The statistical relationship between weather-type frequencies and corn (maize) yields in southwestern Pennsylvania, USA

Agricultural and Forest Meteorology, 59 ( 1992 ) 149-164

149

Elsevier Science Publishers B.V., Amsterdam

The statistical relationship between weather-type frequencies and corn (maize) yields in southwestern Pennsylvania, USA F. Brian Dilley Department of Geography, Pennsylvania State University, University Park, PA 16802, USA (Received 24 September 1991; accepted 20 January 1992

ABSTRACT Dilley, F.B., 1992. The statistical relationship between weather-type frequenoes and corn (maize) yields in southwestern Pennsylvania, USA. Agric. For. Meteorol., 59" 149-164. Several synoptic climatological classification techniques are used to relate atmospheric circulation and corn (maize) yields in two regions in southwestern Pennsylvania. The July frequency of occurrence of just one climate type is found to correlate highly with corn yield over a 10-year period. The correlation coefficients obtained through this approach are substantially higher than those obtained through more traditional methods relating yield to temperature and precipitation. While the synoptic climatological approach to yield prediction does not make the biological relationship between climatic factors and corn growth explicit, other features in addition to high correlations recommend it: the model does not require specification of particular climate variables and its emphasis on atmospheric circulation makes the approach more useful for climate change research than those based on simple weather observations without reference to atmospheric dynamics.

INTRODUCTION

The relationship between crop yield and climate is of continual interest owing to its importance to food supplies and agriculturally based regional economies. In recent years, the prospect of climate changes arising from global warming has drawn further attention to yield prediction from climatic data (Warrick and Riebsame, 1983; Chen and Parry, 1987). A strong statistical relationship exists between climatic variation and corn yield. Thompson (1969), Huda et al. (1976), Chang ( 1981 ), Shaw (1983), and others clearly show that multiple regression identifies combinations of climate variables which explain a substantial percentage of corn yield variation. However, in this study of 10 years of climate and yield data for 13 counties in southwestCorrespondence to: F.B. Dilley, Department of Geography, Pennsylvania State University, University Park, PA 16802, USA.

0168-1923/92/$05.00 © 1992 Elsevier Science Publishers B.V. All rights reserved.

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ern Pennsylvania, synoptic climatological methods reveal a much stronger relationship than that obtained using conventional multivariate methods. In this synoptic approach, daily weather patterns are classified into a smaller number of climate types whose frequency of occurrence is used to predict average yield per acre. Synoptic climate classification identifies configurations of weather or airmass characteristics. For example, some summer days might be hot and humid, with clear skies, high pressure and little wind. Conversely, a low pressure system accompanied by a cold front might produce days with clouds and heavy rain. These two systems would be classified differently, and their effects on crops would be different as well, Four out of five of the techniques for synoptic climate classification evaluated produced better predictions of corn yield than did linear combinations of discrete climate variables for the same region and time period. These results and other features of the synoptic approach suggest that it merits further consideration. Perhaps the best known regression model of the relationship between weather and corn yield is that of Thompson (1969). Thompson suggested that in the Corn Belt of the USA, yields will be greatest when: ( 1 ) June temperatures are average; (2) July and August temperatures are below average; ( 3 ) September through June precipitation is average; (4) June rainfall is low; ( 5 ) July rainfall is above average. He judged the last condition particularly important. Huda et al. ( 1976 ) found that in India: "Above-averageweeklytotals of rainfall had a favourable effect on yield during emergence, but a markedlyreducedeffectduring silkingand tasselling (flowering)to maturity. Above-averagedaily maximum temperatures were favourablefor yield during a 4-week period prior to silking. However,higher-than-averagedaily maximumsdepressed yields during maturation. But above-averagedaily minimum temperatures gave a favourable effect during tassellingand silking." (p. 33. ) In a study by Chang ( 1981 ), yield in hybrid varieties was improved by long day lengths but inhibited by high night temperatures in the tropics. Corn yield simulation computer models similarly include many climatic variables such as wind, humidity, radiation, cloud cover, vapor pressure, and temperature (Terjung et al., 1984). However, regression studies employing multiple climate variables suffer from a number of potential problems identified by Katz ( 1977 ): ( 1 ) the number of climate variables included in the model may outweigh the number of observations; (2) the relationship between individual variables and yield is likely to be non-linear; ( 3 ) the climate variables themselves may be highly correlated; (4) important variables may be inadvertently left out of the model. Newer, more sophisticated techniques address some of these problems. For example, the use of principal components analysis (PCA) eliminates correlation between the variables and reduces the number of variables to a small number of principal components

