Surface and 700 hPa Atmospheric Circulation Patterns for the Great Lakes Basin and Eastern North America and Relationship to Atmospheric Teleconnections

J. Great Lakes Res. 25(1):45–60 Internat. Assoc. Great Lakes Res., 1999

Surface and 700 hPa Atmospheric Circulation Patterns for the Great Lakes Basin and Eastern North America and Relationship to Atmospheric Teleconnections Robert V. Rohli1,*, Anthony J. Vega2, Michael R. Binkley1, Stephen D. Britton3, Heather E. Heckman3, James M. Jenkins3, Yuichi Ono3, and Deborah E. Sheeler3 1Department

of Geography Water Resources Research Institute Kent State University Kent, Ohio 44242-0001 2Department

of Anthropology, Geography, and Earth Science Clarion University Clarion, Pennsylvania 16214 3Department

of Geography Kent State University Kent, Ohio 44242-0001

ABSTRACT. Many studies have identified continental-scale atmospheric circulation regimes, and some have been employed for various regions, but none have involved a regional categorization of circulation around the Great Lakes basin. Such an analysis is important not only because of the economic and recreational importance of the lakes, but in an effort to relate the regional circulation types to the broaderscale modes of atmospheric circulation, such as that forced by El Niño (ENSO). In this study, rotated principal components analysis (RPCA) is performed on the monthly mean sea-level pressure field around the Great Lakes basin, and in a separate analysis, on the mean 700 hPa field in eastern North America. An average-linkage clustering algorithm is applied to the RPCA scores to classify the monthly surface circulation in the Great Lakes region and the 700 hPa circulation over eastern North America. The classification is used to determine whether the various categories of regional circulation patterns are coincident with distinct hemispheric-scale flow regimes. To do this, indices of the modes of variability in some of the most well-known atmospheric teleconnections during months that fall within each circulation mode are subjected to ANOVA tests by cluster. Results suggest that the regional atmosphere over the Great Lakes basin undergoes long-term shifts in preferred modes of circulation. Furthermore, flow variability associated with the 700 hPa North Atlantic Oscillation (NAO) and Pacific/North American (PNA) teleconnections are more strongly tied to variability in both the Great Lakes regional surface circulation and the 700 mb eastern North American flow regimes than is the ENSO-forced Southern Oscillation. INDEX WORDS:

Synoptic climatology, atmospheric circulation, Great Lakes basin.

INTRODUCTION Variability in atmospheric circulation regimes comprises a major component of the climatology of mid-latitude regions. For example, air masses of both Arctic and tropical source regions exert dominance over the Great Lakes basin during various *Corresponding

months, but for any given month of the year the degree of influence of both air mass types varies interannually. Variations in the strength and persistence of regional-scale atmospheric circulation patterns are the most direct cause of the degree of influence of air masses, but the regional steering flow is embedded within broader-scale circulation patterns. One purpose of this research is to produce an au-

author. E-mail: rrohli:@kent.edu

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tomated classification of monthly circulation patterns for identification of the major modes of surface and lower tropospheric circulation over the Great Lakes region and eastern North America (respectively). Since pressure (or height, for upperlevel analysis) inequalities and gradients drive wind systems, spatial patterns of sea level pressure (SLP) (or 700 hPa [approximately 2900 to 3100 m] level geopotential height) fields are employed in the classification process. A second purpose is to determine whether significant serial correlation exists in the various circulation regimes. Third, this work seeks to determine whether long-term trends exist in frequency of the composite flow patterns generated. Finally, this research examines the degree of linkage between the regional- and continental-scale circulation and several hemispheric-scale circulation modes, such as the atmospheric component of the El Niño phenomenon. LITERATURE REVIEW Atmospheric Classification There are two general approaches to the classification process. Manual classifications proceed with the researcher visually examining a series of synoptic weather maps and using his/her intuition to classify each map into a pre-defined category. Although some advantages to manual classification exist, these techniques suffer from two major problems: lack of replicability of the study (even by the same researcher) and the labor intensive nature of the process (Yarnal 1993). For instance, Murray (1993) questions whether the trend toward more frequent southerly synoptic types during the 1980s over the British Isles is a result of systematic bias in Lamb’s (1972) manual classification system, or whether climate variability/change is actually involved. Automated classifications require the use of a computer to conduct the classification. One important family of automated techniques involves the use of eigenvector analysis to identify the most common spatial patterns among a data set to be classified. Automated cluster analysis can then be performed to identify the most logical groupings of daily or monthly mean weather map patterns. Automated techniques alleviate some of the problems of manual classifications because they eliminate most bias in the selection process and they require far less time to produce. However, many subjective decisions are still required in automated analysis, such as what the appropriate spatial and temporal domains should be, what the appropriate type of dis-

persion matrix should be, the type of rotation scheme to apply to the original eigenvectors (if any), how many eigenvectors to retain for analysis, and how many clusters should be created (Yarnal 1993). Oliver (1991) provides an excellent overview of the structure of atmospheric classifications with past examples. Circulation patterns can be classified using either manual or automated techniques. Manual techniques (e.g., Lamb 1972, Muller 1977, Comrie and Yarnal 1992, Yarnal and Frakes 1997) are advantageous in that they allow a researcher to use his/her experience to identify circulation categories. However, they are extremely labor-intensive and involve considerable subjectivity. Automated atmospheric circulation classifications became possible with the arrival of computerassisted statistical analysis, and they alleviate many of the disadvantages of manual techniques. However, subjectivity is still involved in such classifications (Key and Crane 1986). One family of such techniques is based on statistical correlation of atmospheric variables across space and was pioneered by Lund (1963). However, correlation methods suffer from several inherent problems (Yarnal 1993); chief among them is the lack of consideration of spatial variance in the procedure. Another method uses discriminant analysis (Diab et al. 1991), but is seldom employed because the classification must be implemented using a priori synoptic types. Three related eigenvector-based statistical procedures are commonly used: common factor analysis (CFA), empirical orthogonal function (EOF) analysis, and principal components analysis (PCA). Because these techniques detect only standing oscillations (and not traveling waves) (Horel 1984), they are more ideally suited to climatological analysis. Differences among these are detailed by Yarnal (1993), who emphasizes that EOF is less preferred in synoptic climatological work, and CFA involves some difficulties for atmospheric application. On the other hand, in recent years several excellent applied climatological studies have employed PCA. More specifically, Yarnal (1993) provides a variety of reasons that principal components analysis (PCA) is the most appropriate eigenvector technique in atmospheric circulation classification. For example, Davis and Walker (1992) use PCA in combination with cluster analysis to develop an upper-air synoptic climatology of the western United States. Similar automated classifications using PCA in specific regions include the work on

