Articles | Volume 3, issue 2
https://doi.org/10.5194/wcd-3-505-2022
© Author(s) 2022. 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-3-505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Apr 2022
Research article |
| 20 Apr 2022
| 20 Apr 2022
Quantifying climate model representation of the wintertime Euro-Atlantic circulation using geopotential-jet regimes
Joshua Dorrington
CORRESPONDING AUTHOR
Department of Atmospheric, Oceanic, and Planetary Physics, University of Oxford, Oxford, UK
Kristian Strommen
Department of Atmospheric, Oceanic, and Planetary Physics, University of Oxford, Oxford, UK
Federico Fabiano
Institute of Atmospheric Sciences and Climate (ISAC-CNR), Bologna, Italy
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In this work we examine the ability of global climate models in representing the atmospheric circulation in the upper troposphere, focusing on the eventual benefits of an increased horizontal resolution. Our results confirm that a higher horizontal resolution has a positive impact, especially in those models in which the resolution is increased in both the atmosphere and the ocean, whereas when the resolution is increased only in the atmosphere no substantial improvements are found.
Federico Fabiano, Virna L. Meccia, Paolo Davini, Paolo Ghinassi, and Susanna Corti
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Global warming not only affects the mean state of the climate (i.e. a warmer world) but also its variability. Here we analyze a set of future climate scenarios and show how some configurations of the wintertime atmospheric flow will become more frequent and persistent under continued greenhouse forcing. For example, over Europe, models predict an increase in the NAO+ regime which drives intense precipitation in northern Europe and the British Isles and dry conditions over the Mediterranean.
Cited articles
Anstey, J. A., Davini, P., Gray, L. J., Woollings, T. J., Butchart, N.,
Cagnazzo, C., Christiansen, B., Hardiman, S. C., Osprey, S. M., and Yang, S.:
Multi-model analysis of Northern Hemisphere winter blocking: Model biases
and the role of resolution, J. Geophys. Res.-Atmos.,
118, 3956–3971, https://doi.org/10.1002/JGRD.50231, 2013. a
Athanasiadis, P. J., Yeager, S., Kwon, Y.-O., Bellucci, A., Smith, D. W., and
Tibaldi, S.: Decadal predictability of North Atlantic blocking and the NAO,
NPJ Clim. Atmos. Sci., 3, 1–10, 2020. a
Baker, H. S., Woollings, T., and Mbengue, C.: Eddy-Driven Jet Sensitivity to
Diabatic Heating in an Idealized GCM, J. Climate, 30, 6413–6431,
https://doi.org/10.1175/JCLI-D-16-0864.1, 2017. a
Baldwin, M. P. and Dunkerton, T. J.: Stratospheric Harbingers of Anomalous
Weather Regimes, Science, 294, 581–584, https://doi.org/10.1126/SCIENCE.1063315,
2001. a
Barnes, E. A. and Hartmann, D. L.: Testing a theory for the effect of latitude
on the persistence of eddy-driven jets using CMIP3 simulations, Geophys.
Res. Lett., 37, L15801, https://doi.org/10.1029/2010GL044144, 2010. a, b
Barnes, E. A. and Polvani, L.: Response of the Midlatitude Jets, and of Their
Variability, to Increased Greenhouse Gases in the CMIP5 Models, J. Climate, 26, 7117–7135, https://doi.org/10.1175/JCLI-D-12-00536.1, 2013. a, b, c
Barnes, E. A. and Screen, J. A.: The impact of Arctic warming on the
midlatitude jet-stream: Can it? Has it? Will it?, WIREs Clim Change, 6, 277–286, https://doi.org/10.1002/wcc.337, 2015. a, b
Barriopedro, D., García-Herrera, R., Lupo, A. R., and Hernández,
E.: A Climatology of Northern Hemisphere Blocking, J. Climate, 19,
1042–1063, https://doi.org/10.1175/JCLI3678.1, 2006. a
Baur, F., Hess, P., and Nagel, H.: Kalendar der Grosswetterlagen Europas
1881–1939, Bad Homburg (DWD), 1944. a
Beerli, R. and Grams, C. M.: Stratospheric modulation of the large-scale
circulation in the Atlantic-European region and its implications for surface
weather events, Q. J. Roy. Meteor. Soc., 145,
