ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 23, NO. 5, 2006, 665–677

Variations in Typhoon Landfalls over China

Emily A. FOGARTY∗1, James B. ELSNER1, Thomas H. JAGGER1,

Kam-biu LIU2, and Kin-sheun LOUIE3

1Department of Geography, The Florida State University, Tallahassee, Florida

2Department of Anthropology and Geography, The Louisiana State University

3Department of Public and Social Administration, City University of Hong Kong

(Received 27 April 2005; revised 9 February 2006)

ABSTRACT

The interannual variability in typhoon landfalls over China is investigated using historical and modernrecords. For the purpose of substantiating and elaborating upon the claim of north to south variation intropical cyclone activity over China, a north-to-south anti-correlation in yearly activity is confirmed in thehistorical cyclone records. When cyclone activity over the province of Guangdong is high (low), it tendsto be low (high) over the province of Fujian. A similar spatial variation is identified in the modern recordsusing a factor analysis model, which delineates typhoon activity over the southern provinces of Guangdongand Hainan from the activity over the northern provinces of Fujian, Taiwan, Zhejiang, Shanghai, Jiangsu,and Shandong. A landfall index of typhoon activity representing the degree to which each year follows thispattern of activity is used to identify correlated climate variables. A useful statistical regression model thatincludes sea-level pressure differences between Mongolia and western China and sea-surface temperature(SST) over the northwestern Pacific Ocean during the summer explains 26% of the interannual variabilityof the landfall index. It is suggested that a stronger than normal north-south pressure gradient increasesthe surface easterly wind flow over northern China; this, coupled with lower SST over the Pacific, favorstyphoons taking a more westerly track toward southern China.

Key words: typhoons, southern China, historical record, climate variables

doi: 10.1007/s00376-006-0665-2

1. Introduction

Typhoons are among the most destructive of allnatural disasters. When Typhoon Wanda hit HongKong in 1962, damage and casualties were widespread,130 people were killed and at least 53 went missing.More recently, in 2003, Typhoon Nepartak broughttorrential rains and strong winds to the west coast ofHainan causing 197 million dollars (US) in economiclosses. More than 1.7 million people were affected and800 homes were destroyed (China Daily, 2003). Theeconomic loss in China from typhoon-generated stormsurge averaged over the three costliest events since1990 amounts to 2.7 billion USD (Kentang, 2000).Understanding the nature and causes of variability intropical cyclone activity over China is an importantstep toward improving risk assessment.

A tropical cyclone is a warm-core non-frontalsynoptic-scale cyclone, originating over tropical or sub-tropical waters, with organized deep convection and aclosed surface wind circulation about a well-defined

center. Typhoons are tropical cyclones of the west-ern North Pacific (WNP) that have maximum sus-tained one-minute averaged wind speeds of 33 m s−1

or greater. Tropical storms are tropical cyclones hav-ing winds between 17 m s−1 and 32 m s−1. Numerousclimate studies of typhoon activity are available (Wuand Lau, 1992; Lander, 1994; Chan, 1994; Chan andShi, 1996, 2000; Lander and Guard, 1998; Chan et al.,1998; Chen et al., 1998; Wang and Chan, 2002). Thesestudies focus mostly on factors affecting the variabilityin overall tropical cyclone activity. In general, tropicalcyclones form over the warm ocean where sea surfacetemperatures (SST) exceed 26.5◦C. While a necessarycondition, warm ocean waters are far from being suffi-cient for typhoon formation. Additional environmen-tal conditions include weak vertical wind shear, ther-modynamic instability and middle troposphere mois-ture (Gray, 1968, 1979; Emanuel, 1988; Molinari andVollaro, 1989; DeMaria et al., 2001). Large-scale at-mospheric and oceanic factors also play a role in mod-ulating typhoon activity.

*E-mail: eaf1217@garnet.fsu.edu

666 VARIATIONS IN TYPHOON LANDFALLS OVER CHINA VOL. 23

The earliest historical records of tropical cyclonesare from China, where the documented history spansabout 3500 years. The record length and the quality ofthese historical data vary from region to region. Yet,from an early period, the Chinese were aware of theessential features of typhoons, including their atten-dant storm surges. Meticulous records document thefrequency of tropical cyclone landfalls over parts ofChina (Louie and Liu, 2003). In particular, the avail-ability of records from two provinces in China (Chanand Shi, 2000; Liu et al., 2003) provides a unique op-portunity to compare patterns of typhoon landfall ac-tivity across the centuries. Records of tropical cycloneactivity from the second half of the 20th century followfrom advances in satellite and aircraft reconnaissance.The most reliable data period begins in the mid-1960swith the advent of satellites. However, since we areconcerned with landfall data, we use data back to 1945.These modern records support a spatially more com-prehensive analysis of typhoon activity in China, butthey are temporally more limited. Thus in the presentwork we make use of both the historical and mod-ern records of typhoon landfalls to better understandcoastal typhoon variability.

