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HOME >Climate & Weather Linkage > El Nino / Southern Oscillation (ENSO) > Global ENSO Temperature & Precipitation Regressions Information

Global ENSO Temperature and Precipitation Linear Regressions

  Temperature Precipitation Information on Data, Methods, and Interpretation  


Monthly mean data from 1948-2010 is used in this analysis:
(1) Gridded temperature data from the CPC GHCN analysis (0.5x0.5 degrees). Fan and van den Dool, 2008: A global monthly land surface air temperature analysis for 1948-present. JGR, Vol. 113, D01103.
(2) Gridded precipitation data is from the CPC Unified precipitation analysis (0.5x0.5d degrees). Xie, P., M. Chen, and W. Shi, 2010: CPC unified gauge-based analysis of global daily precipitation. AMS 24th Conf. on Hydrology. Jan.18 -21, 2010, Atlanta, GA.
(3) 3-month averages of the Nino-3.4 index, which is also known as the Oceanic Nino Index and is based on ERSST.v3b data.


Departures or anomalies are formed by subtracting the 1981-2010 base period of monthly means. For each three month season, temperature and precipitation anomalies are regressed onto the standardized Niño-3.4 index (displayed in the top panel). Prior to the regression, all datasets are de-trended (the linear trend is removed). This helps to ensure that the variability shown here is ENSO-related. In the bottom panel, the correlation, or the "strength" of the linear fit, is calculated between Nino-3.4 and the anomalies. The correlation coefficients are multiplied by 100 so are expressed in percentages.


ENSO impacts over the globe are largely linear, which means that the warm state (El Niño) is generally associated with the precipitation and temperature anomalies displayed in the figures. The cold state (La Niña) can be visualized by flipping the sign of the anomalies in the figures. For example, El Niño is usually associated with drier than average conditions over the Maritime Continent (region north of Australia). La Niña is associated with wetter than average conditions over the Maritime Continent. The units of the regression map are in degrees C or mm/day per standardized Niño-3.4 value.
The correlation maps can be thought of as showing the significance of the anomalies in the top panel. Larger percentages indicate that the relationship shown in the regression map is stronger. Percentages closer to zero means that the anomalies shown in the top panel are insignificant.

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