A multivariate approach to the evaluation of the climatic regions and climatic resources of China

Geofomm, Vol. 24, No. 4, pp. 357-380,1993 Printed io orcat Britain

al1~7185193 $6.00+0.00 @ 1993Pergmon Press Ltd

A Multivariate Approach to the Evaluation of the Climatic Regions and Climatic Resources of China

GLENN R. MCGREGOR,*

Birmingham, U.K.

Abstract: A climatic regionalization of China is developed based on the multivariate analysis of temperature and precipitation data for 279 locations. Principal components for the description of the spatial variation of China’s climate resource are derived by principal component analysis. The principal components for climate description are temperature, winter moisture, midsummer moisture and seasonality. Agglomerative clustering of the four principal component scores for the individual locations produced 25 homogeneous spatial clusters or climate regions. A geographical nomenclature for the climate regions is presented. A comparison is made of the statistical climatic regionalization developed in this study with existing.empirical classificationsof China’s climate, and the application of the statistical regionalization to climate resource assessment is discussed.

Introduction The environmental potential of an area is dependent on a large range of factors. One of these factors is the climatic factor which, along with land, water and biological factors, plays an important role in determining the environmental and, thus, economic development potential of an area. In any evaluation of an area’s environmental potential much attention is given to climate as this sets the broad limits for a range of physical resource based economic activities. Basic questions asked in the early stages of the evaluation of an area’s climatic potential are: does the climatic resource vary over space and time, and what is the exact nature and availability of the climatic resource? In addition to the compilation of climate statistics which establish the nature and availability of the climate resource there is the process of climate classification which considers the spatial characteristics of the climate resource. *School of Geography, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.

The basic purpose of climate classification is to identify geographical areas possessing similar climate and, thus, similar climatic potential. Climatologists have applied a number of empirical and genetic schemes to the problem of climate classification or regionalization. Common to both of these approaches are the problems of the selection of classification criteria, whether the classification is to be univariate of multivariate in nature, and how to deal with the spatially continuous climate variable. Empirical schemes emphasize the use of observed climatic normals, usually temperature and precipitation. The basic purpose of an empiric classification is to produce a series of climate regions which describe the average climate without any emphasis on a specific application or without regard for the causes of climate (IIENDERSON-SELLERS and ROBINSON, 1986). The genetic approach to climate classification and regionalization emphasizes atmospheric dynamics and, therefore, regionalizes climate in terms of its causes and controls. Of these two approaches it is the empirical approach which has often been used in evaluating the spatial characteristics of an area’s

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358 climatic potential. Traditionally, empirical schemes involve the allocation of locations to qualitatively determined a priori climatic clusters or groups. A large range of empirical climate classification schemes exist (MILLER, 1953; THORNTHWAITE, 1948; BAGNOULUS and GAUSSON, 1957; BUDYKO, 1958; MALMSTROM, 1969; OLIVER and WILSON, 1987), which is testimony to the fact that no single scheme has been universally accepted. Amongst the reasons for this are that many schemes have been developed for specific applications such as agriculture, water resource planning and air conditioning; used only a limited range of climatic elements that may not be functional in discriminating climate regions for environments other than the one for which the scheme was originally developed; been based on climate-related phenomena such as vegetation with the assumption that these phenomena are integrals of climate. More recently climatologists have considered the utility of multivariate statistical approaches, similar to those used by numerical taxonomists, for climatic classification and regionalization. Although this approach is essentially empirical in nature, as the regionalization is often based on the use of climatic normals, it differs from the traditional empirical approaches in the sense that multivariate approaches use statistical classification criteria for grouping locations based on their climatic similarity as measured by a coefficient of similarity or proximity. Climate regions are thus developed in a post priori fashion. In addition to the apriori andpostpriori contrasts is the fact that statistical agglomerative approaches to climatic regionalization are usually based purely on climatic parameters, which removes the problem of using non-climatic parameters, such as vegetation, which are often relied on in a traditional empirical classification of climate (KOPPEN, 1931; BLAIR, 1942). These contrasts, in addition to the fact that empirical classifications suffer from the problem of locating the boundary between climate regions, are often based purely on monthly averages which fail to portray climate dynamics and are univariate or at best bivariate in nature while climate is a multivariate phenomena, have resulted in many climate classifiers favouring multivariate statistical techniques which are perceived as possessing relative objectivity in comparison with the traditional empirical classification schemes.

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Common amongst the statistical approaches to climatic regionalization is the eigentechnique principal component analysis (PCA) which has been applied in conjunction with agglomerative statistical techniques such as cluster analysis (CA) to a number of climatic regionalization problems (STEINER, 1965; McBOYLE, 1971; PRESTON-WHYTE, 1973; AOYADE, 1977; WILMOTT, 1978; RICHMAN and LAMB, 1985; RONBERG and WANG, 1987; PUVANESWARAN, 1990; PERIAGO et al., 1991; WHITE et al., 1991, BONELL and SUMNER, 1992). Associated with the increasing interest in the application of PCA to climate regionalization problems has been an increase in the number of studies comparing regionalizations developed by different procedural options available in PCA. While these studies are important for establishing inter-option variability of the regionalizations developed by PCA there is a distinct lack of studies comparing the regionalizations developed using eigentechniques such as PCA with traditional empirically based regionalizations. Such comparisons will reveal whether there is a divergence or convergence of the regionalizations developed by the two contrasting methods. Such comparisons are important as much climatic resource planning is based on empiric classification schemes which are often assumed to capture the spatial nature of a region’s climatic resources. Further, the multivariate regionalizations developed based on eigentechniques such as PCA may be used for verification or modification of existing empirical regionalizations which are often bivariate in nature. The main objective of this paper is to develop a statistically based climatic regionalization of China using the eigentechnique PCA and the multivariate classification technique CA. The regionalization is developed as a basis for understanding the spatial variation of climate potential in China and identifying regions that possess similar climatic potential. No attempt is made here to establish a climatic nomenclature for the regionalization as this would necessarily involve the establishment of subjective criteria and recourse to the original empirical data. This analysis is therefore concerned principally with a statistically based spatial differentiation of climatic regions and the presentation of a geographical cli-

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matic region nomenclature climatic potential.

outlining areas of similar

The climatic regionalization of China, based on a multivariate analysis of temperature and precipitation data for 279 locations, will be compared with three existing empirical climatic classifications of China in order to establish its potential utility as an aid to climate resource assessment and planning.

