The Correlation Between Fall and Winter Temperature Anomalies in the Midwest during ENSO Events

Size: px

Start display at page:

Download "The Correlation Between Fall and Winter Temperature Anomalies in the Midwest during ENSO Events"

Phillip Young
5 years ago
Views:

1 The Correlation Between Fall and Winter Temperature Anomalies in the Midwest during ENSO Events Morgan E. Brown Iowa State University Mentors: Ray Arritt and Alan Czarnetzki ABSTRACT According to the National Oceanic and Atmospheric Administration (NOAA), ENSO events are defined as the average of a three-month temperature departure from normal (anomalies) in the equatorial Pacific region. Using this definition and past research, El Niño and La Niña years dating back to 1949 were identified, with more intense temperature anomalies classified as strong ENSO events. Temperature data were collected and anomalies were calculated for the fall months of September, October, and November (SON) and the succeeding winter months of December, January, and February (DJF). These fall and winter anomalies were compared with each other using a statistical software program that calculated R squared values and graphed the linear regression of the anomaly plots. Pacific Decadal Oscillation (PDO) data were also considered in this study. Little is known about the dynamics of the PDO, but its shift between warm and cold phase could influence the effects of the ENSO phases. To further understand the ENSO/PDO relationship, yearly ENSO anomalies were compared during each PDO phase, which also appears to be the key factor in the correlation of Midwest fall and winter temperature anomalies with ENSO events. The strongest correlations are evident during La Niña winters during the warm phase of the PDO. Understanding the dynamics of the PDO can help explain the physical mechanisms between the PDO and ENSO events, which can be a useful seasonal forecast tool. 1. Introduction ENSO phases play a role in winter temperatures in the Midwest (Ropelewski and Halpert, 1986), thus correlations between fall and winter temperatures during ENSO events may prove useful as seasonal forecasting tools. Gutzler and Preston (1997) suggest that the Pacific Decadal Oscillation (PDO) phase could influence the effects of the ENSO phases. Their study on the predictability of precipitation in the southwest United States shows that the highest predictability occurred during the warm phase of the PDO, which began in 1977 (Barnett and Gershunov, 1998). Based on Gutzler and Preston s results, I hypothesize the PDO phase to be an important factor in the correlation between fall and winter temperature anomalies in the Midwestern US. Incorporating the PDO phases into the ENSO phases may increase the accuracy of the prediction. To test this hypothesis, fall and winter temperature anomalies in the Midwest were analyzed with respect to the ENSO and PDO phases. Each ENSO and PDO phase was classified and the comparisons were done on each classification separately.. Data Collection a) Region of Study and Data Sources Data were collected from first order stations within the Great Plains, Northern Plains, Great Lakes and Ohio Valley regions. For accuracy, stations chosen for this study needed to have complete monthly temperature data for the winter months of December, January, and February (DJF) and fall months of September, October, November, (SON) and the station could not have changed location at any time from 195 to 5. Figure 1 illustrates the sites that met all of the preceding conditions. All monthly temperature data was obtained from the Midwest Region Climate -

2 Center s MICIS. web database courtesy of Jeff Boyne, NWS La Crosse. b) Determination of El Niño and La Niña Winters The classification of ENSO events is generally a subjective process. However, in September 3, NOAA established specific criteria to define the occurrence of ENSO events called the Oceanic Niño Index (ONI). This is defined to be the 3-month averages of sea surface temperature (SST) departure of ±5 o Celsius from normal within the Nino 3.4 region (1W7W, 5N-5S). The Nino 3.4 region SST departures from normal are noted to be critically important in determining major shifts in tropical rainfall, which teleconnect to rain and temperature anomalies around the globe (Ropelewski and Halpert, 1986). At the time of this study, the ONI was not available, thus the ENSO events were classified using the previous subjective CPC 3-month classifications, based on the general equatorial region off the coast of Peru. This data is no longer available, but relevant information is reproduced in table Analysis a) Anomalies To calculate the fall anomalies at each station, the average temperatures for the months of SON were subtracted from the average of all SON temperatures over the entire period. Similarly, winter anomalies were calculated by subtracting the average of the yearly winter months of DJF for a specific station from the total average of DJF for all years in the study. The anomalies were used to calculate the correlation coefficient using statistical software that simultaneously plotted a linear regression, as shown in figure. Fig 1. Stations that met the criteria set for data collection in this study. The red line indicates the division between the North and South zones used in the analysis process. -

