Time-series of Atmospheric Motion Vectors (AMV) can be used to detect changes in location and intensity of the polar jet, which is related to inter-annual variations of the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO).
28 October 2021
21 October 2021
By Alessio Lattanzio, Marie Doutriaux Boucher, Roger Huckle, Oliver Sus, L. Medici, Mike Grant, Jaap Onderwaater, Régis Borde, Rob Roebeling and Joerg Schulz
The NAO, in connection with the polar vortex, directly steers the location and intensity of the polar jet. As illustrated in Figure 1, a positive NAO directs the polar jet towards northern Europe, while a negative NAO directs it southward.
The jet is the outer edge of the polar vortex; it is a permanent feature of atmospheric circulation that is present at both poles. This use case aims to demonstrate the use of Meteosat-derived AMVs for detecting known changes in the NAO, and discusses the potential of using AMVs for monitoring climate change.
The NAO is a large-scale atmospheric pressure see-saw driving the weather and climate patterns in the northern hemisphere (Hurrell and Deser, 2010). The phase of the NAO, is expressed by the NAO index. This index is calculated from the difference in sea-level pressure between the Arctic (commonly over Iceland) and the subtropical Atlantic (commonly over the Azores) regions.
Even if it always expresses the same atmospheric pattern, the NAO index can be calculated using pressure values from different ground stations. In this case, we use the daily NAO index provided by the National Centers for Environmental Prediction (NCEP) Climate Prediction Center (Barnston and Livezey, 1987). The daily NAO index values, as derived by NCEP, are plotted for the period 2009-2013 in Figure 2.
In the same figure, two winters are highlighted:
- Winter 2009-2010, a colder and longer winter than normal in Western and Northern Europe (Cattiaux et al., 2010), was characterised by a strong negative NAO index.
- Winter 2011-2012, a particularly mild winter over Europe, was characterised by a strong positive NAO index.
NAO variations have a direct impact on the polar jet or jet stream. Jet streams are geostrophic winds. i.e., their strength only depends on the pressure gradient and the Coriolis force. In the northern hemisphere, they flow from west to east, at speeds higher than 30m/s (108 km/h) and they are located in the upper level of the troposphere between 100 and 400 hPa (Kington and Ley, 1999).
Doutriaux-Boucher et al., 2016 first attempted to show the link between the NAO and jet stream derived using meteosat observations comparing two single days: 1st December 2010 and 1st December 2011. For this analysis the Meteosat Second Generation (MSG) AMV Release 1 data have been exploited (EUMETSAT, 2015).
This connection has been analysed in more detail by Lattanzio et al., 2019 extending the comparison for a complete December January February (DJF) season. They first compared AMV patterns for a day during a season with a strong negative NAO phase (20 December 2009) and a day during a season with a strong positive NAO phase (20 December 2011). Figure 3 presents the AMV patterns during these two days, only considering AMVs with a Quality Indicator (QI) higher than 30. The QI provides an indication of the robustness of the retrieval (Holmlund, 1998). During the day with a negative NAO phase (20 Dec 2009), the median latitude of the detected AMVs is about 14 degrees lower and their corresponding median speed is about 5 m/s slower, compared to the day with a positive NAO phase (20 Dec 2011).
In their conference paper, Lattanzio et al., 2019 also analysed the seasonal anomaly of AMV latitude and AMV wind speed for the winter (DJF) with mostly negative NAO indices (2009-2010), and the winter with mostly positive NAO indices (2011-2012). The results of this comparison are shown in Figure 4, which presents the time-series of NAO indices, median AMV latitudes, and median AMV wind speeds during the months December, January, February, and March of these winters.
It can be seen that the AMV latitudes during the winter with negative NAO indices (2009-2010) are about 15 degrees lower than during the winter with positive NAO indices (2011-2012). Similarly, a slight difference in AMV wind speeds is observed, with about 5 m/s slower wind speeds during periods with negative NAO indices.
The inclusion of Meteosat First Generation (MFG) in the latest release allowed extending the analysis back to 1981, covering a period of 38 years. The trend of both position and speed (see Figure 5) of the jet stream is in line with the prediction of several model and reanalysis (Irvine et al., 2016).