Classification of Atmospheric Circulation Patterns the upper Mississippi River basin by Keables (1988), and the identification of the weather types of Australia by Drosdowsky (1993). Several studies have used PCA to identify the broad-scale modes of circulation variability over the northern hemisphere and North America in particular (Horel 1981, Wallace and Gutzler 1981, Hsu and Wallace 1985, Mo and Livezey 1986, Barnston and Livezey 1987). Collectively, these studies suggest that certain geographic locations over North America tend to be preferred zones of geopotential height variability at certain levels in the atmosphere. However, none of these studies classify the time series of circulation patterns. Furthermore, the hemispheric scale of analysis employed in these studies precludes the possibility of detecting variability in regional- to synoptic-scale flow. One such area for which the hemispheric scale of analysis may mask regional flow patterns is the Great Lakes region. By subdividing the circulation modes into a useful number of groups and creating composite maps of the months within each group, common features of the flow can be identified within each group. From this, an analysis of the time series of occurrence of each flow pattern can be produced. Furthermore, the groupings of months can be examined to determine the degree to which the regional- to synoptic-scale classification identifies differences in hemispheric-scale flow pattern that the months represent. Much of the theoretical work regarding the use of PCA in map-pattern classification of atmospheric circulation has been summarized by Yarnal (1993). For instance, Yarnal (1993) suggests that the correlation matrix is the most appropriate type of dispersion matrix, an orthogonal rotation scheme should be employed, and ideally, the PCA-generated scores should be clustered. White (1988) notes that PCA and other eigenvector-based techniques are the only common statistical tools for analyzing spatial and temporal variability in a data set simultaneously. More detailed reviews of the mathematical manipulations employed in PCA are provided by Dunteman (1989). Atmospheric Teleconnections Atmospheric teleconnections are long-range correlation fields in some atmospheric variable (usually pressure or geopotential height, since these fields largely govern atmospheric flow) that represent planetary-scale disturbances produced by standing long waves (Esbensen 1984). The telecon-

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nection that has the widest impact on circulation variability and associated climatic anomalies worldwide is the atmospheric response to the oceanic El Niño phenomenon, the Southern Oscillation (SO). This teleconnection consists of a see-saw of atmospheric mass between the tropical eastern and western Pacific Ocean first recognized by Hildebrandsson (1897) and Walker and Bliss (1932). The SO is known to produce oscillations in SLP, upperlevel heights, and wind fields worldwide (Bjerknes 1969, Horel and Wallace 1981, Yarnal 1985). These changes in circulation are manifested in various parts of North America and the world by precipitation and temperature anomalies (e.g., van Loon and Madden 1981, Yarnal 1985, Ropelewski and Halpert 1987, Halpert and Ropelewski 1992). The Southern Oscillation Index (SOI) can be used to describe the phase of the SO for a given month. The SOI is often calculated as the monthly standardized anomaly of the difference in SLP between Tahiti and Darwin, Australia. A negative (positive) SOI is considered to represent the atmospheric response to the El Niño (La Niña) phenomenon (Philander 1990). However, it should be noted that as will be shown in the “Data and Methods” section, the index can be created using other techniques. A second teleconnection whose relationship to flow patterns over the Great Lakes basin is examined is a “wave train” known as the Pacific-North America (PNA) pattern. This teleconnection consists of pressure (height) nodes over the subtropical and extratropical north Pacific Ocean with additional nodes over western Canada and the Gulf of Mexico Coast of the United States, and appears consistently through the low to middle troposphere (Wallace and Gutzler 1981, Hsu and Wallace 1985). From a flow perspective, during some periods circulation within the North American westerlies is influenced by an anomalous ridge over the western part of the continent and simultaneous trough over the east. These standing waves produce an anomalously strong north-south component of flow in the westerlies over central North America (a positive PNA index). During other times, the ridge-trough configuration is de-amplified or even reversed, so that the flow becomes more strongly west-east, or even southwest-northeast (negative PNA index). Flow reminiscent of the PNA pattern has been shown to affect temperatures (Wallace and Gutzler 1981, Leathers et al. 1991, Hansen et al. 1993), sea level pressures (Hansen et al. 1993), and upperlevel geopotential height patterns in the U.S. (Harman 1991, Leathers and Palecki 1992). Some

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degree of association between the PNA index and U.S. precipitation has been identified by Leathers et al. (1991). Finally, variability in hemispheric-scale flow associated with the North Atlantic Oscillation (NAO) is related to regional flow types over the Great Lakes. The NAO is a north-south seesaw in atmospheric mass in the low to middle troposphere over the subtropical and extratropical Atlantic Ocean (Wallace and Gutzler 1981). The seesaw acts as the Azores High and the Icelandic Low experience pressure (height) anomalies of opposite sign. A NAO index can be computed as the mean monthly SLP difference between Ponta Delgadas, Azores Islands and Akureyri, Iceland, standardized by month. Periods when the NAO is positive (negative) are associated with an anomalously intense (weak) Azores high and Icelandic Low and strong (weak) westerly flow across the midlatitude North Atlantic Ocean. Extremes in the NAO have been found to be associated with temperature anomalies in eastern North America (Rogers 1984). However, there is some dispute about the importance of the NAO globally. For instance, Lamb and Peppler (1987) suggest that “it is unlikely that the NAO’s importance for global climate equals or even approaches that of the Southern Oscillation.” Other teleconnections at monthly time scales have been identified in the literature but do not seem to be as important as the three discussed above. One example of a teleconnection of secondtier importance is the Tropical/Northern Hemisphere (TNH) pattern, for which action centers exist at the 700 hPa level just off the Pacific northwest coast of the United States, an oppositely signed center near or just to the north of the Great Lakes, and a broad center of the same sign as the Pacific center near Cuba (Barnston and Livezey 1987). A second hemispheric-scale flow pattern that may affect smaller-scale flow variability in the United States is the West Pacific (WP) pattern. Wallace and Gutzler (1981) suggest that this pattern appears at the 500 hPa level and is associated with zonal and meridional flow anomalies across the north Pacific Ocean. The East Atlantic (EA) pattern has been identified at the 500 hPa level (Wallace and Gutzler 1981). This pattern has upper-level height variability centers near the British Isles, Canary Islands, and the Black Sea, and a positive index is indicative of high heights over the North Atlantic and low heights over the subtropical Atlantic and eastern Europe. Finally, the Pacific Transition (PT) pattern has been shown to exist in the spring and summer

