Recent Unusual Mean Winter Temperatures Across the Contiguous United States

Recent Unusual Mean Winter Thomas R. Karl1, Robert E. Livezey2 Temperatures Across the Contiguous United States

Abstract United States, even in an unusually cold (or warm) winter, can include a month of relatively mild (or cold) weather or A long-time series (1895-1984) of mean areally averaged winter contain small portions of the country that have relatively temperatures in the contiguous United States depicts an unprece- mild (or cold) weather throughout the winter. dented spell of abnormal winters beginning with the winter of 1975-76. Three winters during the eight-year period, 1975-76 through 1982-83, are defined as much warmer than normal (abnormal), and the three consecutive winters, 1976-77 through 1978-79, 2. Data much colder than normal (abnormal). Abnormal is defined here by the least abnormal of these six winters based on their normalized departures from the mean. When combined, these two abnormal The data set used to obtain areally weighted average winter categories have an expected frequency close to 21%. Assuming that temperature departures was originally used and described by the past 89 winters (1895-1984) are a large enough sample to esti- Diaz and Quayle (1978). The data, which begin in 1931, con- mate the true interannual temperature variability between winters, sist of monthly averages of temperatures for each of 344 state we find, using Monte Carlo simulations, that the return period of a series of six winters out of eight being either much above or much climatic divisions (CDs) in the contiguous United States, below normal is more than 1000 years. This event exceeds the calcu- and, prior to 1931, monthly averages of statewide averages of lated return period of the three consecutive much colder than normal temperature derived by simply averaging the data from all winters (1976-77 through 1978-79) all falling into a much below stations within a state. A national average is derived by normal category, i.e., one that is expected to contain approximately 10% of the data. The more moderate winters of 1981-82 and 1983-84 areally averaging the CDs to calculate state averages, and can also be considered abnormal by relaxing the limits necessary for then areally averaging the states to form a national average an abnormal classification, but this gives a return period of 467 years temperature. for the spell of eight abnormal winters in the nine consecutive win- Diaz and Quayle (1978) adjusted the data set prior to 1931 ters 1975-76 through 1983-84. by using the differences between the statewide average data and the statewide CD averages over an 18-year overlap period (1931-1948). They indicated that during that overlap 1. Introduction period, the station data were an excellent approximation to the CD data. Recently, those data were further scrutinized In recent years, there have been a considerable number of (Karl et al, 1983), and additional adjustments to the data press reports regarding unusual winter weather across the were necessary prior to 1931 for 11 western states (from United States, and a series of technical articles have ad- Montana to New Mexico and westward to the Pacific Ocean) dressed this topic (Diaz and Quayle, 1978; Diaz and Quayle, due to changes in station distribution. Karl et al (1983) de- 1980; Changnon, 1979; Cayan etal., 1982). In response to re- scribe these adjustments in some detail. cent requests for climatological information regarding win- After 1930, the CD averages are obtained from all stations ter weather in the United States over the decade 1973-74 within the CD reporting temperature and precipitation. through 1982-83, some interesting information has emerged; Since the cooperative weather observing stations make up specifically, the unusually large interannual fluctuations of the vast majority of these stations, it is noteworthy that be- the mean areal average winter temperatures across the ginning in the 1930s, there has been an increasing number of United States during the winters 1975-76 through 1982-83. cooperative weather observers reporting the maximum and These data are derived from cooperative and First Order minimum temperatures based on the 24 hours ending in the weather reporting stations in the contiguous United States. morning instead of the evening. Schaal and Dale (1977) The potential bias in this data set is discussed, as well as some noted that this practice produced a "cold" bias in the CD av- of the more subtle statistical problems encountered in the es- erages for stations in Indiana. timation of the return period for this group and other groups Based on the change in observation times, as derived from of abnormal winters. It should be recognized at the outset original manuscript records and as published in Climatological that an areal average mean winter temperature across the Data (U.S. Dept. of Commerce, 1980a), there were approximately one-third more a.m. observers in 1980 than in 1931. This figure was derived from the records for seven states: Cali- fornia, Colorado, Illinois, Indiana, New York, North Caro- lina, and Washington. If these states are representative of the 1 National Climatic Data Center, Asheville, NC 28801. 2 Climate Analysis Center, Washington, DC 20233. entire United States, then an estimate of the cold bias in the time series due to observing schedules can be made using © 1984 American Meteorological Society data derived from Mitchell (1958), Baker (1975), Schaal and