Climate Classifications of China A number of classifications of China’s climate have been developed by Chinese scientists (CHU, 1929; LU, 1949; TAO, 1949; ZHANG, 1959, 1982; HUANG, 1986). Of these the classification of ZHANG (1959) represents a major effort by the government of the Peoples Republic of China, through the Climate Regionalization Group of the Working Committee on Natural Regionalization of China, to establish a climatic regionalization of China (DOMROS and GONGBING, 1988). The objective of such a regionalization was to provide information on the climatic resources of China, mainly for agricultural development, by mapping the climatic indices accumulated air temperature (10°C base) and aridity. Based mainly on accumulated air temperature, six major climate types were identified by ZHANG (1959). A additional seventh type was established for the plateau area of western China with altitudes in excess of 3000 m. The major climate types were found to correspond with eight climate regions. A six-class aridity scale was used to subdivide the climate regions into 32 climate provinces and a further 68 climate areas. The classification of ZHANG (1959) formed the basis for further research on the problem of developing a climatic regionalization of China (DOMROS and GONGBING, 1988). Two new classifications followed in the 1980s. The classification of CHEN (1982) was meteorological-agricultural in nature whereas HUANG’s (1986) regionalization was climatological-physiographical in nature. CHEN’s (1982) regionalization emphasized a range of meteorological variables considered sensitive for agricultural production with emphasis placed on temperature variables. Based on temperature criteria nine non-plateau climate types were established with

a further five identified for the plateau area of western China. Further subdivision of climate types was made based on an assessment of five categories of humidity/ aridity, resulting in 31 climate types (Figure 1). Currently the classification of HUANG (1986) is considered to be the leading climate classification of China (DOMROS ,199O). As for the classifications of ZHANG (1959) and CHEN (1982), the classification of HUANG (1986) is empirical in nature but is notable for its emphasis on non-climatic criteria, namely land use and topography. The classification has four levels. Climate realms constitute the broadest level. Realms are further subdivided into climate belts, climate regions and climate provinces. The level most often quoted and considered the principal level of the classification is that of the climate belts which have been established on the basis of temperature criteria (Figure 2). Climate belts have been further subdivided on the basis of precipitation, with vegetation and land use also considered, to produce 21 climate regions. Climate region physiography is used to identify 45 climate provinces. Because of the use of land use and physiography the HUANG (1986) classification can be considered as a climatophysiographic regionalization which in many ways resembles the physical regionalization of China presented by MEI’E et al. (1985). Further details on the specific criteria used in the classifications of ZHANG (1959), CHEN (1982) and HUANG (1986) may be found in the original publications or DOMROS and GONGBING (1988).

Data and Methods Monthly precipitation and temperature data for 279 State Meteorological Administration of China climate stations were obtained from DOMROS and GONGBING (1988). Station details can be found in DOMROS and GONGBING (1988, pp. 4-14). For all locations mean monthly figures are based on the period 1951-1980. From the monthly precipitation and temperature data 14 variables are derived. These variables form the basis of this analysis (Table 1). The choice of variables was somewhat limited by the fact that temperature and precipitation data are monthly values only. For both the temperature and

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Figure 1. CHEN’s (1982) climatic classification of China. Climate zones/types are as follows: I, cold temperate; II, middle temperate; III, warm temperate; IV, northern subtropical; V, middle subtropical; VI, south subtropical; VII, peripheral tropical; VIII, middle tropical; IX, equatorial tropical; HI, cold plateau; HII, subcold plateau; III, temperate plateau; HIV, subtropical mountain; HV, southern tropical mountain plateau. The five humidity/aridity categories are: A, humid; B, subhumid; C, subarid; D, arid; E, extremely arid. Adapted from DOMROS and GONGBING (1988).

precipitation data two broad types of variables were derived: those that represent annual characteristics and those that represent summer and winter monsoon seasonal characteristics as defined by the months June, July, August (JJA) and December, January February (DJF), respectively. The variables MEANT, MGlO, MG20, TOTP and MG083 (Table 1) represent annual characteristics. These are important as they have a bearing on the nature and magnitude of the seasonal variables. MGlO is important agriculturally as it is a proxy measure of the length of the growing season. The day equivalent of this vari-

able has been used widely in empirically based classifications of China’s climate developed by Chinese scientists (ZHANG, 1956; CHEN, 1982; HUANG, 1986). MG20 is an approximation of the number of winterless months [WC was originally used by KOPPEN (1931)]. MEANT and TOTP give a general indication of the average hygrothermal environment which has implications for hydrology, agriculture, land management and human physioclimates. MG083 has implications for hydrology and agriculture in that it is an indication of the temporal distribution of rainfall. If rainfall is evenly distributed

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B

Rgure 2. HUANG’s (1986) climatic classification of China. Climate zones/types are as follows: I, cold temperate; II, middle temperate; III, warm temperate; IV, northern subtropical; V, middle subtropical; VI, southern subtropical; VII, peripheral tropical; VIII, middle tropical; IX, equatorial tropical; HO, plateau alpine; HI, plateau subalpine; I-III, plateau temperate. The four humidity/aridity categories are: A, humid, B, subhumid; C, semi-arid; D, arid. Adapted from DOMROS and GONGBING (1988).

amongst the 12 calendar months then each month should receive 8.3% of the annual total. High MG083 values for a location therefore indicate a situation tending towards an even temporal distribution of rainfall on an annual basis. The seasonal variables JANT and JANP and JULT and JULP indicate the centre of the winter and summer monsoon seasons, respectively. It should be noted that season centre here refers to a spatial average over a large latitudinal range and really

represents the true temporal centre for central China as the timing of the arrival of the summer and winter monsoons varies geographically. JULJANT represents temperature seasonality. The SUM and WIN variables portray the degree of concentration of annual rainfall into the respective summer and winter monsoon months of JJA and DJF. JULJANP represents rainfall seasonality. Two multivariate statistical techniques are used in this study to develop a statistically based climatic

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362 regionalization of China. These are PCA and CA. All data were standardized before proceeding with these analyses. PCA, along with other eigentechniques such as empirical orthogonal analysis, factor analysis and discriminant function analysis, has become an important tool in climatic regionalization studies because it facilitates the reduction of large data sets to manageable, physically interpretable abstractions by expressing the data set variance in a reduced number of variable dimensions (WHITE et al., 1991). While it is not the intention to discuss the theory of PCA here, as this can be found elsewhere (MATHER, 1976; TAYLOR, 1976; RICHMAN, 1986), it should be noted that there has been mounting discussion on the procedural options that exist in PCA. As noted by BONELL and SUMNER (1992) two fundamental issues must be addressed in undertaking PCA, namely, whether rotation of principal components is needed and, secondly, if rotation is required the nature of the rotation algorithm to be used. It. appears that the majority of workers to date have opted for orthogonal (VARIMAX) rotations over oblique (OBLIM) rotations. Recent comparative studies of regionalizations produced by VARIMAX and OBLIM (RICHMAN, 1986; WHITE ef al., 1991; BONELL and SUMNER, 1992) have indicated that OBLIM produces the most stable results in terms of a ‘simple structure’ of components (RICHMAN, 1986) and yields regionalizations which vary little between rotation and non-rotation, However, as noted by STONE (1989), MURATA (1990) and