as a broad east-west band of like-signed anomalies in the subtropical Pacific, with an additional center of like sign in eastern Russia and a center of opposite sign often appearing in southwestern Canada. Other teleconnections such as the East Atlantic Jet, East Pacific, North Pacific, East Atlantic /Western Russia, Scandinavia, and Subtropical Zonal patterns have been described in the literature along with indices describing the mode of variability in a given month (Barnston and Livezey 1987). However, since this analysis suggests that none show significant relationships with smaller-scale flow variability in the eastern United States or Great Lakes region, these teleconnections are not described here. DATA AND METHODS Monthly gridded SLP and 700 hPa geopotential height data are selected from a 1977-point equalarea grid (National Centers for Environmental Prediction 1996). SLP data are extracted for 21 points within the region bounded by 37°N, 48°N, 75°W, and 95°W (Fig. 1), while 700 hPa geopotential height data for 117 points within the region bounded by 25°N, 55°N, 60°W, and 105°W (Fig. 2) are selected. The 1962 to 1994 period is used for analysis, and spatial resolution of data points is approximately 350 km. The two vertical levels are chosen because many climatic problems that would be affected by the presence of the Great Lakes rely on moisture advection, which is represented by atmospheric circulation between the surface and the 700 hPa level. The spatial domain of the SLP analysis is chosen to represent regional-scale (rather than synoptic-scale) flow patterns, with the mid-latitude part of the Great Lakes basin being the focus of attention. In an attempt to avoid problems associated with height adjustment over the mountainous regions of the North American West, the spatial domain of the 700 hPa level analysis is restricted to the area east of the Rockies. Likewise, the eastern edge of the 700 hPa domain is chosen to detect variability associated with surface storms in the western Atlantic and the Bermuda High. The northern and southern boundaries of the 700 hPa analysis are selected to coincide with the zone of greatest mid-latitude seasonal flow variability. The monthly data are standardized by month (by subtracting the mean and then dividing by the standard deviation) to remove the natural seasonal cycles. Principal components analysis (PCA) is then

Classification of Atmospheric Circulation Patterns

FIG. 1.

FIG. 2.

SLP grid points used in the analysis.

700 hPa grid points used in the analysis.

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performed on the standardized data to reveal the primary modes of variability in SLP and geopotential heights over the region. Because the unrotated solutions to PCA produced unrealistic spatial patterns, varimax rotation techniques are utilized (Richman 1986). The varimax criteria maximizes the sum of the variances of the squared loadings within each column of the loading matrix, resulting in a new set of orthogonal coordinate axes where each new coordinate axis has either large or small loadings of the variables on it (Dunteman 1989). The sets of scores for all temporal observations are then subjected to an average-linkage cluster analysis (Kalkstein et al. 1987). Monthly standardized indices of the SOI, PNA, NAO, and other teleconnections (Climate Prediction Center (CPC) 1998) as well as chronological month numbers (from 1 for January 1962 through 396 for December 1994) are collected and entered into ANOVA procedures to determine whether the F-ratio indicates that, for a given teleconnection, between-cluster variability significantly exceeds the within-cluster variability (or vice versa). If the for-

TABLE 1.

PC1 PC2 PC3 PC4 PC5 PC6

TABLE 2.

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10

mer is true, then the synoptic-scale circulation types can be considered to represent various phases of hemispheric-scale variability. It should be noted that the teleconnection indices produced by CPC are based on rotated PCA scores of the various components of standardized 700 hPa heights. RESULTS AND DISCUSSION PCA and Cluster Analysis Based on a subjective determination of the natural “drop-off” eigenvalues using a plot of component vs. eigenvalue (a “scree plot”—Cattell 1966), three components are retained for rotation in the SLP analysis, while six are retained for the 700 hPa analysis (Tables 1 and 2). The retained components collectively explain 91.0 percent of the data set variance for the SLP analysis, and 88.8 percent of the variance in 700 hPa heights in the study region. Rotation redistributed the proportions of explained variance as shown in Tables 1 and 2. After applying the average-linkage algorithm, six clusters of months result for the SLP analysis and

Eigenvalues of the correlation matrix for unrotated and rotated SLP analysis. Eigenvalue (Unrotated) 14.626 2.469 2.009 1.004 0.269 0.212

Proportion of Explained Variance 0.696 0.118 0.096 0.048 0.013 0.008

Eigenvalue (Rotated) 7.567 6.252 5.284 — — —

Proportion of Explained Variance 0.360 0.298 0.252 — — —

Eigenvalue of the correlation matrix for unrotated and rotated 700 hPa analysis. Eigenvalue (Unrotated) 42.536 23.065 16.906 9.637 6.535 5.240 2.008 1.514 1.284 1.184

Proportion of Explained Variance 0.364 0.197 0.144 0.082 0.056 0.045 0.017 0.013 0.011 0.010

Eigenvalue (Rotated) 23.679 20.494 18.117 15.254 15.065 11.310 — — — —

Proportion of Explained Variance 0.202 0.175 0.155 0.130 0.129 0.097 — — — —

Classification of Atmospheric Circulation Patterns eight for the 700 hPa analysis. The number of clusters retained is based on scree plots of the normalized root-mean-square distance at each iteration of the hierarchical agglomerative clustering procedure, as well as some a priori knowledge of the approximate number of circulation types in the region (Dunteman 1989). It should be noted that 5 months remain unclassified in the SLP analysis along with 35 months in the 700 hPa analysis. Complete lists of months included within each cluster for both the sea level and 700 hPa analysis are provided in Appendices A and B, respectively. Composite Maps Sea Level Pressure Analysis For the sea level analysis, the six composite patterns for each of the clusters are shown in Figures 3a–f. These clusters represent 391 (98.7 percent) of the 396 months (Table 3). The composite map of months in Cluster A (Fig. 3a) shows the effects of high pressure in the southeastern portion of the study area. This pattern is suggestive of either a midlatitude anticyclone along the East Coast or an expansion of the surface Bermuda subtropical anticyclone in summer. However, the fact that this pattern is as prevalent in the cooler season as in the warm season (Table 4) dismisses the Bermuda High as a major factor in the formation of this pattern. The pattern also suggests cyclone passage over the northern Great Lakes, and is especially evident during the time of year when the lakes are warmer than the air above them, destabilizing the atmosphere. Clusters B and C (Figs. 3b–c) show especially weak pressure gradients over the region, with a slight suggestion of higher pressure to the southeast of the region and lower pressure to the northeast. Such a pattern would be expected to produce only light zonal (west to east or vice versa) surface winds across the region. These patterns display a slight preference for occurring during transition seasons (Table 4). In the case of Cluster C, the pressure pattern suggests that frequent and/or intense anticyclones are migrating from northwest to southeast, while cyclones are common and/or intense in migrating from southwest to northeast. The composite map of Cluster D (Fig. 3d) shows a clear meridional (north-south) orientation to the isobars, with higher pressure to the east and lower pressure to the west. Such a pattern would be expected to be associated with southerly surface

