Mark O’Malley WFO Pleasant Hill Kansas City Chapter of the AMS December 4, 2006

1. Arctic Oscillation (AO)/North Atlantic Oscillation (NAO) and Applications to Medium Range Forecasting Mark O’Malley WFO Pleasant Hill Kansas City Chapter of the AMS December 4, 2006

2. Arctic Oscillation (AO) Definition Arctic Oscillation (AO) is defined as the leading mode of Empirical Orthogonal Function (EOF) analysis of monthly mean 1000mb height during 1979-2000 period. Based on height anomalies poleward of 20o latitude in the Northern Hemisphere from the NCEP/NCAR reanalysis dataset at a horizontal resolution of (lat,lon)=(2.5°X2.5°). The seasonal cycle has been removed from the monthly mean height field. AO is sometimes referred to as the northern annular mode

3. AO Positive – Warm Phase Characterized by intensification of the polar vortex with lower than normal pressures over the Arctic region. Meanwhile, higher than normal pressures become evident over the Central Atlantic (and to some degrees the North Central Pacific) inducing an enhanced belt of zonal westerlies across the northern latitudes (near 45o latitude). Illustration from National Geographic Magazine, 2000

4. AO Negative – Cool Phase Characterized by higher than normal pressures over the Arctic region and a breakdown of the polar vortex. Lower than normal pressures develop over the Central Atlantic and Central Pacific. This leads to a more meridional flow pattern across the Northern Hemisphere. Illustration from National Geographic Magazine, 2000

5. Vertical Cross section of the Northern Hemisphere Polar Vortex 1000mb height anomalies does not necessarily correlate directly with midlevel height anomalies (particularly in non-cold season). In general, the stronger the anomaly, the more correlation through the depth of the atmosphere will be seen. AO Index measured as 1000mb height anomaly from monthly mean. Negative anomaly = Positive AO value

6. Average DJF 500 mb heights and Anomalies based on AO phase Enhanced Polar vortex and contraction of the midlatitude westerlies Breakdown of the Polar vortex with relaxation and increased meridional component of the midlatitude westerlies.

7. DJF Temperature & Precipitation Anomalies based on AO Phase

8. Controversy : Is the Arctic Oscillation (AO) really just the North Atlantic Oscillation (NAO) in disguise?

9. AO/NAO Controversy : Temperature Response

10. AO/NAO Controversy : Arguments For vs. Against a Separate Teleconnection Mode • Separate Mode AO center of action covers more of the Arctic, with a larger horizontal scale and more zonal symmetry than the more regionalized NAO. (Thompson and Wallace, 1998) The AO signal has a strong interconnection with troposphere/stratosphere variability, while the NAO is primarily confined to the troposphere. (Kodera and Kuroda, 2004) (Wang et al., 2005) Correlation between AO and NAO is near 74% interannually and near 75% during the winter season. There also exists several periods where AO and NAO measures are nearly out of phase leading to slightly different spatial variability of surface air temperature. (Wettstein and Mearns, 2002) (Wang et al., 2005)

11. AO/NAO Controversy : Arguments For vs. Against a Separate Teleconnection Mode • The Same Mode The Empirical Orthogonal Function (EOF) of the AO pattern has no “straightforward interpretation as a covariance structure” and is “mainly a reflection of similar behavior in the Pacific and Atlantic basins”. Therefore, AO cannot be truly viewed as a teleconnection pattern. (Ambuam et al., 2001) Tests of Rotated Principle Component Analysis (RPCA) on the AO pattern fail to show correlation between one-point teleconnection maps and the “centers of action” and the AO pattern is an artifact of the EOF analysis and not a true teleconnection pattern. (Livezey, 2006) “It follows that the NAO and AO are synonyms: they are different names for the same variability, not different patterns of variability. The difference between the terms is in whether that variability is interpreted as a regional pattern controlled by Atlantic sector processes or as an annular mode whose strongest teleconnections lie in the Atlantic sector.” (Wallace, 2000)

12. AO/NAO Compositing Methodology • Utilized normalized monthly AO and NAO indices for the cold season months of Nov-Mar with monthly average temperature departures from the 1971-2000 normals. Period of record 1950-2006. • Average temperature departures were categorized as above, near, or below average based on a tercile ranking of 1971-2000 normals. • Monthly AO and NAO indices were characterized as above, near, or below normal based on approximately 1 standard deviation from the normalized value of 0. Consideration was also given to provide an “even” distribution. (AO Below < -1.5, AO Above > 1.0) (NAO Below < -1.0, NAO Above > 1.0) This approach agrees well with previous work done by Wettstein and Mearns, 2002 ( ± 1.0) and Wu et al., 2005 ( ± 1.2).