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TABLE 1. Average wintertime adjustments for a change of observation time from 1700 LST to 0800 LST.

Observation Changes from to City, State Adjustment (°C) (LST) (LST) Source

Washington, D.C. -1.0 1700 0800 Blackburn (1983) St. Paul, MN -1.0 1700 0800 Baker (1975) Indianapolis, IN -0.9 1700 0800 Schaal & Dale (1977) Austin, TX* -1.4 1730 0730 Mitchell (1958) Bismarck, ND* -1.2 1730 0730 Mitchell (1958) Columbus, OH* -1.3 1730 0730 Mitchell (1958) Denver, CO* -0.6 1730 0730 Mitchell (1958) Fresno, CA* -0.2 1730 0730 Mitchell (1958) Philadelphia, PA* -0.8 1730 0730 Mitchell (1958) Spokane, WA* -0.4 1730 0730 Mitchell (1958) Tampa, FL* -0.4 1730 0730 Mitchell (1958) Average -0.836

* January values are used to estimate wintertime adjustment.

Dale (1977), and Blackburn (1983). Each of these authors be obtained. In 1980, 10% of the stations used in the CD av- calculated adjustments of various locations in the United erages were associated with urban areas with populations in States for changes in observation schedules. Those locations excess of 50 000. Assuming there has been no systematic in- include: 1) Washington, D.C., 2) Indianapolis, Ind., 3) St. crease or decrease in the percentage of stations monitoring Paul, Minn., 4) Austin, Tex., 5) Bismarck, N.D., 6) Colum- temperatures in urban areas of this size [this assumption ap- bus, Ohio, 7) Denver, Colo., 8) Philadelphia, Pa., 9) Spo- pears reasonable based on station distribution maps as plot- kane, Wash., 10) Tampa, Fla., and 11) Fresno, Calif. The ted in Decadal Census of Weather Stations (U.S. Depart- preferred a.m. and p.m. observation times are 0800 LSTand ment of Commerce, 1958)], then the overall urban influence 1700 LST respectively, except in the Central Time Zone, on the time series of winter temperatures would be 0.1 X where 0700 LST and 1700 LST prevail, as derived from Cli- 0.23°C or 5.57 X 10~4°C yr-1. This "warm" bias would apply matological Data (U.S. Department of Commerce, 1980a) for to the entire period of wintertime data for 1895-1984. We the seven states previously listed. When averaged across the would conclude that during the more recent 50 years of rec- 11 cities listed in Table 1, the average change in mean winter ord, the bias due to observation changes is estimated to be an temperature attributable to a switch from 1700 LST to 0800 order of magnitude larger than the bias due to urban effects. LST is approximately —0.8°C. This value can be used as a On the other hand, for some states the systematic time of ob- rough estimate of the wintertime temperature bias in 1980 servation changes and their associated biases can be miti- compared to 1931 if all stations had converted to a.m. from gated by the opening, closing, and relocations of stations, as p.m. observing schedules. Since only 33% of the observers demonstrated in Appendix A. converted to morning observations, the appropriate estimate In light of this discussion, the time series in Fig. 1 are pre- of the bias for the winter of 1980-81 would be -0.28°C. This sented for the contiguous 48 states. In this figure, one time is equivalent to —5.58 X 10"3oC yr"1 over the 50-year period series has been corrected for biases due to observation times since 1931. and urbanization while the other has no corrections. In our It would be remiss to consider the observation time bias subsequent analyses, both corrected and uncorrected time and omit the systematic "warm" bias that may have occurred series are used for comparative purposes. Small trends in the since the turn of the century due to urban influences. Numer- time series should be viewed cautiously for all of the reasons ous studies have documented these effects (Landsberg, discussed in this section. Emphasis should be placed on the 1981). Mitchell (1953) has separated out and quantified the large year-to-year climate fluctuations. urban influence on the mean seasonal and annual temperatures for six approximately equal areas in the contiguous United States by using data from 77 cities for the years 1900 to 1940. During this time period, the population of the United 3. Procedures States nearly doubled, as it did from 1940 to 1980 (U.S. De- The recent spell of large departures from normal of the mean partment of Commerce, 1980b). Mitchell's calculations sug- winter temperature is evident beginning in 1975-76 (Fig. 1). gest a 0.230°C increase in mean winter temperatures across The normalized departures (Z) from the long-term mean for these urban areas in the United States for the 1900 to 1940 these winter temperatures are listed in Table 2, where period. Assuming the relationship between the rate of city growth and urban temperature increase can be extrapolated for the period 1940 through 1980, and that the thermal effects ST of a city begin to become significant when it grows to a population of 50 000 or more, then a crude estimate of the urban and Ti is the mean winter temperature in any given year, T is "warm" bias on the national average winter temperature can the mean winter temperature of all the winters in the time series,