From modern records we know that approximatelyone third of all tropical cyclones originate over theWNP, making it the most prolific of the tropical cy-clone basins. The WNP basin supports the develop-ment of tropical cyclones year round, however the ac-tive season extends from May through November witha peak in late August or early September. The mod-ern record, from 1945–2003, indicates that on aver-age 28 tropical cyclones occur in the WNP each year.Approximately 61% of these become typhoons. MostWNP tropical cyclones track west-northwestward atlow latitudes (i.e., 15◦N latitude or lower). Approx-imately one-third continue on a west-northwestwardtrack and make landfall in East Asia south of about25◦N latitude. A larger portion of the remaining two-thirds turn northward, gain latitude, and eventuallyturn eastward as they enter the higher latitude west-erly winds (Lander, 1996).

Variations in typhoon activity have been related tovarious climate patterns. During El Nino events, whenSSTs in the central and eastern equatorial Pacific arehigher than normal, tropical cyclones are relativelymore likely to form east of about 160◦E longitude(Lander, 1994). According to Chan (1985) typhoonstend to form farther east during El Nino events and areconsequently more likely to remain east of China. Thecorollary is that typhoons are relatively more likely toeffect China during La Nina years (Saunders et al.,2000). This ENSO-typhoon relationship is supportedby an analysis of historical and modern data in Elsnerand Liu (2003). Moreover, Wu et al. (2004) found asignificant relationship between late season landfalls

over China and ENSO and Ho et al. (2004) inferredthat changes in typhoon tracks are associated with thewestward expansion of the subtropical northwesternPacific high.

From the above studies there is a clear consen-sus among researchers that large scale climate pat-terns affect tropical cyclone genesis regions and sub-sequent tracking. However, agreement has not beenreached on how large-scale circulation patterns influ-ence typhoon landfall variability. Here we address thisquestion directly. More specifically, in this study weare interested in examining the case for a north-southanti-correlation in typhoon activity along the coast ofChina. This interest is prompted by the work of Liuet al. (2003) who speculated an inverse relationshipacross latitudes between the frequency of typhoons oc-curring over northern China to those occurring overGuangdong Province in the south (Liu et al., 2003).A similar anti-correlation across latitudes in U.S. hur-ricane frequency was noted and discussed in Elsner etal. (2000).

We begin with an examination of the historicalrecord using correlation and log-linear regression ana-lyzes. Results verify a north-south variation in cycloneactivity. The modern record is subsequently analyzedto examine this variation in greater spatial detail. Inthis regard, we employ a factor analysis model. Out-put from the model is then used to study possible cli-mate factors that might influence this variation. Datasources and methods are described in section 2. In sec-tion 3 we verify the inverse relationship between tropi-cal cyclone landfall frequency over Guangdong and Fu-jian. In section 4 we describe the factor analysis proce-dure applied to the modern typhoon counts includinga test of the statistical significance of the results. Ageneralized least-squares regression of the factor scoresonto the relative typhoon frequencies provides a land-fall index. Section 5 examines climate patterns thatare correlated with the landfall index. A summaryand conclusions are given in section 6. The originalityof this work centers on the examination of both his-torical and modern landfall records and in the use ofa factor model for identifying the dominant pattern ofspatial variability in typhoon landfalls.

2. Data Description

What causes variations in the frequency of ty-phoons over China? In order to describe and modelthe spatial variability in typhoons, this study makesuse of both modern and historical typhoon recordsand modern atmospheric data obtained from the Na-tional Centers for Environmental Prediction/NationalCenter for Atmospheric Research (NCEP/NCAR) re-analysis project. The area examined is the WNP ex-tending to the Chinese coastal provinces of Guang-

NO. 5 FOGARTY ET AL. 667

dong (including Hong Kong), Fujian, Zhejiang, Shang-hai, Jiangsu, and Shandong, including the islands ofHainan and Taiwan (Fig. 1). In this section we de-scribe the historical and modern data sources and pro-vide a brief description of the analysis methods.

2.1. Historical Typhoons over Guangdong andFujian Province

The historical tropical cyclone data used in thisstudy are obtained from Liu et al. (2003). Accord-ing to their study, records of past typhoon activityare available in two groups of Chinese historical doc-uments. First, ever since the Northern Song Dynasty(AD 960–1126), the Chinese government has kept acontinuous record of typhoon strikes reported by lo-cal administrative authorities (Louie and Liu, 2003;Liu et al., 2003). The second source comes from localgazettes. These gazettes were written or last updatedduring the 19th and 20th centuries, although somedate back as early as the mid 16th century (Louie andLiu, 2003). According to Louie and Liu (2003), thegazettes contain a catalog of major cultural and natu-ral events, including typhoon strikes. Annual counts oftyphoons affecting Guangdong Province for the years1000–1909 were made available as were counts affect-ing Fujian Province for the years 1000–1920. It is likelythat counts are underestimated prior to 1600 and thatstorms much weaker than typhoon intensity may nothave been recorded. On the other hand, weaker stormscausing considerable damage could have been recordedas typhoons. Here we use the word “tropical cyclone”to denote events in the historical list. We consideronly the common years of 1600–1909 (310 consecutive

years) for the present analysis. The reliability of thecount data are likely not uniform over this 300-year pe-riod. Societal development and the state of the sciencemakes it likely that the early counts are less reliablethan later counts. Since our focus here is on interan-nual variability, this trend in increasing reliability isnot likely to significantly affect our results.