ATMOSPHERIC CIRCULATION AND MAIZE YIELDS

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(Meyer et al., 1991 ). The synoptic climatological approach presented here requires no a priori decisions about which particular climate variables might be important for corn growth. Instead, each day is assigned to a group of days with similar characteristics, defining a climate class or type. All weather elements of the days in each class influence the model results. This holistic representation better reflects the fact that growing corn responds to the totality of each day's weather, not to the levels of a set of discrete variables. Representing climate as a single variable, the frequency of occurrence of a key climate type during the month of July, minimizes the number of variables and the amount of data required to construct the model. Another objection of Katz to statistical models is that they are essentially black boxes with no guarantee that the relationships they uncover are biologically meaningful. In his view, this limits their application to the problem of climate change, since it becomes difficult to make inferences about the relationship between yield and any given climate variable. The prediction of yield through the frequency of a single climate type is indeed a black box. The best climate type for predicting yield is selected statistically and no physiological link between climate and the corn plant is specified. However, the ability to link yield variations to atmospheric circulation offers a significant advantage over current approaches to yield modeling in climate change research. Simulations of the impacts of climatic change on crop yields are currently based on temperature and precipitation predictions from atmospheric General Circulation Models (GCM) with doubled carbon dioxide. Yet regional GCM predictions of temperature and precipitation are impugned by the fact that they differ widely among the major climate models (Schlesinger and Mitchell, 1987). Synoptic climatology, on the other hand, relates the surface climate at a particular location to the larger scale atmospheric circulation. It provides a better physical basis for understanding regional climates than do simple observations on individual climate variables. This is particularly true when the weather of the area is tied to synoptic-scale and global circulation, as is that of Pennsylvania (Yarnal and Leathers, 1988). The findings presented here suggest that yield fluctuations are associated with variations in circulation patterns, at least in such areas where the climate is synoptically controlled. Since at least one major GCM accurately simulates circulation patterns for the USA under current CO2 levels (Hewitson and Crane, 1992 ), doubled CO2-GCM circulation might become a better basis for yield modelling than predicted temperature or precipitation. MATERIALS

AND METHODS

Study area

This work was part of a year-long study by a research group at the Pennsyl-

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Fig. 1. The counties of the Southwest (SW) and West Central (WC) districts of Pennsylvania.

vania State University. The focus of this effort was to classify 10 years of daily Pennsylvania weather (1 January 1978-31 December 1987), comparing and contrasting six techniques of synoptic climate classification in the context of a series of environmental applications. In addition to agriculture, the classification results were applied to scenarios of atmospheric ozone concentration, acid rain, runoff, and stream acidity. With one exception, the synoptic climate classification schemes were designed to be generalizable over the entire state. The technique which proved most useful in the agricultural application was based on data taken from a single location (Pittsburgh Airport ). The State of Pennsylvania publishes average per-acre yields of corn harvested for grain for all counties in the state in the 'Cropland Livestock Annual Summary'. Groups of half a dozen or so counties form districts, of which the

ATMOSPHERIC CIRCULATION AND MAIZE YIELDS

15 3

Southwest (SW) and West Central (WC) surround Pittsburgh. The SW district consists of Allegheny, Fayette, Greene, Somerset, Washington and Westmoreland counties, while the WC district consists of Armstrong, Beaver, Butler, Clarion, Indiana, Jefferson and Lawrence (Fig. 1 ). The synoptic climate and crop yield model was developed using both county- and district-level yield data from the SW and WC districts. Data and methods