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FIG. 1. Time series of mean winter temperatures (1895 is the winter of 1895-96) depicted by solid dots. Horizontal dashed line is the long-term mean, solid line is ±1.179 or ±1.253 standard deviations from the mean in (a) and (b), respectively.

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TABLE 2. Normalized mean winter temperatures with and without in terms of success or failure of each trial when the probabil- corrections for observation times and urban influences. ity of a success in any given trial is known; i.e., the probability of obtaining three tails in any independent set of three Normalized Departures (Z) coin tosses (trials). Our problem can be set up in terms of the Z- Scores BPM as we demonstrate in the next section, but unfortu- Winter No Correction Correction nately the interpretation of the results is unclear partly due to 1975-76 1.179 1.326 . the lack of independence between winters (trials), but more 1976-77 -1.389 -1.253 importantly because of the lack of independence between 1977-78 -1.907 -1.775 sets. Since our set of eight winters was determined a posteriori 1978-79 -3.251 -3.120 1979-80 0.442 0.604 (after the winters had already occurred), our set of winters is 1980-81 1.189 1.366 not truly independent of other possible sets that could have 1981-82 -0.981 -0.821 been formed using only a portion of the winters within the 1982-83 1.467 1.657 1975-76 to 1982-83 time period. For these reasons, the 1983-84 -1.150 -0.982 proper use of the BPM is not straightforward and can lead to unrealistically high return periods. The details are discussed in the following subsection. and sj is the standard deviation of the mean winter tempera- a. Binomial probability model tures. The values of Z reported in Table 2 have been calculated using separate estimates of s^and 7Yor the corrected and un- The BPM can be used to calculate the probability of obtaining any number of successes P{b) in a given number of trials, corrected data. Of particular interest are the three warm win- providing the probability of a success is known. The BPM is ters 1975-76,1980-81, and 1982-83, and the run of three cold given by: winters from 1976-77 to 1978-79. The objective is to estimate the return period of a series of eight winters having six or more winters at least as cold or as warm as the six abnormal winters during the years 1975-76 through 1982-83. The return period of such a series of win- where p is the probability of a success and b is the number of ters is estimated using the following assumptions: 1) the vari- occurrences of a success in n trials. As an analogy to our ability of the 89-year record presented in Fig. 1 is representa- problem, one can imagine an eight-sided die that is thrown tive of the true interannual temperature variability between eight times and the number of successes (a throw of a one or winters; 2) the mean winter temperature regime across the an eight) is recorded after each throw. The probability of United States has not changed significantly during this pe- throwing a one or an eight is 0.25 on any single toss of the die. riod; and 3) the mean winter temperatures are normally The BPM gives a probability of .004227 for six, seven, or distributed. eight successes in eight throws, which we refer to as one set of We assume that Jjis constant. It is implicit in the assump- trials. tion of normality that sequences of relatively few years (say a This is approximately equivalent to a return period of one decade) can have noticeably different standard deviations. success in every 237 (1/0.004227) sets of eight throws of the Regarding the second assumption, examination of Fig. 1 in- die or, in our analogy, 237 sets of eight winters. Since there dicates that trend removal would have only minimal effects are eight tosses of the die in each set, the average number of on either an overall estimate of interannual variability or the tosses needed to obtain a one or eight, six or more