is 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 areas have greater variability in weather than average, and blue areas have less variability than average. In El Niño years, many areas have more variable weather than normal, while in La Niña years, many areas have less variable weather than normal. Even though La Niña may affect weather outside the tropics less than El Niño does, the effects of La Niña may be more predictable because the impact are less variable or uncertain.
The analysis and forecast of probability distributions and extreme event risks associated with El Niños and La Niñas can be improved by advances in large ensemble predictions. Sardeshmukh, Compo, and Penland's results illustrate the need for a large number of climate model simulations to distinguish between the "noise" associated with natural variability and the ENSO signal of an increase or decrease in the likelihood of climate extremes, especially to estimate reliably changes outside of the extratropics. This work also documented that the response to ENSO conditions is not a mirror image (symmetric). The weather event responses to El Niños tend to be stronger but more variable whereas the responses to La Niñas, though more consistent, are not as large.
Enhanced climate forecasting capabilities are needed to enable regional and national managers to plan for the impact of extreme weather events in response to future climate variability, and change. Improvements in the "physics" used in the construction of climate models can and will enhance simulation of climate variability (such as ENSO) and the associated impact or signal. However, equally important, and perhaps delivering a greater bang for the buck, is the approach of running a large number of simulations with an existing state-of-the-art climate model in order to resolve changes in the risk of extreme events associated with different climate conditions.
By documenting the importance of large sample size, this study will help other climate scientists refine their own research on detecting and forecasting ENSO impacts on weather making fu