Figure 2 shows time series plots of the annualtropical cyclone counts for both Guangdong and Fu-jian. While we analyze the time series separately, itis likely that some tropical cyclones are counted inboth provinces. Collectively there are 539 cyclones.This equates to an average rate of 1.7 tropical cyclonesper year. Guangdong experiences considerably morestorms compared with Fujian, with an average rate of1.5 tropical cyclones per year and a maximum of 13cyclones in a single year. This compares to an averagerate of 0.2 per year and a maximum of 2 in a singleyear for Fujian. In this study we are interested in theco-variability of tropical cyclone landfalls across thetwo provinces.

2.2. Modern Typhoon Records

The modern typhoon record consists of 6-hourlypositions (latitude and longitude in tenths of a de-gree) and intensities (5 kt intervals of maximum sus-tained (1-minute average) wind speed) for all westernNorth Pacific tropical cyclones. The values are basedon the Annual Tropical Cyclone Reports issued by theJoint Typhoon Warning Center (JTWC) in Hawaii.Extracted from these records is an annual count oftropical cyclones that made landfall over the period1945–2003 between the months of May to November.

Tropic of Cancer

Inte

rnat

iona

l Dat

e Li

ne

±General Study Area

Hainan

Guangdong

Fujian

Taiwan

Zhejiang

Shanghai

Jiangsu

Shandong

Fig. 1. The western North Pacific (WNP) tropical cyclone basin. The coastal provincesof China considered in this study are labeled.

668 VARIATIONS IN TYPHOON LANDFALLS OVER CHINA VOL. 23

1600 1700 1800 19000

4

8

12(a) Guangdong Mean = 1.5

1600 1700 1800 1900Year

0

4

8

12 (b) Fujian Mean = 0.2

Fig. 2. Annual counts of Guangdong and Fujian Province typhoons for the years1600–1909. The counts were extracted from historical documents (see Louie and Liu,2003).

Landfalls are counted by coastal provinces includ-ing Guangdong (with Hong Kong), Fujian, Zhejiang,Shanghai, Jiangsu, and Shandong, including the is-lands of Hainan and Taiwan.

For each province, a count is added to the annualtotal if a storm reached typhoon intensity (33 m s−1)somewhere over the WNP and subsequently made a di-rect passage through the province. In this way, morethan one province may count a single storm. Also,the storm is counted even if the winds at the time ofpassage through the province are less than typhoonintensity. Direct passage is defined as the straight-linepath connecting 6-h positions of the storm’s circula-tion center. Counts are determined with the help ofa geographic information system. A total basin-widecount of the number of typhoons is also used in theanalysis to compute relative frequencies. The WNPbasin extends from the international dateline to Asia.

These data show that 61% of all WNP tropical cy-clones reached typhoon intensity. Furthermore, outof the 963 tropical cyclones that formed, 405 (42%)made landfall over at least one of China’s coastalprovinces including the islands of Hong Kong, Taiwanand Hainan. Guangdong sees the most storms with anaverage annual rate of 1.8 per year. Its neighbor to thenorth has an annual rate of 1.2 per year which com-pares with 1.3 per year for Hainan and 1.4 per year forTaiwan. The northern provinces have fewer typhoons.The average annual rate for Zhejiang is 0.6. This com-pares with 0.1, 0.2, and 0.1 for Shanghai, Jiangsu, andShandong, respectively. The annual counts are plottedas time series in Fig. 3. Here we see the large interan-nual variations in counts. Before modeling these data

we change the annual counts to relative frequenciesbased on the total number of storms occurring overthe WNP basin. Here we compare rates between mod-ern and historical storms. The Guangdong recordsshow 1.5 per year in the historical record comparedto 1.8 in the modern record . These rates are com-parable suggesting historical tropical cyclone countsare most likely records of typhoons not tropical cy-clones. In fact since the historical rate is lower it maybe that historical records consist of somewhat strongertyphoons. Similarly, the Fujian modern records indi-cate more typhoons, implying that the Fujian histori-cal records report strong typhoons rather than weakertropical storms.

2.3. ENSO and the PDO

Two large-scale atmospheric/oceanic index vari-ables are used in the study to investigate possible rela-tionships to the pattern of Chinese typhoons. The ElNino-Southern Oscillation (ENSO) refers to system-atic variations of the ocean and atmosphere charac-terized by SST and sea-level pressure (SLP) changesin the tropical Pacific Ocean. El Nino is the warmoceanic phase, which is characterized by anomalouslywarm SSTs in the eastern and central tropical Pa-cific. The opposite phase, La Nina, is characterizedby anomalously cool SSTs in the same region. Theatmospheric component of ENSO is characterized bya zonal SLP variation called the Southern Oscillation.The Southern Oscillation Index (SOI) represents thedifference in SLPs between Tahiti and Darwin, Aus-tralia.