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BONELL and SUMNER (1992), where CA is to follow PCA, VARIMAX rotation is preferred because this produces orthogonal components which are uncorrelated, which satisfies the assumptions of CA, whereas oblique components produced in OBLIM may be correlated. As it is the intention to use CA in this analysis to develop a regionalization the VARIMAX rotation scheme was chosen. Further to the problem of the selection of a rotation algorithm is the choice of the basic operational mode of decomposition of the data matrix (BONELL and SUMNER, 1992). The operational mode, which determines the nature of the dispersion matrix, may be chosen from a range of six modes (RICHMAN, 1986). Following RICHMAN (1986) an ‘R mode’ PCA analysis was performed. This utilizes the Pearson Product Moment correlation coefficient as the type of dispersion matrix. PCA involves the linear transformation of m original variables into it new variables or components, Each new component is a linear transformation of the old component and successively accounts for as much of the total variance as possible (DAVIS, 1973). As only a few components may account for the majority of the total variance it may be unnecessary to retain all components. Several methods exist for determining the number of components to retain, including the tests of NORTH et aE. (1982), the N rule test of PRIESENDORFER (1988) and CATELL’s (1966) ‘scree test’. The latter of these methods which has been used widely in statistical climate regionalization

Table 1. Variables used in PCA

Variable JANT JULT MEANT JULJANT MGlO MG20 JANP WIN JULP SUM TOTP JULJANP MG083

Variable description January mean dry bulb temperature (“C) July mean dry bulb temperature (“C) Mean annual dry bulb temperature (“C) JULT - JANT (“C) Number of months with mean dry bulb temperature above 10°C Number of months with mean dry bulb temperature above 20°C January mean precipitation (mm) Percentage of annual precipitation in months December to February July mean precipitation (mm) Percentage of annual precipitation in summer months June to August Mean annual precipitation (mm) Ratio of JULP to JANP Number of months with precipitation greater than 8.3% of the mean annual

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problems was also used in this study for determining the number of components to retain. The retained components were used to calculate principle component (PC) scores using the ANDERSON-RUBIN (1956) method. PC scores for the retained components were subsequently mapped and a physical interpretation of the spatial pattern of individual component scores made. Physical interpretation of the components was aided by an analysis of the PC loadings on the original variables (DAVIS, 1973; PUVANESWARAN, 1990). Mapping of PC scores, while aiding in the identification of the broad spatial variations of climate, may not be instructional in revealing distinct climate regions. For this reason many climate regionalization studies proceed to CA which aids in the classification of stations into unique spatial groups based on their similarity. These spatial groups or clusters are the climate regions. As for PCA several decisions have to be made before proceeding with CA. Amongst these are decisions relating to the choice of a divisive or agglomerative clustering procedure and whether the clustering algorithm chosen is hierarchical or nonhierarchical (RONBERG and WANG, 1987), the choice of a similarity measure and the number of clusters or climate regions. An agglomerative hierarchical clustering procedure was used in this study to identify homogenous spatial groups or climate regions. As noted by WILMO’IT (1978) this is the preferred technique when the number and nature of the climate regions is unknown. A variety of hierarchical clustering procedures exist for fusing two clusters to form a new cluster (KALKSTEIN et al., 1987). Of these it is Ward’s method which has gained most favour in climatological problems and is used in this study. Ward’s method has been used successfully by a number of workers developing climatic regionalizations (WILMOTT, 1978; GOSSENS, 1985; WINKLER, 1985; REICH, 1986; ANYADIKE, 1987; EASTERLING, 1989; STONE, 1989). BONELL and SUMNER (1992) have also noted that Ward’s method is favoured in regionalization problems because it is for use with populations greater than 100, does not assume normality and is based on mutually exclusive subsets. Additionally, in a recent comparative study of clustering algorithms, RICHMAN and GONG (1992) have noted that

363 Ward’s method fared best amongst a range of hierarchical methods, including average linkage, single linkage and complete linkage, as it was not prone to fragmentation or chaining effects. The similarity measure used in this study is the squared Euclidian distance. This similarity function is preferred when the number of variables are small (WILLIAMS, 1971), as in the case of this study, and because Ward’s fusion strategy is suited to this similarity measure (BONELL and SUMNER, 1992). RICHMAN and GONG (1992) have also noted that the Euclidean distance in combination with Ward’s hierarchical clustering method gave the least variable results in terms of cluster membership compared to combinations of other distance measures and hierarchical clustering algorithms. One of the basic problems in clustering spatial data is establishing the number of spatial clusters or regions. Various subjective and objective rules exist for establishing the number of clusters in a classification problem. Following the objective approach of CLARK and HOSKING (1986, p. 40) for establishing the number of classes (K> such that K = 1 + 3.3(log1,-,N), where N is the number of cases, 279 cases should be grouped into nine classes. However, clustering at this level would give only a very coarse climatic regionalization given the large land area of China. In establishing the number of clusters (climate regions) the following subjective criteria were used. Clustering into four groups was initially performed to establish the broad geographical regions with similar climates. Four was chosen as this matches the number of climate realms identified by HUANG (1986), which may be considered as macro-climatic regions (DOMROS and GONGBING, 1988). This was, however, modified to five clusters or macro-climatic regions as one of the four clusters was composed only of five of the 279 cases. Further clustering at coarser levels did not result in these cases being subsumed into lowerlevel clusters. Five clusters were therefore used to define macro-climatic regions. Sub-macro-climatic regions were defined on the basis of having at least three cases in the spatial cluster. This rule, however, had to be modified for the fifth macro-climatic region as discussed below. The SPSS-X programs FACTOR and CLUSTER were used to perform the PCA and CA analyses.

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Results and Discussion Spatial variation of the climate resource

Performance of the ‘R mode’ rotated VARIMAX PCA and the application of CATELL’s (1966) ‘scree test’ revealed four PCs to be retained. Together the four PCs explained 85.6% of the total variance. Loadings on the variables of the four PCs were used to aid in the physical interpretation of the components. All variables comprising the PCs possessed high communalities apart from two, indicating that all four PCs account for a high percentage of variable variance (Table 2). The efficiency of the reduced set of our PCs in accounting for variable variance is therefore considered adequate for describing the spatial variation of climate. The four PCs for climate description have been named temperature, winter moisture, midsummer moisture and seasonality. The reasons for this nomenclature are outlined below. Temperature component. This PC alone accounts for 48.2% of the variance. Variables with high PC loadings [in excess of + 0.7 (PUVANESWARAN, 1990)] are JANT, JULT, MEANT, MGlO and MG20, which are all temperature-related variables. For this reason this PC is considered to describe temperature.

