El Niño and Probability

Prashant Sardeshmukh, Gilbert Compo, and Cécile Penland
NOAA/ESRL Physical Sciences Laboratory

Science Writer: Susan Bacon
University of Colorado
Dry river bed representing drought

Research and 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.

Link to Figure 2 Image Figure 2. Contours indicate the expected value of Dks (the amplitude of the maximum difference in probability, change N(0,20) to N(0,1) and N(,2) to N(μ,σ) between sampled normal distributions N(0,20) and N(,2), based on 50 000 Monte Carlo trials of (a) 25 and (b) 180 samples. Green shading indicates values significant at the 5% level, that is, the sample N(,2) distribution can be distinguished from the sample N(0,20) distribution. Scattered points represent the magnitudes of the mean and standard deviation of JFM-mean 500-mb heights in the El Niņo (red) and La Niņa (blue) GCM ensembles relative to those in the neutral ensemble, (a) using only the first 25 members of the 180 available members in the GCM ensembles, and (b) using all 180 members.
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.
Figure 3. The change in the probability of extreme values of JFM-mean precipitation estimated from the AGCM for El Niño (left) and La Niña (right) ensembles. Green (orange) shading indicates an increase (decrease) of the probability of extreme values. The shading contours are 5% and 25%. Link to Figure 3 Image
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.
Link to 
		Figure 4 Image Figure 4. The difference between the standard deviations of the 500-mb height El Niño and neutral GCM ensembles (top panel) and La Niña and neutral GCM ensembles (bottom panel).The 500-mb height plots are drawn at 2-m intervals starting at 1-m. Positive values, or an increase in variability, are indicated by red whereas negative values, or a decrease in variability, are indicated by blue shading.

In addition to highlighting the need for large samples, the researchers found that El Niño and La Niņa 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 Niņa 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 Niņa may be more predictable because the impact are less variable or uncertain.

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