Articles | Volume 2, issue 4
https://doi.org/10.5194/wcd-2-1033-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-2-1033-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Nov 2021
Research article |
| 04 Nov 2021
| 04 Nov 2021
Flow dependence of wintertime subseasonal prediction skill over Europe
Constantin Ardilouze
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Damien Specq
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Lauriane Batté
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Christophe Cassou
CECI, Université de Toulouse, CNRS, CERFACS, Toulouse, France
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Cited articles
Ardilouze, C., Batté, L., and Déqué, M.: Subseasonal-to-seasonal
(S2S) forecasts with CNRM-CM: a case study on the July 2015 West-European
heat wave, Adv. Sci. Res., 14, 115–121, 2017. a
Bach, E., Motesharrei, S., Kalnay, E., and Ruiz-Barradas, A.: Local
Atmosphere–Ocean Predictability: Dynamical Origins, Lead Times, and
Seasonality, J. Climate, 32, 7507–7519, 2019. a
Barnston, A. G. and Livezey, R. E.: Classification, seasonality and persistence
of low-frequency atmospheric circulation patterns, Mon. Weather Rev.,
115, 1083–1126, 1987. a
Batté, L. and Déqué, M.: Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts, Geosci. Model Dev., 9, 2055–2076, https://doi.org/10.5194/gmd-9-2055-2016, 2016. a
Cassou, C.: Intraseasonal interaction between the Madden–Julian oscillation
and the North Atlantic Oscillation, Nature, 455, 523–527, 2008. a
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,
Geophys. Res. Lett., 37, L20704, https://doi.org/10.1029/2010GL044613, 2010. a, b
Chevallier, M., Massonnet, F., Goessling, H., Guémas, V., and Jung, T.: The
role of sea ice in sub-seasonal predictability, in: Sub-Seasonal to Seasonal
Prediction, Elsevier, 201–221, https://doi.org/10.1016/B978-0-12-811714-9.00010-3, 2019. a
de Andrade, F. M., Young, M. P., MacLeod, D., Hirons, L. C., Woolnough, S. J.,
and Black, E.: Subseasonal Precipitation Prediction for Africa: Forecast
Evaluation and Sources of Predictability, Weather Forecast., 36,
265–284, 2021. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, 2011. a
Domeisen, D. I.: Estimating the frequency of sudden stratospheric warming
events from surface observations of the North Atlantic Oscillation, J.
Geophys. Res.-Atmos., 124, 3180–3194, 2019. a
Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzagüena, B., Baldwin, M. P., Dunn-Sigouin, E., Furtado, J. C., Garfinkel, C. I.,Hitchcock, P., Karpechko, A. Y., Kim, H., Knight, J., Lang, A. L., Lim, E.-P., Marshall, A., Roff, G., Schwartz, C., Simpson, I. R., Son, S.-W., and Taguchi, M.: The role of the stratosphere in
subseasonal to seasonal prediction: 2. Predictability arising from
stratosphere-troposphere coupling, J. Geophys. Res.-Atmos., 125, e2019JD030923, https://doi.org/10.1029/2019JD030923, 2020. a
Domeisen, D. I., White, C. J., Afargan-Gerstman, H., Muñoz, A. G., Janiga, M. A., Vitart, F., Wulff, C. O., Antoine, S., Ardilouze, C., Batté, L., Bloomfield, H. C., Brayshaw, D., Camargo, S. J., Charlton-Pérez, A., Collins, D., Cowan, T., del Mar Chaves, M., Ferranti, L., Goméz, R., González, P. L., González Romero, C., Infanti, J. M., Karozis, S., Kim, H., Kolstad, E. W., LaJoie, E., Lledó, L., Magnusson, L., Malguzzi, P., Manrique-Suñén, A., Mastrangelo, D., Materia, S., Medina, H., Palma, L., Pineda, L. E., Sfetsos, A., Son, S.-W., Soret, A., Strazzo, S., and Tian, D.: Advances in the subseasonal prediction of extreme events, B. Am. Meteor. Soc., in review, 2021. a
Dorrington, J. and Strommen, K.: Jet speed variability obscures Euro-Atlantic
regime structure, Geophys. Res. Lett., 47, e2020GL087907, https://doi.org/10.1029/2020GL087907, 2020. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Falkena, S. K., de Wiljes, J., Weisheimer, A., and Shepherd, T. G.: Revisiting
the identification of wintertime atmospheric circulation regimes in the
Euro-Atlantic sector, Q. J. Roy. Meteor. Soc.,
146, 2801–2814, 2020. a
Ferranti, L., Corti, S., and Janousek, M.: Flow-dependent verification of the
ECMWF ensemble over the Euro-Atlantic sector, Q. J. Roy.
Meteor. Soc., 141, 916–924, 2015. a
Ferranti, L., Magnusson, L., Vitart, F., and Richardson, D. S.: How far in
advance can we predict changes in large-scale flow leading to severe cold
conditions over Europe?, Q. J. Roy. Meteor.
Soc., 144, 1788–1802, 2018. a
Ferro, C.: Fair scores for ensemble forecasts, Q. J. Roy.