Early works on variations in typhoon activity over

NO. 5 FOGARTY ET AL. 669

1950 1960 1970 1980 1990 20000246 (a) Hainan

1950 1960 1970 1980 1990 20000246(b) Guangdong

1950 1960 1970 1980 1990 20000246 (c) Fujian

1950 1960 1970 1980 1990 20000246(d) Taiwan

1950 1960 1970 1980 1990 20000246 (e) Zhejiang

1950 1960 1970 1980 1990 20000246(f) Shanghai

1950 1960 1970 1980 1990 2000Year

0246 (g) Jiangsu

1950 1960 1970 1980 1990 2000Year

0246(h) Shangdong

Mean = 1.3 Mean = 1.8

Mean = 1.2 Mean = 1.4

Mean = 0.6 Mean = 0.1

Mean = 0.2 Mean = 0.1

Fig. 3. Annual counts of typhoons over the period 1945–2003 by coastal provinces. The counts areextracted from the modern records using a geographic information system (GIS). Storms that reachedtyphoon intensity and subsequently went on to strike China (not necessarily at typhoon intensity)are counted by determining an intersection of the track with the province outline. Storms strikingmore than one province are counted once for each province.

the WNP have indicated a relationship with ENSO.During El Nino conditions, tropical cyclone for-mation tends to shift farther to the east (Lan-der, 1994) and the cyclone’s subsequent tracks tendto be toward higher latitudes (Elsner and Liu,2003), thus we speculate that variations in the SOIwill correlate with the typhoon landfall pattern.Monthly values of the SOI are obtained from theNOAA-CIRES (National Oceanic and AtmosphericAdministration-Cooperative Institute for Research inEnvironmental Sciences) Climate Diagnostics Cen-ter (CDC), Boulder, Colorado, from their website athttp://www.cdc.noaa.gov/. The Pacific decadal oscil-lation (PDO) is a persistent atmospheric pressure pat-tern over the North Pacific Ocean with energy varia-tions on the decadal timescale. It is the dominantfactor in monthly SST variability in the North Pacific(north of about 20◦N latitude) (Mantua et al., 1997).A PDO index (PDOI) is defined as the leading princi-pal component of North Pacific monthly SST variabil-ity (poleward of 20◦N latitude). The warm phase ofthe PDO (positive values of the PDOI) is character-ized by negative SSTs and positive SLPs in the NorthPacific.

2.4. SLP and SST

Additional atmospheric and oceanic data are usedin this study to investigate possible relationships to thepattern of Chinese typhoons not explained by ENSO

or the PDO. The National Centers for NCEP/NCARreanalysis data are available from the CDC. The dataare the result of a data assimilation using observa-tional data from 1948 to the present and a fixed versionof a numerical forecast model. The data are dividedinto six categories including pressure level, surface,surface flux, other flux, tropopause level and sigmalevel (Kalnay et al., 1996). The present study uti-lizes two first-order variables from the dataset. Thefirst variable is SLP. We are interested in the large-scale atmospheric surface pressure patterns across Asiaand the Pacific that might influence the pattern of ty-phoons over China. Monthly data are averaged overthe months of May through November. The secondvariable is SST over the middle latitudes of the north-western Pacific Ocean. These data are examined inorder to ascertain whether or not anomalous SSTs arerelated to tropical cyclone steering mechanisms andthus to the pattern of landfalls over China. The re-gion chosen is 30◦N to 60◦N and 150◦E to 180◦W andthe time period is June through August.

3. Historical Tropical Cyclone Counts: Guang-dong versus Fujian

We begin the analysis with an examination of thehistorical record. The counts of tropical cyclones af-fecting Guangdong and Fujian over the period 1600–1909 provide a long record for examining co-variability

670 VARIATIONS IN TYPHOON LANDFALLS OVER CHINA VOL. 23

between these two regions (Fig. 2). Years of above-average activity over Guangdong are out of phase withyears of above-average activity over Fujian. The Pear-son correlation coefficient verifies this with a valueof −0.35. Assuming that tropical cyclone activity inone year does not influence TC activity in the nextyear, the approximate 95% significance level is −0.11.Thus we find a significant negative correlation (anti-correlation) between historical tropical cyclone countsover Guangdong and Fujian.

Although the Pearson correlation provides a quan-titative estimate of this anti-correlation, its magnitudeis sensitive to temporal trends. To control for longterm trends in the data (real or observational), we ex-amine the relationship using a regression model. Thearrival of tropical cyclones on the coast can be con-sidered a Poisson process (Elsner et al., 2001; Solowand Moore, 2000; Parisi and Lund, 2000; Elsner andKara, 1999; Bove et al., 1998). The Poisson log-linearregression is a generalized linear model (glm) in whichthe mean of a response variable is related to explana-tory variables through a regression equation. Here weuse the glm to regress the annual Guangdong tropicalcyclone counts on the year and annual Fujian cyclonecounts. Analogous to the residual sum of squares inordinary linear regression, the goodness of fit for a glmis measured by the (scaled) deviance. The deviance isa measure of the discrepancy between observed andfitted values. It serves as a generalization of the usualresidual sum of squares for non-normal data. The mag-

nitude of the drop in scaled deviance upon adding Fu-jian counts to the model that includes the year as apredictor is significant assuming a χ2 approximationwith a p-value of 0.002. Thus, even after accountingfor the possibility of trends in the data, annual countsof Guangdong cyclones are significantly inversely re-lated to those over Fujian.