A prime objective of this study was to determine which of several techniques for climate classification works best for predicting crop yields. Most of these techniques systematically classify daily weather into a smaller number of climate groups. Daily weather is defined by one or several variables in relation to a particular point on the Earth's surface (i.e. Pennsylvania). The six techniques are: ( 1 ) visual 'Subjective' classification of daily weather maps into a small group of classes by consensus among the researchers, based on the polar cyclone model (Barchet and Davis, 1984; Yu and Pielke, 1986 ); (2) the 'Temporal Synoptic Index (TSI)', a principal component-based approach derived from four-times daily observations on seven weather variables from a single weather station (Kalkstein and Corrigan; 1986; Kalkstein et al., 1987); (3) the 'Pressure Correlation method', through which daily surface pressure patterns are classified according to their correlation with each other (Lund, 1963; Yarnal, 1984); (4) spatial 'Principal Component Analysis (PCA)' of a grid of surface pressures (Richman, 1981; Stone, 1989); (5) an east-west, surface-pressure 'index' developed by members of the research group; (6) 'Compositing' of surface pressure maps. Compositing and Indexing differ from the other four techniques. Indexing was rendered comparable by dividing the daily interval-scale values of the index into 13 class intervals and treating them as nominal types. Compositing is not classificatory, as it aggregates rather than differentiates daily weather patterns. A synoptic classification system provides a set of rules for partitioning the variation in daily weather. However, there are no clear cut criteria for deciding where to draw the lines between climate classes. If the number of classes were allowed to equal the number of days in the data set, within-class variation would be minimal but between-class variation would be maximized. On the other hand, if the number of classes were reduced to one, between-class variation would disappear and within-class variation would be maximized. Some synoptic climatological methods permit weather patterns to remain un-

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classified as a means of reducing within-class variation. In the end, the optim u m number of classes and percentage of unclassified weather patterns depends upon the application. In all synoptic climate classification methods in this study, the number of classes was constrained to about 10-15. This insured a fair comparison between methods. It is also easier to conceptualize the characteristics of the climate classes when they remain few in number. Owing to space limitations, discussion here will be confined to the TSI and Compositing techniques, the most useful in the corn-yield application. The Pressure Correlation method produced results only marginally inferior to those of the TSI and could warrant consideration when the data for constructing the TSI are unavailable. The TSI employs a Principal Component Analysis (PCA) of four-timesdaily weather observations. Data for the analysis consisted of seven variables: visibility, temperature, dew point temperature, atmospheric pressure, wind speed, wind direction, and sky cover, taken from a National Oceanic and Atmospheric Administration (NOAA) National Climate Data Center TD- 1440 data tape for Pittsburgh, Pennsylvania. Seven variables times four daily observations provided 28 values per day. A PCA of the data in this form would be expected simply to extract the seasons, the predominant source of variation among these variables. The seasonal signal was removed by first converting each day to an average of itself and the six previous and successive days, and subtracting the resulting 13 day mean from each original observation. The resulting deviation from an approximate 2-week average restricts the variation to daily fluctuations around an interseasonal average. A small number of missing days was replaced by substituting 13 day means for the values of the missing observations. Thirteen days was deemed appropriate on the basis of a spectral analysis by Hewitson and Crane (1992) and because it is a period encompassing the duration of most synoptic-scale climate events. A PCA of the matrix of 3652 days by 28 de-seasoned variables redistributed the variation within the data set into 28 unrotated principal components. Such components are uncorrelated with each other but their correlation with the original variables is maximized. Consequently, they can represent a substantial portion of the total variation in the data while remaining fewer in number than the original variables. Each component has an eigenvalue which is proportional to the percentage of the total variation in the data set that the component explains. Only those components judged to explain a significant proportion of the total variation are retained. The first eight components derived from the daily Pittsburgh airport weather observations had eigenvalues above 1 (a commonly accepted minimum value). Of these, we retained only the first five, each of which explained a minimum of 5% of the total variation, for a total of 62% of the total variation explained. To a certain extent, it is possible to interpret the components in terms of their correlation" with the original variables. Temperature, dew point, and sky cover correlated