3 Year Old OND Old JFM ONI SON ONI DJF ENS O Strength Year Old OND Old JFM ONI S ON ONI DJF ENS O Strength ??? C???.8 C W - N.7.6 W C C -.8 C W - W W W - N.7.3 W N N N N N -..1 N N W W N N.4.3 N N N -.3 N C C.1 C N N -.1 N C+ C.8. C W + W W C- N N C- C C W W W C- C- -.8 C?? W - W -.4 N N N N N N N W W W?? N N N W W W?? N N N C+ C+.6.7 C N N C N N N W N.9.8 W W - W N C C C W W W W + W W W - W N N N N W - N.3. N N N N W W.9 1. W?? W - W.4 1 W C- C C W - W W N N N C C C W + W W C- N C C C+.1.6 C W + W W C C..6 C C+ C+.7.8 C+ C- C C C- C C- 1 N N N C+ C.6.6 C+ W W W + Table 1. Season classifications of cold and warm events as classified by Glenn Lussky, NWS La Crosse. Column 1 is the winter season. Columns and 3 are taken from the subjective 3-month CPC analyses, based on the equatorial SST analyses from 15W to the dateline. (OND indicates October- December of year 1; JFM indicates January-March of year ). Columns 4 and 5 are the ONI 3-month averages for the period listed. (SON indicates the September-November average for year 1; DJF indicates the December-February average for the succeeding winter). Column 6 is the event classification used for this study. This assignment was made based on columns and 3, using subjective assessment of the Nino 3.4 trace. Items labeled with W s indicate warm events. Items labeled with C s indicate cold events. The correlation coefficient (r) statistically measures the strength of a linear relationship. R was calculated over all years and stations for a particular ENSO classification (i.e., strong El Niño years, all La Niña years, etc). See table for a complete list of these classifications. These r values are between 1 and 1, with values near 1 indicating a stronger correlation and values near zero indicating little or no correlation. For this study, correlation is reported as an R value, or the fraction of the variation of the y variable as a result of the linear fit. The qualitative relationship of this data is visible in the plotted linear regression, with a positive slope indicating a positive correlation and a negative slope indicating a negative correlation. -

4 Warm PDO Cold PDO El Niño 1977, 1979, *198, 1951, 195, 1953, *1957, 1986, 1987, 199, 1958, 1963, 1965, 1968, *1991, 199, 1994, 1969, *197, 1976 *1997, 1,, 5 La N iña 1983, 1984, *1988, *1949, 195, 1954, * , *1998, *1999, 196, 1964, *197, 1971 *1973, 1974, *1975 Table. Describes the division of the years into ENSO and PDO phases. * Indicates a strong ENSO classification. PDO and ENSO phases are defined in text. b) Incorporating the PDO To explore the ENSO/PDO relationship, PDO phases were incorporated into the ENSO classifications. Each ENSO classification was subdivided into warm and cold PDO phases. Warm PDO phase occurred from 1977 through 5 and the cold PDO phase occurred in the years prior to Table outlines the subdivisions of each year according to strength, ENSO phase, and PDO phase. Linear regressions and R values were calculated as previously stated for each ENSO classification: ENSO events, strong ENSO events, and the incorporated PDO phase. c) Zonal Comparison The final subdivision was intended to Fig a. El Niño anomalies plot for all El Niño years between 1949 and 5. R =.1. Anomalies plotted are for the months of SON (September, October, November) and DJF (December, January, February). Fig b. La Niña anomalies plot for all La Niña years. R =

