Climate Diagnostics Center - Climate Research Spotlight Article http://www.cdc.noaa.gov/spotlight

1

El Niño and Probability

Prashant Sardeshmukh, Gilbert Compo,and Cécile PenlandNOAA-CIRES Climate Diagnostics Center

Science Writer: Susan BaconUniversity of Colorado

Overview

The laws of probability are useful for day-to-day activities, such as beating a friend ata game of poker, trying to win the lottery,and even guessing the chances that afavorite sports team will win a big game.

As researchers from NOAA's ClimateDiagnostics Center (CDC) recently reported,these rules of chance are also essential forclimate forecasting. In a paper published inthe Journal of Climate, PrashantSardeshmukh, Gilbert Compo, and CécilePenland used probability calculations toshow that a large set of data is the key forlearning how the El Niño SouthernOscillation (ENSO) affects weather outsideof the tropics. Furthermore, a dataset thislarge can be used to detect the impact ofENSO related shifts in the mean state andchanges in variability on the risk of seasonalclimate extremes.

The results from this study indicate that it'snot enough to use instrumental datacollected from thermometers, barometersand other weather-measuring devices overthe past 100 years, since this instrumentalrecord contains no more than a few dozenENSO events. Rather, the CDC researchteam found that using climate models tocreate larger data sets can give additionaluseful information about how ENSO affectsweather around the world.

Benefits of Large Datasets

The importance of using a large data set canbe illustrated in a simple study of cointosses. Any honest coin has a 50-50 chanceof landing on heads or tails, which meansthat if a coin is flipped 100 times, it's likelythat about 50 of the flips will show heads,and about 50 will show tails. With 100 flips,the coin-tosser will have a good chance ofpredicting a 50 percent chance (probability)of getting a heads or a tails on any one of theflips. But if the coin is flipped only a fewtimes, this 50-50 split may be harder toachieve. It's easy to imagine that 5 tossescould produce 1 heads and 4 tails, or 3 headsand 2 tails, or all heads and no tails.Drawing a conclusion about the probabilityof getting a heads or tails on any toss basedon only 5 flips - a small sample size - canlead to inaccurate results.

Similar challenges exist when trying to use asmall data set to understand the effects ofENSO [a phenomenon in which sea surfacetemperatures in the Eastern Pacific Oceanbecome higher or lower than the long-termaverage and cause changes that reverberatethrough the atmosphere perturbing weatherpatterns] on a system as complex as climate.But now, with increasingly sophisticatedmodels and computer power, researchersdon't have to rely only on the instrumentalrecord. The climate science community canalso create large data sets (ensembles) to

Climate Diagnostics Center - Climate Research Spotlight Article http://www.cdc.noaa.gov/spotlight

2

more accurately identify the links betweenENSO and resulting weather patterns.

ENSO Impacts

ENSO is one of the most significant, yetpredictable, shifts in global weather. Itsimpacts are easiest to see in tropical areas,but it can also affect weather outside of thetropics. For instance, an El Niño winter, inwhich sea surface temperatures are higherthan normal in the east central Pacific,brings cooler and wetter weather to theSouthern United States, and warmer anddrier weather to the Pacific Northwest. A LaNiña winter in the east central Pacific, onthe other hand, has lower than normal seasurface temperatures and causes nearlyopposite weather patterns.

Describing ENSO impacts on weather notonly provides better information on how theclimate system works in general, but it alsogives researchers clues about the likelihoodthat extreme weather events - from floodsand droughts, to blizzards and heat waves -will take place in areas outside of thetropics. This advance warning helps peopleprepare, and so reduces the loss of lives andother damage extreme weather can cause.

Researchers know there is a connectionbetween ENSO and weather outside of thetropics, but remain challenged whenforecasting how a specific ENSO event willaffect weather in these areas. One reasonthat forecasts are difficult is that ENSO'simpact on weather outside of the tropics issmall, and so is almost obscured by theclimate's natural variability in these areas.That's why a large sample size is important:Only with enough data points canresearchers distinguish natural variabilityfrom the outside influence.

Figure 1. Probability density functions (PDFs) inthe left panel illustrate the impact of changes in themean on the probability of extreme values (leftpanel). PDFs in the right panel illustrate impact ofchanges in both the mean and the spread on theprobability of extreme values. Green (orange)shading indicates an increase(decrease) of theprobability of extreme values.

Distinguishing ENSO from naturalvariability is helpful because even if ENSOhas small effects on average weatherpatterns, it may still cause significantchanges in the overall distribution. Forexample, even a slight change in averageweather pattern in response to ENSO canalter the likelihoods of extreme weatherevents, such as droughts, floods and storms.This idea is illustrated by Figure 1, where asmall shift in the bell curve results in asignificant change in the probability ofhaving an extreme value. In the bell curvewith the shift, there is no longer an equalchance of having an extreme positive valueor an extreme negative value - rather, in thisnew graph it is four times more likely tohave an extreme positive than an extremenegative. If the shifts illustrated in thegraphs represented ENSO's effect on rainfallin an area, both the right and left panelswould suggest that the area would be muchmore likely to be flooded with heavy rainsthan to suffer through a drought.

Research and Results

To look at how ENSO affects weatheroutside of the tropics, Sardeshmukh,Compo, and Penland first studied the

Climate Diagnostics Center - Climate Research Spotlight Article http://www.cdc.noaa.gov/spotlight

3

historical record of ten El Niño years and tenLa Niña years. They then used a climatemodel to create a larger sample size of 180El Niño years, 180 La Niña years and 180normal years, and compared how weatherresponses - such as temperature andprecipitation - differed around the globeduring the ENSO years. By comparing thehistorical record with the model results, theresearchers could confirm that the modelwas on target.