ATMOSPHERIC CIRCULATION AND MAIZE YIELDS

155

most highly with the first component, temperature and pressure with the second, visibility with the third, wind direction with the fourth, and wind speed with the fifth. Days were clustered into air-mass types on the basis of their five component scores. Each score equals the sum of the correlations of the original 28 variables with a single principal component for a given day. Ward's method was used to assign the days to clusters according to the similarity of their component scores (i.e. their proximity in five-dimensional space). The final number of clusters was determined using the R-square method, which compares the sum of the squared variation within clusters to the total squared variation (SAS Institute Inc., 1985). This ratio is zero when the number of clusters is one, and one when the number of clusters equals the number of individuals being clustered. Eleven clusters or climate types were judged adequate to separate the data distinctly, a number comparable to those obtained using the other synoptic classification methods. Type 1 is the largest, comprising approximately 23% of the days. Types 2 and 3 each accounted for roughly 16% of the days, with the remaining types ranging from 5 to 7%. Types l, 2, 3, and 5, tended to occur most frequently in summer, while types 4~ 6~ 7~ 8, and 1 l, predominated in winter. Another methodology, Compositing, was also useful in relating synoptic circulation and yields. July composites of mean sea-level pressure were constructed for three groups of years having above-average, below-average, and average yields. Values from daily surface-pressure grids were averaged to form a single July pressure pattern for each group of years. Compositing was also used to identify the average pressure characteristics of one of the TSI synoptic climate types. The 'Crop and Livestock Annual Summary' published by the Pennsylvania Crop Reporting Service, contains the average yield per acre of corn harvested for grain in each county each year. The Summary also includes monthly regional temperature and precipitation averages and average departures from normal from the National Weather Service. These include data from the Butler, Donora and Uniontown weather stations in the Southwest Plateau region in which the 13 Southwestern and West Central counties are located. These temperature and precipitation data were correlated with the yield data in a multiple regression model to use as a benchmark for evaluating the effectiveness of the synoptic climatological methods for predicting corn yields. Each of the synoptic climate classification methods provided a climate type for every day in July from 1978 to1987. From a climatic standpoint, July, during which tasseling, silking, and pollination occur, is a critical m o n t h in the growth cycle of the corn plant (Thompson, 1969; Shaw, 1983 ). A multiple regression model was used to determine whether fluctuations in corn yields occur as a function of the frequency of occurrence of any particular suite of

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F.B. DILLEY

July weather conditions. Each classification method was evaluated separately for climate types whose July variation correlated strongly with yields. In each regression model, the dependent variable consisted of ten annual average per-acre corn yield observations. The independent variables were the July frequencies of all climate types from one synoptic classification method. Compensation for the small sample size of ten yield observations was achieved through spatial replication. Each regression was run 15 times using per-acre yields from each of the 13 individual counties plus the aggregated districtlevel yields. No more than two independent variables were permitted to enter the model. The SAS RSQUARE procedure maximizes the R 2 between single or multiple independent v~iriables and a dependent variable, using linear regression to evaluate all possible combinations (SAS Institute Inc., 1985 ). The R 2 adjusted for degrees of freedom (R ~ ) provided a measure of the strength of the relationship between yields and climate type frequencies in the individual counties and averaged over each district. R 2 is nearly identical to R 2 when the sample size is large but diminishes with smaller sample sizes. The root mean squared error (r.m.s.e.), mean absolute error (m.a.e.) and d statistic (Willmott, 1984) provided additional criteria for evaluating the performance of the methods. RESULTS The general relationships between temperature, precipitation, and yield found by Thompson (1969) hold true in the two study regions. In the SW district the July and August temperature departure correlated negatively with yield (R = - 0 . 6 1 ). For both the Southwest and West Central study regions, July precipitation departures from normal correlated positively with corn yield (SW R = 0.5; WC R = 0.29 ) confirming that above-average July rainfall is associated with higher yields. Of the other weather variables, none correlated strongly with yield, although virtually all R values at least had the expected sign. These results, plus the importance of the month of July in the growth cycle of the corn plant, suggested that July should be the focus of the synoptic climatological part of the study. R a2 values for the climate type or types most correlated with district-level yields are given in Table 1. In both districts, almost all models with climatetype frequencies as independent variables resulted in higher correlations than those employing weather variables. The TSI method had the highest Ra2 values in both districts. Other synoptic climate classification techniques, particularly Pressure Correlation and Indexing, gave better results in one district than in the other. Climate types which were good predictors in one district were not always the best predictors in the other, probably due to spatial vari-" ation in the characteristics of the weather types. Randomizing the order of

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ATMOSPHERICCIRCULATIONAND MAIZEYIELDS TABLE 1 Adjusted R 2 values of best corn-yield predictors in southwestern Pennsylvania Model

1-Predictor

R~

2-Predictor

R2

Type 3 July frequency Type 11 July frequency Type 10 July frequency Cyclonic type July frequency

0.72

0.85

Type 8 July frequency July and August temperature departures

0.14

Types 3 and 8 July frequencies Types 11 and 8 July frequencies Types 10 and 9 July frequencies Cyclonic and High-centeredto-south July frequencies Types 5 and 12 July frequencies July and August temperature and July precipitation departures

Type 3 July frequency Type 11 July frequency Type 8 July frequency High-centered-tosouth type July frequency

