Abstract
Global Climate Models (GCMs) generally exhibit significant biases in the representation of large-scale atmospheric circulation. Even after a sensible bias adjustment these errors remain and are inherited to some extent by the derived downscaling products, impairing the credibility of future regional projections. In this study we perform a process-based evaluation of state-of-the-art GCMs from CMIP5 and CMIP6, with a focus on the simulation of the synoptic climatological patterns having a most prominent effect on the European climate. To this aim, we use the Lamb Weather Type Classification (LWT, Lamb British isles weather types and a register of the daily sequence 736 of circulation patterns 1861-1971. METEOROL OFF, GEOPHYS MEM; 737 GB; DA 1972; NO 116; PP 1-85; BIBL 2P1/2, 1972), a subjective classification of circulation weather types constructed upon historical simulations of daily mean sea level pressure. Observational uncertainty has been taken into account by considering four different reanalysis products of varying characteristics. Our evaluation unveils an overall improvement of salient atmospheric circulation features consistent across observational references, although this is uneven across models and large frequency biases still remain for the main LWTs. Some CMIP6 models attain similar or even worse results than their CMIP5 counterparts, although in most cases consistent improvements have been found, demonstrating the ability of the new models to better capture key synoptic conditions. In light of the large differences found across models, we advocate for a careful selection of driving GCMs in downscaling experiments with a special focus on large-scale atmospheric circulation aspects.
This is a preview of subscription content, access via your institution.






Availability of data and materials
The data and methods used in this paper are briefly illustrated in the associated paper notebook, available in the public GitHub repository at https://github.com/SantanderMetGroup/notebooks/tree/devel (2020_Lamb_ClimDyn.* files). Details on data accessibility and software code reproducing the analyses in this paper is provided.
References
Addor N, Rohrer M, Furrer R, Seibert J (2016) Propagation of biases in climate models from the synoptic to the regional scale: implications for bias adjustment. J Geophys Res Atmos 121(5):2075–2089. https://doi.org/10.1002/2015JD024040
Anstey JA, Davini P, Gray LJ, Woollings TJ, Butchart N, Cagnazzo C, Christiansen B, Hardiman SC, Osprey SM, Yang S (2013) Multi-model analysis of northern hemisphere winter blocking: model biases and the role of resolution. J Geophys Res Atmos 118(10):3956–3971. https://doi.org/10.1002/jgrd.50231
Barriopedro D, Fischer EM, Luterbacher J, Trigo RM, Garcia-Herrera R (2011) The Hot Summer of 2010: redrawing the temperature record map of Europe. Science 332(6026):220–224. https://doi.org/10.1126/science.1201224
Berckmans J, Woollings T, Demory ME, Vidale PL, Roberts M (2013) Atmospheric blocking in a high resolution climate model: influences of mean state, orography and eddy forcing. Atmos Sci Lett 14(1):34–40. https://doi.org/10.1002/asl2.412
Bladé I, Liebmann B, Fortuny D, Oldenborgh GJV (2011) Observed and simulated impacts of the summer NAO in Europe: implications for projected drying in the Mediterranean region. Clim Dyn 39(3–4):709–727. https://doi.org/10.1007/s00382-011-1195-x
Boé J (2018) Interdependency in multimodel climate projections: component replication and result similarity. Geophys Res Lett 45(6):2771–2779. https://doi.org/10.1002/2017GL076829
Brands S, Herrera S, Fernández J, Gutiérrez JM (2013) How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa? Clim Dyn 41(3):803–817. https://doi.org/10.1007/s00382-013-1742-8
