Although the exact mechanisms involved in the relationship between North American weather patterns and the El Niño Southern Oscillation (ENSO) are still under investigation, previous studies have provided evidence that strong El Niño episodes may be correlated with above normal winter precipitation and below normal temperatures to a varying degree across Texas. Conversely, studies have also shown that La Niña episodes may be associated with a significant decrease in precipitation.

To further investigate this relationship, this study amassed surface station records in Texas for the 23-year period 1975-1997. This period includes three relatively strong El Niño events: 1982-83, 1986-87 and 1991-92. Average seasonal departures from normal values for temperature, mixing ratio and precipitation were then compiled. These three values were compared to the Multivariate ENSO Index (MEI) developed by Wolter (1999). The MEI takes into account six atmospheric and oceanic parameters and is positive (negative) El Niño (La Niña) episode. The results were then averaged for each of the ten climatic zones of Texas prescribed by the U.S. Geological Survey.

Results from the comparisons show that there may exist a significant positive correlation between the MEI and above normal precipitation for some climate zones for the fall (Oct-Dec) and winter (Jan-Mar) seasons. The addition of the surface mixing ratio parameter as a measure of the amount of water vapor present in the air may provide insight into the process(es) by which ENSO is linked to precipitation anomalies. Our results show evidence for a positive correlation of mixing ratio with the MEI during the winter and spring (Apr-Jun) months with increasing correlation for regions further away from the Gulf of Mexico. Temperature anomalies show a significant negative correlation with the MEI during the spring and a positive correlation during the summer months. Masked in the analysis may be other mechanisms that influence the parameters on seasonal and yearly time scales. Examples of these would be the influence of temperature data by the “urban heat island effect” and the influence on precipitation data by tropical cyclones.