Note: All gridded temperature, precipitation, and snowfall
datasets are from 1950-current and departures are based on 1981-2010 mean values.
- Surface temperature is obtained from the 0.5 lat x 0.5 lon gridded GHCN+CAMS Temperature dataset (Fan and van den Dool, 2008).
- Surface precipitation is obtained from the real-time 0.25 lat x 0.25 lon
gridded, gobal precipitation dataset of 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. Data can be found
- The Oceanic Niño Index (ONI) is 3-mo. running mean of
ERSST.v3 SST anomalies in the
Niño-3.4 region. Values of the ONI can be found at
A 3-mo. season with an ONI value equal to or greater than
0.5 is classified as an El Niño. A 3-mo. season equal to or less than -0.5 is classified as a La Niña. El Niño and
La Niña episodes are defined when the threshold is met for at least 5 consecutive 3-mo. overlapping seasons
(i.e. DJF, JFM, FMA, etc). A list of the episode years included in each composite is located at the very bottom of
DESCRIPTION OF IMAGES:
TOP ROW: "Composite"
- (left panel) The average precipitation, temperature or snow departures during El Niño or La Niña for a 3-month season.
- (right panel) Frequency (%) of occurrence for the signal at each shaded grid point in the left panel (out of all
individual El Niño/ La Niña years). For example, out of all JFM La Niña years, how often was precipitation
above-average over the Ohio Valley? Answer: 60-70% (or roughly 10-11 years out of 16 cases)
MIDDLE ROW: "Trend"
The recent trend is estimated by the Optimal Climate Normal (OCN) of Huang et al. (1996). For temperature, the OCN is
the average of the last 10 years minus the 1981-2010 climatology for each season. The same is true for precipitation
except it is averaged over the last 15 years. OCN is an input to operational seasonal climate forecasts.
- (left panel) OCN of precipitation/ temperature departures
- (right panel) Frequency (%) of occurrence for the signal at each shaded
grid point in the left panel (out of the last 10 or 15 years).
BOTTOM ROW: "Composite + Trend"
Incorporates the influence of recent trends (second row) on the ENSO composites (top row). The ENSO composites
(top row) are re-calculated using "high frequency" (HF) precipitation/ temperature data in order to best remove the
influence of the OCN. To calculate the HF data at each grid point, the raw temperature (precipitation) time series
is filtered by removing the 11-year (15- year) running mean. The beginning and end points of the time series are
removed using the most recent 11-year (15-year) running mean. The HF ENSO composites are then added to the most
recent OCN (second row).
- (left panel) The El Niño/ La Ni&mtilde;a composites in the top
row "adjusted" by the most recent trend (OCN) in the second row.
- (right panel) Frequency (%) of occurrence for the signal at each
shaded grid point in the left panel (out of all El Niño/ La Niña years).
ENSO composites will be updated annually by May 31st.
Huang, J., H.M. van den Dool, and A.G. Barnston, 1996:
Long-Lead Seasonal Temperature Prediction Using Optimal Climate Normals. J. Climate, 9, 809–817.
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
Fan , Y., and H. van den Dool, 2008: A global monthly land surface air temperature analysis for 1948–present. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D01103, 18 PP., 2008 doi:10.1029/2007JD008470