Brands S, Herrera S, Gutiérrez J (2014) Is Eurasian snow cover in October a reliable statistical predictor for the wintertime climate on the Iberian Peninsula?: Is the wintertime climate in Iberia driven by Eurasian snow cover? Int J Climatol 34:1615–1627. https://doi.org/10.1002/joc.3788
Buehler T, Raible CC, Stocker TF (2011) The relationship of winter season North Atlantic blocking frequencies to extreme cold or dry spells in the ERA-40. Tellus A Dyn Meteorol Oceanogr 63(2):174–187. https://doi.org/10.1111/j.1600-0870.2010.00492.x
Busuioc A, Chen D, Hellström C (2001) Performance of statistical downscaling models in GCM validation and regional climate change estimates: application for Swedish precipitation: STATISTICAL DOWNSCALING FOR SWEDISH PRECIPITATION. Int J Climatol 21(5):557–578. https://doi.org/10.1002/joc.624
Cannon A (2020) Reductions in daily continental-scale atmospheric circulation biases between generations of Global Climate Models: CMIP5 to CMIP6. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab7e4f
Casanueva A, Rodríguez-Puebla C, Frías MD, González-Reviriego N (2014) Variability of extreme precipitation over Europe and its relationships with teleconnection patterns. Hydrol Earth Syst Sci 18(2):709–725. https://doi.org/10.5194/hess-18-709-2014
Casanueva A, Kotlarski S, Fischer A, Flouris A, Kjellstrom T, Lemke B, Nybo L, Schwierz C, Liniger M (2020) Escalating environmental summer heat exposure—a future threat for the European workforce. Regional Environ Change 20(2), https://doi.org/10.1007/s10113-020-01625-6
Chang EKM, Guo Y, Xia X (2012) Cmip5 multimodel ensemble projection of storm track change under global warming. J Geophys Res Atmos 117(D23), https://doi.org/10.1029/2012JD018578
Chang E, Yau A (2016) Northern hemisphere winter storm track trends since 1959 derived from multiple reanalysis datasets. Clim Dyn 47. https://doi.org/10.1007/s00382-015-2911-8
Colle BA, Zhang Z, Lombardo KA, Chang E, Liu P, Zhang M (2013) Historical Evaluation and Future Prediction of Eastern North American and Western Atlantic Extratropical Cyclones in the CMIP5 Models during the Cool Season. J Clim 26(18):6882–6903. https://doi.org/10.1175/JCLI-D-12-00498.1
Cover TM, Thomas JA (2006) Elements of Information Theory. John Wiley & Sons, Hoboken
D’Andrea F, Tibaldi S, Blackburn M, Boer G, Déqué M, Dix MR, Dugas B, Ferranti L, Iwasaki T, Kitoh A, Pope V, Randall D, Roeckner E, Strauss D, Stern W, Van den Dool H, Williamson D (1998) Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979–1988. Clim Dyn 14(6):385–407. https://doi.org/10.1007/s003820050230
Davini P, Corti S, D’Andrea F, Rivière G, von Hardenberg J (2017) Improved winter European atmospheric blocking frequencies in high-resolution global climate simulations. J Adv Model Earth Syst 9(7):2615–2634. https://doi.org/10.1002/2017MS001082
Dawson A, Palmer TN (2015) Simulating weather regimes: impact of model resolution and stochastic parameterization. Clim Dyn 44(7):2177–2193. https://doi.org/10.1007/s00382-014-2238-x
Dawson A, Palmer TN, Corti S (2012) Simulating regime structures in weather and climate prediction models: REGIMES IN WEATHER AND CLIMATE MODELS. Geophys Res Lett 39(21):n/a–n/a, https://doi.org/10.1029/2012GL053284
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kaallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J, Park B, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. https://doi.org/10.1002/qj.828
Diaconescu EP, Laprise R (2013) Can added value be expected in RCM-simulated large scales? Clim Dyn 41(7–8):1769–1800. https://doi.org/10.1007/s00382-012-1649-9
Dosio A (2016) Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models: BIAS-ADJUSTED CLIMATE CHANGE INDICES. J Geophys Res Atmos 121(10):5488–5511. https://doi.org/10.1002/2015JD024411