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winds. Its year-round occurrence (Table 4) suggests that moisture advection from the Gulf of Mexico source region may be important in all months. Clusters E and F are composed of only 14 and 8 months respectively, and therefore conclusions based on such a small sample size should be made with caution. Nevertheless, Cluster E months seem to contain frequent and/or intense Colorado wave cyclone passage from southwest to northeast over or near the Great Lakes. Finally, Cluster F suggests storms over the central U.S. in the western part of the study region, with higher pressure to the north of the Great Lakes. Such a pattern implies easterly surface winds over the Great Lakes, and may explain why the cluster contains so few months. The nonparametric Kruskal-Wallis test identifies some significant temporal clustering in the time series of months in the SLP data. The percentiles of the chi-square distribution can be used to identify the level of significance of the Kruskal-Wallis W statistic (Burt and Barber 1996), and the W statistic of 10.08 is significant only at approximately p = 0.08. Mann-Whitney tests (Burt and Barber 1996) reveal some clusters that do differ significantly from one another, taken two at a time. Table 5 shows that only the three largest clusters have chronological month numbers (beginning with 1 for January 1962 and ending with 396 for December 1994) that differ significantly from the other clusters. More specifically, the most common monthly SLP pattern (Cluster A) appears to have been especially frequent earlier in the time series than the next two most common patterns (Clusters B and C). Thus, a slight tendency for reduced pressure gradients in the latter part of the time series may exist. However, little strong evidence for climatic change at this scale is evident. 700 hPa Analysis The 700 hPa analysis revealed eight clusters that collectively represent 361 (91.2 percent) of the 396 months (Table 6). Composite 700 hPa maps of the most common patterns, Clusters A and B (Figs. 4a and 4b) show a mean zonal flow across the Great Lakes region, which reflects the tendency for westerly flow across the midlatitudes. The Cluster A pattern shows relatively low 700 hPa heights over the Great Lakes basin, suggesting an expanded circumpolar vortex. Table 7 shows that this pattern is slightly more common in the cool season than at other times, even after the monthly standardization. Clusters B, C, and D display less seasonal prefer-

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A

D

B

E

C

F

FIG. 3a–f. Composite maps of sea level pressures (+ 1000 hPa) during months that fall within each cluster, by cluster. a = Cluster A; b = Cluster B, etc.

Classification of Atmospheric Circulation Patterns A

B

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E

F

C

G

D

H

FIG. 4a–h. Composite maps of 700 hPa pressures (isohypse labels in meters) during months that fall within each cluster, by cluster. a = Cluster A; b = Bluster B, etc.

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TABLE 3. Number and percentage of months included within each cluster of SLP charts. Cluster A B C D E F Unclassifiable Total

Number of Months Months Included 152 130 67 20 14 8 5 396

Percentage of Total Months 38.4 32.8 16.9 5.1 3.5 2.0 1.3 100.0

TABLE 4. Number of months included in each cluster by month, for SLP analysis. A B January 11 13 February 15 9 March 12 9 April 13 10 May 11 17 June 14 9 July 13 11 August 10 11 September 12 11 October 13 11 November 15 8 December 13 11 Total 152 130

C 4 6 8 6 4 5 6 8 5 5 6 4 67

Cluster D E 1 4 1 1 0 1 1 1 1 0 2 2 0 1 3 1 3 2 3 1 2 0 3 0 20 14

F Unclassifiable 0 0 1 0 2 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1 8 5

ence throughout the year (Table 7), and their lower numbers of included months (compared to Cluster A) make generalizations about seasonality more difficult. Months in Cluster B have flow patterns that appear similar to those in Cluster A, but with isohypses generally located farther to the north than in Cluster A. This suggests the northward retreat of the circumpolar vortex, and these months are likely to be associated with higher temperatures than for corresponding months comprising Cluster A. Furthermore, Cluster B seems to display a slight preference for warm season months (even after the standardization process), so this flow pattern tends to represent anomalously warm months in the warm season. Cluster C (Fig. 4c) shows weak flow in the southern part of the region, as indicated by relatively weak isohypse gradients. However, the iso-

TABLE 5. clusters.

Temporal characteristics of sea level

Median Chronological Interquartile Cluster Month Number Range ab Least Recent E 179.5 126.0–205.0 Dab 218.5 111.5–256.0 Fab 199.5 113.0–265.5 Aa 198.5 93.0–302.5 Bb 194.5 107.0–308.0 Most Recent Cb 211.0 116.0–304.0 Clusters with the same superscript do not differ significantly from one another, according to the Mann-Whitney statistic at p = 0.05.

hypses are also displaced to the north over the Great Lakes, as compared to the patterns for Clusters B and (especially) A. Cluster C months may show a slight preference for transition seasons, indicating that the weak composite flow pattern may be a transition season phenomenon. The composite map of Cluster D months (Fig. 4d) again shows similar flow over the Great Lakes, but has stronger flow to the south of the region and has no seasonal preference. Clusters E through H are composed of only 12 through 7 occurrences, respectively, and therefore represent 700 hPa configurations under rare or extreme conditions. The most striking feature of the composite flow during Cluster E months (Fig. 4e) is the strong meridionality to the west of the Great Lakes basin, with an intense longwave trough over the eastern United States. Such a pattern would be expected to advect polar air southward into the TABLE 6. Number and percentage of months included within each cluster of 700 hPa circulation patterns. Cluster A B C D E F G H Unclassifiable Total

Number of Months Months Included 184 70 38 33 12 9 8 7 35 396

Percentage of Total Months 46.5 17.7 9.6 8.3 3.0 2.3 2.0 1.8 8.8 100.0

Classification of Atmospheric Circulation Patterns TABLE 7. analysis.

January February March April May June July August September October November December Total

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Number of months included in each cluster by month, for 700 hPa

A 16 19 20 14 12 14 12 14 12 15 19 17

B 5 6 6 7 7 7 6 3 7 6 5 5

C 3 2 2 3 4 6 2 2 3 4 3 4

D 3 3 0 4 4 2 3 3 1 3 3 4

Cluster E 3 2 1 0 0 0 0 1 1 1 1 2

184

70

38

33

12

western Great Lakes region. Indeed, this cluster is largely composed of winter months (Table 7), many of which produced devastating cold conditions in the eastern U.S., such as January 1977, January 1985, and December 1989. Cluster F (Fig. 4f) is a summer-dominated group (Table 7). The composite map displays a 700 hPa confluence from the west-southwest and high heights in the Great Lakes region. In addition, height gradients in the Southeast are extremely weak in this pattern, perhaps because of an anomalously strong surface Bermuda High that is relatively unimpeded vertically. Despite the fact that the heights were standardized by month, eight of the nine members of this cluster still occur between June and October, when the Bermuda High is usually at its peak extent. Moreover, all months in Cluster F occurred prior to 1979. The absence of occurrences since 1979 supports Davis et al.’s (1997) observation that there has been a centurylong net removal of atmospheric mass over the Bermuda High. In Cluster G (Fig. 4g), flow is zonal, but the circumpolar vortex has retreated northward despite the presence of a weak trough over the eastern Great Lakes basin, and five of the eight instances occur in August or September (Table 7). Finally, Cluster H (Fig. 4h) shows ridge-to-trough flow over the basin, but because heights are very high across the study area it is also a summer-dominated pattern (Table 7). The Kruskal-Wallis W statistic of 21.608 (p =