Plotting of TEMP PC scores reveals a weak zonal

pattern (Figure 3). TEMP scores are highest in southern China and generally decrease to the north and west. The northward decrease of TEMP scores is largely due to the latitudinal control of insolation receipts with associated affects on mean temperatures and the number of months with temperatures greater than 10 and 20°C. The high component loadings on MG20, JULT and MEANT indicate that these variables are spatially the most sensitive of the TEMP component. The rapid decrease of TEMP scores from above 50 in the southeast to scores below 20 in the west is due to altitudinal control on temperatures. The steep gradient of scores in the vicinity of 122”E delineates the transitional zone between lowland eastern China and upland western China, with rapid decreases in temperatures experienced over relatively short distances due to altitudinal effects imposed by the massive Tibetan plateau relief. Although a zonal orientation of PC score isolines is discernable for southern China, northeastern China displays more of a southwest-northeast orientation. In this area TEMP scores decrease away from the coast, representing the decreasing effects of oceanic warming and increasing continentality, and the increasing effect of cold monsoonal northeasterly flows generated in the area of the Siberian high pressure. If TAMP score magnitude is taken as a measure of heat, northwest China is warmer than northeast China. The relative warmth of northwest China compared to

Table 2. Component eigenvalues, percentage of total variance and component variable loadings Component 1 Eigenvalue 7.22 Percentage of total variance 48.2 Communality Variable JANT JULT MEANT JULJANT MGlO MG20 JANP WIN JULP SUM TOTP JULJANP MG083

0.96 0.91 0.99 0.89 0.90 0.89 0.89 0.82 0.97 0.89 0.94 0.23 0.65

0.72 0.87 0.87 0.41 0.84 0.91 0.38 0.33 0.21 -0.32 0.42 -0.11 0.02

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4

2.91 1.51 1.23 19.4 10.1 8.2 Component loading 0.02 0.35 0.13 0.15 0.13 0.17 0.75 0.78 -0.08 -0.67 0.43 -0.45 0.22

0.30 0.01 0.23 -0.36 0.24 0.16 0.24 -0.23 0.95 0.14 0.64 -0.01 0.03

0.59 0.11 0.39 -0.76 0.33 0.08 0.36 0.23 0.07 -0.56 0.42 -0.01 0.78

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,

. Rgure 3. Spatial distribution

of the temperature

northeast China may be due to thermal continentality which is most pronounced in the dry summer months, especially for the large intramontane basins in this area. The general lack of moisture in this region is due to the inability of moist unstable southwesterly monsoonal flows to penetrate this region because of topographic blocking and distance from the moisture source. Consequently little of the available energy is used in evaporation. Excess energy in the form of sensible heat therefore goes into heating the atmosphere, thus creating the relative warmth compared to northeast China where available moisture acts as an energy sink through evaporation. Winter moisture component. The winter moisture component explained 19.4% of the total variance

(TEMP) PC scores.

with the variables JANP and WIN, both indicative of winter moisture conditions, dominating this component . The spatial distribution of WINT scores shows considerable spatial variability (Figure 4). Areas of greatest winter moisture supply are in eastern China, especially south of the Yangtse river. The spatial concentration of moisture in this area may be accounted for in terms of upper westerly troughs which produce west to east travelling transient disturbances and the occurrence of frontal zones. In the winter months China is dominated by westerly flows, although at the surface (O-2000 m) northwest to northeast flows associated with the winter mon-

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Figure 4. Spatial distribution

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of the winter moisture (WINT) PC scores.

soon may dominate. The upper westerlies that travel to the south of the Tibetan plateau manage to penetrate southern China bringing with them transient disturbances of variable magnitude. The total number of cyclones associated with upper westerly troughs reaches a maximum in January to April (DOMROS and GONGBING, 1988) and bring with them considerable amounts of cyclonic precipitation to southeastern China. Frontal precipitation is also a significant feature in southern China in the winter months. Frontal zones are produced by the interaction of transformed polar continental air masses and warm southwesterly airflows. These quasistationary fronts are orientated along the southeast coast of China in winter, bringing considerable rainfall to this area. Frontal rainfall processes may be

enhanced by orographic forcing in the area of the south China Hills. WINT scores decrease to the west and north of southern China (Figure 4), indicating the dry nature of these areas in the winter months. Slightly higher WINT scores over northwest and northeast China compared to western China represent precipitation from a northern belt of fronts developed over these areas due to the interaction between westerly and winter monsoon flows. Generally the spatial distribution of WINT scores (Figure 4) indicates the dryness of China in the winter months. From the spatial pattern of WINT scores winter China may be subjectively divided into slightly moist northwest and northeast regions, an extensive dry area running from the Yellow Sea and expanding

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to the west and a comparatively wet region in southeastern China where transient disturbances in the upper westerlies, frontogenesis and orography play a role in enhancing precipitation totals. Midsummer mokture component. This component accounts for 10.1% of the total variance with JULP possessing extremely high component loadings. For this reason this PC was named midsummer moisture (MSUM). MSUM PC scores are generally greatest throughout southern China (Figure 5). Characteristic of the MSUM score spatial distribution is a cellular pattern, indicating a marked spatial variability in moisture. Noticeable are concentrations of moisture in southern Yunan, southwest Sichuan, southern

36: Guangxi and southeast Liaoning provinces (Figure 5). These localized concentrations of moisture are due to orographic forcing of unstable southwesterly summer monsoon flows. The areas west of the 200 MSUM score isoline are those which do not experience the southwest monsoon due to their great distance from the moisture source and topographic blocking of the southwesterly flows, which may only reach a depth of 2000 m. Aridity is a marked feature of northwest China as indicated by the large areas with MSUM scores below zero. In southern, eastern and northern China a steep spatial gradient from high near coastal MSUM scores to scores below 200 further inland may reflect a regime of flow divergence. While southwesterly flows converge at the coast further inland they diverge, producing in effect rainshadow areas. The 200 MSUM score line re-

Figure 5. Spatial distribution of the midsummer moisture (MSUM) PC scores.

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368 sembles very closely the southwest-northeast orientation of the July 150 mm rainfall isohyet (DOMROS and GONGBING, 1988). This may be used to divide midsummer China into a moist south southwest to east northeast half and a dry west to northwest half. Notable also is the drier area in western Fujian, Jianxi and Hunan provinces south of the Yangtse due to rainshadow effects. Season&y

component

{SEAS).

This component contributed 8.2% to variance explanation. High component loadings of opposite sign on the variables JULJANT and MGO83 indicate that this is a seasonality component. An inverse association exists between these two variables such that large (small) July - January temperature differences (tempera-

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ture range) are associated with a small (large) number of months with precipitation greater than 8.3% of the annual rainfall. There is thus an inverse relationship between temperature range and the temporal concentration of rainfall. High negative SEAS scores indicate areas where there is a large temperature range and an uneven distribution of precipitation. The spatial distribution of SEAS scores (Figure 6) shows an increase in seasonality with increasing latitude. M~imum seasonality occurs in the extreme northwest and northeast of China while minimum seasonality occurs in the subtropical to tropical latitudes. The comparable lack of seasonality in southern China is related to the more even temporal dist~butio~ of precipitation

s

Figure 6. Spatial distribution of the seasonality (SEAS) PC scores.

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and the smaller summer-winter temperature differential. SEAS is also a good indicator of continentality and partially agrees with the spatial pattern of TEMP scores (Figure. 3).

The climate regionalization At a broad level five macro-climatic regions may be identified on the basis of a hierarchical clustering of the four PC scores for the 279 locations. Of the 279 locations 97.9% of them fall into four of the five macro-climatic regions. The remaining 2.1% fall into the fifth region representing southern Yunan and Guangxi provinces and south and western Taiwan.