3732–3750, https://doi.org/10.1002/qj.3653, 2019. a, b
Bellucci, A., Athanasiadis, P. J., Scoccimarro, E., Ruggieri, P., Gualdi, S.,
Fedele, G., Haarsma, R. J., Garcia-Serrano, J., Castrillo, M., Putrahasan,
D., Sanchez-Gomez, E., Moine, M. P., Roberts, C. D., Roberts, M. J., Seddon,
J., and Vidale, P. L.: Air-Sea interaction over the Gulf Stream in an
ensemble of HighResMIP present climate simulations, Clim. Dynam., 56,
2093–2111, https://doi.org/10.1007/s00382-020-05573-z, 2021. a, b
Bowie, E. H. and Weightman, R. H.: Types of storms of the United States and
their average movements, US Government Printing Office, https://www.jstor.org/stable/24520675?seq=1 (last access: 14 April 2022), 1914. a
Brunner, L., Schaller, N., Anstey, J., Sillmann, J., and Steiner, A. K.:
Dependence of Present and Future European Temperature Extremes on the
Location of Atmospheric Blocking, Geophys. Res. Lett., 45,
6311–6320, https://doi.org/10.1029/2018GL077837, 2018. a
Butchart, N., Anstey, J. A., Hamilton, K., Osprey, S., McLandress, C., Bushell, A. C., Kawatani, Y., Kim, Y.-H., Lott, F., Scinocca, J., Stockdale, T. N., Andrews, M., Bellprat, O., Braesicke, P., Cagnazzo, C., Chen, C.-C., Chun, H.-Y., Dobrynin, M., Garcia, R. R., Garcia-Serrano, J., Gray, L. J., Holt, L., Kerzenmacher, T., Naoe, H., Pohlmann, H., Richter, J. H., Scaife, A. A., Schenzinger, V., Serva, F., Versick, S., Watanabe, S., Yoshida, K., and Yukimoto, S.: Overview of experiment design and comparison of models participating in phase 1 of the SPARC Quasi-Biennial Oscillation initiative (QBOi), Geosci. Model Dev., 11, 1009–1032, https://doi.org/10.5194/gmd-11-1009-2018, 2018. a
Cassou, C.: Intraseasonal interaction between the Madden–Julian Oscillation
and the North Atlantic Oscillation, Nature, 455, 523–527,
https://doi.org/10.1038/nature07286, 2008. a, b
Cattiaux, J., Douville, H., and Peings, Y.: European temperatures in CMIP5:
origins of present-day biases and future uncertainties, Clim. Dynam.,
41, 2889–2907, https://doi.org/10.1007/S00382-013-1731-Y, 2013. a, b
Charlton-Perez, A. J., Ferranti, L., and Lee, R. W.: The influence of the
stratospheric state on North Atlantic weather regimes, Q. J.
Roy. Meteor. Soc., 144, 1140–1151, https://doi.org/10.1002/qj.3280,
2018. a, b
Charney, J. G. and DeVore, J. G.: Multiple Flow Equilibria in the Atmosphere
and Blocking, J. Atmos. Sci., 36, 1205–1216,
https://doi.org/10.1175/1520-0469(1979)036<1205:MFEITA>2.0.CO;2, 1979. a, b
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S.,
Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.:
Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled
Model, J. Adv. Model Earth Sy., 11, 185–209,
https://doi.org/10.1029/2018MS001369, 2019. a
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman,
P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri,
M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L.,
Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis
Project, Q. J. Roy. Meteor. Soc., 137, 1–28,
https://doi.org/10.1002/QJ.776, 2011. a
Cortesi, N., Torralba, V., González-Reviriego, N., Soret, A., and
Doblas-Reyes, F. J.: Characterization of European wind speed variability
using weather regimes, Clim. Dynam., 53, 4961–4976,
https://doi.org/10.1007/S00382-019-04839-5, 2019. a
Corti, S., Molteni, F., and Palmer, T. N.: Signature of recent climate change
in frequencies of natural atmospheric circulation regimes, Nature, 398,
799–802, https://doi.org/10.1038/19745, 1999. a
Davini, P. and D'Andrea, F.: Northern Hemisphere atmospheric blocking
representation in global climate models: Twenty years of improvements?,
J. Climate, 29, 8823–8840, https://doi.org/10.1175/JCLI-D-16-0242.1, 2016. a
Davini, P. and D'Andrea, F.: From CMIP3 to CMIP6: Northern hemisphere
atmospheric blocking simulation in present and future climate, J. Climate, 33, 10021–10038, https://doi.org/10.1175/JCLI-D-19-0862.1, 2020. a, b