times, is enhanced swings of temperature in recent years. The effects approximately once in every 1896 (8 times 237) single throws of small changes in the values of Z, are addressed in Section 4. of the die. Such a value could be improperly related to our A chi-square goodness of fit test indicates that there is no problem and be interpreted as a return period of once in substantial reason to reject the hypothesis that the observed every 1896 winters. mean winter temperatures are from a normal distribution. This analogy fails because the BPM does not properly ac- Using the assumption of a normal distribution, the limits count for the overlapping nature of the time series we are of the two extreme categories can be defined by the smallest considering. While each toss of the die is defined as a member absolute value of Zfor the six unusual winters. For the uncor- of only one particular set of eight tosses, a priori (before the rected data, this value occurred in 1975-76, and as such the toss of the die), each of the 89 winters could belong to one of limits of the categories associated with the unusually cold eight consecutive sets of eight winters (except for those at the and warm winters have normalized departures of — 1.179 and ends of the time series). For example, the first three winters of 1.179. In a normal distribution, roughly 75% of the area the period 1975-76 through 1982-83 could just as easily have under its probability density function has a value of Z be- been considered part of the set of the previous five winters. tween — 1.179 and 1.179. Therefore, there is 0.25 probability The climatologist can arrange his sets any way that suits his of any one winter being as cold (or colder) or as warm (or interest, since the mean winter temperatures are already warmer) as the limits defined by the Z's of — 1.179 and 1.179. known. Furthermore, the time series has some year-to-year We use Monte Carlo simulations to estimate the return pe- persistence, namely a lag one correlation coefficient of 0.11, riod of the recent spell of unusual winters despite the appar- so that each winter is not completely independent of those in ent attractiveness of the Binomial Probability Model (BPM). previous years. Positive persistence from winter to winter in The BPM is often applied to problems which can be specified the United States has also been reported by Namias (1978a).

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b. Monte Carlo simulations O >< o Return period estimates were made through the use of Monte Carlo simulations. A first order autoregressive process was used to generate 200 sets of 10000 pseudo-random numbers (X) with a mean of zero and a standard deviation of one. Sx Each Xis used to represent a mean winter temperature. Any given X at time t is defined by

X = rX -i + Z , (3) (N t t t

and Zt is an independent normal random deviate with mean < is estimated after 200 replications of time series are generated. o

uc* N A .3 A

2 >« 4. Results 2 z Monte Carlo simulations were run using the information in :: z Table 2 with and without the estimated corrections for changes in observation times and urban effects. The results 2 Z of these simulations are contained in Table 4. The categories in the first three simulations are defined such that each of the as >H winters of 1975-76 to 1982-83, except those of 1979-80 and OH £ OO >H 1981-82, is considered unusual. The return period is the expected number of years between independent recurrences of r- >H the prescribed event. We estimate the return period by divid- ing the observed number of events in each simulated series ^O >H into the length of the series (10 000 years). Each return period OS U lO >H given is the average of 200 such estimates. The first simulation's return period estimate (Table 4) is ^ z about 500 years longer than the second simulation, which uses the uncorrected time series. This sensitivity is attributed ^ z to the fact that the limits of each category are defined by the

