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Numerical classification of climateKazutake Kyuma aa The Center for Southeast Asian Studies , Kyoto University ,KyotoPublished online: 22 May 2012.

To cite this article: Kazutake Kyuma (1972) Numerical classification of climate, Soil Science andPlant Nutrition, 18:4, 155-167, DOI: 10.1080/00380768.1972.10433288

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(Soil Science and Plant Nutrition, Vol. 18, No. 4, p. 155-167, 1972)

NUMERICAL CLASSIFIUATION OF CLil\IATE The Method and Its Application to the Climate of Japan

Kazutake KYUMA

The Center for Southeast Asian Studies, Kyoto University

REClUVED FEBRUARY 22, 1972

The present paper deals with a classifica­tion of the climate of Japan for setting up climatic regions as a basis of classification of alluvial soils. By definition alluvial soils are those which have undergone little change under the influences of pedogenetlc processes. Accordingly the ordinary classificatory method based on the profile morphology does not work effectively for these soils. The morphological features, if any at aU, do not properly reflect the influence, especially, of climate.

In order to establish a soil series classifica· tion in alluvial lands, therefore, climatic regions have to be set up first, so that an adequate series separation can be made among otherwise similar soils occurring under clearly different climate. This approach is a violation of the basic principle of the modem soil clas­sification, that a differentiating characteristic must be a property of the things to be clas­sified. We have to admit, however, that al· luvial soils are not " soils" in the strictest sense of the word, for they are not ~et "his­torical natural bodies" formed in complex in­teraction with environmental conditions.

There are many schemes of climatic clas­sification and several of them have been adopt· ed for classifying the climate of Japan. The results, however, are not necessarily satisfac­tory from our point of view in that some are too rough, whereas others are so detailed and complex that a regional division is not readily delineated on a map. In the present study we attempt to test the applicability of a numerical taxonomic method Jn combination with discriminant functions. to the classifica•

155

tion of climate. Besides objectivity and reproducibility which are said to be inherent in the numerical method, we expect to have readily mappable climatic regions as a result of classification.

DATA AND METHODS

The climatic data were taken from the cli­matic table attached to WADACHI's book on "Climate of Japan " (1). Mean monthly tem· peratures and mean monthly precipitations are the basic data used commonly in many of the climatic classification schemes, e.q., Kop .. PEN's and TIIORNTUWAITE's, In this study we also use these two sets of data for 107 stations out of 112 stations cited in the book, omitting only the stations on mountain tops.

These 107 stations, as listed in Table 1, are distributed fairly uniformly throughout the country and are presumed to represent varia­tions of the climate of low-altitude regions of Japan. The data are averages of the observa­tions usually over 30 years and at the shortest over 10 years.

A method of numerical taxonomy as propos­ed by SaKAL and SNEATH (2) consists of the following steps :

i. Standardization of the original data t() make them dimensionless

il. Computation of similarity coefficient (s) iii. Sorting (or Cluster analysis) Both product-moment correlation coefficient

and taxonomic distance (or Euclidean distance) between a pair of taxonomic units (single sta­tions or clusters of stations, as in our case)

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156 K. KYUMA

Table 1. Location and moisture data of the sample stations according to THORNTHWAITE'a method

Station Latitude Altitude Water Preeipi• Water Moisture Climatic

No. Name oN m Need (PE) tation Surplus lndex.(Im) Type

501 WAKKANAl 45°25' 1.8 526 111.1) 617 1.1.7.1.1 AC I 2

502 JWlOW 44°22 1 1·1 569 1)24 755 1)2.8 A~'

50) ASAHIKAWA. 4)9 461 111o) 575 1092 517 90.0 DJ.B1 I 5olt ABASiiiRI 41.1001 1 J7o6 ,~,., 867 )21.1 59o7 Bz~'

505 NEHUW 4,3°20 1 27.5 .512 10)5 52) 102.) ACa' 506 KUSHIW 42°59 1 )2o0 51'- 10it9 5.35 104.2 ACa' 507 OBIHUlO 42°55 1 )9o0 557 9.30 J7l 67.0 a,ea• 508 URAKAWA 42°101 )Jo5 558 107'- 516 92.1t 134~'

509 SAPFOW 4.)0 0) 1 16.9 591 1118 527 89.2 B4B1' 510 SUTTSU 42°47 1 15o7 591 1,)12 721 122.1 ABt' 511 MUOORAN 42019 1 1.12.6 587 11)5 548 9).5 B4B1• 512 K>RI 420Q6t 18.7 588 tolt4 456 77o7 B,3B1' 51J ESASHl 41°52 1 29.6 621 119J 572 92ol B~t-Bt'