Davini, P., Fabiano, F., and Sandu, I.: Orographic resolution driving the improvements associated with horizontal resolution increase in the Northern Hemisphere winter mid-latitudes, Weather Clim. Dynam. Discuss. [preprint], https://doi.org/10.5194/wcd-2021-51, in review, 2021. a
Davini, P., Cagnazzo, C., Gualdi, S., and Navarra, A.: Bidimensional
diagnostics, variability, and trends of northern hemisphere blocking,
J. Climate, 25, 6496–6509, https://doi.org/10.1175/JCLI-D-12-00032.1, 2012. a
Dawson, A.: eofs: A Library for EOF Analysis of Meteorological, Oceanographic,
and Climate Data, J. Open Res. Softw., 4, 14,
https://doi.org/10.5334/JORS.122, 2016. a
Dawson, A. and Palmer, T. N.: Simulating weather regimes: impact of model
resolution and stochastic parameterization, Clim. Dynam., 44,
2177–2193, https://doi.org/10.1007/s00382-014-2238-x, 2015. a
Delworth, T. L. and Zeng, F.: The impact of the North Atlantic Oscillation on
climate through its influence on the Atlantic meridional overturning
circulation, J. Climate, 29, 941–962,
https://doi.org/10.1175/JCLI-D-15-0396.1, 2016. a
Delworth, T. L., Zeng, F., Zhang, L., Zhang, R., Vecchia, G. A., and Yang, X.:
The central role of ocean dynamics in connecting the North Atlantic
oscillation to the extratropical component of the Atlantic multidecadal
oscillation, J. Climate, 30, 3789–3805,
https://doi.org/10.1175/JCLI-D-16-0358.1, 2017. a
Deser, C., Tomas, R. A., and Peng, S.: The transient atmospheric circulation
response to North Atlantic SST and sea ice anomalies, J. Climate, 20, 4751–4767,
https://doi.org/10.1175/JCLI4278.1, 2007. a
Domeisen, D. I. V., Grams, C. M., and Papritz, L.: The role of North Atlantic–European weather regimes in the surface impact of sudden stratospheric warming events, Weather Clim. Dynam., 1, 373–388, https://doi.org/10.5194/wcd-1-373-2020, 2020. a, b
Dorrington, J.: On the variability and forced response of atmospheric regime
systems, PhD thesis, University of Oxford,
https://ora.ox.ac.uk/objects/uuid:5567d432-e429-4b7c-838a-aea4014d2923 (last access: 14 April 2022),
2021. a
Dorrington, J. and Fabiano, F.: Reanalysis and Model regime datasets, regression metrics, and example analysis code, GitHub [data set], https://github.com/joshdorrington/GJR_hist_climate_data (last access: 14 April 2022), 2022. a
Dorrington, J. and Strommen, K. J.: Jet Speed Variability Obscures
Euro-Atlantic Regime Structure, Geophys. Res. Lett., 47, 15,
https://doi.org/10.1029/2020gl087907, 2020. a, b
Driouech, F., Déqué, M., and Sánchez-Gómez, E.:
Weather regimes–Moroccan precipitation link in a regional climate change
simulation, Global Planet. Change, 72, 1–10,
https://doi.org/10.1016/J.GLOPLACHA.2010.03.004, 2010. a
Dunstone, N., Smith, D., Scaife, A., Hermanson, L., Eade, R., Robinson, N.,
Andrews, M., and Knight, J.: Skilful predictions of the winter North
Atlantic Oscillation one year ahead, Nat. Geosci., 9,
809–814, https://doi.org/10.1038/ngeo2824, 2016. 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
Fabiano, F., Christensen, H. M., Strommen, K., Athanasiadis, P., Baker, A.,
Schiemann, R., and Corti, S.: Euro-Atlantic weather Regimes in the PRIMAVERA
coupled climate simulations: impact of resolution and mean state biases on
model performance, Clim. Dynam., 54, 5031–5048, https://doi.org/10.1007/s00382-020-05271-w, 2020. a, b, c, d
Fabiano, F., Meccia, V. L., Davini, P., Ghinassi, P., and Corti, S.: A regime view of future atmospheric circulation changes in northern mid-latitudes, Weather Clim. Dynam., 2, 163–180, https://doi.org/10.5194/wcd-2-163-2021, 2021. a, b, c
Falkena, S. K., de Wiljes, J., Weisheimer, A., and Shepherd, T. G.: Detection
of interannual ensemble forecast signals over the North Atlantic and Europe
using atmospheric circulation regimes, Q. J. Roy. Meteor. Soc., 148, 434–453, https://doi.org/10.1002/QJ.4213, 2021. a