CM >< least unusual (lowest | Z |) winter. The correction for the bias in the time series causes the "least unusual" warm winter, - z 1975-76, to appear more unusual (warmer) and the cold winter of 1976-77 to appear less unusual, thus determining the N z z z z z z z limits of the category (Table 2). The effect of including the A R> M N N H H \O i^n^ 'ô^ iT^î v^ô ^^ ^-h^ M^^ slight persistence is not so dramatic, but it does tend to re- duce the return period estimate on the order of 50 years. > o The 200 replications of the pseudo-random sequences '•3 u £ permit us to quantify the uncertainty inherent in our estimates s u c o ^ M M M M 6 i w of the return periods. Listed in Table 4 are the 5% and 95% £ u £ confidence limits (i.e., the 90% confidence interval) asso-

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TABLE 4. Return period estimates based on Monte Carlo simulations using various limits to define unusual winters within the period 1975-76 through 1983-84.

Minimum Correction for Return Period 5 and 95% Number of Observation Times of Confidence Simulation Category Winters r and Urban Effects? Event (yrs) Limits (yrs)

1 |Z| > 1.253 6 of 8 0.11 yes 1164 1094-1243 2 |Z| > 1.179 6 of 8 0.11 no 625 595-657 3 |Z| > 1.179 6 of 8 0.00 no 681 651-714 4 Z< -1.253 3 of 3 0.11 yes 550 524-578 5 Z< -1.389 3 of 3 0.11 no 1010 949-1080 6 |Z| > 0.821 8 of 9 0.11 yes 467 449-486 7 |Z| >0.981 8 of 9 0.11 no 2096 1924-2303 8 |Z| >0.982 7 of 9 0.11 yes 341 330-352