514 HAKODATE 41°lt9 1 ),).) 6oo 12cYt 6olt 100.6 AB1' 515 TAN ABU 41°17' .lo1 609 1451 842 1)8.1t AB1' 516 HACIUNOHE 40°)2 1 27.4 625 1.1.00 475 76.1 BJB1• 517 AOM.>RI 40°49 1 ).6 62) 1424 801 128.6 AB1' 518 FUKAURA 400)9 1 67.7 664 1576 912 1)7o) A.B1' 519 AKITA. )9°4) 1 9.1 670 1785 1116 166.6 AB1' 520 SAKATA. .)8°54 1 2.0 696 1969 127) 182.8 AB I 1 521 YAMAGATA .)8°15 1 150.6 679 1250 571 84.0 B4D1' 522 K>RIOKA )9°42 1 154.5 6.)5 1205 570 89.8 B4Bt' 52) MIYAKO .39°)9 1 42.7 61t2 1275 6)) 98o5 B4B1 I 524 ISiliNOJ.lAKI )8°26• 4).) 666 1101 4J5 65.) B.3B1• 525 S£1/DAl )8°16' )8.4 674 1.215 541 80.2 B4B1' 526 JVKUSiiiMA J7°45 1 67 • .) 717 1146 429 59o9 B2B2' !i27 lNAWA.SIIlllO J70J4' 526 6)2 1518 886 140.) ABt I 528 SUIRAKAWA )7°07 1 )5,).7 667 1418 751 112.5 AB1• 529 ONAHAMA )6°57' :hl 705 1428 723 102o7 ABt' 5)0 MI'OO )6°2)' 29.2 726 1.)92 666 91.8 a4a2 t

5.32 U'I'SUNOHIYA )60JJ' 119.8 727 1507 780 107·'• ADz• ,, MA!BASifl )6°24' 1Uo7 751 12)0 479 6)o7 B,]Bzl Sl4 KUMAGAYA. )60091 ,30.0 764 1286 522 68.2 B.)Bz' 5.35 CIIICHIBU )5°59 1 ;us.o 72.3 1448 725 100.4 AB2'

5.36 'OOKYO ).5°41' 4.1 795 1567 772 97·0 l'l~cBa'

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Numerical Classification of Climate 157

Table 1 (Continued)

Station Latitude Altitude Water Precip:l.- Water Moisture Climatic

No. Name ON Ill Need (PE) tation surplus Index( Im) Type

537 YOKOHAMA ,),5°26 1 .39·5 784 1656 872 111.1 ABa'

538 CHOSHI .35°43 1 26.6 784 16ft7 86) 110.1 AB;z•

539 KATSIJURA 35°09 1 u.s 794 20)7 1243 1,56.6 ABa'

,540 TOMISAKI )4..055 1 12.2 617 1814 997 122.1 All;a'

5lt1 OSHIMA 340ft6t 190o5 785 )016 22)1 28'-.2 ADa'

542 HACIUJOJ IMA .330CJ6t 79·7 903 .3253 2.350 260,2 All)'

SltJ AIKAWA ,38°01' ''*·'* 738 1,564 826 111,8 ADz'

544 NIIGATA 37°.5!5' a.o 743 1744 1001 1)4,8 ABz'

545 XAKADA 370()61 t.hlt 740 )OJ It :229/e :no.1 AD,a t

.546 TOfAMA )6°42 1 8.6 7.52 :3)01 15ft9 205.9 ABa'

547 FUSUIKI ,)6°47 1 1ll.1 754 2218 1464 194.0 ABa'

548 W'AJIKA. 37°23' ,5.6 719 .3178 1459 20),0 ADa' .549 KANAZAWA :J6o)J' 27.0 759 2485 1726 227.6 AB,a'

550 FUKUI .]60Q)I 9o2 776 2376 1600 2o6.J ABa'

551 TSUR!JGA 35°.39 1 1.1tr 786 2424 16.38 208.6 Ana•

.552 TAKAYAMA )6°09 1 .560.3 646 1737 1091 168.9 ABl'

553 NAGANO .]6°40' 418.1 690 978 z8a 41.6 .BaD1'

.554 KARUIZAWA. .36°20' 9)4.o 569 1.364 795 1J9·8 ACa'

.555 MATSIJIDTO .36()15 1 61o.o 669 1012 )It) :a.:J BaB1'

556 IIDA 35°.31' 481.8 705 1.55~ 84.9 120,,. Mlt'

557 KOFU .35°40 1 271.7 767 1228 461 60.2 a3s2•

ssa J\JNATSU ),50.)0' 859.6 612 1697 taBs 177·2 ~1'

.560 MISIIIMA .35°07 1 20,1 79.3 18)2 1039 1.:n.o ADa'

561 NAGATSllllO .3'*0 36' st.. 'f 833 1870 1037 124.6 Alla'

562 SIUZUOKA 34-0 58 1 u.; 836 2278 14.53 176.0 Mla'

S63 OMAJ:ZAKI 3ft036' 44,7 834. 2197 1.)6.) 16),,. ABa'

564 HAMA.HATSU .)4,042 1 3lo7 810 1871 1o61 131·1 ABa'

56.5 NAGOYA .35°101 ;u.J 798 1514 716 89.7 Bt.Ba •

566 GIFll 35°24. 1 12.8 800 1791 991 123.9 ADa•

568 IUKONil: 35°161 87o) 769 1.59.3 82lt 107.1 ~· 569 KAM.ItfAM-' )4°51 1 69.2 786 1946 1160 147.6 ADa'

570 UENQ J40ft61 159·3 74.3 1.375 6.)3 a,,o D.<.,Da'

.571 TSU )4°ft)• 1o9 8oltr 1687 88.3 109.8 Ana'