Faranda, D., Masato, G., Moloney, N., Sato, Y., Daviaud, F., Dubrulle, B., and
Yiou, P.: The switching between zonal and blocked mid-latitude atmospheric
circulation: a dynamical system perspective, Clim. Dynam., 47,
1587–1599, https://doi.org/10.1007/s00382-015-2921-6, 2016. 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, https://doi.org/10.1002/qj.2411, 2015. a, b, c
Frame, T. H. A., Methven, J., Gray, S. L., and Ambaum, M. H. P.:
Flow-dependent predictability of the North Atlantic jet, Geophys. Res. Lett., 40, 2411–2416, https://doi.org/10.1002/GRL.50454, 2013. a
Franzke, C., Woollings, T., and Martius, O.: Persistent Circulation Regimes
and Preferred Regime Transitions in the North Atlantic, J. Atmos. Sci., 68, 2809–2825, https://doi.org/10.1175/JAS-D-11-046.1, 2011. a
Garrido-Perez, J. M., Ordóñez, C., Barriopedro, D.,
García-Herrera, R., and Paredes, D.: Impact of weather regimes on wind
power variability in western Europe, Appl. Energ.,
https://doi.org/10.1016/j.apenergy.2020.114731, 2020. a
Ghil, M.: Climate Change: Multidecadal and Beyond, 1st edn., vol. 6, edited by: Chang, C.-P., Ghil, M., Latif, M., and Wallace, J. M., World Scientific
Publ. Co./Imperial College Press, 31–51, https://doi.org/10.1142/9789814579933_0002, 2015. a
Gold, E.: Aids to forecasting: types of pressure distribution, Gt. Brit.
Meteorological office, Geophysical memoirs, no. 16, Meteorological office,
Neill & co., Edinburgh, London, 1920. a
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.:
Balancing Europe's wind-power output through spatial deployment informed by
weather regimes, Nat. Clim. Change, 7, 557–562,
https://doi.org/10.1038/NCLIMATE3338, 2017. a, b
Gray, L. J., Anstey, J. A., Kawatani, Y., Lu, H., Osprey, S., and Schenzinger, V.: Surface impacts of the Quasi Biennial Oscillation, Atmos. Chem. Phys., 18, 8227–8247, https://doi.org/10.5194/acp-18-8227-2018, 2018. a
Gutjahr, O., Putrasahan, D., Lohmann, K., Jungclaus, J. H., von Storch, J.-S., Brüggemann, N., Haak, H., and Stössel, A.: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, 2019. a
Haarsma, R., Acosta, M., Bakhshi, R., Bretonnière, P.-A., Caron, L.-P., Castrillo, M., Corti, S., Davini, P., Exarchou, E., Fabiano, F., Fladrich, U., Fuentes Franco, R., García-Serrano, J., von Hardenberg, J., Koenigk, T., Levine, X., Meccia, V. L., van Noije, T., van den Oord, G., Palmeiro, F. M., Rodrigo, M., Ruprich-Robert, Y., Le Sager, P., Tourigny, E., Wang, S., van Weele, M., and Wyser, K.: HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR – description, model computational performance and basic validation, Geosci. Model Dev., 13, 3507–3527, https://doi.org/10.5194/gmd-13-3507-2020, 2020. a
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a
Hannachi, A., Straus, D. M., Franzke, C. L. E., Corti, S., and Woollings, T.:
Low-frequency nonlinearity and regime behavior in the Northern Hemisphere
extratropical atmosphere, Rev. Geophys., 55, 199–234,
https://doi.org/10.1002/2015RG000509, 2017. a
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
Hochman, A., Messori, G., Quinting, J. F., Pinto, J. G., and Grams, C. M.: Do
Atlantic-European Weather Regimes Physically Exist?, Geophys. Res. Lett., 48, e2021GL095574, https://doi.org/10.1029/2021GL095574, 2021. a
Hoerl, A. E. and Kennard, R. W.: Ridge Regression: Biased Estimation for
Nonorthogonal Problems, Technometrics, 12, 55–67,
https://doi.org/10.1080/00401706.1970.10488634, 1970. a
Hoskins, B. J., James, I. N., and White, G. H.: The shape, propagation and
mean-flow interaction of large-scale weather systems., J. Atmos. Sci., 40, 1595–1612 https://doi.org/10.1175/1520-0469(1983)040<1595:TSPAMF>2.0.CO;2,
1983. a
Huth, R.: A circulation classification scheme applicable in GCM studies,
Theor. Appl. Climatol., 67, 1–18,
https://doi.org/10.1007/S007040070012, 2000. a
Jiménez-Esteve, B. and Domeisen, D. I.: The tropospheric pathway of the