0.44

0.68

Type 9 July frequency September through June precipitation departures

0.43

Types 3 and 6 July frequencies Types 3 and 6 July frequencies Types 8 and 10 July frequencies Cold front and High-centeredto-south July frequencies Types 9 and 4 July frequencies September through June and July precipitation departures

Southwest district

TSI Pressure Correlation PCA Subjective

Index Weather variables

0.68 0.34 0.37

0.30

0.85 0.40 0.49

0.24 0.40

West central district

TSI Pressure correlation PCA Subjective

Index Weather variables

0.26 0.47 0.25

0.18

0.57 0.48 0.57

0.62 0.20

the district average per-acre yields resulted in correlation coefficients close to zero. This test was included to insure that RSQUARE was not simply picking up spurious relationships in the data. Temporal Synoptic Index Type 3 was the single type most strongly associated with yield, with an R 2 of 0.72 ( R = 0.87 ) in the Southwest district and 0.44 ( R = 0 . 7 1 ) in the West Central district. Of all TSI types, the frequency of Type 3 was most strongly related to yield in 10 of the 13 counties. The frequency of occurrence of Type 3 paired with that of one other type was the

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best two-variable predictor of yield in 11 out of the 13 counties. The second climate type of the pair was variable. The pairing of Type 3 with Types 6, 7, 8, and 11, all occurred more than once over the set of counties as the most highly associated with yield. Model comparison statistics in Table 2 for the single most predictive type provided by each synoptic method show that TSI Type 3 compares favorably to the best predictors from other methods as well. The question remains as to which specific TSI Type 3 climate characteristics might be biologically beneficial to the corn plant. Type 3 days regularly fall in the middle of the range of the average July characteristics for all 11 TSI climate types (Table 3 ). The typical Type 3 day is one of exceptionally low visibility and more humid on average than most of the other types. It is calm with light winds from the south, with rain occurring on some 40% of the days TABLE2

Model comparison statistics showing average performance across 13 Pennsylvania counties of weather types best predicting yield for each synoptic climate classification method Model

Type

r.m.s.e,

r.m.s.e.~

r.m.s.e.u

m.a.e,

d

TSI

3 11 8

7.69 8.26 8.99 9.32 9.16

5.50 6.41 7.46 7.98 7.89

5.22 4.89 4.85 4.57 3.76

6.22 6.89 7.34 7.28 7.40

0.79 0.70 0.68 0.61 0.54

Pressure Correlation PCA

Subjective Index

Cyclonic 9

TABLE 3

Average July daily specifications of TS1 climate types from four daily observations at Pittsburgh Airport Type

Visibility

Wind speed

Temp.

Dew point

Pressure

Sky cover

(kin)

(m s -~ )

(°C)

(°C)

(mbar)

(%)

Wind dir. (deg. from N)

1 2 3* 4 5 6 7 8 9 10 11

14.4 10.8 8.9 12.4 22.6 14.7 11.0 22.5 20.6 16.0 11.3

2.9 2.7 2.9 3.2 3.1 1.5 3.4 2.7 2.7 4.1 6.5

23.1 21.8 23.3 23.8 20.4 21.5 26.6 18.3 20.6 20.7 22.6

16.2 16.4 18.1 18.6 12.1 13.4 20.4 10.5 11.8 14.5 19.2

1017.1 1016.1 1016.6 1012.9 1017.9 1021.2 1016.3 1018.6 1018.1 1014.0 1008.7

0.3 0.3 0.3 0.3 0.2 0.1 0.3 0.3 0.3 0.2 0.2

176.8 147.0 180.8 194.6 200.7 102.8 196.2 166.7 172.7 263.0 235.0

Average High Low

15.0 22.6 8.9

3.3 6.5 1.5

22.1 26.6 18.3

15.6 20.4 10.5

1016.1 1021.2 1008.7

0.3 0.3 0.1

185.1 235.0 102.8

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ATMOSPHERIC CIRCULATION AND MAIZE YIELDS

(Table 4 ). While other types are characterized by more precipitation per event and higher percentages of days experiencing rain, Type 3 days occur frequently during July (Fig. 2 ). Type 2 days, also frequently occurring and rainy, often accompanied Type 3 episodes. Owing to the nature of the model, it is not possible to attribute causality to frequent Type 3 occurrences and high yields or specify physiological mechanisms through which Type 3 claaracterTABLE4

Average daily July precipitation and percentage of days with rain occurring at Pittsburgh

Airport

TSI type

Rainfall (mm)

Standard deviation

% days with rain

11 2 10 3 7 1 4 8 6 5 9

6.94 6.26 3.97 4.10 2.22 1.88 1.48 0.85 0.19 0.32 0.00

3.6 10.6 7.7 9.5 2.9 4.4 2.8 0.9 0.5 1.9 0.0

1.00 0.55 0.45 0.39 0.38 0.24 0.33 0.67 0.13 0.03 0.00

.25

.24 .23

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1

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.15

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.i

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.O5 .04

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5

9

4

10

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6

July TSI Climate Type

Fig. 2. July TSI climate types rankedby frequencyof occurrence.