572 OWAS& )40oJ.• tlt.lt 794. ft119 ;))3.5 418.9 .AJ3a 1

573 SliiONOMISAKI 33°27' 7).2 854. 2510 16,56 19.3.6 ~I

,74 WAKAYAMA J4°1it' 13.6 8.34. 1)86 552 66,1 B.)Da' .57.5 KASIIIWA.RA )4°)0' 6) 796 1.)61 565 71.0 D3B2'

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158 K. KYUMA

Table 1 ,(Continued) Station Latitude Altitude Water Precipl- Water Moisture Climati

No, Name ON m Need (PE) tation Surplus Index(Im) Type c

576 KYOTO .35°01 1 40.9 800 1488 688 86,1 B4,Ba' 577 OSAKA J4°J9 1 6.7 84.) 1a74 4.)1 51.1 DaDa' 578 KOBE .)4°41 1 58o1 8.)1 1a96 466 56.1 BaBa' 579 SUMOTO J4°aor 109oJ 8o6 1514. 708 87.7 B4Ba' sao TOYOOKA J5°Ja 1 J1o7 76a ao61 1a99 170.6 ADa' 581 SA IGO J6°1a 1 a6.a 74.1 1760 1019 1J7o5 ADa' 58 a YON AGO J5°a6• 6.5 766 18a4. 1058 1.)8.1 ADa' 58J SAKAI J50JJI a.1 79a 1940 1148 14.4..9 ADa' 584 MAT SUE J5°a7• 17.1 776 1974. 1aOO 154. • .) ADa' 585 HAMADA .)4°54 1 18.0 786 16a7 841 107.1 ADa' 586 !HIHJNOSEKI JJ057' 4.6.a 814 16.)0 816 100,a ADa' 587 JIIllOSHIMA J4°a2 1 a9.1 8o5 15a1 717 89.1 D4Da' 588 OKAYAMA .)4°41 1 J,J 610 109a a8a .)4.9 B1Da' 589 TADOTSU .)4°16' 4.0 8a6 1111 a85 J4,6 D1Da' 590 TOKUSJIIMA J'•0o4.• 1 • .) 6aJ 1567 744. 90.5 B4,Ba' 591 MUllOTOMISAK I JJ015 1 184.7 8J5 a450 1615 19.).4 ADa' 59 a KOCIU JJOJ!tl 0.5 8Ja 255a 1720 ao6,6 ADa' 59J ASIIIZURI JZOI•J' a9,8 900 a4JO 15JO 169.9 AI3J' 59ft UWAJIMA JJ0 14' 4a,o 847 16J'• 787 92o9 o4Ba' 595 MAT SUYAMA JJ0 50' J2.a 818 1JJ1 51J 6a.7 B.3Ba' 596 IZUJIARA J401a' ao.B 799 a110 1.)11 164..1 ADa' 597 TOM IE Ja0 J7 1 a6.7 859 aoo1 114-Z 1.).).1 AOJ' 598 NAGASAKI .)2°44" 26.9 8.)0 1966 11.)6 1.)6.8 ADa' 600 JIIRADO JJ0a2• ss.a 81lt a156 1.)4a 164.8 ADa' 601 SAGA JJ0 15 1 4.1 8.)6 1792 956 114 • .) ABa' 6oa FUKUOKA JJOJ5' 2.1 sao 1597 777 94ro7 B4.Ba' 60.) IIZUKA JJOJ91 J5o9 796 1768 990 1a4.o ADz' 60'• OITA JJ01lt 1 4,6 BoLa 159J 789 98,2 D4.Ba' 606 KIJMAHJTO .)2°49 1 J7o9 841 1757 916 109.0 ADz' 607 AKtJNE )2°01' J7o7 857 2082 1aaG 14..).1 ABJ' 608 MAKIJRAZAKI .)1°161 a9.9 882 a079 1198 1J.:;i,8 ABJ' 6o9 KAGOSHIMA J10J4.' 4..8 879 2168 1a89 146.7 ADJ' 610 MIYAZAKI ;n°55' 7.4 87J a5a7 1654 189.5 ADJ' 611 YAKUSHIMA J0°a7' 1lt.a 961 .)6.52 a691 a79,9 AD3' 61a NAZE a8°2.)' a.7 1065 a997 19)a 181.9 ABt.'

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Numerical Classification of Climate

Taxonomic Di•tanct 2.0 1.8 1.6 1.4 1.% 1.0 0.8 0.8

I I

Fiar. 1. A dendrogram for 107 stations representing the climate of Japan based on taxonomic distance-weighted pair-group sorting method

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160 K.KYUMA

are commonly used as the coefficients of similarity. The former is categorized a pat· tern coefficient, while the latter a magnitude coefficient.

Several sorting methods have been proposed. A previous experience by other authors (3) showed that choice of a sorting method has relatively little effect on the resulting clas· sification. We adopted in the present study the weighted pair·group method, which allows in one cycle of clustering just one taxonomic unit to join a cluster formed in a previous cycle, giving equal weighting to both the joining unit and the cluster.

Use of a computer is indispensable in each of these three steps. The computer programs were prepared by ourselves in FORTRAN language. A F ACOM 23Q-60 at the Computer Center of Kyoto University was used through· out the study.