Another way to see this inverse relationship is withconditional probability. A conditional probability isthe probability of an event given the occurrence ofsome other event. For instance, the conditional prob-ability of a Guangdong tropical cyclone will be highergiven that Fujian is spared. Here we estimate the an-nual mean number of tropical cyclones over Guang-dong when there are no Fujian cyclones, then we usethe mean number as the annual rate parameter in aPoisson distribution to generate the probability dis-tribution of Guangdong cyclones. We do the samefor years in which there are at least one Fujian trop-ical cyclone. The probability distributions are com-pared in Fig. 4. Here we see that the probability of anactive year over Guangdong (more than 3) is higherwhen Fujian receives no cyclones. Having establishedan anti-correlation in landfall activity between Guang-dong and Fujian using historical documentary records,we are next interested in examining whether or notsuch an inverse relationship exists in the modern in-strumental records. Here we have access to typhoonlandfall counts all along the coast so we take a some-what different approach.

0 2 4 6 8 10 12 14

0.0

0.1

0.2

0.3

0.4 Histogram of Guangdong given Fujian=0

0 2 4 6 8 10 12 14

0.0

0.1

0.2

0.3

0.4

Histogram of Guangdong given Fujian >0

Number of storms per year

Ann

ual P

roba

bilit

y A

nnua

l Pro

babi

lity

Fig. 4. The annual probability of Guangdong typhoons conditioned on Fujian typhoonactivity. (a) The probability of a Guangdong typhoon in years without a Fujian ty-phoon. (b) The probability of a Guangdong typhoon in years with at least one Fujiantyphoon.

NO. 5 FOGARTY ET AL. 671

4. Modern Typhoon Counts: A Landfall Index

Typhoons typically form and track westward to-ward China; as they approach the coast they some-times curve northwestward steering clear of China. Onaverage, direct hits on the northern provinces are lesslikely than hits on the southern provinces as the north-ern coastline juts to the northwest. To lessen the effectthis disparity in counts might have on our analysis,we combine the counts from Shanghai, Jiangsu, andShandong. Figure 5 shows the annual typhoon ratesin the the six regions, including the combined northernprovinces which are labeled as SJS.

To normalize the annual counts we use relative fre-quency, where the relative frequency of a tropical cy-clone is calculated by dividing the annual count by thetotal number of typhoons to occur in the WNP basinfor the entire season. Relative frequencies are obtainedfor the 6 coastal regions. Correlations in annual fre-quencies between each of the regions is shown in Table1. The matrix of values (correlation matrix) is in orderfrom south to north, with Hainan southernmost andthe region of SJS northernmost. On balance, the cor-relations are positive indicating that when activity ishigh (low) in one province it tends to be high (low) inthe others. The exception is Guangdong and Hainan.When typhoon activity is high over the south it tendsto be lower over the north. Interestingly, the cutoffbetween negative and positive correlation occurs be-tween the provinces of Guangdong and Fujian. Nextwe employ a factor analysis model as a way to describeand quantify this overall pattern in annual typhoonfrequencies.

4.1. Factor Analysis Model

A factor analysis model attempts to approximate

Hainan

Guang

dong

Fujian

Taiwan

Zhejia

ng SJS0.0

0.5

1.0

1.5

2.0

Ave

rage

Num

ber

of

Typ

hoon

s P

er Y

ear

Fig. 5. The average annual count (rate) of tropical cy-clones affecting coastal provinces of China. The rates areobtained by counting storms that made direct passage overthe province. The period of record extends over 1945–2003. Whisker length on the bars indicates the magnitudeof the standard error.

the correlation matrix, with the object being to de-cide whether the data are consistent with a prescribedstructure. Factor analysis is used to study patterns ofrelationships among dependent variables. The purposeof a factor analysis model is to describe, if possible,covariance relationships among the variables in termsof a few underlying, but unobservable, random quan-tities called factors. Factors that contribute to thecorrelation structure observed in the correlation ma-trix are the dependent variables being observed. Thesingle factor model is composed of a response vectorR that is equal to the single vector of factor load-ings F times the factor scores (s) plus an error vec-tor (e). The factor model formula is R = F · s + e ,where the random variable s has mean 0 and variance1. The errors e form a random vector with uncorre-lated components that are also uncorrelated with s,having mean 0 and variance less than 1. The responsevector R is the centered and scaled proportion term.The expected value of R is zero and the variance isequal to the transpose of the single vector factor load-ings F times F plus the diagonal of the variances,Var(R)=FT · F + diag(σ2). The factor loadings areestimated using maximum likelihood methods. Oncefactor loadings are determined, factor scores are esti-mated using generalized least squares, fitting the re-sponse R using the inverse of the uniqueness as weightswith the factor loadings as the predictors. The depen-dent variables are the annual typhoon frequencies inthe 6 regions of coastal China.