The five macro-climatic regions and a suggested geographical nomenclature are presented in Figure 7. Division of China into four geographically dominant macro-climatic regions appears to be based principally on the temperature component and, secondly, the winter moisture component. The temperature component appears to be responsible for the division into regions I and III, with the remaining area encompassed by regions II and IV, although similar in terms of the temperature component, subdivided on the basis of the winter moisture component. Region V which is geographically limited appears to have been delineated on the basis of the winter and midsummer moisture PCs.

Figure 7. Five macroclimatic regions. The regional nomenclature is: I, greater northern China; II, north, middle, central and southern China; III, Qinghai-Tibetan plateau region; IV, central eastern China; V, southwest and north and South China Seas region.

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370 Although the five regions give a broad appreciation of the distribution of the major macro-climatic regions closer examination of the PC score patterns indicates that within these macro-climatic regions variation of the various climatic PCs is apparent. This is especially true for the two moisture PCs. Subdivision of the five macro-climatic regions by further hierarchical clustering produced 25 regions (Figure 8). Of these four consisted of one location only. These areas are retained as separate regions as clustering at lower levels did not result in these locations being allocated to larger homogeneous regions. They are therefore considered truly heterogeneous. The geographical areas associated with the 25 climate regions and thus possessing similar climate resources are presented in Table 3.

The climatic regionalization

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and climate resource

assessment

An understanding of the nature of the climate regions may be gained by considering the range of climate values for the 25 regions. For the 11 variables with component loadings greater than 0.7 variable values within one standard error of the respective region mean are presented in Table 4. For those regions defined on the basis of less than four locations the maximum and minimum values are presented. For regions defined on the basis of one location only (Ig2, Ig3, IIh, IVd and IVe) a single parameter value is given. The data in Table 4 forms the basis for developing an

Ib

Figure 8. The 25 climate regions derived from PA and CA. nomenclature refer to Table 3.

For regional

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24 Number 4/1993 Table 3. Geographical areas associated with the 25 climatic regions

Region Ia Ib Icl Ic2 Id

Igl Ig2 Ig3 Ih IIa IIb IIC IId IIe IIf IIIa IIIb IIIC IVa IVb IVC IVd IVe IVf Va Vb

Geographical areas Northern greater Hinggan mountains Middle Heilogjiang river and Songen Plain Sungari, Wusuli and lower Heilogjiang river, and northern Zhangguangcai-Ling area Xilongol and Bailingmiao plateau area and southwest greater Hinggan mountain area Southern greater Hinggan mountains, Xiliao river plain and hills, Northern Hebei mountains, lower Liaohe river plain area and Liaodong peninsula Tarim basin, Turpin and Hami basins, Beishan Gobi, Gaxun Gobi and Junggar basin areas, Alxa plateau and eastern Ordos area Northern Bailingmiao-Erenhot area Altay mountains Tianshen mountain area Qilian mountains, Qaidam basin area Jiandong peninsular, Huanghe-Huaihe-Haihe river plain area, central Shandong mountain area Sharuii plateau, southern Sham&eastern Gansu plateau area Qinling and Daba mountains, Sichuan basin, Yunnan Plateau Northern plains and hills of the middle and lower Changjiang river area, Guixhou plateau Southern Guangxi basin, northern Hainan, Leixhou peninsular Coastal hills and plain Guangdong area Eastern Qinghai-Tibetan plateau area South Tibetan river area Western Sichuan and eastern Tibet plateau Southern plains and hills of the middle and lower Changjiang river area, west Chiangjiang low mountains South Chiuangjiang low mountains, hills and basin area Coastal plains of Fujian, Naming mountains, northern and eastern Guangxi area Taipei-Chilung area Hualien-eastem Taiwan area South Hainan, southern Yunan Hengchun and Penghu area Taiwan Southeast Guangxi, Hengduan mountains

understanding of the climatic potential of each region as there is a 68% chance that the region variable mean lies between the values presented in Table 4. Such information can be used for preliminary climatic resource planning. For example, the two variables MGlO and MG20 could be used in unison for a preliminary evaluation of those regions which possess climatic potential for agricultural production. To differentiate between potential tropical and nontropical agricultural regions, for example, the relationship of MGlO to MG20 should be considered. Regions with large numbers of months with temperatures in excess of 10°C but low to zero number of months with temperatures greater than 20°C would indicate that while non-tropical agricultural production is possible tropical agricultural production is not. A further step in the evaluation process could also consider precipitation in association with MGlO and MG20. Regions If, IIb and IIc, while having a similar thermal potential for agricultural production, possess contrasting hygric potential. Specifically, for region If there is a 68% chance that the mean number of months with MG20 lies between 2.6 and 4.0

months, making this region marginal for subtropical agricultural production. Regions IIb and IIc possess similar thermal characteristics to region If and also appear marginal for subtropical agricultural production. However, based on hygric considerations region If, with mean midsummer rainfall having a 68% chance of falling between 16.8 and 26.2 mm, is moisture-limited for subtropical agricultural production and indeed any agricultural production despite this region possessing the required thermal potential. This contrasts with regions IIb and IIc which are not moisture-limited vir-d-t& summer agricultural production. The climatic potential of regions IIb and IIc may also be compared with region IIf which has a greater number of months with temperatures in excess of 20°C and higher precipitation levels. Therefore, based on hygrothermal considerations, region IIf has much greater climatic potential for subtropical/tropical agricultural production compared to regions IIb and 11~. The empirical data presented in Table 4, in addition to the regionalization, also have applications in areas

