Hurrell North Atlantic Oscillation (NAO) Index (PC-based)

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Hurrell North Atlantic Oscillation (NAO) Index (PC-based)
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The principal component (PC)-based indices of the North Atlantic Oscillation (NAO) are the time series of the leading Empirical Orthogonal Function (EOF) of SLP anomalies over the Atlantic sector, 20°-80°N, 90°W-40°E. These indices are used to measure the NAO throughout the year, tracking the seasonal movements of the Icelandic low and Azores high. These movements are illustrated in the Figures on this page. Positive values of the NAO index are typically associated with stronger-than-average westerlies over the middle latitudes, more intense weather systems over the North Atlantic and wetter/milder weather over western Europe.

Key Strengths

Key Strengths

PC-based indices are more optimal representations of the full spatial patterns of the NAO

May be less noisy than station-based indices

Key Limitations

Key Limitations

Not available as far back as station-based indices

Dependent on any inherent weaknesses in the source data set and its gridding scheme

Please cite data sources, following the data providers' instructions
Suggested Data Citation
None
Dataset DOIs
None
Hosted Climate Index Files
  1. Details
    Missing Value: -999.00 Units: std.dev. Start Date: Updated through:
    Citation

    NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Updated regularly. Accessed DD Month YYYY [list date you accessed the data].

    Notes
     As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.
  2. Details
    Missing Value: -999.00 Units: std.dev. Start Date: Updated through:
    Citation

    NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Updated regularly. Accessed DD Month YYYY [list date you accessed the data].

    Notes
     As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.
  3. Details
    Missing Value: -999.00 Units: std.dev. Start Date: Updated through:
    Citation

    NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Updated regularly. Accessed DD Month YYYY [list date you accessed the data].

    Notes
     As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.
  4. Details
    Missing Value: -999.00 Units: std.dev. Start Date: Updated through:
    Citation

    NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Updated regularly. Accessed DD Month YYYY [list date you accessed the data].

    Notes
     As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.
  5. Details
    Missing Value: -999.00 Units: std.dev. Start Date: Updated through:
    Citation

    NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Updated regularly. Accessed DD Month YYYY [list date you accessed the data].

    Notes
     As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.
  6. Details
    Missing Value: -999.00 Units: std.dev. Start Date: Updated through:
    Citation

    NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Updated regularly. Accessed DD Month YYYY [list date you accessed the data].

    Notes
     As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.
  7. Details
    Missing Value: -999.00 Units: std.dev. Start Date: Updated through:
    Citation

    NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Updated regularly. Accessed DD Month YYYY [list date you accessed the data].

    Notes
     As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.
Data Access
None
Usage Restrictions
None

Expert Developer Guidance

Expert Developer Guidance

Since there is no unique way to define the spatial structure of the NAO, it follows that there is no universally accepted index to describe the temporal evolution of the phenomenon. Most modern NAO indices are derived either from the simple difference in surface pressure anomalies between various northern and southern locations, or from the PC time series of the leading (usually regional) EOF of sea level pressure (SLP). Many examples of the former exist, usually based on instrumental records from individual stations near the NAO centers of action, but sometimes from gridded SLP analyses. A major advantage of most of these indices is their extension back to the mid-19th century or earlier.

A disadvantage of station-based indices is that they are fixed in space. Given the movement of the NAO centers of action through the annual cycle, such indices can only adequately capture NAO variability for parts of the year. Moreover, individual station pressures are significantly affected by small-scale and transient meteorological phenomena not related to the NAO and, thus, contain noise.

An advantage of the PC time series approach is that such indices are more optimal representations of the full NAO spatial pattern; yet, as they are based on gridded SLP data, they can only be computed for parts of the 20th century, depending on the data source.

For a more detailed discussion of issues related to the NAO indices and related indices such as the Northern Annular Mode (NAM) and Arctic Oscillation (AO), see Hurrell and Deser (2009) and Hurrell et. al (2003), linked in Key Publications 2 and 3 below.

James Hurrell, NCAR

 

Technical Notes

The PC-based NAO indices produced by NCAR's Climate Analysis Section are based on Hurrell (2003), cited below. They are currently offered as ascii text files for winter, monthly, seasonal, and annual values. As is the nature of PC-based indices, every time additional data is used to compute the EOF the individual PC values will likely change. It is thus recommended that one downloads an entire climate index each time they wish to update their holdings.