Since we are only concerned with defining the lead-ing factor, we use a one-factor model. Model solutionsare the same whether we use a principal componentor maximum likelihood method. The resulting fac-tor loadings are plotted in Fig. 6. As expected, theyshow a north-south split with opposite signs on theloadings for Guangdong and Hainan compared to Fu-jian, Taiwan, Zhejiang and SJS. The split occurs be-tween Guangdong and Fujian. Thus the factor anal-ysis model appears to capture the main correlationstructure of annual typhoon activity along the Chinesecoast, and the results are consistent with the inverserelationship noted in the historical counts.

The magnitude of the factor score represents thedegree to which the year represents the dominate co-variability in relative typhoon frequencies. We call thetime series of the 59 factor scores the landfall index(LI), as it represents the important variation in ty-phoon landfalls. A plot of the LI is shown in Fig.7. Positive values of the LI indicate a propensity forstorms to recurve and track north of the Tropic ofCancer, whereas negative values of the LI indicate theopposite effect with a larger proportion of landfalls oc-curring south of the Tropic of Cancer. Plots showingthe tracks of typhoons during the year with the maxi-mum (minimum) value of the LI are also shown in Fig.7. Thus it appears that the factor model as summar-

672 VARIATIONS IN TYPHOON LANDFALLS OVER CHINA VOL. 23

Table 1. Matrix of correlations in annual typhoon frequencies.

Hainan Guangdong Fujian Taiwan Zhejiang SJS

Hainan 1.000 0.139 –0.268 –0.274 –0.197 –0.143

Guangdong 1.000 –0.190 –0.348 –0.357 –0.296

Fujian 1.000 0.595 0.436 0.350

Taiwan 1.000 0.386 0.158

Zhejiang 1.000 0.523

SJS 1.000

Note: SJS represents Shanghai, Jiangsu, and Shandong.

++

++

-- Tropic of Cancer

Inte

rna

tio

na

l D

ate

Lin

e

Fig. 6. Values of the factor loadings from a factor analysis model using a maximumlikelihood method. HN: Hainan, GD: Guangdong, FJ: Fujian, TW: Taiwan, ZH: Zhe-jiang, SJS: Shanghai, Jiangsu, Shandong. The factor loading sign is plotted in thecorresponding map region.

ized by the LI describes the north-south variationin typhoon landfalls. Years with straight-moving ty-phoons that influence southern China have a large neg-ative LI and years with recurving typhoons that influ-ence northern China have a large positive LI.

4.2. Statistical Significance

Since there are no direct methods to test the sta-tistical significance of the factor-analysis results, weemploy Monte Carlo simulations. Simulations are per-formed to test whether the north-south split in factorloadings could have occurred by chance. The annualfrequency of storm occurrences in each province is ran-domized with respect to year. For example, in 1945the relative frequency of Guangdong typhoons was 8percent, but under a single randomization it is 13 per-cent. One can constrain all the rows and columns andsimulate the possible combinations of counts and as-sign probabilities to them. This is based on work fromPatefield (1981) which gives an efficient method of gen-erating r× c tables with given row and column totals.

This is a multinomial sampling scheme where the fre-quencies are randomized but the marginal totals arepreserved.

Here the relative frequencies of landfalls are ran-domized 1000 times. Samples are drawn from the con-ditional distribution of the table given the margins.Rows contain the annual basin totals and the columnsare the annual province totals; an additional columnis included that contains the sum of basin-wide activ-ity that did not affect any province. An assumption ismade that the number of storms in each year and theproportion of storms in the basin for each province isconstant; only the relative frequency of landfalls perregion changes for each simulation. From these as-sumptions we simulate the number of storms for eachprovince for each year. For every simulation, we re-peat the one-factor model and compute the interceptand slope of a regression of the loadings against theprovince order from north to south.

The slope from a regression of the loadings on lati-tude order (south to north) is +0.21 using the observed

NO. 5 FOGARTY ET AL. 673

1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001

Year

-2

-1

0

1

2

3La

ndfa

ll In

dex

(a)

Fig. 7. (a) A landfall index (LI) obtained by linearly projecting the factor loadingsfrom the factor analysis model onto the annual landfall frequencies using linear regres-sion. Tracks of typhoons in (b) 1959 when the LI was large and positive, and in (c)1995 when the LI was large and negative.

typhoon landfall frequencies. The null hypothesis isthat the magnitude of the slope is zero, correspondingto the result that there is no relationship in landfallfrequencies across the provinces. The number of sam-ples in the simulation with an absolute value of theslope greater than or equal to 0.21 under the inde-pendence assumption is 6, so the p-value is 6/1000 =0.006. Thus the null hypothesis that the magnitudeof the slope is zero is rejected. The small p-value in-dicates that the observed north-south one-factor splitdoes not occur at random.