Meteor. Soc., 140, 1917–1923, 2014. a
Ferro, C. A., Richardson, D. S., and Weigel, A. P.: On the effect of ensemble
size on the discrete and continuous ranked probability scores, Meteorological
Applications: A journal of forecasting, practical applications, training
techniques and modelling, Met. Apps., 15, 19–24, 2008. a
Hamouda, M. E., Pasquero, C., and Tziperman, E.: Decoupling of the Arctic
Oscillation and North Atlantic Oscillation in a warmer climate, Nature
Climate Change, 11, 137–142, 2021. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], accessed on 29 October 2021, https://doi.org/10.24381/cds.bd0915c6, 2018. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Jung, T., Hilmer, M., Ruprecht, E., Kleppek, S., Gulev, S. K., and Zolina, O.:
Characteristics of the recent eastward shift of interannual NAO
variability, J. Climate, 16, 3371–3382, 2003. a
Keeley, S. P. E., Sutton, R. T., and Shaffrey, L. C.: Does the North Atlantic
Oscillation show unusual persistence on intraseasonal timescales?,
Geophys. Res. Lett., 36, L22706, https://doi.org/10.1029/2009GL040367, 2009. a, b
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis:
General specifications and basic characteristics, J.
Meteorol. Soc. Jpn. Ser. II, 93, 5–48, 2015. a
Kolstad, E. W., Wulff, C. O., Domeisen, D. I., and Woollings, T.: Tracing North
Atlantic Oscillation forecast errors to stratospheric origins, J.
Climate, 33, 9145–9157, 2020. a
Lee, R. W., Woolnough, S. J., Charlton-Perez, A. J., and Vitart, F.: ENSO
modulation of MJO teleconnections to the North Atlantic and Europe,
Geophys. Res. Lett., 46, 13535–13545, 2019. a
Lin, H., Brunet, G., and Derome, J.: An observed connection between the North
Atlantic Oscillation and the Madden–Julian oscillation, J. Climate,
22, 364–380, 2009. a
Mariotti, A., Baggett, C., Barnes, E. A., Becker, E., Butler, A., Collins, D. C., Dirmeyer, P. A., Ferranti, L., Johnson, N. C., Jones, J., Kirtman,B. P., Lang, A. L., Molod, A., Newman, M., Robertson, A. W., Schubert, S., Waliser, D. E., and Albers, J.:
Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and
Beyond, B. Am. Meteorol. Soc., 101, E608–E625,
2020. a
Mayer, K. J. and Barnes, E. A.: Subseasonal Forecasts of Opportunity
Identified by an Explainable Neural Network, Geophys. Res. Lett.,
48, e2020GL092092, https://doi.org/10.1029/2020GL092092, 2021. a
Merryfield, W. J., Baehr, J., Batté, L., Becker, E. J., Butler, A. H., Coelho, C. A. S., Danabasoglu, G., Dirmeyer, P. A., Doblas-Reyes, F. J., Domeisen, D. I. V., Ferranti, L., Ilynia, T., Kumar, A., Müller, W. A., Rixen, M., Robertson, A. W., Smith, D. M., Takaya, Y., Tuma,M., Vitart, F., White, C. J., Alvarez, M. S., Ardilouze, C., Attard, H., Baggett, C., Balmaseda, M. A., Beraki, A. F., Bhattacharjee, P. S., Bilbao, R., de Andrade, F. M., DeFlorio, M. J., Díaz, L. B., Ehsan, M. A., Fragkoulidis, G., Grainger, S., Green, B. W., Hell, M. C., Infanti, J. M., Isensee, K., Kataoka, T., Kirtman, B. P., Klingaman, N. P., Lee, J.-Y., Mayer, K., McKay, R., Mecking, J. V., Miller, D. E., Neddermann, N., Ng, C. H. J., Ossó, A., Pankatz, K., Peatman, S., Pegion, K., Perlwitz, J., Recalde-Coronel, G. C., Reintges, A., Renkl,C., Solaraju-Murali, B., Spring, A., Stan, C., Sun, Y. Q., Tozer, C. R., Vigaud, N., Woolnough, S., and Yeager, S.: Current and emerging developments in subseasonal to
decadal prediction, B. Am. Meteorol. Soc., 101,
E869–E896, 2020. a
Michelangeli, P.-A., Vautard, R., and Legras, B.: Weather regimes: Recurrence
and quasi stationarity, J. Atmos. Sci., 52, 1237–1256,
1995. a
Minami, A. and Takaya, Y.: Enhanced Northern Hemisphere correlation skill of
subseasonal predictions in the strong negative phase of the Arctic
Oscillation, J. Geophys. Res.-Atmos., 125,
e2019JD031268, https://doi.org/10.1029/2019JD031268, 2020. a
NOAA Climate Prediction Center NAO index: North Atlantic Oscillation (NAO), available at: ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii,
last access: 11 December 2020. a
Pokorná, L. and Huth, R.: Climate impacts of the NAO are sensitive to how
the NAO is defined, Theor. Appl. Climatol., 119, 639–652,
2015. a
Roberts, C. D., Vitart, F., and Balmaseda, M. A.: Hemispheric Impact of North
Atlantic SSTs in Subseasonal Forecasts, Geophys. Res. Lett., 48,
e2020GL0911446, https://doi.org/10.1029/2020GL091446, 2021. a
