipitation seesaw.
Fig. 4 shows the distributions of surface cyclone
tracks in each extreme phase to reveal preferred cyclone
paths that affect regions of increased precipitation.
Cyclone tracks with at least one central pressure at or
below 1000 hPa and a minimum lifetime of 2 days wereextracted from the CDC storm-track data. Weak andshorter-lived weather systems were ignored. Only cyclones that passed through, originated in, or terminatedin box E (box W) were plotted for the ES-wet–WS-drysummers (WS-wet–ES-dry summers). Thus, cyclone
tracks clustered in the targeted boxes. However, regionsof significant cyclogenesis and cyclolysis were scatteredover northern Eurasia. During the ES-wet–WS-drysummers (Fig. 4a), most of the cyclones passing overeastern Siberia developed inside Siberia. After crossing
ES, the cyclones typically moved northeastward toward
the Arctic Ocean and the Kolyma region. Some of themreached the North Pacific. Several cyclones developedin Northeast Europe and Scandinavia (west of 60°E),
and a few entered from the Kara Sea. They subsequently
moved eastward and southeastward to central–eastern
Siberia. A considerable number of cyclones developed
within and then exited ES; cyclogenesis was common inthis region. During the WS-wet–ES-dry summers(Fig. 4b), cyclones entering western Siberia came fromvarious locations: European, central Asia, and the Norwegian Sea. Many cyclones also developed inside western Siberia. This region is strongly cyclogenetic. Most
cyclones moved eastward and northeastward, eventuallyreaching central and eastern Siberia and the Arctic Ocean.
A more comprehensive picture of geographicalchanges in primary cyclone tracks is provided in Fig. 5,

Fig. 6. (a) Time series of summer (JJA) cyclone counts for box E (blue) and box W (red). Dashed (solid) line is raw (smoothed) time series. (b) Sameas (a) except for anomalies.

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which shows composites of cyclone track density
constructed for the opposite phases of the precipitation
seesaw as in Fig. 3. Cyclone track density is the number ofcyclones passing within a certain distance of each grid
point and was computed as described by Sinclair (1994).
Track density was derived by counting the tracks inoverlapping circular cells with a radius of 555 km(approximately 5° latitude). This technique is spatiallysmoother compared to raw counts that are based on nonoverlapping contiguous unit cells (e.g., Sinclair, 1994,
1995). The map for the ES-wet–WS-dry phase (Fig. 5a)shows a high density between 60°–70°N latitude
extending from the eastern end of WS into Kolyma. The
enhanced track density across central-ES was near theincreased precipitation signal in ES (Fig. 2a,c). The
cyclone frequency maxima in central and ES (Fig. 3a) fellwithin this band. At the same time, density was relatively
low over WS. In the WS-wet–ES-dry phase (Fig. 5b),there was a pronounced maximum in the same latitudebelt over WS, and this high concentration of cyclone
tracks overlapped the enhanced cyclonic activity (Fig. 3b)
and the increased precipitation over WS (Fig. 2b). In
contrast, the density was reduced over central and ES. The
composite difference between the two phases (Fig. 5c)shows an east–west dipole, which is almost collocatedwith the precipitation dipole (Fig. 2c).

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Cyclone behavior indicates that regions of enhanced (reduced) cyclone activity nearly coincidewith regions of increased (decreased) precipitation ineach seesaw phase. Results shown here for cyclone
frequency and cyclone tracks agree with the zonal
displacement of high storm-track activity that accompanies the precipitation seesaw as discussed by
Fukutomi et al. (2004).
3.3. Interannual variability of regional cyclone activity
and precipitation
Composite analyses of cyclone occurrence showedthat spatial variability in storm-track activity resulted in
the east–west precipitation pattern in the seesaw
extremes. Temporal variations in summer precipitationin ES and WS are strongly linked to variations inregional cyclone activity. To show this linkage, temporalvariations in summer cyclone counts between ES and
WS were compared. Cyclone centers were counted for
all grid points within box E (ES) and box W (WS).
Fig. 6 presents time series of cyclone counts (i.e., area
total cyclone frequency) for the two boxes in eachsummer from 1973–2002. The series of total counts(Fig. 6a) and anomalies (Fig. 6b) both displayedconsiderable interannual variability. Time series were

Fig. 7. (a) Time series of summer cyclone counts anomalies for box E (solid line) and summer precipitation anomalies averaged over the ES domain(dashed line). (b) Same as (a) except for box W (solid line) and the WS region (dashed line).

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smoothed with a 3-point weighting average (1-2-1) tohighlight lower-frequency interannual (sub-decadal)
range signals. The two time series were out of phase.
The correlation coefficient between raw counts for box
E and box W was −0.40, which was significant at the
95% level. In addition, the correlation for the smoothedcounts was − 0.69. These inverse relationships reflect
the seesaw nature of summer cyclone activity acrossSiberia.
Consider the relationship between summer precipitation and cyclone activity in each region during thesame period (Fig. 7). A comparison of the time series ofregional cyclone counts to those of precipitation
anomalies revealed an apparent synchronous relationship for both the ES and WS region. Comparison of thetwo time series for ES (Fig. 7a) shows a variation in
cyclone counts that is in phase with precipitation
variability. The same is true over WS (Fig. 7b). Correlation coefficients between the two times series were 0.59
and 0.69 for ES and WS, respectively. Both weresignificant at a confidence level exceeding 99.9%. Interannual variability in cyclone frequency in box E (W)accounted for interannual variability in the areaaveraged precipitation in the ES (WS) region.