F 0 0 0 1 0 1 2 1 3 1 0 0

G 0 1 0 1 1 0 0 4 1 0 0 0

H 0 0 0 0 2 0 2 2 1 0 0 0

Unclassifiable 3 0 4 3 3 3 6 3 4 3 2 1

9

8

7

35

0.003) suggests that several of the clusters tend to be comprised of months that have statistically significant serial differences. Table 8 summarizes the results from Mann-Whitney tests on the “Julian” month numbers, testing two clusters against one another at a time. Clusters F and E tend to be composed of early months in the time series, while Cluster G is composed of later months. However, the paucity of months that make up these clusters (Table 6) suggests that these may represent extreme months that happened in either the early or late part of the time series. More striking, however, is the tendency for Cluster A to be composed of events that occur significantly earlier in the time series

TABLE 8.

Temporal characteristics of 700 hPa.

Median Chronological Interquartile Cluster Month Number Range a Least recent F 94.0 66.0–139.0 Eabce 92.0 41.5–203.5 Habcd 116.0 89.0–213.0 Abc 180.0 104.5–280.0 Dde 217.0 86.0–293.0 Cd 202.0 133.0–306.0 Bd 265.0 153.0–328.0 Most recent Gd 278.0 239.5–310.5 Clusters with the same superscript do not differ significantly from one another, according to the Mann-Whitney statistic at p = 0.05.

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than Cluster B and C events, just as was the case at sea level. Thus, the expansion of the circumpolar vortex over the Great Lakes seems to be a less common characteristic of the 700 hPa atmosphere in the early 1990s than in the 1960s.

TABLE 10. Means and standard deviations of the SOI, PNA, and NAO monthly indices by 700hPa cluster. Note that the entire collection of indices (of which the 1962–1994 months are a part) is standardized to a mean of zero and a standard deviation of 1.0.

Relationship to Hemispheric-Scale Flow Neither the SLP nor the 700 hPa analyses produce clusters in which the SOI differs significantly by cluster, according to ANOVA tests (F-ratio = 1.11 and 0.78, respectively, with corresponding p = 0.357 and 0.608). The most noteworthy case of apparent clustering of SOI months appears in Cluster A in the 700 hPa analysis, in which all months from November 1982 through June 1983 (an extremely intense El Niño event) appear in Cluster A. On the whole, however, no statistically significant relationship between cluster and SOI is found, despite the fact that the most intense El Niño event in the time series does fall within the same cluster. The PNA index shows strong differences by cluster for both the SLP (F-ratio = 5.51 and p = .0001) and 700 hPa (F-ratio = 3.80 and p = .0006) analyses. Tables 9 and 10 suggest that Clusters E and F in the SLP analysis and Cluster E in the 700 hPa

Cluster A

SOI –0.17 1.21

PNA 0.08 0.96

NAO 0.02 0.98

B

–0.17 1.07

–0.13 0.81

0.35 0.91

C

–0.52 1.12

–0.02 0.91

0.29 1.05

D

–0.32 1.16

–0.54 1.05

–0.48 0.67

E

–0.38 0.75

0.90 0.93

–1.32 0.58

F

0.13 1.05

–0.57 1.21

0.13 0.88

G

–0.11 0.97

–0.49 1.07

0.56 0.91

H

0.23 1.07

0.16 0.90

–0.16 1.06

TABLE 9. Means and standard deviations of the SOI, PNA, and NAO monthly indices by SLP cluster. Note that the entire collection of indices (of which the 1962–1994 months are a part) is standardized to a mean of zero and a standard deviation of 1.0. Cluster A

SOI –0.14 1.12

PNA –0.26 0.89

NAO 0.21 0.97

B

–0.17 1.08

0.09 0.93

–0.22 0.86

C

–0.34 1.07

0.13 0.96

0.16 1.29

D

–0.64 1.68

–0.08 1.22

0.33 0.68

E

–0.46 0.73

0.85 0.88

–0.89 1.13

F

–0.48 1.53

1.00 1.26

0.15 0.82

analysis are associated with amplified PNA ridgetrough configuration. This observation is especially apparent in Figure 4e. The NAO index also displays strong differences by cluster in both the SLP and 700 hPa analyses (Fratio = 5.70, p = .0001 and F-ratio = 6.99, p = .0001 respectively). Thus, from Tables 9 and 10 it is apparent that strong zonal flow over the north Atlantic Ocean is associated with Great Lakes regional SLP patterns such as those in Figures 3a and 3d and with 700 hPa patterns such as those in Figures 4g, 4b, and 4c. Weak zonal flow over the Atlantic occurs with surface atmospheric configurations over the Great Lakes such as is shown in Figure 3e and 3b and with 700 hPa patterns as shown in Figures 4e and 4d. The classification does not cluster the surface flow patterns associated with extremes of other major teleconnections to a significant extent. In fact, for the EA pattern the clusters show significantly

Classification of Atmospheric Circulation Patterns similar values of the EA index (F-ratio = 0.18, p = 0.9693). All of the clusters have a mean EA index near zero. The one possible exception, however, is the Asian Summer pattern (F-ratio = 2.25, p = 0.0559) for which a positive (negative) phase of the pattern is indicated by above- (below-) normal 700 mb heights throughout southern Asia and northeastern Africa (Climate Prediction Center 1998). When 700 mb heights are above normal (i.e., ridging) near the “action centers,” surface flow over the Great Lakes region appears as in Figure 3c. During months when troughing takes place in the action centers, flow in the Great Lakes region appears as in Figure 3a. Because summer months (J-J-A) are used exclusively in the computation of the Asian Summer pattern, the number of observations in each cluster is relatively low and caution must therefore be exercised in the interpretation of results. In the 700 hPa classification, a few teleconnections show some important differences between months of different clusters. In particular, three “second-tier” teleconnections display impressive differences in flow across North America between months in Clusters 2 vs. 3. For example, the TNH pattern shows an F-ratio of 8.73 (p = 0.0001) for the between- vs. within-group variability. From a flow perspective, when the flow across the eastern United States appears as in Figure 4b, a positive TNH index occurs. This is associated with relatively low heights across the central latitudes of the North Pacific, central Canada, and Scandinavia, while a pattern of above-normal heights exists from central Siberia to the Gulf of Alaska, the southern United States, and the central latitudes of the North Atlantic (Climate Prediction Center 1998). By contrast, the flow shown in Figure 4c would be associated with opposite conditions at the nodes listed above. The WP pattern also shows a significant relationship with 700 hPa flow over eastern North America (F-ratio = 2.57, p = 0.0135), despite the fact that this index is calculated using 500 hPa heights. Flow associated with Figure 4c is linked to positive WP indices, with below-normal heights over the western and central North Pacific and above-normal heights across the high latitudes of the North Pacific. During months in which the flow resembles that of Figure 4b, the WA index tends to be positive and flow variations over the Pacific are in the opposite manner as described above. Finally, the PT pattern index demonstrates strong differences from one 700 hPa cluster to another (Fratio = 6.69, p = 0.0001). For instance, the pattern

