Probability Map Request

Variable: Season:
ENSO Event: ENSO Index:
ENSO-Related Impacts

ENSO-Related Impacts

People often ask, "How will El Niño (or La Niña) affect me?" In terms of the climate impacts of El Niño/La Niña, that question can be translated as "If there's an El Niño (or a La Niña) event, what will the climate be like in a particular season?". That answer will certainly vary by region and season (3-month average, here). Also, it is often difficult to identify what changes in seasonal climate are caused explicitly by El Niño or La Niña events and what changes are merely associated with those events through a potential chain of impacts.

One can say that unusual weather events are the direct result of El Niño (or La Niña) only if that weather event would not have occurred in the absence of El Niño (or La Niña). In every other case, the unusual weather that may seem related to the occurrence of El Niño or La Niña, is an associated impact. In particular, it is common during El Niño and La Niña events to have persistent weather patterns that result in a particular outcome for the seasonal climate. For example, it is common for southern California to receive larger amounts of rain during an El Niño in the January-February-March season. The increased rain, in this case, generally falls as stronger and more frequent storms. Clearly, it would rain in California regardless of El Niño, and strong storms can occur in any year, and even frequent storms may occur in non-El Niño winters. However, an El Niño event increases the likelihood for stronger and more frequent storms, and is thus associated with an increased probability for above normal rainfall in that season. In other regions, more remote from the Pacific, the repeatability of certain climate outcomes exists because the atmospheric circulation changes induced by El Niño (or La Niña) affects sea surface temperatures (SSTs) in other ocean basins. One example of this remote association is eastern Africa where rainfall variability is associated empirically with El Niño/La Niña. However, it is actually the Indian Ocean temperatures, which warm or cool consistently with the tropical Pacific (El Niño/La Niña), that are largely responsible for affecting the rainfall changes over eastern Africa (Goddard and Graham (1999) ).

We have produced global maps that illustrate the probabilities of seasonal temperature and precipitation outcomes that are associated with El Niño and La Niña. The maps are based on historical observed data of temperature and precipitation during the 1950-1995 period. Over that 46 year period we determine the wettest one-third of the years ("above-normal") the driest one-third of the years ("below-normal") and the middle one-third ("near-normal"), at each location and for each 3-month season (e.g. JFM, FMA, ...., DJF). An identical procedure is applied to the temperature fields. Next, we identify the strongest 10 El Niño events and the strongest 10 La Niña events, for each of the 12 seasons, and examine the temperature and precipitation in those 10 events.

The probability maps show the frequency of years that above-normal, near-normal, and below-normal temperature and precipitation occurred during those top 10 El Niño and La Niña years (listed at the bottom of the figures with increasing magnitude from left to right). If one locates an area on the "above-normal" precipitation map for El Niño that shows a value of 0.5, that means that 1/2 or 50% or 5 out of 10 of the past El Niño events resulted in above normal precipitation for that area. The definition of how much rainfall, in terms of percent of normal (200% = 2x normal), and how much of a temperature departure is considered below normal or above normal also depends on season and location.

One may interpret the probability maps as indicating how the odds for a particular climate outcome have shifted given an El Niño or La Niña event. If one had no information about the climate, the odds would be 33% or 0.33 or 1/3 or 1 in 3 chance of ending up in any of the three categories (above-, near-, below-normal). However, as an example, the maps show that over Florida in JFM 7 out of 10 (or 70%) La Niñas resulted in below normal rainfall. Thus, the odds of receiving below-normal rainfall for Florida, in that case, more than doubled. To the extent that what happened in the past is representative of what will happen in the future, these maps can be used to assess the seasonal climate probabilities associated with El Niño and La Niña for each season.

The high resolution (0.5x0.5 degrees) data used to make these maps of probabilistic climate anomalies associated with El Niño and La Niña come from the Climate Research Unit at the University of East Anglia (New, et al., 1999; New, et al., 2000).

The precipitation amounts have also been examined for several locations identified as experiencing strong and repeatable impacts during El Nino (Ropelewski and Halpert, 1987). We have ploted the frequency of occurrence for rainfall anomalies during El Nino years and during La Nina years. The El Nino years are defined as the 20 warmest years out of the last 100 (and La Nina as the 20 coldest years) as indexed by tropical Pacific ocean temperature anomalies. The distribution of these regional rainfall anomalies are compared against the distribution of rainfall in all 100 years. Another comparison is presented in which the El Nino and La Nina anomalies are compared against the rainfall anomalies that happen in a "normal year". "Normal years" are defined as the 20 years (out of 100) in the middle, in which the ocean temperatures in the tropical Pacific are near 0. Tables are included with the plots that show the rainfall and ocean temperature anomalies for the years included in the plots (El Nino, La Nina, and "Normal" years). Click on the region of interest.

Eastern Central Africa (Oct-Dec)
Eastern Southern Africa (Nov-May)
Uganda (Oct-Dec)
India (Jun-Sep)
Indonesia (Jun-Nov)
Thailand (Jun-Aug)
Philippines (Jun-Aug)
Philippines (Oct-May)
Queensland, Australia (Nov-Feb)
Northern South America (Jul-Mar)
Southern Brazil (Nov-Feb)

Note: Region names are only approximate with respect to actual area covered, which is noted specifically on all files.

Data Sources: - SST Data: Kaplan reconstructed global SST. 1856-1991 - Precipitation Data:Global Historical Climate Data Network (GHCN) 1851-1989

Processing: In all cases, SST and precipitation anomalies were averaged over months indicated in OGP web figure showing ENSO impacted areas (and noted in filenames). The data sets were narrowed to include only 100 years, 1890-1989, to insure consistent periods of data for the various regions. "El Nino" years were defined to be the 20 years (20%-ile) in which the SSTa averaged over NINO3 (5S-5N; 150W-90W), and over the months indicated, was the warmest. Similarly for the La Nina (cold) years, shown in the probability distribution plots.

Goddard, L. and N.E. Graham, 1999: The importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa, J. Geophys. Res., 104, 19 099-19 116.
New, M. G., M. Hulme, and P. D. Jones, 1999: Representing twentieth-century space-time climate variability. Part I: Development of a 1961-90 mean monthly terrestrial climatology, J. Climate, 12, 829-856.
New, M. G., and P. D. Jones, 2000: Representing twentieth-century space-time climate variability. Part II: Development of a 1901-96 mean monthly grid of terrestrial surface climate, J. Climate, in press.
Ropelewski, C. F. and M.S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation, Mon. Weather Rev., 115, 1606-1626.

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