Figure 2. Contours indicate the expected value ofDks (the amplitude of the maximum difference inprobability, change N(0,20) to N(0,1) and N(,2) toN(µ,s ) between sampled normaldistributions N(0,20) and N(,2), based on 50 000Monte Carlo trials of (a) 25 and (b) 180 samples.Green shading indicates values significant at the5% level, that is, the sample N(,2) distributioncan be distinguished from the sample N(0,20)distribution. Scattered points represent themagnitudes of the mean and standarddeviation of JFM-mean 500-mb heights in the ElNiño (red) and La Niña (blue) GCMensembles relative to those in the neutralensemble, (a) using only the first 25 members ofthe 180 available members in the GCMensembles, and (b) using all 180members.

An analysis of the model results led theresearch team to conclude that a lot of datais needed to tell how ENSO affects weather.This finding is shown in Figure 2, in whicheach dot represents the modeled effects of

ENSO on average weather patterns at onelocation around the world. Red dotsrepresent El Niño years and blue dotsrepresent La Niña years. Dots in the whitearea show locations at which researcherscan't tell whether ENSO had an effect onweather. In the top graph, using only thefirst 25 instead of 180 model runs, it isimpossible to tell if ENSO affected weatherin most locations. However, in the bottomgraph, where 180 model runs were used, alot more dots fall outside of the white area,which shows that effects of ENSO onweather around the world are easier topinpoint with larger samples.

Figure 3. The change in the probability of extremevalues of JFM-mean precipitation estimated from theAGCM for El Niño (left) and La Niña (right)ensembles. Green (orange) shading indicates anincrease (decrease) of the probability of extremevalues. The shading contours are 5% and 25%.

The large number of model runs providedthe research team with an opportunity toexplore how ENSO related shifts in themean state and changes in variability canalter the risk of extreme events. This type ofanalysis was motivated by an interest indetermining the usefulness of seasonalforecasts to identify predictable climateextremes. In only a few regions in theUnited States are the ENSO related alteredrisk of extreme seasonal mean winterprecipitation significantly different relativeto the risk of extremes under "normal"conditions (Figure 3). Under El Niñoconditions there is a significant increase inthe risk for wet and flooding conditions in

Climate Diagnostics Center - Climate Research Spotlight Article http://www.cdc.noaa.gov/spotlight

4

California (left panel). Under La Niñaconditions there is a significant increase inthe risk for dry and wildfire conditions inFlorida (right panel). Not only is there anENSO related climate signal in these regionsbut the altered risk for climate extremes is apotentially predictable aspect of seasonalclimate.

Figure 4. The difference between thestandard deviations of the 500-mb height ElNiño and neutral GCM ensembles (toppanel) and La Niña and neutral GCMensembles (bottom panel). The 500-mbheight plots are drawn at 2-m intervalsstarting at 1-m. Positive values, or anincrease in variability, are indicated by redwhereas negative values, or a decrease invariability, are indicated by blue shading.

In addition to highlighting the need for largesamples, the researchers found that El Niñoand La Niña events do not have equal andopposite effects on weather patterns. ElNiño seems to cause a stronger, but morevariable response, than La Niña in manyareas around the world. The greatervariability in 500-mbar height during ElNiño years is illustrated in Figure 4, wherered areas with greater variability thanaverage. In El Niño years, many areas havemore vaiable weather than normal, while inLa Niña years, many areas have less variableweather than normal. Even though La Niña

may affect weather outside the tropics lessthan El Niño does, the effects of La Niñamay be more predictable because theimpacts are less variable or uncertain.

Concluding Remarks

The analysis and forecast of probabilitydistributions and extreme event risksassociated El Niños and La Niñas can beimproved by advances in large ensemblepredictions. Sardeshmukh, Compo, andPenland’s results illustrate the need for alarge number of climate model simulationsto distinguish between the "noise"associated with natural variability and theENSO signal of an increase or decrease inthe likelihood of climate extremes,especially to estimate reliably changesoutside of the extratropics. This work alsodocumented that the response to ENSOconditions is not a mirror image(symmetric). The weather event responses toEl Niños tend to be stronger but morevariable whereas the responses to La Niñas,though more consistent, are not as large.

Enhanced climate forecasting capabilitiesare need to enable regional and nationalmanagers to plan for the impact of extremeweather events in response to future climatevariability, and change. Improvements in the"physics" used in the construction of climatemodels can and will enhance simulation ofclimate variability (such as ENSO) and theassociated impact or signal. However,equally important, and perhaps delivering agreater bang for the buck, is the approach ofrunning a large number of simulations withan existing state-of-the-art climate model inorder to resolve changes in the risk ofextreme events associated with differentclimate conditions.

By documenting the importance of large-scale sample size, this study will help other

Climate Diagnostics Center - Climate Research Spotlight Article http://www.cdc.noaa.gov/spotlight

5

climate scientists refine their own researchon detecting and forecasting ENSO impactson weather making future climate weatherforecasts more reliable.

References

Sardeshmukh, P.D., G.P. Compo, and CecilePenland (2000). Changes Associated with ElNiño, Journal of Climate, 13: 4268-4286.

Acknowledgements

Creators, Authors and ContributorsEl Niño and Probability was created bystaff members of theNOAA-CIRES Climate Diagnostics Center.Credits are listed below:

Scientific Authorship:• Prashant Sardeshmukh• Gilbert Compo• Cecile Penland

Science Writer:• Susan Bacon

Web Layout and Design:• Barb DeLuisi• Julia Collins

Content and Graphic Review:• Robert Webb• Cathy Smith

Photo Credits:• "Drought Image"(#K7520) provided

courtesy of the USDA PhotographyCenter:http://www.ars.usda.gov/is/graphics/photos/home.htm.