In order to compare the result of numerical classification, THORNTHWAITE's indices (4) were also obtained with the same procedures as stated in a previous paper (5).

RESULTS AND DISCUSSIONS

Dendrograms based both on correlation coef· ficients and taxonomic distances were obtained, of which the one based on the latter is shown in Fig. 1. As imagined readily, correlation coefficients produce a classification based on similarity in pattern but not in magnitude of data. Therefore, stations located in widely different temperature conditions could be clustered at a high similarity value. For example, Station No. 501 at Wakkanai, the northernmost point in Hokkaido (ca. 45.N), and Station No. 550 at Fukui on the 36th parallel were grouped together at a correlation coefficient value of 0. 82. This sort of absurdity did not occur in the case of classification based on taxonomic distances. A previous study by other authors (6) on numerical classification of soils also showed that the taxonomic dis· tance, or more generally the magnitude coef· ficlents, normally give better representation of our idea of overall similarity among the objects of study than do the pattern coef· ficients. Accordingly, we will confine herefter

our discussions to the classification based on taxonomic distances.

According to ROHLF and SOKAL (c{., Ref. 2), the expected value of distance computed from n characters for indifferent pairs of taxonomic units approaches v2 as n tends to infinity. When n is 24, the expected value of d, taxono· mic distance, is 1. 398 and its 95% confidence limits are 0. 98 and 1. 81, as calculated by interpolation from the figures given by SaKAL and SNEATII (2). Thus the distance values smaller than 0, 98 are indicative of significant similarity in our case.

If we draw a straight line intersecting the distance coordinate at a taxonomic distance value of 0. 8, six groups comprising 10 to 32 member stations are separated together with six independent stations. Roman numerals I to VI are given to the 6 groups from the top to the bottom of Fig. 1. The sample mean vectors of the 6 groups, as shown in Fig. 2, illustrate features of the climate of each group.

Figure 3 is a plot of the stations belonging to each of the groups on a map. From the distribution pattern of the stations we can see the general zonality of the climate and its modification by the ocean currents and orogra· phy. The main body of the Hokkaido Island belongs to one type of climate, which may be called boreal. Oshima Peninsula of Hokkaido and the northernmost part of Honshu is separable from others as subboreal. The east· ern half of southern Tohoku and its continua· tion into northern Kanto, together with the inland part of Chubu, where general elevation is high, belong to temperate climate. Stations located on the Japan sea coast are grouped together regardless of a wide span in latitudes, the climate of which may be a variation of temperate climate, having a precipitation maximum during the winter months. The main part of the so-called Seinandanchi (warm region in southwest Japan) is represented by the group V stations and belongs to warm temperate climate. The Pacific coast of south· western Japan and the eastern perlphery of Kyushu, both of which are under the influence of warm currents, appear to belong to one climatic type, which may be called oceanic warm temperate.

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Numerical Classification of Climate 16l

mrn ·c II llJ 25 25 25

300 20 20

15 15

10 10

5 5 5 100

0 0 0 -5 -5

1234567 8 9101112 1 2 3 4 5 6 7 8 9 101112

25 25 300

20 20

15 15 200

10 10

5 5 s 0 0 0·

-5 -5 -5 1 2 3 4 5 6 7 8 9 10 1112 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 1112

Month Fig. 2. Illustration of the sample mean vectors of the six groups

- temperature •c, ---- rainfall mm.

There are a few exceptional cases, in which stations belonging to one group are located in a region dominantly occupied by the stations belonging to another group, e.g., 527, 552, 554, 558, and 570. Most of these anomalous cases are explained by the high elevations at which these stations are located.

As previously discussed, the 6 groups set up by means of numerical taxonomy appear to represent the major variations of the eli· mate of Japan, and, at the same time, show fair regionality when plottnd on a map. Ac· cordingly, we can set up the following 6 eli· matic regions as the mappable equivalents of the groups,

Group I

I[

III IV v

Climatic Region Ilokkaido-ku Donan·Rikuu-ku Kitakanto-Nairiku·ku Uranlppon·ku Seinandanchi·ku

VI Nankai·ku These climatic regions are delineated in Fig. 3 by thick broken lines.

When it becomes necessary to reduce the number of groups or regions, by referring to the dendrogrnm in Fig. 1, Groups V and VI or Seinandanchi and Nankai·ku may be com­bined together tirst; then, Groups II and III or Donan-RJkuu·ku and Kitakanto·Nalriku-ku may be combined, for these combinatlons are below the taxonomic distance value of 0. 98, the threshold for the distance values. TnOI~NTliWAITE's climatic Indices for the

humidity and thermal efficiency classes are given in Table 1 for the same 107 stations. There are 5 thermal efficiency classes rang• ing from second microtbermal to fourth meso~ thermal, i.e., C,1

, D/, Da', lla', and D.', the last one represented by just one statlon at Naze(No. 612). When plotted on a map, these 5 classes naturally show a certain zonality,

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162 K.KYUMA

•

. ~ FlJr. 3. A map showing climatic regions based on a numerical taxonomic method

but one of them, Da' extends widely, covering mid-Kyushu to the southern part of Tohoku, and that it is represented by more than half of all the stations.