.01

.01

8

11

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F.B.DILLEY

istics stimulate corn growth. The benefit of the synoptic climatological approach lies in its ability to link many associated climate characteristics together in a single statistic. Surface pressure pattern composites corroborate the relationship between TSI Type 3 and yield. Distinctly different atmospheric flow patterns were associated with high-, low- and average-yield years. The years 1984-1986 had above-average yields in both study regions, while 1979, 1981, and 1983 were below average. The years 1978, 1980, and 1982 were average. Only 1987 had high yields in one district and low yields in the other. Figures 3-5 show the average atmospheric flow patterns for the three groups. The below-average years were characterized by a strong high-pressure system centered over the study area (Fig. 3 ), which weakened in the average years (Fig. 4 ). The aboveaverage years were characterized by a weaker, more zonal pressure gradient, with the center of the high located to the southeast (Fig. 5). The July composite pressure pattern from the above-average years is nearly identical to that of the TSI type 3 days (Fig. 6 ). Both Compositing and synoptic climate classification identified the same circulation pattern in connection with high corn yields in southwestern Pennsylvania. The success of the TSI method compared with the other synoptic climatological techniques may be due to the fact that it is based on the characteristics of air masses, as opposed to pressure patterns. Plants would most likely respond physiologically to air-mass characteristics (e.g. temperature, moisture,

Fig. 3. Below-average yield years composite July surface pressure.

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ATMOSPHERIC CIRCULATION AND MAIZE YIELDS

0 " ./

"/j ,~,~

Fig. 4. Average yield years composite July surface pressure.

JLS

....

Fig. 5. Above-average yield years composite July surface pressure.

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F.B. DILLEY

Fig. 6, TSI type 3 composite July surface pressure, 1978-1987.

cloud cover, etc. ), rather than to wind direction or atmospheric pressure directly. Air-mass characteristics as defined by observations at a single location are the basis for TSI climate classification. Interestingly, the correlation between TSI Type 3 occurrences and yield was highest in Allegheny County where the TSI weather observations originated. CONCLUSIONS

There are strong statistical associations between climatic variation and corn yield. In this study, the frequencies of synoptic climate types during July correlated more highly with yield than did temperature and precipitation. While including more discrete variables such as evapotranspiration and solar radiation in a multivariate regression model would doubtless raise correlation coefficients, the amount of climatic information they would introduce would still be finite and, therefore, incomplete. Furthermore, the number of variables in the model would overwhelm the number of observations. The synoptic approach does not isolate or even identify the specific aspects of the climate to which the corn plant responds, but better captures the totality of the complex interactions between climate and growing corn. Of all the synoptic methods compared, the TSI produced the strongest and most consistent relationship between the July frequency of a synoptic climate type and yield. Both the TSI and Compositing identified one specific atmos-

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163

pheric flow pattern which was consistently present during July in high-yield years. Other synoptic climate classification techniques also identified climate types whose July frequency correlated more strongly with yields than did temperature and precipitation. These statistical relationships are affected (to an u n k n o w n extent) by decisions taken by the researcher during the climate classification procedure. There is no way of knowing, for example, whether the Pressure Correlation m e t h o d might have produced better results than the TSI if the climate classes had been only slightly differently construed. It also remains to be seen whether a TSI could be developed for another weather station with similar results, or whether the one in this study would produce the same results over a longer time period. ACKNOWLEDGMENTS The author would like to thank the participants of the synoptic climate classification seminar ( D e p a r t m e n t of Geography, Pennsylvania State University) for their advice and support throughout the process of producing this paper: Dr. Brent Yarnal, Andrew Comrie, Dr. Bruce C. Hewitson, Ken Yelsey and John Draves. I especially t h a n k Dr. Yarnal for his thorough review and editorial c o m m e n t s during the final stages of the writing.