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shown in Figure 4b is conducive to a positive PT index (Climate Prediction Center 1998), while that shown in Figures 4c and 4d are associated with negative indices (Climate Prediction Center 1998). Given that the eastern United States at around 40°N is an “action center” of this summer mode of variability in 700 hPa heights, it is not surprising that the classification distinguishes summer months with different PT flow regimes. SUMMARY/CONCLUSIONS This research attempts to derive major modes of sea level pressure (SLP) and 700 hPa mid-tropospheric atmospheric circulation over the Great Lakes basin. Rotated PCA is combined with average-linkage cluster analysis in the identification of dominant synoptic patterns. The analysis reveals six SLP cluster patterns and eight 700 hPa patterns. Results suggest that few of the SLP patterns demonstrate significant temporal trends. However, the most common pattern is more prevalent earlier in the time series than the second and third most common patterns. This implies a tendency for reduced pressure gradients during the latter portion of the time series. Such a trend is consistent with recent increases in global and hemispheric temperatures and a net reduction in the latitudinal thermal gradient. This trend is supported in the 700 hPa analysis. The most common 700 hPa pattern, with its expanded circumpolar vortex and presumably lower temperature and dew points, also tends to precede the second and third most common patterns. Such results support the notion that the regional atmosphere undergoes shifts consistent with the preferred climatological modes of circulation. While this suggests that recent global warming trends are the primary regional forcing mechanism, other forcing mechanisms such as more frequent and intense recent ENSO events (Trenberth and Hoar 1996) cannot be discounted as a major source of regional climatic variability. However, months within each cluster are not found to be segregated by the Southern Oscillation Index (SOI), the gauge for describing ENSO intensity. Rather, flow variability associated with the 700 hPa North Atlantic Oscillation (NAO) and Pacific/North American (PNA) teleconnection patterns are found to be more strongly tied to variability in both the Great Lakes regional surface circulation and the 700 hPa eastern North American flow regimes. Other 700 hPa teleconnections show some relationships to 700 hPa

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flow over eastern North America, but other than the NAO and PNA only the Asian Summer pattern seems to be tied to the regional surface circulation over the Great Lakes. Further research is needed to identify the primary sources of climate variation and change over the study area so that future impact scenarios, incorporating projected increases in anthropogenic greenhouse gases, may be improved. Subsequent studies may also apply the regional classification developed here to a wide variety of environmental problems around the Great Lakes basin to determine the degree of linkage to atmospheric circulation. REFERENCES Barnston, A.G., and Livezey, R.E. 1987. Classification, seasonality and persistence of low frequency atmospheric circulation patterns. Mon. Wea. Rev. 115: 1083–1126. Bjerknes, J. 1969. Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev. 97:163–172. Burt, J.E., and Barber, G.M. 1996. Elementary Statistics for Geographers, Second Edition. New York: Guilford Press. Cattell, R.B. 1966. The scree test for the number of factors. Multivariate Behavioral Research 1:245–276. Climate Prediction Center (CPC). 1998. United States Department of Commerce, National Oceanic and Atmospheric Administration. http://nic.fb4.noaa. gov:80/data/ teledoc/telecontents.html Comrie, A.C., and Yarnal, B. 1992. Relationships between synoptic-scale atmospheric circulation and ozone concentrations in metropolitan Pittsburgh, Pennsylvania. Atmos. Env. 26B:301–312. Davis, R.E., and Walker, D.R. 1992. An upper-air synoptic climatology of the western United States. J. Climate 5:1449–1467. ——— , Hayden, B.P., Gay, D.A., Phillips, W.L., and Jones, G.V. 1997. The north Atlantic subtropical anticyclone. J. Climate 10:728–744. Diab, R.D., Preston-Whyte, R.A., and Washington, R. 1991. Distribution of rainfall by synoptic type over Natal, South Africa. Int. J. Climatol. 11:877–888. Drosdowsky, W. 1993. An analysis of Australian seasonal rainfall anomalies: 1950–1987. I: Spatial Patterns. Int. J. Climatol. 13:1–30. Dunteman, G.H. 1989. Principal Components Analysis. Newbury Park, California: SAGE Publications. Esbensen, S.K. 1984. A comparison of intermonthly and interannual teleconnections in the 700 mb geopotential height field during the Northern hemisphere winter. Mon. Wea. Rev. 112:2016–2032. Halpert, M.S., and Ropelewski, C.F. 1992. Surface temperature patterns associated with the Southern Oscillation. J. Climate 5:577–593.

Hansen, A.R., Pandolfo, J.P., and Sutera, A. 1993. Midtropospheric flow regimes and persistent wintertime anomalies of surface-layer pressure and temperature. J. Climate 6:2136–2143. Harman, J.R. 1991. Synoptic Climatology of the Westerlies: Processes and Patterns. Washington, D.C.: Association of American Geographers. Hildebrandsson, H.H. 1897. Quelques recherches sur les entres d’action de l’atmosphere. Kunglica Svenska Vetenskapsakademiens Handlingar 29. Horel, J.D. 1981. A rotated principal component analysis of the interannual variability of the Northern Hemisphere 500-mb height field. Mon. Wea. Rev. 109:2080–2092. ——— . 1984. Complex principal component analysis: Theory and examples. J. Climate Appl. Meteor. 23:1660–1673. ———, and Wallace, J.M. 1981. Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev. 109:813–829. Hsu, H., and Wallace, J.M. 1985. Vertical structure of wintertime teleconnection patterns. J. Atmos. Sci. 42:1693–1708. Kalkstein, L.S., Tan, G., and Skindlov, J.A. 1987. An evaluation of three clustering procedures for use in synoptic climatological classification. J. Clim. Appl. Meteorol. 26:717–730. Keables, M.J. 1988. Spatial associations of midtropospheric circulation and upper Mississippi River Basin hydrology. Annals Assoc. Amer. Geog. 78:74–92. Key, J., and Crane, R.G. 1986. A comparison of synoptic classification schemes based on ‘objective’ procedures. J. Climatology 6:375–388. Lamb, H.H. 1972. British Isles weather types and register of the daily sequence of circulation patterns, 1861–1971. Geophys. Mem. 116, London. Lamb, P.J., and Peppler, R.A. 1987. North Atlantic Oscillation: Concept and application. Bull. Amer. Meteor. Soc. 68:1218–1225. Leathers, D.J., and Palecki, M.A. 1992. The Pacific / North American teleconnection pattern and United States Climate. Part II: Temporal characteristics and index specification. J. Climate 5:707–716. ——— , Yarnal, B.M., and Palecki, M.A. 1991. The Pacific / North American teleconnection pattern and United States climate. Part I: Regional temperature and precipitation associations. J. Climate 4:517–528. Lund, I.A. 1963. Map-pattern classification by statistical methods. J. Appl. Meteorol. 2:56–65. Mo, K.C., and Livezey, R.E. 1986. Tropical-extratropical geopotential height teleconnections during the northern hemisphere winter. Mon. Wea. Rev. 114:2488–2515. Muller, R.A. 1977. A synoptic climatology for environmental baseline analysis: New Orleans. J. Appl. Meteorol. 16:20–33.