ciated with each estimate. They are derived from the stand- 5. Conclusions ard deviation (5) of the total number of events (TE) in each of the 200 sets («) of Xt to obtain an estimate of the standard An uncharacteristic spell of abnormal winters in the United deviation of the average TE over all 200 sets This is given States occurred from 1975-76 through 1982-83 as defined by by: nationally areally averaged temperatures. Nationally, six of eight winters during this period were either abnormally SN = s/\fn. (4) warm or cold (|Z | > 1.253). If no time series bias is taken The confidence intervals depicted in Table 4 represent the into consideration, the winter of 1975-76 is the least unusual uncertainty of our knowledge of the "true" return periods (Z = 1.179). If bias in the time series due to changing obser- because we chose to limit our Monte Carlo calculations to vation times and urban effects is considered, then 1976-77 is n = 200 runs of 10000 years (2 X 106 years in all). There are the least unusual (Z = —1.253) of the six abnormal winters. other sources of uncertainty besides the value of Ti that we The estimated mean return period of the corrected time series have not explicitly considered, even if our assumptions of is 1164 years for six or more winters in a series of eight con- stationarity and representativeness are valid. In particular, secutive winters with temperatures as abnormal as the winter our estimates of s^and r, based on a relatively short 89-year of 1976-77, and for the uncorrected time series, the estimated record, are subject to sampling error, and this can seriously return period is 625 years for six or eight winters as abnormal affect the results. We can see, from the first two lines in Table as 1975-76. The more moderate winters of 1983-84 and 4, that a 6% decrease in the limit on Z, which is inversely pro- 1981-82 can also be considered abnormal by lowering the portional to St, is reflected in a 46% decrease in return period. values of Z necessary for an abnormal classification. This Thus, even a small error in St can be important. If r = 0.11 gives the return period estimate for a spell of eight winters overestimates the true autocorrelation, the effect on the re- with |Z | > 0.821 (|Z | > 0.981 for uncorrected data) in the sults, based on the differences between simulations two and nine consecutive winters, 1975-76 through 1983-84, as 467 three, are probably not very important. If we have underes- years (2096 years for uncorrected data). timated the true value of r, then a more important error may The recent variability is either a moderately rare event in a have been introduced. reasonably stationary climate, or it represents climate change; The fourth and fifth simulations in Table 4 pertain to the which of these is the correct view is beyond the scope of this run of three very cold winters from 1976-77 through 1978-79. paper. In one sense at least (a national average temperature), The more recent event of high variability of mean winter the climate of the past several winters has been very unusual, temperatures rivals the unusualness of three consecutive win- and is unprecedented in the past 89 winters. Some research ters as cold as or colder than the winter of 1976-77. It is has already addressed some of the potential causes for the re- noteworthy that this Monte Carlo study estimates the return cent spell of unusual winters by addressing specific unusual period of such an event at roughly 2000 years less than the winters (Namias, 1978b; Namias, 1980; Harnack, 1980; BPM (550 versus 2588 years). Rasmusson and Wallace, 1983), but it is hoped that the work Three additional simulations were run after relaxing the reported here can generate additional interest in this recent limits of the categories defining unusually cold and warm period of unusual mean winter temperatures. How the na- winters. The results of these simulations are listed in Table 4 tion coped with this unusual variability could also be the sub- as the sixth, seventh, and eighth simulations. Note the large ject for climate impact studies. difference (approximately 1500 years) between the return period estimates associated with the corrected versus the uncorrected time series (simulation 6 versus 7). The more stringent classification of unusual winters in simulation 1, despite fewer winters in such a category, resulted in a much Acknowledgments. Thanks are due to Pamela Young and Carolyn Herman of the National Climatic Data Center (NCDC) for their longer return period than in simulations 6 or 8. Note also, computations regarding station changes in observation times, eleva- there is less of a difference between the corrected and uncor- tion, latitude, and longitude. The skillful typing of NCDC's Infor- rected data in simulations 1 and 2 than in simulations 6 and 7. mation Management Section is also acknowledged.

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FIG. 2. Time series and trend line of mean winter temperatures. (1931 is the winter of 1931-32). Horizontal dashed line is the long-term mean and solid dots are mean winter temperatures.

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Appendix A. Site change bias. References