There are also 5 humidity classes for Japa­nese climate, A, D,, Da, Ba, and Bt, of which Bt is represented by only 2 stations at Okaya· ma (No. 588) and Tadotsu (No. 589), while the perhumid class (or A) is represented by nearly 70 stations located all over the country.

Both thermal efficiency and humidity class· es are combined, forming 15 groups. Omit· ting those groups having less than 5 member stations, the following 7 groups remain, repre· sen ted by a total of 93 stations:

Group symbol Number of stations ACa' 5 AD( ~

ADa' 42 ABa' 8 D,Dt' 6 D,DI' 11 B,Ba' 6

Figure 4 shows the distribution of the stations belonging to each of these 7 groups. A com· parlson with Fig. 3 reveals that each of the 7 groups has less Internal geographical coher· ence, so that delineation of climatic regions is more difficult.

As discussed in a previous paper (5), THORN· TIIWAITE's method is developed primarily to classify the climate of the U.S.A. and relies heavily on a parameter, potential evapotrans• piration, which Is derived from a mean month· ly temperature so as to fit U.S. conditions. The method has, of course, certain undisputa·

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Numerical Classification of Climate 163

-<>- A c· 0 ABI

• a.n; 6 All; A a.n;

• .a.n; a AB;

Fill. 4. A map showing grouping of stations according to TUORNTUWAIT's climatic Indices

ble merits, one of which is that water balance for each month can be computed (on an as­sumption of 100 mm water retention by the soil) so that the effect of climate on plant growths may be directly assessed. In view of the objective of the present paper, however, the difficulty in delineating the climatic re• gions is a great drawback to TUORNTHWAITE' s method as compared to the numerical taxono­mic method.

Another advantage of the numerical taxono­my in comparison with TUORNTIIWAITE's method is avoidance of artificial parameters and/or assumptions. Potential evapotranspira­tion is calculated using an empirical formula, the validity of which ls only partially tested in the United States. The numerical taxonomy utilizes the same amount of orlgimil inCorma-

tion but does nQt rely on any such arbitrary parameters or assumptions in the course of classification.

We have not referred thus far to the 6 stations which do not belong to any of the 6 established groups; they are Oshima (No. 541), Hachijojima (No. 542), Takada (No. 545), Owase (No. 572), Yakushima (No. 611), and Naze (No. 612). Although these stations may represent certain variations of the climate of Japan, the areal extention of each variation is not large. Therefore, we thlnk it appropriate to let each of the stations revert to one of the 6 groups.

What is necessary to do with these 6 sta• tion is also necessary when dealiug with the climatic data from the stations located at the margins of the climatic regions. We nlust be able to classify each of these stations into

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one of the 6 groups. The method of discriminant function seems

to serve this purpose effectively, It is one of the multivariate statistical methods and is used for discriminating an individual defined by p variates into one of k established groups. An essential prerequisite for applying this method is a common variance-covariance matrix to the k groups. In our case p is 24 characters (12 mean monthly temperatures and 12 mean monthly precipitations) and k is 6.

To be able to test the equality of the covariance matrices of the 6 groups, according to the method outlined in SEAL's book (7), we must have more than p (=24) member stations for each group. As this condition is not satisfied for 5 out of the 6 groups in this study, let us put forward an a priori assump· tion that the equality is held, and proceed with obtaining discriminant functions.

Using a computer program prepared by the Shionogi Computing Center (8), the discri­minant functions (D.F .) for all the pairs of groups, i.e., ( ~) == 15 pairs, were obtained, the coefficients of which are given in Table 2. In order to check the validity of the method, the 101 stations for which the functions were computed, were reclassified with the discri­minant functions. The following 5 stations were misclassified, most of which are located at the margins of the respective climatic regions, as shown in Fig. 3.

Station No. Grouping by N.T. by D.F.

510 I II 518 IV II 519 IV II 523 III II 561 V VI

The misclasslfied cases are slightly less than 5% of the total and this figure is thought to be satisfactorily small. Therefore, we applied the method to the 6 stations remaining unclas· sified, obtaining a reasonable result as shown below:

Station No. Location Grouping by D.F. 511 542 545

Oshima llachijojima Takada

VI VI IV

572 611 612

Owase Yakushima Naze

VI VI VI

Thus, the discriminant functions obtainea herein apper to be useful for purpose, although the mathematical basis of the method is not sufficiently firm in this particular study.

For the characterization of the 6 established groups, the method of canonical analysis could be applied appropriately, but the same prere· quisitite as in the case of discriminant func· tions renders the use of the method in this study inadquate.

SUMMARY

As a basis for classifying alluvial soils a climatic regional division of Japan was at· tempted using a numerical taxonomic method and discriminant functions. Mean monthly temperature and precipitations for 107 meteoro· logical stations located throughout the country were used for the study. They are thought to represent the major variations of the cli­mate of Japan (except for the mountainous regions).

A dendrogram based on taxonomic distances yielded 6 groups at a level of taxonomic dis· tance of 0. 8, leaving 6 stations unclassified. Each of these groups showed a fairly good regionality, thus enabling us to set up a eli· matic region as a mappable equivalent of the group. Six climatic regions were delineated as shown in Fig 3.