Classification of Atmospheric Circulation Patterns Murray, R. 1993. Bias in southerly synoptic types in decade 1981–90 over the British Isles. Weather 48:152–153. National Centers for Environmental Prediction. 1996. CD-ROM: Version III, Grid Point Data Set. University of Washington Department of Atmospheric Sciences and National Center for Atmospheric Research Data Support Section. Oliver, J.E. 1991. The history, status, and future of climate classification. Phys. Geog. 12:231–251. Philander, S.G. 1990. El Niño, La Niña, and the Southern Oscillation. San Diego: Academic Press. Richman, M.B. 1986. Rotation of principal components. J. Climatology 6:293–335. Rogers, J.C. 1984. The association between the North Atlantic Oscillation and the Southern Oscillation in the Northern hemisphere. Mon. Wea. Rev. 112:1999–2015. Ropelewski, C.F., and Halpert, M.S. 1987. Global and regional scale precipitation patterns associated with the El Niño/Southern Oscilliation. Mon. Wea. Rev. 115:1606–1626. Trenberth, K.E., and Hoar, T.J. 1996. The 1990–1995 El Niño-Southern Oscillation event: Longest on record. Geophys. Res. Letters 23:57–60.

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van Loon, H., and Madden, R.A. 1981. The Southern Oscillation. Part I: Global associations with pressure and temperature in northern winter. Mon. Wea. Rev. 109:1150–1162. Walker, G., and Bliss, E.W. 1932. World Weather V. Mem. Royal Meteor. Soc. 4:53–84. Wallace, J.M., and Gutzler, D.S. 1981. Teleconnections in the geopotential height field during the Northern hemisphere winter. Mon. Wea. Rev. 109:784–812. White, D.A. 1988. Climate Regionalization: A Comparison of Principal Component Analysis Rotation Schemes. Ph.D. dissertation, The Pennsylvania State University, State College, PA. Yarnal, B. 1985. Extratropical teleconnections with El Niño/Southern Oscillation (ENSO) events. Progress in Physical Geography 9:315–352. ——— . 1993. Synoptic Climatology in Environmental Analysis. London, UK: Belhaven Press. ———, and Frakes, B. 1997. Using synoptic climatology to define representative discharge events. Int. J. Climatol. 17:323–341. Submitted: 23 December 1997 Accepted: 7 October 1998 Editorial handling: David J. Schwab

APPENDIX A Months in the Study Period by Sea Level Cluster Cluster A: 1/62, 4/62, 5/62, 12/62, 1/63, 2/63, 3/63, 5/63, 10/63, 12/63, 3/64, 5/64, 6/64, 9/64, 10/64, 11/64, 2/65, 5/65, 11/65, 12/65, 2/66, 3/66, 5/66, 6/66, 10/66, 2/67, 3/67, 4/67, 7/67, 12/67, 3/68, 4/68, 8/68, 9/68, 10/68, 1/69, 5/69, 8/69, 10/69, 11/69, 2/70, 4/70, 5/70, 6/70, 7/70, 9/70, 4/71, 6/71, 7/71, 11/71, 1/72, 7/72, 2/73, 6/73, 8/73, 1/74, 4/74, 7/74, 8/74, 9/74, 10/74, 6/75, 7/75, 8/75, 10/75, 11/75, 1/76, 2/76, 3/76, 6/76, 3/77, 4/77, 5/77, 7/77, 8/77, 6/78, 7/78, 8/78, 10/78, 12/78, 3/79, 6/79, 11/79, 12/79, 11/80, 2/81, 4/81, 6/81, 2/82, 3/82, 4/82, 7/82, 9/82, 10/82, 11/82, 7/83, 9/83, 12/83, 1/84, 6/84, 7/84, 9/84, 11/84, 12/84, 2/85, 3/85, 4/85, 9/85, 10/85, 1/86, 3/86, 9/86, 11/86, 12/86, 5/87, 6/87, 7/87, 10/87, 11/87, 1/88, 2/88, 3/88, 8/88, 9/88, 10/88, 12/88, 1/89, 2/89, 4/89, 2/90, 4/90, 6/90, 11/90, 12/90, 1/91, 2/91, 5/91, 9/91, 11/91, 12/91, 8/92, 9/92, 12/92, 6/93, 11/93, 2/94, 4/94, 5/94, 7/94, 8/94, 10/94, 11/94 Cluster B: 9/62, 10/62, 4/63, 6/63, 7/63, 11/63, 1/64, 2/64, 7/64, 8/64, 1/65, 4/65, 7/65, 8/65, 10/65,

4/66, 7/66, 8/66, 9/66, 12/66, 1/67, 10/67, 11/67, 5/68, 6/68, 11/68, 12/68, 4/69, 6/69, 12/69, 1/70, 8/70, 11/70, 12/70, 1/71, 2/71, 3/71, 5/71, 2/72, 3/72, 8/72, 9/72, 12/72, 1/73, 4/73, 5/73, 7/73, 9/73, 10/73, 11/73, 2/74, 3/74, 5/74, 6/74, 11/74, 12/74, 1/75, 2/75, 3/75, 5/75, 5/76, 12/76, 2/77, 9/77, 12/77, 5/78, 5/79, 8/79, 9/79, 10/79, 1/80, 3/80, 4/80, 5/80, 6/80, 7/80, 8/80, 9/80, 10/80, 1/81, 3/81, 12/81, 1/82, 4/83, 5/83, 2/84, 5/84, 8/84, 5/85, 6/85, 7/85, 2/86, 4/86, 5/86, 6/86, 7/86, 1/87, 8/87, 9/87, 12/87, 5/88, 11/88, 5/89, 6/89, 8/89, 10/89, 11/89, 1/90, 5/90, 9/90, 10/90, 3/91, 7/91, 10/91, 1/92, 2/92, 3/92, 4/92, 7/92, 10/92, 4/93, 5/93, 7/93, 8/93, 9/93, 10/93, 12/93, 3/94, 6/94, 9/94 Cluster C: 3/62, 6/62, 7/62, 11/62, 9/63, 3/65, 6/65, 1/66, 11/66, 5/67, 8/67, 9/67, 1/68, 2/69, 9/69, 3/70, 8/71, 4/72, 5/72, 10/72, 11/72, 4/75, 9/75, 12/75, 4/76, 8/76, 10/77, 2/78, 3/78, 4/78, 11/78, 2/79, 4/79, 7/79, 2/80, 12/80, 5/81, 7/81, 10/81, 11/81, 6/82, 8/82, 8/83, 10/83, 3/84, 11/85, 8/86, 10/86,