Drost HG (2018) Philentropy: information theory and distance quantification with R. J Open Source Softw 3(26):765. https://doi.org/10.21105/joss.00765
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization. Geosci Model Dev 9(5):1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Fabiano F, Christensen HM, Strommen K, Athanasiadis P, Baker A, Schiemann R, Corti S (2020) Euro-Atlantic weather regimes in the primavera coupled climate simulations: impact of resolution and mean state biases on model performance. Clim Dyn 54(11):5031–5048. https://doi.org/10.1007/s00382-020-05271-w
Favà V, Curto JJ, Llasat MC (2015) Relationship between the summer NAO and maximum temperatures for the Iberian Peninsula. Theor Appl Climatol pp 1–15, https://doi.org/10.1007/s00704-015-1547-2
Fealy R, Mills G (2018) Deriving Lamb weather types suited to regional climate studies: A case study on the synoptic origins of precipitation over Ireland. Int J Climatol 38(8):3439–3448. https://doi.org/10.1002/joc.5495, 00000
Fernández J, Frías MD, Cabos WD, Cofiño AS, Domínguez M, Fita L, Gaertner MA, García-Díez M, Gutiérrez JM, Jiménez-Guerrero P, Liguori G, Montávez JP, Romera R, Sánchez E (2019) Consistency of climate change projections from multiple global and regional model intercomparison projects. Clim Dyn 52(1):1139–1156. https://doi.org/10.1007/s00382-018-4181-8
Folland CK, Knight J, Linderholm HW, Fereday D, Ineson S, Hurrell JW (2009) The Summer North Atlantic Oscillation: past, present, and future. J Clim 22(5):1082–1103. https://doi.org/10.1175/2008JCLI2459.1
Fujiwara M, Wright JS, Manney GL, Gray LJ, Anstey J, Birner T, Davis S, Gerber EP, Harvey VL, Hegglin MI, Homeyer CR, Knox JA, Krüger K, Lambert A, Long CS, Martineau P, Molod A, Monge-Sanz BM, Santee ML, Tegtmeier S, Chabrillat S, Tan DGH, Jackson DR, Polavarapu S, Compo GP, Dragani R, Ebisuzaki W, Harada Y, Kobayashi C, McCarty W, Onogi K, Pawson S, Simmons A, Wargan K, Whitaker JS, Zou CZ (2017) Introduction to the sparc reanalysis intercomparison project (s-rip) and overview of the reanalysis systems. Atmos Chem Phys 17(2):1417–1452. https://doi.org/10.5194/acp-17-1417-2017
Harada Y, Kamahori H, Kobayashi C, Endo H, Kobayashi S, Ota Y, Onoda H, Onogi K, Miyaoka K, Takahashi K (2016) The jra-55 reanalysis: representation of atmospheric circulation and climate variability. J Meteorol Soc Jpn Ser II 94(3):269–302. https://doi.org/10.2151/jmsj.2016-015
Hochman A, Alpert P, Harpaz T, Saaroni H, Messori G (2019) A new dynamical systems perspective on atmospheric predictability: Eastern mediterranean weather regimes as a case study. Sci Adv 5(6):eaau0936. https://doi.org/10.1126/sciadv.aau0936
Hulme M, Briffal K, Jones P, Senior C (1993) Validation of gcm control simulations using indices of daily airflow types over the British isles. Clim Dyn 9(2):95–105. https://doi.org/10.1007/BF00210012
Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (2003) An overview of the North Atlantic Oscillation. In: Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (eds) Geophysical Monograph Series, vol 134. American Geophysical Union. Washington, D. C., pp 1–35
Iturbide M, Bedia J, Herrera S, Baño-Medina J, Fernández J, Frías M, Manzanas R, San-Martín D, Cimadevilla E, Cofiño A, Gutiérrez J (2019) The R-based climate4R open framework for reproducible climate data access and post-processing. Environ Model Softw 111:42–54. https://doi.org/10.1016/j.envsoft.2018.09.009
Iturbide M, Casanueva A, Bedia J, Herrera S, Milovac J, Gutiérrez J (2020) On the need of bias adjustment for more plausible climate change projections of extreme heat. Atmos Sci Lett Submitted
Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, Ev Meijgaard, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Regional Environ Change 14(2):563–578. https://doi.org/10.1007/s10113-013-0499-2