For allocating the 6 stations that remained unclassified by the numerical taxonomy to an appropriate one of the 6 groups, the method of discriminant functions was adopted, with an a priori assumption that the prerequisite for the method is satisfied. Though lacking a firm mathematical basis, the discriminant functions proved useful, if we allow for 5 % of misclassified cases.

TIIORNTilWAITE's indices for humidity and thermal efficiency are also obtained for the same 107 stations for the purpose of compari· son. A regional division with respect to TUORNTIIWAITE's climatic types was difficult to achieve.

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14

Tab

le 2

. C

oeff

icie

nts

of

disc

rim

inan

t fu

nctio

ns f

or

15 p

airs

of

the

6 gr

oupS

Cba

rac-

Co

effi

cien

ts o

f D

iscr

imin

ant

Fun

ctio

ns,

for

the

Pai

r o

f G

roup

s te

r N

o.

I -

II

I-

III

I-

IV

I-V

I-

VI

II -

III

1 -o

.170

X 1

01 -o

.),)

t. X

101

-{).

410

X 1

01

-o.5

t.a

x to

1 -o

.6JB

x

to1

-o.t

65

x

tot

2 -o

.191

x

to1

-o.J

87

x

tot

-o.4

4o x

to

t -o

.609

x

tot

-0.7

31 x

10

1 -o

.196

x

to1

J -0

.181

X 1

01 -0

o419

X 1

01 -0

.465

X

101

-o.6

68 x

to

t -o

.824

x

101

-0.2

)7 X

10

1

4 -0

.241

X 1

01 -o

.572

x

to1

-o.5

90 x

to

t -0

.8)2

X

101

-0.9

71 x

to

1 -O

.JJt

x

tot

5 -0

.258

X 1

01 -o

.556

x

to1

-Oo5

75 X

101

-o.7

71 x

to

1 -0

.844

X

101

-{).

297

X

101

6 -0

.210

x

to1

-0.4

52 X

10

1 -o

.sto

x

to1

-0.6

16 X

10

1 -0

.6)7

X 1

01

-o.2

4t

x to

t

7 -{

).20

) X

10

1 -o

.4t6

x

tot

-o.t

.B8

x to

t -o

.571

x

101

-o.5

70 x

10

1 -0

.212

x

101

8 -Q

.)tQ

X

101

-o.6

62 x

1o

t -0

.846

X

101

-0.9

5.3

X 1

01 ..0

o9)9

X

lOt

-0.)

52

X

t01

9 ..

0.29

8 X

101

-0.7

41 X

101

-0

.891

X

101

-o.u

s x

ti

f'

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)2 X

to

2 -o

.!tt

.J x

to

1

10

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52 X

10

1 -O

.t.)

5 X

-101

-0

.572

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101

-0.7

98 X

t0

1 -o

.985

x

101

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X

10t

11

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,5t.

X

101

-{).

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X 1

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. X

t0

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. X

10

1 -o

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x

tot

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10t

12

-o.1

54 x

10

1 -{

).))

7 X

101

-o

.t.)

6 X

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1ot

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t0

1 -0

o18)

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101

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29 X

1W

2 o

.27

t x

io-1

-o

.955

x

10-1

0.

197

x to

-1

-().1

()1.

X 1

0-t

--

Q.)

2) X

1

0-t

1ft

-o.)

zo x

10-

1 0.

274

x 10

-2

-0o1

8o X

to

O

-o.2

B3 x

to

-t

-o.6

97 x

to

-t

0.)

48

X

10

-t

15

-0.4

11 X

Io-

1 ..0

o181

J. X

to

-1

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67 X

trf

J -0

.106

X

tOO

-0.2

18 X

10

° 0.

227

x io

-1

1.6

-o.f

t-97

X 1

.0-1

-o

.729

x 1

0-1

-0.1

.20

X

1cfJ

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t X

tr

f>

-Oo2

95 X

tr

P

-0.2

)2 X

1

0-t

17

-o.2

55 x

1o

-1 -o

.stJ

x 1

o-t

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x

to-1

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.t)6

X

10°

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t6 X

trP

-0

.258

x

s_o-

t

18

-0.)

11.

X

to-1

-o

.,5t.5

x

to-t

-0

o518

X 1

0-l

-0

.105

X

t0°

-0o1

84 X

10°

-0

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X

tO-t

19

-o.3

52 x

10

-1

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t6 x

to

-1 -{).~X

10-1

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x

to-t

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to

? 0.

3.57

x

to-2

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to

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10-

1 -o

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x

10-2

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x

10-1

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o729

X

10

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10_2

21

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5o x

10

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94 x

10

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ot

x 10

-1

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89

x

to-t

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1

0-t

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).44

2 X

to

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488

x 10

-2

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146

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to

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16)

X

10

-t

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10-

1 -0

.977

X

10-2

2J

o.t

41

x

1o

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o.t.

Jo x

10

-1

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87 x

10

-1

0.20

2 x

1o

-t

-o.5

74 x

10-

2 o.

289

x to

-t

24

..

0.58

6 X

1W

2 0.