5 identify any geographical relationships. The data set was divided among the stations into two zones: North and South. The border of these zones extends across the northern borders of Ohio, Indiana, Illinois, Missouri and Kansas (refer to figure 1). An example of a data set classification would be All La Niña years during a Cold PDO phase in the North zone. Again, linear regressions and R values were calculated as previously stated for each classification in these zones Fig 3a. El Niño anomalies plot for strong El Niño years during a warm PDO phase (years between 1977 and 5). R =.9. Anomalies plotted are for the months of SON (September, October, November) and DJF (December, January, February). Fig 3b. La Niña anomalies plot for strong La Niña years during a warm PDO phase. R = Fig 4a. El Niño anomalies plot for strong El Niño years during a cold PDO phase (years between 1949 and 1977). R =.16. Anomalies plotted are for the months of SON (September, October, November) and DJF (December, January, February). Fig 4b. La Niña anomalies plot for strong La Niña years during a cold PDO phase. R =.. -5-

6 EN S O P h a se C la ssific a tio n P D O P h a se Zo n e R V a lu e S ta t S ig C old A ll W a rm N o rth.8 5 y e s C old S trong W a rm S o uth.7 7 y e s C old S trong W a rm y e s C old A ll W a rm S o uth.6 3 y e s C old A ll W a rm N o rth.4 4 y e s W a rm S trong C o ld y e s W a rm S trong W a rm S o u th.4 y e s C old S trong y e s C old A ll y e s W a rm A ll W a rm N o rth. y e s C old A ll C o ld y e s C old A ll C o ld N o rth.1 7 y e s W a rm A ll W a rm y e s N eutra l y e s W a rm A ll W a rm S o uth.1 4 y e s W a rm S trong C o ld S o uth.1 4 C old S trong C o ld S o uth.1 C old A ll C o ld S o uth.1 C old A ll W a rm y e s W a rm A ll y e s W a rm A ll C o ld N o rth.1 W a rm S trong C o ld N o rth.1 W a rm S trong N eutra l ---- W a rm y e s C old S trong C o ld W a rm A ll C o ld N eutra l ---- C o ld C old S trong C o ld N o rth -. 1 W a rm A ll C o ld S o uth -. 1 W a rm S trong W a rm y e s Table 3. Complete list of classifications and correlation value obtained. Table is sorted by correlation values with highest correlations at the top. The strongest negative values are at the bottom of the list. Warm ENSO indicates an El Niño year, whereas Cold ENSO indicates a La Niña year. PDO (Pacific Decadal Oscillation) phases include the years under each ENSO classification as depicted in table, and are defined as described in the text. The Zone column represents the stations that exist in either the North or South zone as shown in figure 1. Classifications with a yes in the Stat Sig column indicates that the results are within the 95% confidence range. Dashes indicate that no subdivision was used, thus all years were considered as a whole. c) Neutral Years Temperature anomalies during neutral years were also plotted. In order for the fall temperature anomalies to be used as a forecasting tool for winter temperature anomalies during ENSO events, it would be expected that non- ENSO events would be random and have no correlation. -6-