8’9 O’ZP I.9Z L’E E’E 0’0 0’9 6X O’PZ I’9-

Z’Z O’PI 8’8 O’E Z’I 0’0 O’Z E’I 0’8 E’N91

O’E S’6E 0’6T 8’EZ O’ZI O’Z 0.t O’P IX O’LIE%

0’9 8’S O’PEZ P’9 O’SE 0’6 O’ZI S’9Z 1'82. 6'22 WI

O’P SIP 6'E P'E O'Z O'E O'S P'E 6'22 9'81Z%

0’9 6’21 O’IEI 1’9 O’PI 0’6 O’Zl S’tiZ S'6Z E'8I

E’8 6'8s O'EE 9'PZ Z'Z S'P 6'L 8'8 P'SE 6'L-

O’S 8’01 0’9ZI I’E 0’6 0’6 O’ZI 6’EZ 1'62 9'91

L’Z L'61 O'II 8'6 0'1 S'I L’Z 0% 8'II S'EZ-

311

IS

E’L O’OZ L’E OT91 I’OZ 97 S’L L’EI 9'LI 8'8

E’E 0’6

P’E 0% 8'91 0'9 1'1 9'Z 93 S'II E'6I I'II-

JII

::7 S’PP Z’OI S’9I 8’OE 0'6E 9'91

Z’S O'IP Z'9Z 2'6 L’I O'P Z’L S’L 6'62 I'LJI

L’9 Z’9I O’SSZ 8’L O’EE E’OI P’S1 0’6Z 9'9E I'61

8'9 O'ES O’PLP L’S 0'91 Z'E E'8 0'11 O'EE L’P-

SC P’8 O’EEI O’P O’LI E’S 0’8 0’91 0'61 6'6 911

59 8’LZ 0’822 E’8 O’LZ Z’S 2’6 8’LI I'IE Z'E

87 0'8E 0'9SI P'Z LX 9'Z S'9 9'8 9'9Z E’8-

II WNI 8'2 0'51 0'821 S'I O'P 8'0 Z'Z O'E 0'6 L’LIaI

E’P 0’61 0’9SI 8’S P’S1 SE Z’9 Z'ZI E'IZ Z'Z PI1

P'E 0.a O’LOI 8'7 2’7. 8'1 S'P 8'9 2'87 I'ZIPI

59 O’OZ 0’782 6X O’ZI S’P O’ZI E'OZ 1'62 2'6

S’L 0'85 O’Z91 L’S S’L E'Z S’L P'P OX E'6-

L’E P’iI O’E9I E’Z O’L L’Z O’L 8'11 O'LI P'S 311

S’Z O'OZ 03% 6'7 i:i S’Z VI O'II E’LZ31

O’L O’PE O’L9I Z’P I’8 L’P 6’8 P'91 S'ZE 8'0-

P’S 0'6P O’LOZ E'E 6'S S'Z 0'9 P'P I'LZ Z’PI-

9II

5:: S’LI 9'ZZ-

P’E O'IE O'IEI I'Z L’E 9'1

EI E'I-

O’S6 P’Z S’P L’Z Z’S

91

UBaZU aopal JOloua prepuels auo u!g~!m saqea ralaunmd a~mq:, [euo!%a~ *p elqul

0’9SZ E’S S’L S’S 9’8 0'91 S'ZE Z'Z-

9'Z 039 O'Z91 63 6‘2 P'O 6'S Z'E 6% 1'91-

8’2 O’ZZ 0’66I L’Z S’P E’E E’S 5'6 8'61 9'E-

0'9 O’LZ 0'89 I'Z L'9 Z'O S"Z VT E'TT 8’LE-

VII

e1

E80E)M

-InrNvr d?fu NM dNvr Oz9w OIf)H LNvm .L-Inr LNvr

E8O!wI kwa-mr d-U-U NIhi dNw Oz?m OIOYV .LNvm m-u uwr

8.0 18.0

4.0

E!P JANJUL MGO83

3.8

2.4 19.3 11.1

Va

JYa

3.2

IIIa

MGlO MG2O JANP

s MEANT MGlO MO20 JANP WIN JULP JANJUL MGO83

E&L MGQ83

MGlO MG20 JANP WIN

k% MEANT

-14.7

5.0

12.0 22.0

IJIb

6.0

2t8

6.0

2::

25.0 5.8

17.0 4.1

Vb

11.0 I*; 29:6 6.2 118.0 10.4 3.5

12:8

1:*:

WC

2:: 13.0 248.0 21.6 7.3

it.;: 12:o

14.9

6.4

20.3

10.9

3.4

-0.8

IIFC

-1.5

Region IV

5.0

-2.3

7.8

rvb

4.0

-4.1

Region V

7.3

11.1

6.0

-7.7

Region III

14.9

Ivd

12.0 8.0 69.0 11.4 193.0 11.2 7.0

GE!

17.1

We

B e

;!

i:

_8

374 outside that of agriculture. Energy planning for human hygrothermal comfort is one of these. From Table 4 it is immediately apparent which regions on an annual and seasonal basis (JANT, JULT, MEANT, JULJANT, MGlO and MG20) are potentially stressful in terms of heat or cold stress. The information in Table 4 could be used in conjunction with a simple psychrometric scheme such as that of TERJUNG (1966) which, although in the absence of relative humidity information, is useful for establishing the broad thermal comfort zones into which the regions fall. For example for region IIb there is a 68% chance that the July mean temperature, representing midsummer, will fall between 14.2 and 16.3”C, which places this region in the cool to possibly mild thermal zone (depending on humidity) of TERJUNG (1966). Therefore, for this region there will be little energy requirements for cooling or heating in the summer months. This contrasts with region IVb where there is a 68% chance that July temperatures will be between 23 and 33°C placing it in the mild to possibly sultry (depending on humidity) comfort zone. For region IVb there will be large energy requirements for maintenance of equitable indoor summer climates. The same sort of analysis could be made for winter energy requirements for heating as it is immediately apparent that there is a large regional contrast in winter thermal conditions which has implications for human thermal comfort and energy requirements. Assuming the necessary research has been undertaken and the association between climatic parameters and energy requirements established, the data in Table 4 could also be used to quantify in general terms the relative levels of regional energy requirements for heating or cooling. The data presented in Table 4 may also be used to shed some light on the reasons for climatic region heterogeneity. With reference to this regions IVd and IVe are of interest. Climatic region heterogeneity in the case of regions IVd and IVe may be attributed to interregional differences of the component elements, JANP, JULP, JANJULT and MG083, indicating that, on the basis of seasonal moisture differences and seasonality, these regions are climatically distinct. Similar comparisons can be made for other regions, such as IIa, IIb and IIc, which seem to be differentiated principally on the basis of the temperature component (Table 4). An attempt to identify the component elements by which the regions are differ-

Geoforum/Volume

24 Number 4/1993

entiated is presented in Table 5. This type of analysis is instrumental in identifying the climatic parameters that account for intramacroclimatic region heterogeneity and thus provides some insight into why interregional differences in climate exist. Additionally climate parameters that are significantly different (Table 5) in association with standard error of mean values (Table 4) may serve as a guide to those parameters most effective for developing an empirically based classification of climate and a regional climatic nomenclature.

Comparison of climate classifications

The most striking contrast between the statistical climatic regionalization presented in this study and those of CHEN (1982) and HUANG (1986) is the orientation of the climate regions in eastern and central China. For the CHEN (1982) and HUANG (1986) regionalizations the regions in this area are zonal (Figures 1 and 2) which contrasts with the southwest-northeast orientation of the regions in the statistical classification (Figure 8). The zonal orientation of regions reflects the dependence on temperature, which is strongly latitude-dependent, as a primary classification criteria in the schemes of CHEN (1982) and HUANG (1986). The azonal orientation of regions in the statistical regionalization represents the influence of the moisture PCs in differentiating between climate regions as seen in the spatial pattern of moisture component scores (Figures 4 and 5) and reflects the dynamics of the advancing and retreating monsoons in east China. At a very broad level there are similarities between the areas encompassed by the cold and middle temperate belts and the warm temperate area of northwest China of the CHEN (1982) and HUANG (1986) classifications (Figures 1 and 2) and region I of the statistical regionalization (Figure 8). All three regionalizations commonly recognize the extreme northern part of northeastern China as a distinct climate zone, although the area occupied by this zone varies between regionalizations. The detail for the Tibetan Plateau area in the CHEN (1982) regionalization can not be replicated in the statistical regionalization because of the paucity of data, while the plateau alpine, plateau subalpine and plateau temperate zones of HUANG (1986) resemble the area