Jacob D, Teichmann C, Sobolowski S, Katragkou E, Anders I, Belda M, Benestad R, Boberg F, Buonomo E, Cardoso RM, Casanueva A, Christensen OB, Christensen JH, Coppola E, De Cruz L, Davin EL, Dobler A, Domínguez M, Fealy R, Fernandez J, Gaertner MA, García-Díez M, Giorgi F, Gobiet A, Goergen K, Gómez-Navarro JJ, Alemán JJG, Gutiérrez C, Gutiérrez JM, Güttler I, Haensler A, Halenka T, Jerez S, Jiménez-Guerrero P, Jones RG, Keuler K, Kjellström E, Knist S, Kotlarski S, Maraun D, van Meijgaard E, Mercogliano P, Montávez JP, Navarra A, Nikulin G, de Noblet-Ducoudré N, Panitz HJ, Pfeifer S, Piazza M, Pichelli E, Pietikäinen JP, Prein AF, Preuschmann S, Rechid D, Rockel B, Romera R, Sánchez E, Sieck K, Soares PMM, Somot S, Srnec L, Sørland SL, Termonia P, Truhetz H, Vautard R, Warrach-Sagi K, Wulfmeyer V (2020) Regional climate downscaling over europe: perspectives from the EURO-CORDEX community. Regional Environ Change 20(2):51. https://doi.org/10.1007/s10113-020-01606-9
Jenkinson A, Collison F (1977) An initial climatology of gales over the north sea. synoptic climatology branch memorandum. Meteorol Off pp 1–62
Jiang B, Pei J, Tao Y, Lin X (2011) Clustering uncertain data based on probability distribution similarity. IEEE Trans Knowl Data Eng 25(4):751–763
Jones PD, Hulme M, Briffa KR (1993) A comparison of Lamb circulation types with an objective classification scheme. Int J Climatol 13(6):655–663. https://doi.org/10.1002/joc.3370130606
Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over europe using a nested regional-climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries. Q J R Meteorol Soc 121(526):1413–1449. https://doi.org/10.1002/qj.49712152610
Jones PD, Harpham C, Briffa KR (2013) Lamb weather types derived from reanalysis products. Int J Climatol 33(5):1129–1139. https://doi.org/10.1002/joc.3498
Jung T, Miller MJ, Palmer TN, Towers P, Wedi N, Achuthavarier D, Adams JM, Altshuler EL, Cash BA, Kinter IJL, Marx L, Stan C, Hodges KI (2012) High-Resolution Global Climate Simulations with the ECMWF Model in Project Athena: Experimental Design, Model Climate, and Seasonal Forecast Skill. J Clim 25(9):3155–3172. https://doi.org/10.1175/JCLI-D-11-00265.1
Jury MW, Herrera S, Gutiérrez JM, Barriopedro D (2019) Blocking representation in the ERA-Interim driven EURO-CORDEX RCMs. Clim Dyn 52(5–6):3291–3306. https://doi.org/10.1007/s00382-018-4335-8
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77(3):437–472. DOI https://doi.org/10.1175/1520-0477(1996)077%3c0437:TNYRP%3e2.0.CO;2, publisher: American Meteorological Society
Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K, Takahashi K (2015) The jra-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn Ser II 93(1):5–48. https://doi.org/10.2151/jmsj.2015-001
Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86
Lamb H (1972) British isles weather types and a register of the daily sequence of circulation patterns 1861-1971. METEOROL OFF, GEOPHYS MEM; GB; DA 1972; NO 116; PP 1-85; BIBL 2P1/2
Maraun D, Shepherd TG, Widmann M, Zappa G, Walton D, Gutiérrez JM, Hagemann S, Richter I, Soares PMM, Hall A, Mearns LO (2017) Towards process-informed bias correction of climate change simulations. Nat Clim Change 7(11):664–773. https://doi.org/10.1038/nclimate3418
Masato G, Hoskins BJ, Woollings TJ (2012) Wave-breaking characteristics of midlatitude blocking. Q J R Meteorol Soc 138(666):1285–1296. https://doi.org/10.1002/qj.990
Masato G, Hoskins BJ, Woollings T (2013) Winter and Summer Northern Hemisphere Blocking in CMIP5 Models. J Clim 26(18):7044–7059. https://doi.org/10.1175/JCLI-D-12-00466.1
Matsueda M, Mizuta R, Kusunoki S (2009) Future change in wintertime atmospheric blocking simulated using a 20-km-mesh atmospheric global circulation model. J Geophys Res Atmos 114(D12), https://doi.org/10.1029/2009JD011919