ENSO-North Atlantic teleconnection, J. Climate, 31, 4563–4584,
https://doi.org/10.1175/JCLI-D-17-0716.1, 2018. a
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019. a
Kidston, J., Scaife, A. A., Hardiman, S. C., Mitchell, D. M., Butchart, N.,
Baldwin, M. P., and Gray, L. J.: Stratospheric influence on tropospheric jet
streams, storm tracks and surface weather, Nat. Geosci., 8, 433–440,
https://doi.org/10.1038/ngeo2424, 2015. a
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation
CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199,
https://doi.org/10.1002/GRL.50256, 2013. a
Laloyaux, P., de Boisseson, E., Balmaseda, M., Bidlot, J. R., Broennimann, S.,
Buizza, R., Dalhgren, P., Dee, D., Haimberger, L., Hersbach, H., Kosaka, Y.,
Martin, M., Poli, P., Rayner, N., Rustemeier, E., and Schepers, D.:
CERA-20C: A Coupled Reanalysis of the Twentieth Century, J. Adv. Model Earth Sy., 10, 1172–1195,
https://doi.org/10.1029/2018MS001273, 2018. a
Lavaysse, C., Vogt, J., Toreti, A., Carrera, M. L., and Pappenberger, F.: On the use of weather regimes to forecast meteorological drought over Europe, Nat. Hazards Earth Syst. Sci., 18, 3297–3309, https://doi.org/10.5194/nhess-18-3297-2018, 2018. a
Legras, B. and Ghil, M.: Persistent anomalies, blocking and variations in
atmospheric predictability., J. Atmos. Sci., 42, 433–471,
https://doi.org/10.1175/1520-0469(1985)042<0433:PABAVI>2.0.CO;2, 1985. a
Li, C. and Wettstein, J. J.: Thermally driven and eddy-driven jet variability
in reanalysis, J. Climate, 25, 1587–1596,
https://doi.org/10.1175/JCLI-D-11-00145.1, 2012. a
Lorenz, D. J. and Hartmann, D. L.: Eddy-zonal flow feedback in the Northern
Hemisphere winter, J. Climate, 16, 1212–1227, https://doi.org/10.1175/1520-0442(2003)16<1212:EFFITN>2.0.CO;2, 2003. a, b
Madonna, E., Li, C., Grams, C. M., and Woollings, T.: The link between
eddy-driven jet variability and weather regimes in the North
Atlantic-European sector, Q. J. Roy. Meteor. Soc., 143, 2960–2972, https://doi.org/10.1002/qj.3155, 2017. a, b
Masato, G., Hoskins, B. J., and Woollings, T. J.: Can the Frequency of
Blocking Be Described by a Red Noise Process?, J. Atmos. Sci., 66, 2143–2149, https://doi.org/10.1175/2008JAS2907.1, 2009. a
Matsueda, M. and Palmer, T. N.: Estimates of flow-dependent predictability of
wintertime Euro-Atlantic weather regimes in medium-range forecasts,
Q. J. Roy. Meteor. Soc., 144, 1012–1027,
https://doi.org/10.1002/qj.3265, 2018. a, b
Michelangeli, P.-A., Vautard, R., Legras, B., Michelangeli, P.-A., Vautard, R.,
and Legras, B.: Weather Regimes: Recurrence and Quasi Stationarity, J. Atmos. Sci., 52, 1237–1256,
https://doi.org/10.1175/1520-0469(1995)052<1237:WRRAQS>2.0.CO;2, 1995. a, b
Molteni, F. and Kucharski, F.: A heuristic dynamical model of the North
Atlantic Oscillation with a Lorenz-type chaotic attractor, Clim. Dynam.,
52, 6173–6193, https://doi.org/10.1007/s00382-018-4509-4, 2019. a
Nakamura, N. and Huang, C. S. Y.: Atmospheric blocking as a traffic jam in the
jet stream, Science, 361, 42–47, https://doi.org/10.1126/SCIENCE.AAT0721, 2018. a, b
O'Reilly, C. H., Minobe, S., and Kuwano-Yoshida, A.: The influence of the Gulf
Stream on wintertime European blocking, Clim. Dynam., 47, 1545–1567,
https://doi.org/10.1007/s00382-015-2919-0, 2016. a
Palmer, T. N.: A nonlinear dynamical perspective on climate prediction,
J. Climate, 12, 575–591,
https://doi.org/10.1175/1520-0442(1999)012<0575:ANDPOC>2.0.CO;2, 1999. a, b
Parker, T., Woollings, T., Weisheimer, A., O'Reilly, C., Baker, L., and
Shaffrey, L.: Seasonal Predictability of the Winter North Atlantic
Oscillation From a Jet Stream Perspective, Geophys. Res. Lett., 46,
10159–10167, https://doi.org/10.1029/2019GL084402, 2019. a, b
Pfahl, S., Schwierz, C., Croci-Maspoli, M., Grams, C. M., and Wernli, H.:
Importance of latent heat release in ascending air streams for atmospheric