G~fo~olume

24 Number 411993

encompassed by region III of this study’s regionalization. Generally, the areas for which the three regionalizations are in greatest agreement are for the northwest, northern central, northeastern and westem China. Beyond these areas there is considerable divergence in the regionalizations of climate due largely to the reliance on temperature as the principal discriminator of climatic regions in the empirical schemes of CHEN (1982) and HUANG (1986). The climate region pattern of the statistical regionalization in central and eastern China follows more closely that of the Koppen classification for China

375 (Figure 9) than that of the two Chinese regionalizations considered above. This is most likely due to the fact that the Koppen classification gives equal emphasis to thermal and moisture factors in classification of climate in contrast to the Chinese empiric schemes. The Cf zone of Koppen is meridional in nature and parallels closely the area encompassed by region IV of this study’s regionalization. Both regionalizations are functional in recognizing northern Taiwan as climatically similar to eastern China. Region II appro~mates to an average degree the area defined as Cw in the Koppen classification: however, it fails to recognize the areas of Dw and Cf climate in

-

Figure 9. Koppen classification of China. The climate zones/types are as follows: Af, tropical moist with no dry season; Aw, tropical with winter dry season; 353, dry steppe climate; BSk, dry cold steppe climate; BWk, desert cold climate; Cf, warm temperate moist with no dry season; Cw, warm temperate with winter dry season; Df, moist snow climate; Dw, snow climate with winter dry season. Adapted from DOMROS and GONGBING (1988).

If3

If

Ie

Id

Ic

Ib

Ia

All - [JANP, WIN, MGO83]

Ib

All - [JULT, MG20, WIN, MGO83]

JULT, MG20, JANP, JULP All - [MGlO, JANP]

All - [JANP, WIN, MGO83]

Id

All - [MGlO, MG20, MG083]

IC

JANP, WIN, JULP

All - [MGlO, MG20, MG083]

All - [JULT MGlO, MG 20, WIN, MG083

All - [MG20, WIN, MG083]

Ie

(a) Macro-climatic region I

JANT, WIN, JULP, JULJANM, MG083

JAN, JULP

JANT, MEANT, JANP, WIN, JULJANM, MG083

ti - [JULT, MEANT, MGlO, MGZO]

All - [JANT, MGlO, MG20, JULJANM]

JANP, WIN, JULP, MG083

MG20, MGO83]

All - [MGIO,

All - [JANT, MGlO, JULJANM, MGO83]

All - [MGlO, MG083]

All - [MG083]

All - [WIN, MG083]

If

Table 5. Matrix of interregional climate variable difference*

All - [MGlO, MG20, JULP, MGO83]

All - [MG20, JANP, WIN, JULP, MGO83]

All - [JANT, MGlO, MG20, WIN, MGO83]

All - [JANT, MG20, JANP, JANP, MGO83]

JANT, WIN JULP, JULJANM

MEANT, MG20, MG083

All - [MGlO, MG20, JANP, WIN, MGO83J

Ih

MG20, MGW]

All - [MGlO,

IIIb

Nil

All - [MG20, JANP, WIN, MGo83]

IIIC

Ail - [JULT, MGlO, MG20, MGO83]

All - [MG20, JANP, WIN, MGO83]

All - (JULT, MGlO, MG20, WIN, MG083] JANP, JULP

All - [MGlO, MGO83] JANP, JULP

All - [JULT, JANP, JULP, MGOg3]

IVb

IVa

All - [JULT, h4EANT, MG20, MGOg3]

Ivb

All - [JULT, MG20, MGO83]

JANT, MEANT, MGlO, WIN, JULJANM

Region IV IVC

All - [MGlO, MG20, WIN, JANP, JULJANM, MGO83]

All - [MGlO, JULJANM, MGO83]

All - [MGO83]

Va

JANT,MEANT, JULT, JANP, JULP, JULJANM

MG20, JANP, WIN, JULP, MGO83]

AII - FIGlO,

AII - [MGlO, MGos3]

All - [MGO83]

All - [MGlO, MG20, WIN]

IIg

All - [MG20]

IIf

All - [JULP]

IIe

(c) Macro-climatic regions III, IV and V

MG20, MGO83]

Ail - [MEANT,

All - [JULT, MG20, JULP]

IId

(b) Macro-climatic region II

All - [MG20, WIN, JULP]

Region III

JANT, JULP, JULJANM, MGOg3

IIC

vb

Region V

*Variables appearing in Table 5 are significantly different at the 0.05 level (two-tailed Student’s ‘P-test). The notation ‘all - [ 1’ should be read as all variables significantly different except those that are bracketed.

mb

IIIa

IIf

IIe

IId

IIC

IIb

IIa

IIb

378 the central area of region II (Figures 8 and 9). This may be in part a result of the lack of climate data for this high-elevation area, thus limiting the identification of this climate type in this study. For west China there is little agreement between the statistical regionalization and that of Koppen’s. The Koppen ice and snow zone partially agrees with region III of this study’s regionalization: however, attempts to draw boundaries in this part of western China are frustrated by the lack of data. Region I, encompassing northwest, northern central and northeastern China, includes the Bw, Bs and Dw climates of Koppen. For the two regionalizations greatest agreement exists for northeast China, where the Dw climate zone of Koppen matches closely the area encompassed by regions Ia, Ib, Ic and Ie (Figures 8 and 9). The statistical regionalization has been functional in recognizing subsets of the Koppen Dw climate as a result of temperature and summer precipitation differences as has the regionalization of CHEN (1982). For the other areas encompassed by regions Id, If, Ig and Ih only limited agreement with the Koppen regionalization exists although the east to west regionalization of the Koppen type B dry climates based on rainfall is recognized by the regionalization presented in this study.

Conclusion The climatic regionalization

of China produced by PCA and CA in general parallels the empirically based climate regionalizations of China and is compatible with the empirical knowledge of the temperature and precipitation climatology of China. The main difference between the climate regionalization presented here and the empirically based ones produced by Chinese scientists is the orientation of the climate regions in eastern China. This is related to the latter’s dependence on the latitudinally dependent temperature variable as the primary classification variable whereas the statistical regionalization is multivariate in nature and reflects the interaction between key climate variables such as temperature and precipitation. Although the statistical regionalization generally verifies the empirical regionalizations developed recently by Chinese scientists there appears to be evidence for encouraging a modification of existing empirical classifications, especially

Geoforum/Volume

24 Number 4/1993

in terms of the regionalization pattern for eastern China which needs to reflect better the hygrothermal characteristics of the East Asia monsoon. Based on a range of temperature and precipitation variables PCA has revealed that four PCs may be used to provide an understanding of the spatial variation of the climate resource in China as represented by thermal and moisture characteristics. Plotting of temperature, winter moisture, midsummer moisture and seasonality PC scores reveals clearly the spatial variation of the climate resource in China. The spatial pattern of scores demonstrates the latitudinal control, through insolation receipts and altitudinal control on temperature, the increasing effects of continentality, distance from the coast and topography on rainfall, and the broad latitudinal control on seasonality. Twenty-five climate regions were identified and are differentiated mainly on the basis of temperature and winter moisture characteristics with midsummer moisture and seasonality accounting for further heterogeneity amongst the regions. As the regions have been identified on the basis of their internal multivariate climatic similarity it may be assumed that intraregional climate forced biophysical processes will possess minimal spatial variability. The regions identified by the application of PCA and CA may therefore represent logical environmental process related regions for agricultural forecasting and environmental planning in general. These regions contrast with the administrative areas presently used as a spatial basis for these purposes. Furthermore the regions identified may serve as areas for making more detailed assessments of resources such as the compilation of climate statistics, which will aid in establishing the exact nature and availability of a region’s climate resource. The intercomparison of climate classifications has also revealed those areas which are commonly identified by both empiric and statistical classification schemes. Those areas demonstrating greatest divergence of climate regionalization results are those which are relatively ‘data-free’ or areas for which overemphasis is given to thermal factors in classification. Data-free areas warrant an increase in the data network density which will facilitate a greater understanding of the spatial variation of the climate