259

X 1

0-l

-{

).87

6 X

1

0-l

o

.t7

2 x

1o

-t

0.22

9 x

to-2

0.

318

x 10

-1

<D

NS

T.

0o25

J X

to

' o

.6)1

x

toJ

o.87

5 x

1oJ

0.11

0 X

le

ft

0o1)

9 X

to

ft-O

e)7

7 X

to

J -

II-

IV

-0.2

41 x

lO

J.

-o.2

48 x

to

t

-o.2

84 x

to

t

-o.3

t.9 x

to

1

-o.3

17 x

to

1

-0.)

00

X

101

-0.2

85 X

10

1

-o.s.

3s x

w

t

-0o5

9J X

tO

t

-0.4

21 X

101

-o.J

t.a

x to

t

-0.2

82 x

to

1

-o.9

02 x

to

-t

-0.1~ X

10°

-0. t

26 X

tr

P

-o.7

08 x

10-

1

-o.lt

-14

x to

-t

-o.2

07 x

to

-1

-o.4

8o x

to

-2

O.t

08 X

10

-t

-o.3

51 x

to

-t

-o.2

2.5

x to

-1

-o.8

28 x

to

-1

-o.8

17 x

to

-t

0.62

2 X

1oJ

II-

V

-0.3

70 x

10

1

-o.!

tt8

x to

t

-o.4

87 x

101

-0.5

91 X

t01

-0.5

12 X

101

-o.w

5 x

to

1

-0.3

68 x

to

t

-0.6

t.)

X

101.

-o.S

87 X

to

1

-0.6

46 X

lO

t

-0.t

.91

X t

01

-0.4

16 X

10

1

0.25

0 x

to-t

0.37

1 x

to-2

-o.6

46 x

to

-t

..()

.tO

t X

10°

-0.1

11 X

t0

° -o

. nr.

x to

-t

-o.1

52 x

to

-1

..O.tO

J x

10-z

-o.2

J9 x

to

-1

..o.1

14 x

to

-t

o.61

t. x

to

-2

o.~Jt x

to

-t

0.85

1 X

to

'

z c 3 .. ... c;· ~

(") ii>

to

Ul ::;;

n t» .... 0 :3

0 - Q E:r t» ~ .... "' Ul

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Tab

le 2

(C

onti

nued

)

Cla

rac-

ter

No.

II-V

I

1 -o

• .r.t>

9 x

tu1

2 -o

.5J9

x t

at

J -o

.64J

X

tat

4 -Q

.7Jt

X

101

5 -o

.s86

x

to1

6 -o

.ft2

7 X

10

1

7 -o

.J67

x t

o1

8 -o

.629

X

lOt

9 -o

.102

X

to2

ta

-o.8

)4 X

t0

1

11

-o.6

45 X

10

t 12

-o

.52

t X

10

t

1)

o.t

57

x

to-t

14

-o.J

77 X

10

-t

15

-Q.1

77 X

tO

O

16

-o.2

45 X

10

°

t7

-Q.2

9t X

t0

°

18

-Q.1

5J X

10

°

19

-o.7~ X

10

-t

20

-o.5

54 X

tO

-t

21

-o.6

72 x

to

-t

22

-Q.2

05 X

tO

-t

2J

-oat

98 X

tO

-t

24

o.8

t5 x

to

-2

aJN

ST.

0a11

4 X

1f

P

Co

effi

cien

ts o

f D

i.&

erim

inan

t F

un

ctio

ns,

fo

r th

e P

air

of

Gro

ups

III

-IV

III-

Y

III-

VI

IV

-y

IV

-VI

-o.7

6o X

to

O

-o.2

05 x

101

-o.J~ X

tO

t -o

.t2

9 X

10

1 -o

.228

X

101

-o.5

27 X

to

O

-Q.2

22 X

ta

t -o

.J/r

i x

tot

-Q.1

69 X

ta

t -o

.29

t X

10

1

-a.4

62 x

10

° -Q

.250

X

tOt

-o.4

o5 X

ta

l -Q

.20J

X

101

-o.J

59 X

10

1

-o.1

79 X

10

a -Q

.26o

X

101

-Q.J

99 X

ta

t -Q

.242

X

101

-o.J

82 X

10

1

-0.1

94 X

to

O

-Q.2

15 X

1Q

t -o

.289

X

101

-o.1

96 x

to

t -Q

.269

X ta

l

-Q.5

8J X

ta

0 -o

.t6

4 X

ta

l -o

.t8

5 X

lO

t -Q

.1o6

X

ta1

-o.1

27 x

to

t

-o.7

24 X

ta

0 -o

.155

X

101

-Q.1

54 X

ta

l -o

.828

X

lff>

-o.8

t7 X

ta

0

-o.t

8J

X

101

-o.2

91 X

10

1 -o

.277

x

ta1

-Q.t

08 X

10

1 -Q

.9J9

X

to'J

-o

.150

X

tQl

-o.'*

"' x

ta

l -o

.575

X

101

-Q.2

9J X

10

1 -o

.425

X

tOt

-0.1

)7 X

tO

t -Q

.J6J

X

t01

-o.s

so x

ta

l -o

.226

X

ta1

-o.4

tJ X

10

1

-o.1

28 X

10

t -o

.279

X

10t

-o.4

JJ X

10

1 -o

.t5

t X

ta

1 -o.~ x

ta

l

-o.9

90 X

10

0 -o

.2JJ

X

101

-o.J

J8 x

ta

l -o

.t)4

X

tat

-Q.2

J9 X

t0

1

-Q.1

2J X

tO

O -o

. 7)6

X

10-2

-Q

.t67

X

10-l

0.