5. Relation of the Landfall Index to ClimateFactors

Results from the historical and modern records in-dicate a significant geographic variation in typhoonlandfall frequency over China on an interannual ba-sis. In years with above-normal landfall activity overGuangdong and Hainan, there are fewer storms overFujian and provinces to the north. What causes thisvariation, and can it be explained by variations in at-mospheric and oceanic variables?

As mentioned, the coherent north-south signal inChina landfalls is expressed as a value for the LI eachyear. We now turn our attention to the possibilitythat a portion of the interannual variation in the LIcan be statistically explained by variations in climatefactors. However, we find little correlation betweenthe LI and the SOI. The SOI is an indicator of the El

Nino-Southern Oscillation (ENSO). Here the SOI isaveraged over the months of May through November.The Pearson’s product-moment correlation coefficientr[LI, SOI] = −0.12. On the other hand, there is a weakcorrelation with the Pacific decadal oscillation (PDO),as measured by the PDOI. Here r[LI, PDO] = −0.20using values averaged from May through November.The PDO is the dominant factor in monthly SST vari-ability in the North Pacific (Mantua et al., 1997). Dur-ing the warm phase of the PDO, there are cooler SSTsand higher SLPs over the northern Pacific. Althoughthis relationship exists, it is not very strong, thus weare motivated to look at other climate signals.

It has been proposed by Liu et al. (2003) that vari-ations in coastal typhoons might be related to regionalclimatological connections such as anomalous SST overthe northwestern Pacific (north of 30◦N) during thetyphoon summer and fluctuations in SLP patterns be-tween western China and Mongolia. They noted thatcooler SST in the northwestern Pacific, especially dur-ing the summer season, could displace the predomi-nant storm tracks to the south. Ho et al. (2004) notedthat SST increases in tropical regions of the WNP tendto coincide with SST decreases over the northern Pa-cific.

An annual index of the north-south SLP gradi-ent between Mongolia and western China (SLPI) ismade by taking SLP differences of the seasonal (May–November) averages for two regions bounded by 40◦–60◦N and 85◦–110◦E (region 1) and 25◦–35◦N and

674 VARIATIONS IN TYPHOON LANDFALLS OVER CHINA VOL. 23

Tropic of Cancer

Inte

rn

atio

nal D

ate

L

ine–(b)

Tropic of Cancer

Inte

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atio

nal D

ate

L

ine–(c)

Fig. 7. (Continued.)

75◦–100◦E (region 2). These regions are based on dif-ferences in the large-scale steering flow between highand low LI years. The SLPI is the region 1 averageminus the region 2 average. An annual index of SST(SSTI) is made by averaging over the months of Juneand August for the region bounded by 30◦–60◦N and150◦–180◦E. Values for the SLPI (SSTI) are in hPa(◦C).

The correlation between SLPI and LI (r[LI, SLPI])is −0.43 and the correlation between SSTI and LI(r[LI, SSTI]) is +0.31. Recall that negative values ofthe LI correspond to southern China landfalls, so anegative correlation indicates that Guangdong land-falls are associated with a strong pressure gradient(high values of SLPI) over western China. Further,the positive correlation between LI and SSTI indicates

NO. 5 FOGARTY ET AL. 675

that a warmer ocean is also associated with south-ern landfalls. Scatter plots of these relationships areshown in Fig. 8. When the SLPI is high, indicatingrelative high pressures over Mongolia and low pres-sures over western China, the LI is low. This im-plies that when the SLP gradient over Mongolia isstrong there is an increase in surface winds in theform of stronger easterlies over northern China whichmay inhibit typhoons from recurving, thereby increas-ing landfall activity over Guangdong Province. Also,when cooler SSTs occur over the northwestern Pacific(north of 30◦N) we see an increased likelihood of ty-phoons over southern China. Extensive and persistentSST anomalies produce substantial variations in at-mospheric heating. We speculate that cooler SST overthe NW Pacific can lead to increased middle and up-per tropospheric troughiness over the northern Pacificthrough decreased turbulent transfer of sensible heat.

A linear regression model further quantifies theseresults. We regress the LI onto year, SLPI and SSTI.The multiple R-squared value indicates that 26% of thevariability in the LI is explained by these two climatefactors plus the trend. The t-value on the coefficientfor SLPI (SSTI) is −3.50 (+2.46) providing a p-valueof 0.001 (0.017). Diagnostic plots (not shown) give usno reason to suspect the underlying model assump-tions. Thus both the SLPI and the SSTI appear tobe individually important in explaining the variabilityin the landfall index. We note that the SSTI shows asignificant decline over the 59-year period. Climato-logically, typhoon tracks are determined by the gen-esis regions and steering flow. The strong pressuregradient indicates an enhanced subtropical high andstronger steering flow at atmospheric levels extend-ing to at least 500 hPa. We examine the geopoten-tial height gradient between region 1 and region 2 andfind that the LI is significantly negatively correlatedr[LI, 850 (hPa) gradient] = −0.35 (p-value = 0.024).Thus the differences in typhoon tracking that we findare due to differences in steering flow.

