National Weather Service Weather Forecast Office

Stephen A. Miller

1. Introduction

The Southern Oscillation(SO for short), and its warm phase, called El Nino, has caused concern due to the much-above-normal precipitation amounts sometimes accompanying this phase of the SO. During the 1982-1983 period(the strongest El Nino ever measured), the Jackson, Mississippi, area received precipitation totaling 61.38" during the November through April period, and 28.51" during the December through February period. The Pearl River at Jackson reached 39.58' on May 25, 1983, due to the rains, becoming the second highest level on record. It is the comparison of the current El Nino with this record setting event that has caused the concern.

The Southern Oscillation is an west to east shifting of warmer than normal waters in the equatorial Pacific Ocean. During the El Nino phase, the warmer than normal waters shift eastward, nearer to the coastal waters of Central and South America. During the opposite phase, called La Nina, the warmer than normal waters shift westward to the western reaches of the Pacific Ocean. It is the accompanying shift in the global scale air circulations that either enhances or decreases precipitation over the Pacific Ocean and other, globally close regions, with the results depending upon the phase of the oscillation.

Sir Gilbert Walker(Walker 1923, 1924, 1928, 1932, 1937) noticed the phenomena, in an attempt to explain, then predict the cycle of the monsoon rains in India, conducted research in the 1920's and 1930's. He found a correlation existed between the difference of the average monthly sea level pressures for Darwin, Australia and Pepeete, Tahiti. It is this difference in average pressure( with some records dating before 1600) which became the first "index" of the SO, and (with some adjustments over the years) is still in use today.

Barnston and Livezey(Barnston and Livezey, 1987), explored the various global circulations, identified thirteen separate teleconnection patterns in the Northern Hemisphere. One of these teleconnections, the North Atlantic Oscillation(NAO), was found to affect the mean circulation pattern in the Southeast, throughout the year. Little research has been found by this author on the interaction between the ENSO, and NAO. At a cursory glance, there appears a correlation between the relative strengths of the SO and NAO, and the weather in the Southeast. This will require further research, though, to quantify and verify.

It is during the El Nino phase of the SO in which enhanced precipitation affects the coastal areas of the Gulf of Mexico, among other regions of the US and world. This paper explores the various stages of the SO and its effects upon average temperature, total precipitation, and total snowfall amounts for winter(defined as the period December through February)in Jackson, MS. Its effects upon temperature, precipitation and snowfall for a period encompassing the six coolest months of the year(the period including the months from November though April) also are covered, and attempts to expose any relationships to the local climatology of the Jackson, Mississippi area.

2. Methodology

The period used in this paper encompasses November through April for the years 1896 through early 1997, and uses climatological sources found on station at the Jackson National Weather Service Office. Several sources were used to define a period as an El Nino, La Nina, or neutral (average) year. The first was a listing of El Nino and La Nina years on the National Oceanographic and Atmospheric Administration (NOAA) Climate Prediction Center's(CPC) El Nino web page at web address . The second was taken from University of Massachusetts' El Nino web site from a thesis written by Kimberly Amaral www.umassd.edu/Public/People/KAmaral/Thesis/ElNinoYears.html>. The third was a paper by Ropelowski and Jones, 1987. There were some slight differences among the three sources. Also, the CPC site did not have available the years before 1950. For each year, when two of the sources agreed upon a year being an El Nino, La Nina or neutral year, that year was marked as such. No attempt was made to relate the strength of the El Nino or La Nina to the departures.

The statistics in this paper were calculated using the Minitab statistical software package and several spreadsheets in Microsoft Excel. Parameters calculated were mean, standard deviation, maximum, minimum, correlation coefficients, and probabilities using tests for normal and non-normal distributions. The hypothesis used for the testing was that the El Nino and La Nina years do not vary significantly from average.

The test used for non-normal distributions was the Student-t test, due to sample sizes for El Nino(28) and La Nina(17) years being less than the required 30 to assume a normal distribution. A 95% confidence interval was used for calculating probabilities. For the correlation coefficients in the following sections, according to Panofksy and Brier, 1958, a coefficient great than 2.6 times the standard deviation, or 0.2613, is considered significant for this sample size. All testing compared the sample statistics with those of the total population. For this study, this includes all the years of recorded data at Jackson.