7 Linear regressions and R values were calculated using the same method as ENSO classifications. 4. Results Figures, 3, and 4 are outputs of the statistical program, with r values calculated from the reported R listed. Figure shows the linear regression of the El Niño (a) and La Niña (b) anomaly plots for the entire period studied. La Niña years have the higher correlation, but both R values are relatively small. Figure 3 shows strong El Niño (3a) and La Niña (3b) anomaly plots, but within the warm PDO phase (years from 1977 to 5). Both R values are significantly higher than those in figure, with La Niña years having a higher correlation. Finally, figure 4 shows the same strong El Niño (4a) and La Niña (4b) anomaly plots, but during the cold PDO phase (years from 1949 to 1977). Neither value shows strong correlation, but the El Niño R value is higher than La Niña. A complete list of classifications is presented in table 3, sorted with r values in descending order. Negative correlation values indicate a negative slope in the linear regression, thus values closer to 1 suggest a stronger negative correlation between fall and winter temperature anomalies. Warm ENSO indicates an El Niño year, whereas Cold ENSO indicates a La Niña year. PDO phases include the years under each ENSO classification as depicted in table. The Zone column represents the stations that exist in either the North or South zone as shown in figure 1. Dashes indicate that no subdivision was used, thus all years were considered as a whole. This chart shows that the higher correlations exist in the warm PDO phase during La Niña events. The north and south zone correlations are spread fairly evenly throughout the table. The statistical significance indicates a 95% confidence that the results found were not a random occurrence. 5. Discussion Gutzler and Preston (1997) indicated that the warm PDO phase allows for more accurate predictability of precipitation during El Niño seasons in the southwest US during a warm PDO phase. Similarly, correlation values reported in this study suggest that the warm PDO plays a factor in the midwestern temperature effects of ENSO events. These results also indicate a relationship between Midwest temperature anomalies and the ENSO phase, which could be useful in forecasting winter temperature anomalies. The R values suggest that the higher correlations between fall and winter temperature anomalies occur during the warm phase of the PDO. Of these, the La Niña years had the strongest correlations. The linear regressions show that the majority of the classifications are positively correlated, with exceptions existing mostly during El Niño seasons. Positive correlations during the La Niña seasons suggest that during a cold ENSO and warm PDO phase overlap, if the fall temperatures are colder than average, we can expect the winter temperatures to be colder than average as well. Similarly, if the fall temperatures are warmer than average, we can expect winter temperatures to be warmer than average. During an overlap of the warm ENSO and warm PDO phase, the results are less certain. Strong ENSO events indicate a negative relationship, whereas all ENSO events indicate a positive relationship. Both correlation values are relatively small, so the strength of the El Niño event will need to be analyzed in order for the seasonal forecast to be useful. Although during the neutral years there is a slight negative correlation, there is no correlation evident during each phase of the PDO. This suggests that the PDO plays a key role in understanding the teleconnections of the ENSO phases. 6. Future Work PDO phase is an important factor in forecasting temperatures during ENSO events. Warm PDO phases in particular seem to have a strong effect on the ENSO events, especially during La Niña. Understanding the dynamics of the PDO is an important part of understanding the physical mechanisms that cause the correlation between ENSO and PDO phases. -7-

8 This study used a subjective classification of ENSO events. It would be beneficial to classify ENSO events based objectively on the new ONI criteria. Also, only average monthly temperatures were used to correlate fall and winter temperature anomalies in this study. Higher correlation values might be evident if high and low monthly temperatures were considered separately. It would also be interesting to see how other meteorological parameters can be affected by ENSO and PDO oscillations, such as precipitation. If more research is done on the PDO and its teleconnections, the correlation between fall and winter parameter anomalies could be a useful forecasting tool during ENSO events. Acknowledgments. Funding provided by COMET (P.I. s Alan Czarnetzki, University of Northern Iowa; Glenn Lussky and Jeff Boyne, NWS La Crosse). Technical advice provided by Ray Arritt, Iowa State University. Assistance provided by Eugene Takle, Daryl Herzmann, Jon Hobbs, Iowa State University; Steve Hu, University of Nebraska, Lincoln; Dave Gutzler, University of New Mexico; Ed Brown, University of Northern Iowa. htm] Rasmusson, E.M., 1984: El Niño: The ocean/atmosphere connection. Oceanus, 7, 5-1. Ropelewski, C.F., and M.S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Mon. Wea. Rev., 114, Trenberth, K.E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, Wang, H., and R. Fu, : Winter monthly mean atmospheric anomalies over the Northern Pacific and North America associated with El Niño SSTs. J. Climate, 13, REFERENCES Barnett, T., and A. Gershunov, 1998: Interdecadal modulation of ENSO teleconnections. Bull. Amer. Meteor. Soc., 79, CPC, cited 5: Cold and warm episodes by season. [Available online at is_monitoring/ensostuff/ensoyears.shtml] Gutzler, D. S., and J. W. Preston, 1997: Evidence for a relationship between spring snow cover in North America and summer rainfall in New Mexico. Geophys. Res. Lett., 4, Horel, J.D., and J.M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with Southern Oscillation. Mon. Wea. Rev., 19, MRCC, cited 5: Online Database. [Available online through restricted access at m]. NOAA, cited 3: NOAA gets U.S. consensus for El Niño/La Niña index, definitions. [Available online at -8-

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012

ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 23 April 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index