This case shows that the patterns in AMV latitude and wind speed are consistent with the expected behaviour due to changes in NAO, and may serve as proxy for observing NOA changes related to shifts in the polar jet. The data record used for this use case was released in September 2021 (refer to Table 1 for details) with the following digital object index: http://doi.org/10.15770/EUM_SEC_CLM_0020.
This study shows the importance that geostationary data such as the Meteosat play in analysing atmospheric climate pattern and in assessing their evolution during time. In particular, the analysis of the polar jet streams provides a key proxy data for the study of the Northern Atlantic Oscillation.
For this study, the following data has been used:
- Atmospheric Motion Vectors from Meteosat Second Generation
- Northern Atlantic Oscillation (NAO) generated at NCEP
|General||Data record name||Atmospheric Motion Vectors Release 2|
|Data record digital identifier||DOI: 10.15770/EUM_SEC_CLM_0020 (0-degree)|
|Data record short description||Reprocessed level-2 geostationary atmospheric motion vector from Meteosat first and second generation|
|Record type||Thematic Climate Data Record|
|Content||Meteosat level-2 atmospheric motion vectors (TCDR)|
|Time period||01 January 1982 – 31 December 2017|
|Instrument||Instruments names||Meteosat Visible and Infrared Imager (MVIRI) Spinning Enhanced Visible and Infrared Imager (SEVIRI)|
|Instrument Data||Input data||
|Output data||Geostationary atmospheric motion vector retrieved using the EUMETSAT algorithm|
|Format||The products are provided in BUFR format and NetCDF|
|Access||EUMETSAT Data Centre||The data set is available from EUMETSAT Data Centre (https://eoportal.eumetsat.int/ EXT)|
Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns, Mon. Wea. Rev., 115(6), 1083–1126, doi:10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2, 1987.
Bradbury, J. A., Dingman, S. L., and Keim, B. D.: New England drought and relations with large scale atmospheric circulation patterns, J. Am. Water Resour. As., 38, 1287–1299, https://doi.org/10.1111/j.1752-1688.2002.tb04348.x, 2002
Cattiaux, J., Vautard, R., Cassou, C., Yiou, P., Masson‐Delmotte, V. and Codron, F.: Winter 2010 in Europe: A cold extreme in a warming climate, Geophysical Research Letters, 37(20), doi:https://doi.org/10.1029/2010GL044613, 2010.
Doutriaux-Boucher, M., A. Lattanzio, O. Hautecoeur, R. Borde, and J. Schulz, Reprocessing of atmospheric motion vectors at EUMETSAT, 13th International Wind Workshop, Monterey, 2016, http://cimss.ssec.wisc.edu/iwwg/iww13/proceedings_iww13/papers/session5/IWW13_Session5_1_Doutriaux-Boucher_final.pdf
EUMETSAT, 2015: Atmospheric Motion Vectors - MSG - 0 degree (CF-015 Release 1) https://doi.org/10.15770/EUM_SEC_CLM_0006.
Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Holmlund, K.: The Utilization of Statistical Properties of Satellite-Derived Atmospheric Motion Vectors to Derive Quality Indicators, Wea. Forecasting, 13(4), 1093–1104, doi:10.1175/1520-0434(1998)013<1093:TUOSPO>2.0.CO;2, 1998.
Hurrell, J. W. and Deser, C., 2010: North Atlantic climate variability: The role of the North Atlantic Oscillation, Journal of Marine Systems, 79(3), 231–244, https://doi.org/10.1016/j.jmarsys.2009.11.002.
Irvine, E. A., Shine, K. P., and Stringer, M. A., 2016: What are the implications of climate change for trans-Atlantic aircraft routing and flight time?, Transportation Research Part D: Transport and Environment, 47, 44–53, https://doi.org/10.1016/j.trd.2016.04.014
Lattanzio, A., Doutriaux-Boucher, M., Schulz, J. and Borde, R., 2019: Representation of Regular Climate Variations in the Eumetsat Atmospheric Motion Vector Climate Data Record. [online] Available from: https://ams.confex.com/ams/JOINTSATMET/mediafile/Manuscript/Paper360663/JSC_2019_Extended_Abstract_Lattanzio.pdf
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