blocking, Nat. Geosci., 8, 610–614, https://doi.org/10.1038/ngeo2487,
2015. a
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F.,
Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N.,
Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., Fisher, M.,
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart,
F., Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N.,
Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher,
M.: ERA-20C: An Atmospheric Reanalysis of the Twentieth Century, J. Climate, 29, 4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1, 2016. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K.,
Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S.,
Schmidli, J., Van Lipzig, N. P., and Leung, R.: A review on regional
convection-permitting climate modeling: Demonstrations, prospects, and
challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475,
2015. a
Richter, J. H., Anstey, J. A., Butchart, N., Kawatani, Y., Meehl, G. A.,
Osprey, S., and Simpson, I. R.: Progress in Simulating the Quasi-Biennial
Oscillation in CMIP Models, J. Geophys. Res.-Atmos.,
125, e2019JD032362, https://doi.org/10.1029/2019JD032362, 2020. a
Roberts, C. D., Senan, R., Molteni, F., Boussetta, S., Mayer, M., and Keeley, S. P. E.: Climate model configurations of the ECMWF Integrated Forecasting System (ECMWF-IFS cycle 43r1) for HighResMIP, Geosci. Model Dev., 11, 3681–3712, https://doi.org/10.5194/gmd-11-3681-2018, 2018. a
Robin, Y., Yiou, P., and Naveau, P.: Detecting changes in forced climate attractors with Wasserstein distance, Nonlin. Processes Geophys., 24, 393–405, https://doi.org/10.5194/npg-24-393-2017, 2017. a
Robinson, W. A.: Does eddy feedback sustain variability in the zonal index?,
J. Atmos. Sci., 53, 3556–3569,
https://doi.org/10.1175/1520-0469(1996)053<3556:DEFSVI>2.0.CO;2, 1996. a
Rodríguez-Fonseca, B., Suárez-Moreno, R., Ayarzagüena, B.,
López-Parages, J., Gómara, I., Villamayor, J., Mohino, E.,
Losada, T., and Castaño-Tierno, A.: A Review of ENSO Influence on the
North Atlantic. A Non-Stationary Signal, Atmosphere, 7, 87,
https://doi.org/10.3390/atmos7070087, 2016. a
Rossby, C. G.: Planetary flow patterns in the atmosphere, Q. J. Roy.
Meteor. Soc., 66, 68–87,
https://empslocal.ex.ac.uk/people/staff/gv219/classics.d/Rossby-planflowQJ40.pdf (last access: 14 April 2022),
1940. a
Scaife, A. A., Camp, J., Comer, R., Davis, P., Dunstone, N., Gordon, M.,
MacLachlan, C., Martin, N., Nie, Y., Ren, H.-L., Roberts, M., Robinson, W.,
Smith, D., and Vidale, P. L.: Does increased atmospheric resolution improve
seasonal climate predictions?, Atmos. Sci. Lett., 20, e922,
https://doi.org/10.1002/ASL.922, 2019. a
Schiemann, R., Athanasiadis, P., Barriopedro, D., Doblas-Reyes, F., Lohmann, K., Roberts, M. J., Sein, D. V., Roberts, C. D., Terray, L., and Vidale, P. L.: Northern Hemisphere blocking simulation in current climate models: evaluating progress from the Climate Model Intercomparison Project Phase 5 to 6 and sensitivity to resolution, Weather Clim. Dynam., 1, 277–292, https://doi.org/10.5194/wcd-1-277-2020, 2020. a
Sein, D. V., Koldunov, N. V., Danilov, S., Wang, Q., Sidorenko, D., Fast, I.,
Rackow, T., Cabos, W., and Jung, T.: Ocean Modeling on a Mesh With Resolution
Following the Local Rossby Radius, J. Adv. Model Earth Sy., 9, 2601–2614, https://doi.org/10.1002/2017MS001099, 2017. a
Shen, B. W., Pielke, R. A., Zeng, X., Baik, J. J., Faghih-Naini, S., Cui, J.,
and Atlas, R.: Is weather chaotic? Coexistence of chaos and order within a
generalized lorenz model, B. Am. Meteorol. Soc.,
102, E148–E158, https://doi.org/10.1175/BAMS-D-19-0165.1, 2021. a
Shepherd, T. G.: Atmospheric circulation as a source of uncertainty in climate
change projections, Nat. Geosci., 7, 703–708, https://doi.org/10.1038/NGEO2253,
2014. a
Shutts, G. J.: The propagation of eddies in diffluent jetstreams: Eddy
vorticity forcing of “blocking” flow fields, Q. J. Roy.