REFERENCES Barchet, W.R. and Davis, W.E., 1984. A Weather Pattern Climatologyof the United States. Pacific Northwest Laboratory, Richland Washington,US Department of Energy, PNL-4889, 80 pp. Chang, J.H., 1981. Corn yield in relation to photoperiod, night temperature, and solar radiation. Agric. Meteorol., 24: 253-262. Chen, R.S. and Parry, M.L., 1987. Policy-OrientedImpact Assessment of Climatic Variations. International Institute for Applied SystemsAnalysis,Laxenburg,Austria, RR-87-7, 50 pp. Hewitson, B.C. and Crane, R.G., 1992. Regional climates in the GISS GCM: synoptic-scale circulation. J. Clim., in press. Huda, A.K.S.,Ghildyal, B.P. and Tomar, V.S., 1976. Contribution of climatic variables in predicting maize yield under monsoon conditions. Agric. Meteorol., 17: 33-47. Kalkstein, L.S. and Corrigan, P., 1986. A synoptic climatological approach for geographical analysis: assessmentof sulfur dioxide concentrations. Ann. Assoc. Am. Geogr, 76:381-395. Kalkstein, L.S., Tan, G. and Skindlov, J.A., 1987. An evaluation of three clustering procedures for use in synoptic climatologicalclassification.J. Clim. Appl. Meteorol., 26:717-730. Katz, R.W., 1977. Assessingthe impact of climate change on food production. Clim. Change, 1: 85-96. Lund, I.I., 1963. Map-pattern classificationby statistical methods. J. Appl. Meteorol., 2: 56-65. Meyer, S.J., Hubbard, K.G. and Wilhite, D.A., 1991.The relationship of climatic indices and variables to corn (maize) yields: a principal components analysis.Agric. For. Meteorol., 55: 59-84. Pennsylvania Crop Reporting Service 1978. Crop and livestock annual summary. Economics,

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Statistics and Cooperatives Service. US Department of Agriculture, Pennsylvania Department of Agriculture, Harrisburg, PA, 79 pp. Richman, M.B., 1981. Obliquely rotated principal components - an improved meteorological map typing technique? J. Appl. Meteorol., 20:1145-1159. SAS Institute Inc., 1985. SAS User's Guide: Statistics, Version 5 Edition. SAS, Cary, NC 6, 957 PP. Schlesinger, M.E. and Mitchell, J.F.B., 1987. Climate model simulations of the equilibrium climatic response to increased carbon dioxide. Rev. Geophys., 25: 760-798. Shaw, R.H., 1983. Estimates of yield reductions in corn caused by water and temperature stress. In: C.D. Raper, Jr., and P.J. Kramer (Eds.), Crop Reactions to Water and Temperature Stresses in Humid, Temperate Climates. Westview, Boulder, CO., pp. 49-65. Stone, R.C., 1989. Weather types at Brisbane, Queensland: An example of the use of principal components and cluster analysis. Int. J. Clim., 9: 3-32. Terjung, W.H., Liverman, D.M. and Hayes, J.T., 1984. Climatic change and water requirements for grain corn in the North American Great Plains. Clim. Change, 6:193-220. Thompson, L.M., 1969. Weather and technology in the production of corn in the U.S. Corn Belt. Agron. J., 61: 453-456. Warrick, R.A. and Riebsame, W.E., 1983. Societal response to CO2-induced climate change: opportunities for research. In: R.S. Chen, E. Boulding and S.H. Schneider (Eds.), Social Science Research and Climate Change: An Interdisciplinary Appraisal. Reidel, Dordrecht, pp. 20-60. Willmott, C.J., 1984. On the evaluation of model performance in physical geography. In: G.L. Gaile and C.J. Willmott (Eds.), Spatial Statistics and Models. Reidel, Hingham, MA, pp. 443-460. Yarnal, B., 1984. A procedure for the classification of synoptic weather maps from gridded atmospheric pressure surface data. Comput. Geosci., 10: 397-410. Yarnal, B. and Leathers, D.J., 1988. Relationships between interdecadal and interannual climatic variations and their effect on Pennsylvania. Ann. Assoc. Am. Geogr., 78:624-641. Yu, C.H. and Pielke, R.A., 1986. Mesoscale air quality under stagnant synoptic cold season conditions in the Lake Powell area. Atmos. Environ., 20:1751-1762.