GeoforunWolume

24 Number 4/1993

resource in these areas. Divergence of the regionalizations due to the overemphasis of thermal factors in the empirical classifications suggests that the existing Chinese empirical classifications need to be modified so as to give greater emphasis to moisture components of the climate and, thus, reflect more the dynamics of the monsoon climates of China. It is acknowledged that the range of climate variables on which the statistical regionalization is based is limited and that a greater range of climate parameters may result in a more effective meaningful climatic regionalization. However, this may be limited by the availability of climate ,data such as solar radiation, cloud cover, wind and humidity, or data required for the calculation of a monsoon index for only a limited number of locations. Future objective approaches to climate regionalization could also include geographical variables such as elevation and a continentality index to produce a geo-climatic regionalization of climate. It should also be noted that the statistical regionalization presented is one based on derived summary measures of the average climate. It is important to note this as climate regions are not static in nature, with regional boundaries likely to change with season. The next step, therefore, in developing a multivariate statistically based climate regionalization, in addition to the inclusion of a greater range of classificatory variables, would be to consider the spatio-temporal characteristics of climate and develop seasonally based regionalizations. Such seasonal regionalizations would reveal those areas which behave similarly in seasonal climatic terms and thus provide a spatial basis for seasonal climate resource planning. Though beyond the scope of this study, mention should be made of the implications that a possible greenhouse forced climate change has for producing a climatic classification such as that presented in this study. For regions such as China that cover a large latitudinal range, current climate change modelling studies have clearly shown that changes in basic climate parameters such as temperature and precipitation will not be uniform. High-latitude environments are expected to experience a greater change relative to low-latitude environments. Non-spatially uniform changes in the vector properties of climatic elements will, at the conceptual level, most likely lead to an alteration in the spatial distribution of climate

379 resources. Exploring the nature of these spatial changes is an obvious avenue for future research. Approaches that offer considerable potential for assessing the sensitivity of a region’s climate resources to climate change have been outlined in some detail by HOSSELL (1992) and RIZZO and WIKEN (1992). These studies have shown clearly that with climate change the spatial distribution of climate resources and thus climate regions will change. This has obvious implications for long-term planning of economic activities that are climate-sensitive. From the results of this study it appears that a statistical approach to climatic regionalization may offer an alternative approach to the evaluation of the spatial distribution of climate resources. However, a cautionary note is appropriate. It must be borne in mind that the pattern of regionalization produced may depend on the number and type of variables used, the number of clusters initially prescribed, the climate station network density and distribution, the PCA procedural option selected and the cluster algorithm chosen. Furthermore, although a statistical approach may produce an effective regionalization recourse must be made to the original empirical data if a climatic nomenclature is to be developed for the regions identified and an understanding of the actual nature of the regional climates gained. Nevertheless, the use of eigentechniques such as PCA for reducing the dimensionality of resource assessment problems which are spatial and multivariate in nature in conjunction with agglomerative clustering techniques offers considerable potential in the area of assessment of environmental potential and the identification of ‘objectivity’ based planning regions.

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380 BONELL, M. and SUMNER, G. (1992) Autumn and winter precipitation areas in Wales, 1982-1983 to 19861987, Znt. J. Clim., 12,77-102. BUDYKO, M. I. (1958) The Heat Balance of the Earth. Office of Technical Services, U.S. Department of Commerce, Washington, DC. CATELL, R. B. (1966) The scree test for the number of PC’s, Mult. Behav. Res., 1,245-276. CHEN, X. (1982) A new approach to the climatic division of China, Actu met. sin., 40,398. CHU, C. (1929) Climatic provinces of China, Mem. Inst. Met. Nanking, 1, l-11. CLARK, W. A. V. and HOSKING, P. L. (1986) Statistical Methods for Geographers. John Wiley, New York. DAVIS, J. C. (1973) Statistics and Data Analysis in Geology. John Wiley, New York. DOMROS, M. (1990) Klimaokologische grundlagen des mischanbaus von marktoreintierten tropischen dauerkulturen auf Hainan, China, Mainzer geogr. Stud., H34, 279-298. DOMROS, M. and GONGBING, B. (1988) Climate of Chintz. Springer, Amsterdam. EASTERLING, D. R. (1989) Regionalization of thunderstorm activity in the contiguous United States, Znt. J. Clim., 9,567-579. GOSSENS, C. (1985) Principal component analysis of Mediterranean rainfall, J. Urn., 5, 379-388. HUANG, B. (1986) Climatic division and physio-graphic division of China: retrospectives and prospects, Paper presented at the German-Chinese workshop of the Climate of China, Institute of Geography, Mainz University. HOSSELL, J. E. (1992) Global warming and the British landscape: the sensitivity of land use and vegetation in Britain to climatic change, Unpublished PhD thesis, University of Birmingham. KALKSTEIN, L. S., TANG, T. and SKINDLOV, J. R. (1987) An evaluation of three clustering procedures for use in synoptic chmatological classification, J. Cfim. appl. Met., 26,717-730. KOPPEN, W. (1931) Versuch einer Klassification der Klimate, vorzugsweise nachihren Buezieshumgen zur pflanzenwelt, Geogr. Z., 6,592-611. LU, A. (1949) The climatic provinces of China, Met. scient. Pap. Acad. sin. Peking, 441446. McBOYLE, G. R. (1971) Climatic classification of Australia by computer, Aust. geogr. Stud., 6,1-14. MALMSTROM, V. H. (1969) A new approach to the classification of climate, Geogrl. J., 68,351-357. MATHER, P. M. (1976) Computational Methods of Multivariate Analysis in Physical Geography. John Wiley, London. MEI’E, R., RENZHANG, Y. and HAOSHENG, B. (1985) An Outline of China’s Physical Geography. Foreign Languages Press, Beijing. MILLER, A. A (1953) Climatology. Methuen, London. MURATA, A. (1990) Regionality and periodicity observed in rainfall variations of the Baiu season over Japan, Znt. J. Urn., 10,627-646. NORTH, G. R., BELL, T. L., CAHALAN, R. F. and MEONG, F. J. (1982) Sampling errors in the estimation of empirical orthogonal functions, Mon. Weath. Rev., 699-706. OLIVER, J. E. and WILSON, L. (1987) Climatic classification, In: The Encyclopedia of Climatology, J. E.

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