Meteor. Soc., 109, 737–761, https://doi.org/10.1002/qj.49710946204, 1983. a, b, c
Slivinski, L. C., Compo, G. P., Sardeshmukh, P. D., Whitaker, J. S., McColl,
C., Allan, R. J., Brohan, P., Yin, X., Smith, C. A., Spencer, L. J., Vose,
R. S., Rohrer, M., Conroy, R. P., Schuster, D. C., Kennedy, J. J., Ashcroft,
L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A.,
Domínguez-Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones,
P. D., Kubota, H., Lee, T. C., Lorrey, A. M., Luterbacher, J., Mock, C. J.,
Przybylak, R. K., Pudmenzky, C., Slonosky, V. C., Tinz, B., Trewin, B., Wang,
X. L., Wilkinson, C., Wood, K., and Wyszyński, P.: An Evaluation of
the Performance of the Twentieth Century Reanalysis Version 3, J. Climate, 34, 1417–1438, https://doi.org/10.1175/JCLI-D-20-0505.1, 2021. a
Small, R. J., Msadek, R., Kwon, Y.-O., Booth, J. F., and Zarzycki, C.:
Atmosphere surface storm track response to resolved ocean mesoscale in two
sets of global climate model experiments, Clim. Dynam., 52, 2067–2089,
2019. a
Smith, D. M., Eade, R., Scaife, A. A., Caron, L. P., Danabasoglu, G., DelSole,
T. M., Delworth, T., Doblas-Reyes, F. J., Dunstone, N. J., Hermanson, L.,
Kharin, V., Kimoto, M., Merryfield, W. J., Mochizuki, T., Müller,
W. A., Pohlmann, H., Yeager, S., and Yang, X.: Robust skill of decadal
climate predictions, NPJ Clim. Atmos. Sci., 2, 13,
https://doi.org/10.1038/s41612-019-0071-y, 2019. a
Sousa, P. M., Trigo, R. M., Barriopedro, D., Soares, P. M. M., Ramos, A. M.,
and Liberato, M. L. R.: Responses of European precipitation distributions
and regimes to different blocking locations, Clim. Dynam., 48,
1141–1160, https://doi.org/10.1007/S00382-016-3132-5, 2016. a
Steinfeld, D., Boettcher, M., Forbes, R., and Pfahl, S.: The sensitivity of atmospheric blocking to upstream latent heating – numerical experiments, Weather Clim. Dynam., 1, 405–426, https://doi.org/10.5194/wcd-1-405-2020, 2020. a
Stephenson, D. B., Hannachi, A., and O'Neill, A.: On the existence of multiple
climate regimes, Q. J. Roy. Meteor. Soc., 130,
583–605, https://doi.org/10.1256/QJ.02.146, 2004. a
Strommen, K., Mavilia, I., Corti, S., Matsueda, M., Davini, P., von Hardenberg,
J., Vidale, P. L., and Mizuta, R.: The Sensitivity of Euro-Atlantic Regimes
to Model Horizontal Resolution, Geophys. Res. Lett., 46,
7810–7818, https://doi.org/10.1029/2019GL082843, 2019. a, b, c, d
Strong, C. and Magnusdottir, G.: Dependence of NAO variability on coupling
with sea ice, Clim. Dynam., 36, 1681–1689, https://doi.org/10.1007/s00382-010-0752-z, 2011. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the
Experiment Design, B. Am. Meteorol. Soc., 93,
485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Trevisan, A. and Buzzi, A.: Stationary Response of Barotropic Weakly Nonlinear
Rossby Waves to Quasi-Resonant Orographic Forcing, J. Atmos. Sci., 37, 947–957, https://doi.org/10.1175/1520-0469(1980)037<0947:srobwn>2.0.co;2,
1980. a
Tyrlis, E. and Hoskins, B. J.: The Morphology of Northern Hemisphere
Blocking, J. Atmos. Sci., 65, 1653–1665,
https://doi.org/10.1175/2007JAS2338.1, 2008. a
Ullmann, A., Fontaine, B., and Roucou, P.: Euro-Atlantic weather regimes and
Mediterranean rainfall patterns: Present-day variability and expected changes
under CMIP5 projections, Int. J. Climatol., 34,
2634–2650, https://doi.org/10.1002/JOC.3864, 2014. a, b
Van Der Wiel, K., Bloomfield, H. C., Lee, R. W., Stoop, L. P., Blackport, R.,
Screen, J. A., and Selten, F. M.: Environmental Research Letters The
influence of weather regimes on European renewable energy production and
demand Recent citations The influence of weather regimes on European
renewable energy production and demand, Environ. Res. Lett, 14, 9,
https://doi.org/10.1088/1748-9326/ab38d3, 2019. a
Vissio, G., Lembo, V., Lucarini, V., and Ghil, M.: Evaluating the Performance