In spite of the arguments suggesting a systematic "cold" bias Baker, D. G., 1975: Effect of observation time on mean temperature in the time series, an empirical case can be made that these estimation. J. Appl. Meteor., 14, 471-476. bias corrections can sometimes be overwhelmed by station Blackburn, T., 1983: A practical method of correcting monthly av- openings, closings, and relocations. Fig. 2 depicts remark- erage temperature biases resulting from differing times of obser- able consistency of the trend lines since 1931 for the three vation. J. Climate Appl. Meteor., 22, 328-330. time series of mean winter temperatures for Asheville, North Cayan, D., R. Harnack, and G. Vallis, 1982: Workshop on weather Carolina; the climate division which contains Asheville; and and climate variability during winter 1981-82. Bull. Amer. Meteor. the state of North Carolina as derived from areally averaged Soc., 63, 1179-1183. climate division data. The Asheville data are taken from a Changnon, S. A., Jr., 1979: Howa severe winter impacts on individ- rooftop location, and are based on a midnight-to-midnight uals. Bull. Amer. Meteor. Soc., 60, 110-114. observation schedule. During the last 52 years, the station Diaz, H. F., and R. G. Quayle, 1978: The 1976-77 winter in the contiguous United States in comparison with past records. Mon. Wea. moved once, approximately 200 m from the Post Office roof- Rev., 106, 1393-1421. top to the Federal Building rooftop. The southern mountain and , 1980: An analysis of the recent extreme winters in the climate division had a 38% increase in the number of AM ob- contiguous United States. Mon. Wea. Rev., 108, 687-699. servers from 1931 to 1980, while the state as a whole had a Dickson, R. R., 1959: Some climate-altitude relationships in the 30% increase. The average latitude and longitude of the sta- Southern Appalachian Mountains. Bull. Amer. Meteor. Soc., 40, tions in these areas has not changed significantly over the last 352-359. 52 years, but the average station elevation has decreased. Harnack, R. P., 1980: An appraisal of the circulation and tempera- Using the station elevations at the beginning of each decade ture pattern for winter 1978-79 and a comparison with the pre- (1930-1980), the average station elevation decreases by 78 m vious two winters. Mon. Wea. Rev., 108, 37-55. for the Southern Mountains and by 43 m for the whole state Karl, T. R., L. K. Metcalf, L. N. Nicodemus, and R. G. Quayle, 1983: Statewide average climatic history [Alabama through Wyoming]. from 1930 to 1955 versus 1956 to 1980. By using the observed National Climatic Data Center Historical Climatological Series change of January mean temperature with elevation for sta- 6-1. National Climatic Data Center, Asheville, N.C. tions in the Southern Appalachian Mountains as reported by Landsberg, H. E., ed., 1981: World Survey of Climatology Volume 3, Dickson (1959), we find that this amounts to a positive bias General Climatology. Elsevier Scientific Publishing Company, of approximately 0.28°C for the Southern Mountains and New York, N.Y., 408 pp. 0.15°C for the whole state. We use the temperature-elevation Mitchell, J. M., Jr., 1953: On the causes of instrumentally observed relationships developed by Dickson (1959) for the whole secular temperature trends. J. Meteor., 10, 244-261. state, as well as for the Southern Appalachian Mountain divi- , 1958: Effect of changing observation time on mean tempera- sion, since most of the decrease of average elevation can be ture. Bull. Amer. Meteor. Soc., 39, 83-89. attributed to mountain stations. Using Washington, D.C.'s Namias, J., 1978a: Persistence of U.S. seasonal temperatures up to one year. Mon. Wea. Rev., 106, 1557-1567. time of observation bias (Table 1) of 1.0°C as an approxima- , 1978b: Multiple causes of the North American abnormal win- tion for the time of observation bias in North Carolina, an ter 1976-77. Mon. Wea. Rev., 106, 279-295. estimate of the negative bias for the changes in observation , 1980: Causes of some extreme Northern Hemisphere climatic time from 1930 to 1980 is -0.38°C for the Southern Moun- anomalies from summer 1978 through the subsequent winter. tains and —0.30°C for the whole state. Since Asheville is the Mon. Wea. Rev., 108, 1333-1346. only city in the Southern Mountains with a population close Rasmusson, E. M., and J. M. Wallace, 1983: Meteorological aspects to 50000, and it has decreased in population since 1930, of the El Nino/Southern Oscillation. Science, 222, 1195-1202. urban influences are ignored for this climate division. After Schaal, L. A., and R. F. Dale, 1977: Time of observation bias and adding in the urban influences for the state as a whole, as dis- "climate change". J. Appl. Meteor., 16, 215-222. cussed in Section 2, the net bias for the Southern Mountains U.S. Department of Commerce, 1958: Decadal Census of Weather Stations, Weather Bureau. U.S. Department of Commerce, Wash- from 1930 to 1980 is estimated to be -0.10°C and for the en- ington, D.C., np. tire state, — 0.15°C. This helps to explain the consistency of , 1980a: Climatological Data, 1931-1980, California, Colorado, the trend lines in Fig. 2. So, in spite of the observation time Illinois, Indiana, New York, North Carolina, and Washington. bias, its importance can sometimes be mitigated by station U.S. Department of Commerce, Washington, D.C., np. relocations, but it would be presumptuous to make such an , 1980b: Statistical Abstract of the United States, Bureau of the assertion for the entire nation. Census. U.S. Department of Commerce, Washington, D.C., np.

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