3. Discussion and Results

A. Total Precipitation

After calculating the statistics, it was found that for precipitation, both total liquid equivalent and snowfall, little significant correlation exists between the deviation from average of the precipitation amount and the type of year. For example, although the average for the winter precipitation amount is higher for El Nino years and lower for La Nina years, testing showed a high probability that the sample means are not statistically different from the mean of the total population. Tables 1 and 2 show the results of tests performed on the winter precipitation totals. After comparison between deviation from average and type of year, correlation coefficients of -0.0228 for El Nino, 0.1013 for neutral, -0.1073 for La Nina years, and -0.0329 for all years were calculated for the winter period. Both are very small correlations below the 0.2613 cutoff for a significant correlation, suggesting that the relation between precipitation departure and type of year is from random fluctuations. In Table 2, and following tables displaying test results from the "Z" and Student-t tests, "Z" value is the calculated value from the Z normalcy test with "Z - P Value" the associated probability( percentage divided by 100). The "T - Value" and

"T - P Value" are the same values from the Student-t test. One item that was in evidence for La Nina years is a small number of above normal precipitation amounts, as shown by Table 1. Another observation is the smaller variability of the data for La Nina years, in evidence by the smaller standard deviation. It is also interesting to note that the two highest winter precipitation totals for 1896 to 1997 are for neutral, rather than El Nino years.

Expanding the time period to November through April, the statistics show a slightly different pattern. Average total precipitation, for both El Nino and La Nina years, is less than for neutral years. With El Nino years, the difference between neutral and El Nino average precipitation is not significantly different.  One can see that the precipitation total for the 1982-1983 period has become the largest. Still, amounts for El Nino years are distributed across the entire range of values. With La Nina years, the difference in average precipitation is almost significant at the level tested for. If the confidence level is reduced, one could say, that for the November through April period, La Nina years are drier than average. This is with an 88% confidence level. Also, precipitation totals during La Nina years are less variable than either El Nino or neutral years, as exhibited by the smaller standard deviation for these years. Tables 3 and 4 list the statistics calculated for the November through April period. 

After calculating the correlation coefficients for this period, small numbers were the result; -0.1144 for El Nino years, 0.1926 for neutral, -0.1190 for La Nina years, and -0.0291 for all observations compared to type of year, again suggesting the results are from random fluctuations for El Nino and neutral years.

B. Snowfall

Considering snowfall in Jackson, the key item to remember is that measurable snowfall has occurred only during 40% of the years or less(37% during the winter months, 40% for the November through April period), regardless of the type of year. Table 5 shows these percentages listed according to period and type of year.   As one can see from these numbers, measurable snowfall totals are slightly less during El Nino years than in neutral years, more so during La Nina years. Table 6 includes the basic statistics for snowfall for the winter and November through April periods for all years. One can see from this table, La Nina years again have less snow than neutral or El Nino years, and are less variable in the total amounts for the period when it does occur. Correlation coefficients calculated are summarized in Table 8. Again, the numbers are small and seem to be the result of randomness.

Table 5: Percentages of El Nino, Neutral, and La Nina years with Measurable Snowfalls

Table 6: Winter and November Through April Snowfall Statistics

Calculation of the test statistics, shown in Table 7, shows El Nino years are not significantly differ from neutral years in total snowfall amounts for winter and the November through April period. La Nina years also do not significantly differ from average at a 95% confidence level. However, one could say, with a 78% confidence interval, that La Nina years result in less snow than average. Again, though, correlations are low between snowfall amount for the November through April period and type of year for La Nina, El Nino, and neutral, and all years inclusive, suggesting this is due to randomness. The results are again summarized in Table 8.

Table 7: November - April and Winter Snowfall Departures - Z and Student T Test Results

C. Average Temperature

In this study, average temperature showed the most correlation between departure from average and type of year; warm for La Nina, cooler for El Nino. For winter, the correlation coefficient was 0.2890, and for November through April, it was 0.3220. Both are modest, yet significant, positive correlations. The coefficients are summarized in Table 13. One can also see, the correlation for El Nino years for the November through April period is above the 0.2613 significance level, with the significance level taken as an absolute number.

Tables 9 and 10 summarize the basic mean temperature statistics with Tables 11 and 12 summarizing the test results. The average of all El Nino winters is 1.4 degrees below the population average of 48.6 degrees, and 0.9 degrees below the November through April average of 59.4 degrees. La Nina years were 1.4 degrees above for winter and 1.3 degrees for November through April. . After calculating the statistics for the test of means, there was a point two to four percent chance(depending on testing method and of the data) that the data for El Nino years could be part of the average. Thus, one can state that El Nino years are warmer than the average, with 96% to 98% confidence, for winter and November through April periods.   One can see that El Nino years tend to cluster at the cooler end of the figure with La Nina years clustering near warmer end. With La Nina, period of the data is important. With winter data, an 89% to 90% confidence level of La Nina years being warmer than average was calculated. For the November through April period, the confidence level rises to over 98%.