Robertson, A. W., Vigaud, N., Yuan, J., and Tippett, M. K.: Toward Identifying
Subseasonal Forecasts of Opportunity Using North American Weather Regimes,
Mon. Weather Rev., 148, 1861–1875, 2020. a
Seager, R., Kushnir, Y., Nakamura, J., Ting, M., and Naik, N.: Northern
Hemisphere winter snow anomalies: ENSO, NAO and the winter of 2009/10,
Geophys. Res. Lett., 37, L14703, https://doi.org/10.1029/2010GL043830, 2010. a
Specq, D., Batté, L., Déqué, M., and Ardilouze, C.: Multimodel
forecasting of precipitation at subseasonal timescales over the southwest
tropical Pacific, Earth Space Sci., 7, e2019EA001003, https://doi.org/10.1029/2019EA001003, 2020. a, b
Sun, L., Perlwitz, J., Richter, J. H., Hoerling, M. P., and Hurrell, J. W.:
Attribution of NAO Predictive Skill Beyond 2 Weeks in Boreal Winter,
Geophys. Res. Lett., 47, e2020GL090451, https://doi.org/10.1029/2020GL090451, 2020. a
Thompson, D. W. and Wallace, J. M.: The Arctic Oscillation signature in the
wintertime geopotential height and temperature fields, Geophys. Res.
Lett., 25, 1297–1300, 1998. a
World Climate Research Programme (WCRP): CMIP6 data, available at: https://esgf-node.llnl.gov/search/cmip6/, last access: 29 October 2021. a
Vautard, R.: Multiple weather regimes over the North Atlantic: Analysis of
precursors and successors, Mon. Weather Rev., 118, 2056–2081, 1990. a
Vigaud, N., Robertson, A. W., and Tippett, M. K.: Predictability of recurrent
weather regimes over North America during winter from submonthly reforecasts, Mon. Weather Rev., 146, 2559–2577, 2018. a
Vitart, F.: Monthly forecasting at ECMWF, Mon. Weather Rev., 132,
2761–2779, 2004. a
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D., Woolnough, S., Wu, T., Won, D.-J., Xiao, H., Zaripov, R., and Zhang, L.: The
subseasonal to seasonal (S2S) prediction project database, B.
Am. Meteor. Soc., 98, 163–173, 2017 (data available at: http://s2sprediction.net/, last access: 29 October 2021).
a, b
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., H. Douville, H., Franchisteguy, L., Ethé, C., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK experiments with CNRM-CM6-1, J. Adv. Model. Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019. a
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J., Lazo, J. K., Kumar, A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V., Bharwani, S., MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B., Graham, R., Kjellström, E., Becker, E., Pegion, K. V., Holbrook, N. J., McEvoy, D., Depledge, M., Perkins-Kirkpatrick, S., Brown, T. J., Street, R., Jones, L., Remenyi, T. A., Hodgson-Johnston, I., Buontempo, C., Lamb, R., Meinke, H., Arheimer, B., and Zebiak, S. E.:
Potential applications of subseasonal-to-seasonal (S2S) predictions,
Meteorol. Appl., 24, 315–325, 2017. a
Woollings, T., Franzke, C., Hodson, D., Dong, B., Barnes, E. A., Raible, C.,
and Pinto, J.: Contrasting interannual and multidecadal NAO variability,
Clim. Dynam., 45, 539–556, 2015. a
Yamagami, A. and Matsueda, M.: Subseasonal Forecast Skill for Weekly Mean
Atmospheric Variability Over the Northern Hemisphere in Winter and Its
Relationship to Midlatitude Teleconnections, Geophys. Res. Lett.,
47, e2020GL088508, https://doi.org/10.1029/2020GL088508, 2020. a
Zheng, C., Chang, E. K.-M., Kim, H., Zhang, M., and Wang, W.: Subseasonal to
seasonal prediction of wintertime northern hemisphere extratropical cyclone
activity by S2S and NMME models, J. Geophys. Res.-Atmos., 124, 12057–12077, 2019. a
Zuo, J., Ren, H.-L., Wu, J., Nie, Y., and Li, Q.: Subseasonal variability and
predictability of the Arctic Oscillation/North Atlantic Oscillation in
BCC_AGCM2. 2, Dynam. Atmos. Oceans, 75, 33–45, 2016. a
Short summary
Forecasting temperature patterns beyond 2 weeks is very challenging, although occasionally, forecasts show more skill over Europe. Our study indicates that the level of skill varies concurrently for two distinct forecast systems. It also shows that higher skill occurs when forecasts are issued during specific patterns of atmospheric circulation that tend to be particularly persistent.
These results could help forecasters estimate a priori how trustworthy extended-range forecasts will be.
Forecasting temperature patterns beyond 2 weeks is very challenging, although occasionally,...
