Climate Indices

Indices are diagnostic tools used to describe the state of a climate system. Climate indices are most often represented with a time series; each point in time corresponds to one index value.

An index can be constructed to describe almost any atmospheric event, from summer monsoon rainfall in India, to pressure differences at two locations in the Pacific Ocean, to spatially-averaged sea surface temperatures. Each of these indices are created with a specific purpose: to monitor climate.

Creating the Southern Oscillation Index

Example: Generate the Southern Oscillation Index by standardizing the differences in sea-level pressure anomalies at Tahiti and Darwin.
Locate Dataset and Variable
  • Select the "Datasets by Catagory" link in the blue banner on the Data Library page.
  • Click on the "Climate Indicies" link.
  • Select the Indicies Tahiti dataset.
  • Click on the "sea level pressure" link under the Datasets and Variables subheading.
  • Click on the "sea level pressure" link again under the Datasets and Variables subheading. CHECK
    Notice that sea level pressure anomaly and standardized sea level pressure are also variables contained in this dataset. These are variables that will be computed in this example, however you may select them in the future to save time.
Select Temporal Domain
  • Click on the "Data Selection" link in the function bar.
  • Enter the text Jan 1960 to Apr 2004 in the Time text box.
  • Press the Restrict Ranges button and then the Stop Selecting button. CHECK
Calculate Anomalies
  • Click on the "Filters" link in the function bar.
  • Choose the anomalies command. CHECK EXPERT
    This operation calculates the difference between monthly climatology and the original data. These anomalies are not standardized.
Standardize (Normalize) Tahiti Anomalies
  • Click on the "Expert Mode" link in the function bar.
  • Type the following command under the text already there:

    [T] standardize
  • Press the OK button. CHECK
    The standardize function divides each anomaly by the standard deviation of the data. The result is a time series of standardized sea level pressure anomalies at Tahiti.
Enter Darwin, Australia Standardized Anomaly Data
  • In the Expert Mode text box, enter the following lines below the text already there: 

    SOURCES .Indices .Darwin .slp .full
  T (Jan 1900) (Apr 2004) RANGEEDGES
  yearly-anomalies
  [T]standardize
  sub
  • Press the OK button. CHECK
    The above set of commands adds another variable to the interface, standardized sea level pressure anomalies from Darwin, then subtracts the two variables with the sub command. The result is a dataset of standardized anomalies at Tahiti minus standardized anomalies at Darwin, Australia from January 1960 to April 2004. The Southern Oscillation Index is defined as the difference between standardized sea level pressure anomalies at these two locations.
View Results
  • To see the results of this operation, choose the time series viewer. CHECK
Southern Oscillation Index from January 1960 to April 2004
The resulting image is a graph of the SOI over the previous 44 years. Negative values represent higher-than-average surface pressures at Darwin and lower-than-average surface pressures at Tahiti, which in turn, often correspond to El Niño conditions. The exceptionally strong El Niño event of 1982 / 1983 is prominent in this figure.

Creating the Niño 3 Index

Example: Generate the Nino 3 Index by spatially averaging sea surface temperature anomalies over the range 90° W - 150° W, 5° S - 5° N.

Locate Dataset and Variable
  • Select the "Datasets by Catagory" link in the blue banner on the Data Library page.
  • Click on the "Air-Sea Interface" link.
  • Select the NOAA NCEP EMC CMB GLOBAL Reyn_Smith dataset.
  • Select the "Reyn_SmithOIv2" link under the Datasets and Variables subheading.
  • Click on the "monthly" link again under the Datasets and Variables subheading.
  • Select the "Sea Surface Temperature Anomaly" link again under the Datasets and Variables subheading. CHECK
Select Spatial Domain
  • Click on the "Data Selection" link in the function bar.
  • Enter the text 90W to 150W, 5S to 5N in the appropriate text boxes.
  • Press the Restrict Ranges button and then the Stop Selecting button. CHECK
Calculate Spatial Average
  • Click on the "Filters" link in the function bar.
  • Choose the Average over "XY" command. CHECK EXPERT
    This operation calculates the spatial average of the data.
    *NOTE: Make sure to select the combined "XY" in the filters menu. Multiple variable selections are located to the right of the individual variables in the filters menu.
View Results
  • To see the results of this operation, choose the time series viewer. CHECK
Niño 3 Index from December 1981 to Present
The resulting image is a time series of the Niño 3 Index. The Niño indices (i.e., Niño 1+2, Niño 3, Niño 3.4, Niño 4) are used to analyze El Niño and La Niña conditions, and each index represents a different region in the Pacific.