The winter (December thru March) station-based index of the NAO is based on the difference of normalized sea level pressure (SLP) between Lisbon, Portugal and Stykkisholmur/Reykjavik, Iceland since 1864. The SLP anomalies at each station were normalized relative to the 120-year period 1864--1983. Normalization is used to avoid the series being dominated by the greater variability of the northern station.From June 2003 through February 2010, the Ponta Delgada station reports were intermittent, with a large gap in reports from June 2003 through May 2008. 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.

Accessed DD Month YYYY [list date you accessed the data]. They are also associated with above-normal precipitation over northern Europe and Scandinavia and below-normal precipitation over southern and central Europe. Both the long-term means and standard deviations are based on the period 1864-1983. NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). Last modified 24 Apr 2020. We also used NCEP/NCAR Reanalysis point data to fill in July 2012 in the Reykjavik SLP record.NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). The extra precision is provided for users who are making further calculations where rounding to the appropriate precision is best done at the end of the calculations. During particularly prolonged periods dominated by one particular phase of the NAO, abnormal height and temperature patterns are also often seen extending well into central Russia and north-central Siberia. Updated regularly. Updated regularly. Both the long-term means and standard deviations are based on the period 1864-1983. 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. Hurrell (1995) normalized relative to 1864--1994.The SLP values at each station were normalized by removing the long-term mean and by dividing by the long-term standard deviation. 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.

An extended version of the index can be derived for the winter half of the year by using a station in the southwestern part of the Iberian Peninsula (Hurrell, 1995). The daily NAO index for the past 120 days. Values ascribed to the year of the January.

The NAO index is obtained by projecting the NAO loading pattern to the daily anomaly 500 … The calculated timeseries use Ponta Delgada observational data from Jan. 1865 - May 2003, June 2008- Jan 2010, and Mar 2010-Mar 2013,  and NCEP/NCAR Reanalysis point data from June 2003-May 2008 and for Feb 2010. North Atlantic Oscillation (NAO) Index. The NAO exhibits considerable interseasonal and interannual variability, and prolonged periods (several months) of both positive and negative phases of the pattern are common.The NAO index is obtained by projecting the NAO loading pattern to the daily anomaly 500 millibar height field over 0-90°N. The standard NAO index data file has been provided in the same format since 1998. 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. Contact Adam Phillips (asphilli (AT) ucar.edu) and/or Jim Hurrell (jhurrell (AT) ucar.edu).Winter index of the NAO based on the difference of normalized sea level pressures (SLP) between Lisbon, Portugal and Stykkisholmur/Reykjavik, Iceland since 1864. Note: The station index value for year N refers to an average of December year N-1 and January, February, and March year N. (Example: The 1899 value contains the average of December 1898 and January, February, and March 1899. )The SLP values at each station were normalized by removing the long-term mean and by dividing by the long-term standard deviation.
The NAO loading pattern has been chosen as the first mode of a Rotated Empirical Orthogonal Function (EOF) analysis using monthly mean 500 millibar height anomaly data from 1950 to 2000 over 0-90°N latitude. The NAO calculated from Gibraltar and SW Iceland is also given (Jones et al., 1997). Updated regularly. Accessed DD Month YYYY [list date you accessed the data]NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). 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.

JISAO data. References: When using the NAO data, cite "Updated from Jones et al."

Here we give data for SW Iceland (Reykjavik), Gibraltar and Ponta Delgada (Azores).

Lisbon minus Stykkisholmur Normalized December-March Average SLP Anomalies. Normalization is used to avoid the series being dominated by the greater variability of the northern station. The daily NAO index correpsponds to the NAO patterns, which vary from one month to the next. Questions about these datasets?


Each daily value has been standardized by the standard deviation of the monthly NAO index from 1950 to 2000 interpolated to the day in question. Updated regularly. NAO Index Data provided by the Climate Analysis Section, NCAR, Boulder, USA, Hurrell (2003). 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.