e areas. One reason that forecasts are difficult is that ENSO's impact on weather outside of the tropics is small, and so is almost obscured by the climate's natural variability in these areas. That's why a large sample size is important: Only with enough data points can researchers distinguish natural variability from the outside influence.
Distinguishing ENSO from natural variability is helpful because even if ENSO has small effects on average weather patterns, it may still cause significant changes inthe overall distribution. For example, even a slight change in the average weather pattern in response to ENSO can alter the likelihoods of extreme weather events, such as droughts, floods and storms. This idea is illustrated by Figure 1, where a small shift in the bell curve results in a significant change in the probability of having an extreme value. In the bell curve with the shift, there is no longer an equal chance of having an extreme positive value or an extreme negative value - rather, in this new bell curve it is four times more likely to have an extreme positive than an extreme negative. If the shifts illustrated in the graphs represented ENSO's effect on rainfall in an area, both the right and left panels would suggest that the area would be much more likely to be flooded with heavy rains than to suffer through a drought.
Research & Results
To look at how ENSO affects weather outside of the tropics, Sardeshmukh, Compo, and Penland first studied the historical record of ten El Niño years and ten La Niña years. They then used a climate model to create a larger sample size of 180 El Niño years, 180 La Niña years, and 180 normal years, and compared how weather responses - such as temperature and precipitation - differed around the globe during the ENSO years. By comparing the historical record with the model results, the researchers could confirm that the model was on target.
An analysis of the model results led the research team to conclude that a lot of data is needed to tell how ENSO affects weather. This finding is shown in Figure 2, in which each dot represents the modeled effects of ENSO on average weather patterns at one location around the world. Red dots represent El Niño years and blue dots represent La Niña years. Dots in the white area show locations at which researchers can't tell whether ENSO had an effect on weather. In the top graph, using only the first 25 instead of 180 model runs, it is impossible to tell if ENSO affected weather in most locations. However, in the bottom graph, where 180 model runs were used, a lot more dots fall outside of the white area, which shows that effects of ENSO on weather around the world are easier to pinpoint with larger samples.
The large number of model runs provided the research team with an opportunity to explore how ENSO related shifts in the mean state and changes in variability can alter the risk of extreme events. This type of analysis was motivated by an interest in determining the usefulness of seasonal forecasts to identify predictable climate extremes. In only a few regions in the United States are the ENSO related altered risk of extreme seasonal mean winter precipitation significantly different relative to the risk of extremes under "normal" conditions (Figure 3). Under El Niño conditions there is a significant increase in the risk for wet and flooding conditions in California (left panel). Under La Niña conditions there is a significant increase in the risk for dry and wildfire conditions in Florida (right panel). Not only is there an ENSO related climate signal in these regions but the altered risk for climate extremes is a potentially predictable aspect of seasonal climate.
In addition to highlighting the need for large samples, the researchers found that El Niño and La Niña events do not have equal and opposite effects on weather patterns. El Niño seems to cause a stronger, but more variable, response than La Niña in many areas around the world. The greater variability in 500-mbar height during El Niño years is illustrated in Figure 4, where red ar