4. Summary

As the preceding section illustrated, the effects of the El Nino Phase of the Southern Oscillation upon precipitation in the Jackson area are erratic, thus hard to predict. It is this variability over the entire range of observations that keeps the average for El Nino years from deviating significantly from the average of all years. With temperature, what is considered the "usual effects" of El Nino occur. El Nino years result in cooler than average temperatures for the winter and November through April periods in Jackson with a 96% to 98% confidence interval.

The La Nina phase, on the other hand, tends to be less variable with respect to precipitation, thus easier to quantify and predict. This holds true for melted liquid precipitation, and snowfall. For liquid and solid precipitation, the tested 95% confidence level wasn't achieved, but not by much. Confidence levels ranged for all precipitation ranged from 80% to almost 90%.

With respect to temperature, again, what is considered "usual" is confirmed. La Nina years tend to result in warmer than usual winters. This is also extended to the November through April period.

References and Other Resources:

 

• Barnston, A. G., and Livezey, R. E., 1986: Classification, Seasonality, and Persistence of Low-Frequency Atmospheric Circulation Patterns; Monthly Weather Review; Vol. 115, pgs. 1083-1126.

   

• Fraedrich, K., 1987: El Nino/Southern Oscillation Predictability; Monthly Weather Review; Vol. 116, pgs. 1001-1012.

   

• Livezey, R. E., and Mo, K. C., 1987: Tropical-Extratropical Teleconnections during the Northern Hemisphere Winter. Part II: Relationships between Monthly Mean Northern Hemisphere Circulation Patterns and Proxies for Tropical Convection; Monthly Weather Review; Vol. 115, pgs. 3115-3132.

   

• Panofsky, H. A., and Brier, G. W., 1958(reprint 1976): Some Applications of Statistics to Meteorology; The Pennsylvania State University

   

• Rasmusson, E. M., and Carpenter, T. H., 1982: Variations in Tropical Sea Surface Temperature and Surface Wind Fields Associated with the Southern Oscillation/ El Nino; Monthly Weather Review; Vol. 110, pgs. 354-384.

   

• Ropelowski, C. F., and Jones, P. D., 1987: An Extension of the Tahiti-Darwin Southern Oscillation Index: Notes and Correspondence; Monthly Weather Review; Vol. 115, pgs. 2161-2165.
• Schaefer, R. L., and Anderson, R. B, 1989: The Student Edition of Minitab; Addison-Wesley Publishing Co., Inc.
• Trenberth, K. E., and Shea, D. J., 1987: On the Evolution of the Southern Oscillation; Monthly Weather Review; Vol. 115, pgs. 3078-3096.
• Van Loon, H., and Madden, R. A.,1981:the Southern Oscillation. Part I:Global Associations with Pressure and Temperature in the Northern Winter; Monthly Weather Review; Vol. 109, pgs. 1150- 1162.

   

• ____________, and ______________ ,1981:the Southern Oscillation. Part II: Associations with Changes in the Middle Troposphere in the Northern Winter: Monthly Weather Review; Vol 109, pgs. 1163-1168.

   

• Walpole, R. E., and Myers, R. H.,1985:Probability and Statistics for Scientists and Engineers, Third Edition: Macmillan Publishing Co.

   

• Wright, P. B., 1984; Relationships between the Indices of the Southern Oscillation: Monthly Weather Review; Vol.112, pgs. 1913-1919.
• ______, 1924: Correlation in Seasonal Variations of Weather, IX:A

  Further Study of World Weather; Mem. Indian Meteor. Dept., Vol 24, pgs. 275-332.

• ______, 1928: World Weather III; Mem. Royal Meteor. Soc., Vol 2, pgs. 81-95.
• ______, and Bliss, E. W. , 1930: World Weather IV; Mem, Royal Meteor. Soc., Vol 3, pgs. 81-95.
• ______, and _________, 1932: World Weather V; Mem, Royal Meteor. Soc., Vol 4, pgs. 53-84.
• ______, and _________, 1937: World Weather VI; Mem. Royal Meteor., Vol 4, pgs. 119-139.