The NCAR Sea Level Pressure dataset is used for the calculation of the various NAO PC-based indices.

Key Figures

NAO PC-based JJA index.

The principal component (PC) time series of the leading EOF of JJA SLP anomalies over the Atlantic sector (20-80N, 90W-40E) serves as an alternative index (see Hurrell (2003) and Hurrell (1995) below). The JJA PC timeseries is shown below in color, and the station based index is given by the thick black line. The correlation between the two is 0.60 over the period 1899-2017. The black dots on the EOF panel show the location of the stations used in the JJA station-based index. (Climate Data Guide; A. Phillips)

NAO PC-based SON index.

The principal component (PC) time series of the leading EOF of SON SLP anomalies over the Atlantic sector (20-80N, 90W-40E) serves as an alternative index (see Hurrell (2003) and Hurrell (1995) below). The SON PC timeseries is shown below in color, and the station based index is given by the thick black line. The correlation between the two is 0.74 over the period 1899-2017. The black dots on the EOF panel show the location of the stations used in the SON station-based index. (Climate Data Guide; A. Phillips)

NAO PC-based annual index.

The principal component (PC) time series of the leading EOF of annual SLP anomalies over the Atlantic sector (20-80N, 90W-40E) serves as an alternative index (see Hurrell (2003) and Hurrell (1995) below). The annual PC timeseries is shown below in color, and the station based index is given by the thick black line. The correlation between the two is 0.91 over the period 1899-2017. The black dots on the EOF panel show the location of the stations used in the annual station-based index. (Climate Data Guide; A. Phillips)

NAO PC-based DJF index.

The principal component (PC) time series of the leading EOF of DJF SLP anomalies over the Atlantic sector (20-80N, 90W-40E) serves as an alternative index (see Hurrell (2003) and Hurrell (1995) below). The DJF PC timeseries is shown below in color, and the station based index is given by the thick black line. The correlation between the two is 0.88 over the period 1899-2018. The black dots on the EOF panel show the location of the stations used in the DJF station-based index. (Climate Data Guide; A. Phillips)

NAO PC-based DJFM index.

The principal component (PC) time series of the leading EOF of DJFM SLP anomalies over the Atlantic sector (20-80N, 90W-40E) serves as an alternative index (see Hurrell (2003) and Hurrell (1995) below). The DJFM PC timeseries is shown below in color, and the station based index is given by the thick black line. The correlation between the two is 0.93 over the period 1899-2020. The black dots on the EOF panel show the location of the stations used in the DJFM station-based index. (Climate Data Guide; A. Phillips)

NAO PC-based MAM index.

The principal component (PC) time series of the leading EOF of MAM SLP anomalies over the Atlantic sector (20-80N, 90W-40E) serves as an alternative index (see Hurrell (2003) and Hurrell (1995) below). The MAM PC timeseries is shown below in color, and the station based index is given by the thick black line. The correlation between the two is 0.9 over the period 1899-2018. The black dots on the EOF panel show the location of the stations used in the MAM station-based index. (Climate Data Guide; A. Phillips)

NAO PC-based monthly index.

The principal component (PC) time series of the leading EOF of monthly SLP anomalies over the Atlantic sector (20-80N, 90W-40E) serves as an alternative index (see Hurrell (2003) and Hurrell (1995) below). The principal component timeseries is shown below in color, and the station based index is given by the thick black line. The correlation between the two is .81 over the period January 1899 - July 2018. The black dots on the EOF panel show the location of the stations used in the monthly station-based index. (Climate Data Guide; A. Phillips)

Other Information

Earth system components and main variables
Type of data product

Years of record
to
Data time period extended
Yes, data set is extended
Timestep
Monthly, Seasonal, Annual
Formats:
Input Data

NCAR (Trenberth & Palino) Sea Level Pressure

Vertical Levels:
Missing Data Flag
None
Ocean or Land
None
Spatial Resolution
None
Model Resolution (reanalysis)
None
Data Assimilation Method
None
Model Vintage (reanalysis)
None