115

X

100

o.t

o6

x

to0

-o.t

8J

X

tOO

-o.)

U X

tO

-t

-o.7

25 X

t0

-l

0.1.

52 X

10

° 0.

111

X

100

-Q.t

ft9

X

10°

-o.s

n x

to

-1

-o.2

00 X

10

° o.

612

x to

-1

-o.5

09 x

to

-1

-o.'t

76 x

to

-t

-o. 7

76 x

to

-1

-Q.2

22 X

10

° -o

.)O

t x

1o-t

-Q

.t75

X

t0°

-o.t

56

x t

o-t

-o

.85

t X

10

-t

-Q.2

65 X

10

° -o

.695

X

tO-t

-Q

.250

X

10°

0.27

0 X

10

-2 -o

.so

o x

to

-1 -o

.129

X

10°

-o.5

27 X

1

0-t

-o

.t)2

X

10°

-o.8

J7 X

t0

-2

-o.t

88

x t

o-t

-Q

. 740

X

t0-1

-Q.t~ X

10

-l

-o.6

56 X

1

0-l

o.t

w x

to-

1 0

.2t4

X

t0-2

-o.5

22 X

tO

-t

-o.t

t9 x

to

-1

-o.6

6J X

1

0-l

-o.J

07 x

to

-t

-o.1

95 x

to

-1 -o

.628

X

tO-t

0.

112

X

10-t

-o

.J2

t x

ta-1

-o.1

27 X

1

0-l

-Q

.t60

X

t0-2

-o

.to

7 x

1

0-t

0a

111

X

10-t

0.

200

X

10-2

-o.1

12 X

10

° -o

.227

x

to-t

-o

,.487

X

tO-t

0.

890

X

10

-t

0.6J

O X

10

-t

-o.u

4 x

to0

-o.8

7J x

10-

2 -o

.2J7

X

10

-l

0ot0

5 X

10

° o.

899

x 1

0-t

o.z!

Q x

ta

l 0.

474

X

tal

Oo7

58 X

to

J 0a

2JO

X

tal

a.5

t4 X

to

J

y-

VI

-Q.9

86 X

10

° -o

.t2

z x

tal

-Q.1

56 X

10

1

-Q.1

J9 X

10

1

-Q.7

J6 X

ta

0

-Q.2

1J X

to

O

o.11

5 x

to-t

o.t

JS x

to

0

-o.t

Jt x

to

t -o

.t8

8 X

ta

l

-o.t

54

x

ta1

-o.t

a5 X

10

1

-o.9

J2 x

to

-2

-o.4

t4 x

to

-1

-Q.t

12 X

10

°

-Q.t

!ri

X

10°

-o.t

8o

X

toO

-o

.791

x to

-t

-o.5

52 x

to

-1

-o.5

4A x

to

-1

-o.4

JJ x

to

-1

-o. 9

09 X

10

-2

-o.2

6o x

to

-t

-o.t

49

X

10

-l

o.2S

tt x

to

J

... g ~ ~ c::: ::'! >

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Numerical Classification of Climate 167

Acknowledgement. The author gratefully acknowt­~dges the help extended by 1\fr. Y. Sakal in pre· paring the computer program for the numerical taxonomy, and the assistance offerred by Misses U. Nakaoku, E. Shimizu, and F. Imanaka in the course

,of data processing and manuscript writing. The author is deeply indebted to Dr. C. Asano and Mr. U. Uesaka of the Shlonogi Computing Center for Mathematical, Statistical, and Biometric Analysis, for their useful diacussions and help in providing the -computer programs.

REFERENCES

1) WADACftl, K. (ed.), " Nippon-no Kiko (Climate of Japan)," Tokyodo, Tokyo, 1958, p. 492

:2) SoKAI., R.R. and SNEATH, P.H.A., "Principles

of Numerical Taxonomy," Freeman and Co., San Francisco, 1953, p. 359,

3) MoORE, A. W. and RUSSELL, J.S., Geoderma, 1, 139 (1967)

4) THOliNTHWAlT£, C.W., Geop. Rev., 38, 55 (1948)

5) KYUMA, K., Tonan Ajia KenkyH (Southeast Asian Studies), 9, 136 (1971)

6) CUANALO De La C., II. E. and WEBSTER, R., J, Soil Sci., :u, 340 (1970)

7) SEAL U.L., "Multivariate Statistical Analysis for Biologists," Methuen and Co., England, 1964 (Translated by M. Shiotani, 1970, Kyori· tsu-Shuppan, Tokyo)

8) StUOTANI, M. and AsANO, C., "Tahenryo Kal· seki·Ron (Multivariate Statistical Analyses)," Kyoritsu-Shuppan, Tokyo, 1967, p, 236

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