4. Discussion
We examined the relationships between the seesaw
pattern in summer precipitation and the concurrent cyclone activity over northern Eurasia. Statistics of cyclone
frequency and cyclone tracks were used to measure cyclone behavior. Composite analyses presented a coherent
picture of cyclone behavior affecting east–west patterns
of precipitation in the seesaw extremes. Zonal displacement of cyclone activity played a key role in determining
the seesaw pattern. Furthermore, results from comparisons of the temporal characteristics confirm the importance of regional cyclone frequency to the variability of
precipitation in eastern and western Siberia. Consequently, the low-frequency changes in regional cyclone activity
could result in the precipitation seesaw rhythm noted overthe past 30 years.
Fukutomi et al. (2004) noted that the causes ofinterannual variability in cyclone (i.e., storm track)
activity associated with the precipitation seesaw remain
uncertain. Mechanisms responsible for year-to-yearchanges in large-scale baroclinicity over northernEurasia and its link to other climate signals are not yet
understood. To elucidate a relationship, the relationships

Fig. 8. (a) Time series of summer cyclone counts anomalies for box W (thin solid line) and the winter (DJF) NAO index (thick solid line). (b) Same as(a) except for summer precipitation anomalies (thin solid line) for the WS region.

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between the Siberian regional cyclone activity, precipitation, and various climate indices were investigated(data not presented). No concurrent climate factors were
found to produce the interannual signals. However, we
did observe noteworthy two-season lagged relations.
Panels a and b of Fig. 8 show that the preceding winter
NAO is statistically related to the summer surface
cyclone frequency and precipitation over WS. The
correlation coefficient between the winter NAO and thesummer cyclone frequency in box W was − 0.39 for
1973–2002, which was marginally significant at the
95% confidence level. The correlation coefficient for
precipitation had a higher value (− 0.47) with the 99%
confidence level. Summer cyclone frequency andprecipitation in box E (ES) were poorly correlatedwith the winter NAO index. Similarly, the statistical
relationships between WS regional 500-hPa cyclone
frequency and the winter NAO index were examinedusing available data (not shown). The resulting
correlation coefficient was − 0.38 for 1958–1997(− 0.51 for 1968–1997), which marginally exceeded
the 95 (99.5)% confidence level. These inverse relationships indicate that enhanced (reduced) summer cyclone
activity and precipitation in WS followed low- (high-)
winter NAO. However, mechanisms to explain these
coherent signals are lacking. A question raised here iswhether the climate conditions associated with the winter NAO influence subsequent summer storm activity in
WS. Several recent studies have suggested potential
impacts of the winter NAO and the following spring–summer surface climate signals over the Eurasiancontinent (e.g., Bojariu and Gimeno, 2003; Yu andZhou, 2004; Wang and You, 2004). The winter NAO is astatistical predictor for subsequent summer climate overEurasia; however, physical mechanisms underlying thedelayed impact remain elusive.
A current concern is how the interannual backgroundconditions influence summer cyclone activity and precipitation across northern Eurasia. What are the sourcesof variability? If the winter NAO changes summer storm
activity as hypothesized from statistics, how is theclimate memory transmitted to control summer storm
activity? A plausible mechanism could be the seasonal
progress of land-surface hydrological processes associated with continental snow mass and soil moistureprovided by snowmelt water and feedback into theatmosphere (e.g., Barnett et al., 1989; Yasunari et al.,
1991; Bamzai and Marx, 2000). However, soil moisturedoes not appear to be the bridge linking winter–springsnow cover and subsequent summer conditions overnorthern Eurasia (e.g., Shinoda, 2001; Robock et al.,2003). Puzzling issues remain for the role of a carrier of397

climate signals as discussed by Hori and Yasunari(2003). Further careful investigation with regard to thewinter–spring snow cover, snow mass, and soil
moisture is required to detect the primary driver of thedominant mode of summer hydroclimate variability innorthern Eurasia.
Acknowledgments
The authors wish to thank two anonymous reviewers
for their comments. We want also to thank Hiromichi
Igarashi (FRCGC/JAMSTEC) for helpful discussions,and Nobuhiko Endo (FRCGC/JAMSTEC) for assistanceto processing the cyclone climatology data. Storm-tracks
dataset based on SLP is available online at the NOAA/CDC (ftp://ftp.cdc.noaa.gov/Datasets/map/storm/). Global Cyclone Climatology dataset based on geopotential
height is provided at CIMSS, University of Wisconsin–
Madison (ftp://stratus.ssec.wisc.edu/pub/cyclones/).
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