of Climate Models Based on Wasserstein Distance, Geophys. Res. Lett., 47, 21, https://doi.org/10.1029/2020GL089385, 2020. a
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., Douville,
H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G.,
Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, 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
Wang, L., Ting, M., and Kushner, P. J.: A robust empirical seasonal prediction
of winter NAO and surface climate, Sci. Rep., 7, 279,
https://doi.org/10.1038/s41598-017-00353-y, 2017. a
Weisheimer, A., Schaller, N., O'Reilly, C., MacLeod, D. A., and Palmer, T.:
Atmospheric seasonal forecasts of the twentieth century: multi-decadal
variability in predictive skill of the winter North Atlantic Oscillation
(NAO) and their potential value for extreme event attribution, Q. J. Roy. Meteor. Soc., 143, 917–926,
https://doi.org/10.1002/qj.2976, 2017. a
White, R. H., Hilgenbrink, C., and Sheshadri, A.: The Importance of Greenland
in Setting the Northern Preferred Position of the North Atlantic Eddy-Driven
Jet, Geophys. Res. Lett., 46, 14126–14134,
https://doi.org/10.1029/2019GL084780, 2019. a
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D.,
Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson,
S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J.
G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L.,
Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and
Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and
GC3.1) Configurations, J. Adv. Model Earth Sy., 10,
357–380, https://doi.org/10.1002/2017MS001115, 2018.
a
Woollings, T. and Blackburn, M.: The North Atlantic Jet Stream under Climate
Change and Its Relation to the NAO and EA Patterns, J. Climate, 25,
886–902, https://doi.org/10.1175/JCLI-D-11-00087.1, 2012. a
Woollings, T., Hannachi, A., and Hoskins, B.: Variability of the North
Atlantic eddy-driven jet stream, Q. J. Roy. Meteor. Soc., 136, 856–868, https://doi.org/10.1002/qj.625, 2010. a, b, c
Woollings, T., Czuchnicki, C., and Franzke, C.: Twentieth century North
Atlantic jet variability, Q. J. Roy. Meteor. Soc., 140, 783–791, https://doi.org/10.1002/qj.2197, 2014. a
Woollings, T., Barnes, E., Hoskins, B., Kwon, Y. O., Lee, R. W., Li, C.,
Madonna, E., McGraw, M., Parker, T., Rodrigues, R., Spensberger, C., and
Williams, K.: Daily to decadal modulation of jet variability, J. Climate, 31, 1297–1314, https://doi.org/10.1175/JCLI-D-17-0286.1, 2018. a, b, c
Woollings, T. J., Hoskins, B., Blackburn, M., and Berrisford, P.: A new Rossby
wave-breaking interpretation of the North Atlantic Oscillation, J. Atmos. Sci., 65, 609–626, https://doi.org/10.1175/2007JAS2347.1, 2008. a
Yamazaki, A. and Itoh, H.: Vortex-vortex interactions for the maintenance of
blocking. part I: The selective absorption mechanism and a case study,
J. Atmos. Sci., 70, 725–742,
https://doi.org/10.1175/JAS-D-11-0295.1, 2013. a
Zhang, W., Kirtman, B., Siqueira, L., Clement, A., and Xia, J.: Understanding
the signal-to-noise paradox in decadal climate predictability from CMIP5 and
an eddying global coupled model, Clim. Dynam., 56, 2895–2913,
https://doi.org/10.1007/s00382-020-05621-8, 2021. a, b
Short summary
We investigate how well current state-of-the-art climate models reproduce the wintertime weather of the North Atlantic and western Europe by studying how well different "regimes" of weather are captured. Historically, models have struggled to capture these regimes, making it hard to predict future changes in wintertime extreme weather. We show models can capture regimes if the right method is used, but they show biases, partially as a result of biases in jet speed and eddy strength.
We investigate how well current state-of-the-art climate models reproduce the wintertime weather...