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2/87, 3/87, 4/87, 6/88, 7/88, 3/89, 7/89, 9/89, 12/89, 7/90, 8/90, 6/91, 8/91, 5/92, 1/93, 2/93, 3/93, 1/94, 12/94 Cluster D: 8/62, 12/64, 9/65, 6/67, 10/70, 9/71, 10/71, 12/71, 11/77, 9/78, 8/81, 5/82, 12/82, 1/83, 2/83, 6/83, 10/84, 8/85, 4/91, 11/92

Cluster E: 8/63, 2/68, 3/69, 6/72, 7/76, 9/76, 10/76, 1/77, 6/77, 1/78, 1/79, 9/81, 1/85, 4/88 Cluster F: 2/62, 7/69, 3/73, 12/73, 3/83, 11/83, 4/84, 6/92 Unclassifiable: 4/64, 7/68, 11/76, 12/85, 3/90

APPENDIX B Months in the Study Period by 700 hPa Cluster Cluster A: 2/62, 6/62, 10/62, 11/62, 12/62, 1/63, 3/63, 7/63, 11/63, 3/64, 4/64, 7/64, 8/64, 1/65, 2/65, 3/65, 8/65, 10/65, 1/66, 2/66, 3/66, 4/66, 6/66, 8/66, 10/66, 12/66, 1/67, 2/67, 5/67, 9/67, 10/67, 3/68, 5/68, 6/68, 10/68, 11/68, 12/68, 7/69, 9/69, 11/69, 12/69, 3/70, 4/70, 6/70, 7/70, 8/70, 9/70, 11/70, 1/71, 2/71, 3/71, 4/71, 5/71, 10/71, 12/71, 2/72, 3/72, 4/72, 6/72, 7/72, 8/72, 11/72, 12/72, 2/73, 3/73, 4/73, 6/73, 8/73, 12/73, 1/74, 2/74, 4/74, 5/74, 6/74, 7/74, 8/74, 11/74, 12/74, 1/75, 2/75, 3/75, 9/75, 10/75, 11/75, 12/75, 1/76, 5/76, 6/76, 7/76, 8/76, 9/76, 10/76, 2/77, 3/77, 6/77, 9/77, 10/77, 11/77, 12/77, 1/78, 2/78, 4/78, 5/78, 10/78, 12/78, 1/79, 3/79, 5/79, 7/79, 10/79, 11/79, 2/80, 3/80, 4/80, 5/80, 11/80, 5/81, 8/81, 12/81, 1/82, 2/82, 3/82, 11/82, 12/82, 1/83, 2/83, 3/83, 4/83, 5/83, 6/83, 10/83, 11/83, 12/83, 1/84, 2/84, 3/84, 9/84, 2/85, 6/85, 9/85, 11/85, 8/86, 11/86, 1/87, 9/87, 12/87, 1/88, 3/88, 4/88, 5/88, 7/88, 8/88, 11/88, 6/89, 7/89, 8/89, 9/89, 10/89, 1/90, 2/90, 5/90, 7/90, 12/90, 3/91, 4/91, 7/91, 8/91, 10/91, 11/91, 3/92, 4/92, 6/92, 8/92, 9/92, 10/92, 11/92, 2/93, 3/93, 4/93, 11/93, 12/93, 2/94, 6/94, 9/94

Cluster D: 5/62, 4/63, 1/64, 12/64, 4/65, 4/67, 12/67, 1/69, 2/69, 12/70, 11/71, 11/73, 5/75, 6/75, 2/79, 4/79, 1/80, 8/80, 7/81, 10/81, 5/82, 10/82, 11/84, 2/86, 5/86, 7/86, 9/86, 7/87, 8/87, 12/92, 6/93, 8/93, 10/94

Cluster B: 1/62, 4/62, 2/63, 5/63, 5/64, 10/64, 7/65, 11/65, 5/66, 3/67, 11/67, 7/68, 2/70, 5/70, 9/72, 10/72, 3/74, 9/74, 2/76, 12/76, 3/78, 6/78, 6/79, 8/79, 6/80, 7/80, 9/80, 12/80, 4/81, 6/81, 4/82, 6/82, 7/82, 8/82, 9/83, 5/84, 6/84, 7/84, 12/84, 4/85, 5/85, 7/85, 12/85, 1/86, 3/86, 10/87, 2/88, 10/88, 12/88, 1/89, 2/89, 3/89, 4/89, 11/89, 4/90, 6/90, 9/90, 10/90, 11/90, 1/91, 2/91, 9/91, 9/93, 10/93, 1/94, 3/94, 4/94, 5/94, 8/94, 11/94

Cluster L: 1/81, 3/81, 11/81, 6/88

Cluster C: 6/63, 6/64, 11/64, 5/65, 6/65, 12/65, 8/68, 4/69, 6/71, 1/73, 9/73, 10/73, 10/74, 8/75, 4/76, 4/77, 5/77, 7/77, 9/78, 11/78, 12/79, 7/83, 3/85, 10/85, 10/86, 12/86, 2/87, 5/87, 6/87, 11/87, 9/88, 3/90, 5/91, 6/91, 12/91, 1/92, 2/92, 1/93

Cluster Q: 3/62, 9/68, 4/84

Cluster E: 8/63, 12/63, 2/64, 9/66, 2/68, 3/69, 1/70, 11/76, 1/77, 10/80, 1/85, 12/89 Cluster F: 9/64, 9/65, 6/67, 4/68, 10/69, 9/71, 7/73, 7/78, 8/78 Cluster G: 8/62, 2/81, 9/82, 8/84, 8/85, 4/86, 5/89, 8/90 Cluster H: 7/66, 5/69, 8/69, 8/71, 7/75, 9/79, 5/93 Cluster I: 9/62, 5/73 Cluster J: 7/67, 8/67, 7/94 Cluster K: 7/62, 4/75, 9/81

Cluster M: 6/69, 7/71, 1/72, 3/76, 8/77 Cluster N: 9/63, 5/72, 4/87 Cluster O: 10/63, 1/68, 3/87, 12/94 Cluster P: 11/66, 5/92

Unclassifiable: 10/70, 8/83, 10/84, 6/86, 7/92, 7/93