13. AO/NAO Compositing Methodology • Determine the number (and probability) of occurrences of above, near, or below average temperatures for each category of AO/NAO. • Determine Statistical Significance/Risk Analysis • Assume a null hypothesis that the distribution is random and has no dependence on AO/NAO • Perform a Student-t test to look at all possible distributions and determine the likelihood (probability) that the given AO/NAO distribution could be a random distribution (heavily dependant on sample size)

14. AO/NAO Compositing Methodology • Results show that the AO/NAO distribution has strong statistical significance with less than a 5% chance that the distribution is random

15. AO Compositing Results

16. NAO Compositing Results

17. Applications to Medium Range Forecasting • Assumptions • In order for monthly AO/NAO values to result in greater than 1 standard deviation above or below normal, there must significant stretches within the month (on a weekly basis) where AO/NAO values are much above or below the normalized average • In order for monthly temperatures to fall in the below or above tercile, there must exist significant stretches within the month (on a weekly basis) where temperatures are much above or below average • Given the strong statistical significance on a monthly basis and the conceptual model of what defines periods of strongly positive and negative AO/NAO, time periods shorter than a month must also have a strong correlation between AO/NAO phase and temperature departure

18. Applications to Medium Range Forecasting Example MCI Daily Temperature Data Period of Oct 9-22 characterized by highly negative AO

19. Applications to Medium Range Obtaining Data CPC Website : http://www.cpc.noaa.gov/products/precip/CWlink/daily_ao_index/teleconnections.shtml Different Ensemble solutions Correlation coefficient – generally poor past 7 days. Performance best during the cold season. AO measure typically increases and decreases faster than predicted by ensemble members

20. Summary • Monthly AO/NAO phase shows a strong correlation with monthly temperature departures across the Lower Missouri River Valley during the cold season. • It is assumed that this correlation can be applied to shorter time scales on the order of several days to weeks. • There exists a strong possibility that the combination of forecast AO/NAO phase with situational awareness and pattern recognition could be successfully used to improve temperature forecasts in the medium range days 5-7 against the climatologically weighted GFS guidance.

21. References Ambaum, Maarten, B.J. Hoskins, and D.B. Stephenson, 2001: Acrtic Oscillation or North Atlantic Oscillation? J. Climate, 14, 3495-3507. Higgins, R.W., Y. Zhou and H.-K. Kim, 2001: Relationships between El Niño-Southern Oscillation and the Arctic Oscillation: A Climate-Weather Link. NCEP/Climate Prediction Center ATLAS 8. Livesey, Robert, 2006: North Atlantic and Arctic Oscillations. COMET Climate Variability Course. Boulder, August 2006. 9pp. Thompson, D.W.J, and J.M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1297-1300. Thompson, D.W.J, and J.M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 1000-1016. Thompson, D.W.J, and J.M. Wallace, 2000: Annular modes in the extratropical circulation. Part II: Trends. J. Climate, 13, 1018-1036.

22. References Wallace, J.M., D.W.J. Thompson, and Z. Fang, 2000: Comments on “Northern Hemisphere Teleconnection Patterns during Extreme Phases of the Zonal-Mean Circulation”. J. Climate, 13, 1037-1039. Wallace, J.M., 2000: On the Arctic and Antarctic Oscillations. 2000 NCAR Advanced Studies Program Summer Colloquim on Dynamics of Decadal to Centennial Climate Variability. 40pp. Wang, Dongxiao, C. Wang, X. Yang, and J. Lu, 2005:Winter Northern Hemisphere surface air temperature variability associated with the Arctic Oscillation and North Atlantic Oscillation. Geophys. Res. Lett, 32, in print. Wettstein, Justin and L.O. Mearns, 2002: The Influence of the North Atlantic-Arctic Oscillation on Mean, Variance, and Extremes of Temperature in the Northeastern United States and Canada. J. Climate, 15, 3586-3600. Wu, Aiming, W.W. Hsieh, A. Shabbar, G.J. Boer, and F.W. Zwiers, 2005: The nonlinear association between the Arctic Oscillation and North American winter climate. Climate Dynamics, 26, 865-879, doi:10.1007/s00382-006-0118-8.