6. Summary and Conclusions

The variability of the typhoon landfall patternin China is investigated using historical and modernrecords. The work is motivated by a recent hypoth-esis of an inverse relationship between the frequencyof typhoons over northern China to those over Guang-dong Province in the south (Liu et al., 2001; Elsnerand Liu, 2003). The study makes use of the mod-ern instrumental record of typhoons over the coastalprovinces of China and documentary evidence of ty-phoons over Guangdong and Fujian. Indices for ENSOand the PDO are combined with regionally-averagedSLPs and SSTs to examine climate factors related to

-1 0 1 2 3 4 5

SLPI (hPa)

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13.0 13.5 14.0 14.5 15.0

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fall

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Fig. 8. (a) Scatter plot of the LI and the SLPI, and(b) scatter plot of the LI and SSTI. The solid line is theleast-squares regression line.

the pattern of China landfalls. The originality of thiswork rests with the examination of both historical andmodern landfall records and in the use of a factormodel for identifying the dominant pattern of spatiallandfall variability.

The historical records provide a hint at the im-portant landfall pattern by showing a statistically sig-nificant negative correlation between the frequency oftropical cyclone landfalls over Guangdong and Fujian.When tropical cyclone activity over Guangdong is high(low), it tends to be low (high) over Fujian. The re-lationship is examined using correlation, conditionalprobabilities, and log-linear regression. The dominantlandfall pattern is identified in the modern recordsof typhoon counts in coastal provinces using a factor

676 VARIATIONS IN TYPHOON LANDFALLS OVER CHINA VOL. 23

analysis model. The model delineates the southernprovinces of Guangdong and Hainan from the north-ern provinces of Fujian, Taiwan, Zhejiang, Shanghai,Jiangsu, and Shandong. When typhoon activity ishigh over the southern provinces, it tends to be lowover the northern provinces. This result is new andprovides a way to examine climate influences on geo-graphic variations in typhoon activity. The statisticalsignificance of the factor analysis model is tested us-ing Monte Carlo simulations. A landfall index (LI) ofannual activity representing the degree to which eachyear follows this pattern of activity is used to identifycorrelated climate variables.

The relationship between the dominant spatial pat-tern of China landfalls, as described by the LI, and cli-mate is first examined using indices for ENSO and thePDO. Although the SOI is not linearly related to theLI, there is a weak relationship with the PDO. Ex-amination of additional variables that might explainthe landfall pattern indicates that a north-south SLPgradient between western China and Mongolia mightbe used as a predictor of regional typhoon activity.When the SLP gradient features relatively high pres-sures over Mongolia and low pressures over westernChina, typhoons are more likely to track westward to-ward southern China. In contrast, when the SLP gra-dient weakens, recurving typhoons are more likely tovisit the northern provinces of China.

Additionally, ocean surface temperatures over thehigher latitudes of the northwestern Pacific are cor-related with the pattern of typhoon landfalls. Whentemperatures are cooler than normal, typhoons are rel-atively more likely over southern China. A linear re-gression model that includes sea level pressure differ-ences between Mongolia and western China and SSTover the northwestern Pacific explains 26% of the inter-annual variability of the LI. A stronger than normalsurface pressure gradient increases the easterly windflow over China forcing the fledgling storms to trackwestward toward the southern coast. Moreover, colderwater over the WNP shifts the subtropical high pres-sure farther to the west and south, thus keeping trop-ical cyclones over lower latitudes and tracking themtoward Hong Kong. These results are consistent withthe speculation put forth in Liu et al. (2003) that ty-phoons are more abundant across Guangdong whenthe climate is drier and windier in northern and cen-tral China.

The major conclusions of this study are:

1. Historical typhoon records from the Guang-dong and Fujian provinces of China dating backto 1600 are inversely correlated on the annualtimescale.

2. A factor model applied to modern typhooncounts in the coastal provinces of China indicates

a north-south split in activity, with the split oc-curring between Guangdong and Fujian.

3. The factor model is statistically significantagainst a null hypothesis of a random distribu-tion of landfalls.

4. The factor scores provide a landfall index (LI)of the dominant geographical pattern in Chinalandfall activity from year to year.

5. The LI is not significantly related to either theSOI or the PDO.

6. The north-south SLP gradient over central Asiaand SSTs over the higher latitudes of the north-western Pacific Ocean are significantly related tothe LI on an annual basis, although the varianceexplained is only 26%.

The study can be improved. For example, it wouldbe interesting to examine the geographic pattern ofstrong typhoons over China. It is likely to be similarto that of the general typhoon pattern, but it mightbe more pronounced. More importantly, it would berevealing to examine the results of a factor model ap-plied to the historical records. This would allow amore detailed comparison of the historical and modernrecords. Unfortunately, typhoon counts for provincesother than Guangdong and Fujian are unavailable atthis time. Finally, a closer examination of other cli-mate variables might reveal additional relationshipswith the typhoon landfall pattern.

Acknowledgments. Partial support for this study

was provided by the U. S. National Science Foundation

(BCS-0213980). The views expressed herein are those of

the authors and do not reflect those of the funding agency.

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