McSweeney C, Jones R, Lee RW, Rowell D (2015) Selecting CMIP5 GCMs for downscaling over multiple regions. Clim Dyn 44(11):3237–3260. https://doi.org/10.1007/s00382-014-2418-8
O’Reilly CH, Minobe S, Kuwano-Yoshida A (2016) The influence of the gulf stream on wintertime European blocking. Clim Dyn 47(5):1545–1567. https://doi.org/10.1007/s00382-015-2919-0
Otero N, Sillmann J, Butler T (2018) Assessment of an extended version of the Jenkinson-Collison classification on CMIP5 models over Europe. Clim Dyn 50(5):1559–1579. https://doi.org/10.1007/s00382-017-3705-y
Pereira S, Ramos A, Rebelo L, Trigo R, Zêzere J (2018) A centennial catalogue of hydro-geomorphological events and their atmospheric forcing. Adv Water Resour 122:98–112. https://doi.org/10.1016/j.advwatres.2018.10.001
Perez J, Menendez M, Mendez FJ, Losada IJ (2014) Evaluating the performance of CMIP3 and CMIP5 global climate models over the north–east Atlantic region. Clim Dyn 43(9):2663–2680. https://doi.org/10.1007/s00382-014-2078-8
Poli P, Hersbach H, Dee DP, Berrisford P, Simmons AJ, Vitart F, Laloyaux P, Tan DG, Peubey C, Thépaut JN, Trémolet Y, Hólm EV, Bonavita M, Isaksen L, Fisher M (2016) ERA-20C: an atmospheric reanalysis of the twentieth century. J Clim 29(11):4083–4097. https://doi.org/10.1175/JCLI-D-15-0556.1
Prein AF, Bukovsky MS, Mearns LO, Bruyère CL, Done JM (2019) Simulating North American weather types with regional climate models. Front Environ Sci 7:36. https://doi.org/10.3389/fenvs.2019.00036
R Core Team (2020) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/
Ramos AM, Cortesi N, Trigo RM (2014) Circulation weather types and spatial variability of daily precipitation in the Iberian Peninsula. Front Earth Sci 2. https://doi.org/10.3389/feart.2014.00025, 00037
Rex DF (1950) Blocking action in the middle troposphere and its effect upon regional climate. Tellus 2(3):196–211. https://doi.org/10.1111/j.2153-3490.1950.tb00331.x
Rohrer M, Brönnimann S, Martius O, Raible CC, Wild M, Compo GP (2018) Representation of Extratropical Cyclones, Blocking Anticyclones, and Alpine Circulation Types in Multiple Reanalyses and Model Simulations. J Clim 31(8):3009–3031. https://doi.org/10.1175/JCLI-D-17-0350.1
Sharma KK, Seal A (2019) Modeling uncertain data using monte Carlo integration method for clustering. Expert Syst Appl 137:100–116. https://doi.org/10.1016/j.eswa.2019.06.050
Sillmann J, Croci-Maspoli M (2009) Present and future atmospheric blocking and its impact on European mean and extreme climate. Geophys Res Lett 36(10):L10,702. https://doi.org/10.1029/2009GL038259
Sousa PM, Trigo RM, Barriopedro D, Soares PMM, Ramos AM, Liberato MLR (2017) Responses of European precipitation distributions and regimes to different blocking locations. Clim Dyn 48(3–4):1141–1160. https://doi.org/10.1007/s00382-016-3132-5
Strommen K, Mavilia I, Corti S, Matsueda M, Davini P, von Hardenberg J, Vidale PL, Mizuta R (2019) The sensitivity of euro-Atlantic regimes to model horizontal resolution. Geophys Res Lett 46(13):7810–7818. https://doi.org/10.1029/2019GL082843
Stryhal J, Huth R (2017) Classifications of Winter Euro-Atlantic Circulation Patterns: An Intercomparison of Five Atmospheric Reanalyses. J Clim 30(19):7847–7861. https://doi.org/10.1175/JCLI-D-17-0059.1
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of cmip5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Trigo RM, DaCamara CC (2000) Circulation weather types and their influence on the precipitation regime in Portugal. Int J Climatol p 23, DOI 10.1002/1097-0088(20001115)20:13%3C1559::AID-JOC555%3E3.0.CO;2-5
Turco M, Sanna A, Herrera S, Llasat MC, Gutiérrez JM (2013) Large biases and inconsistent climate change signals in ENSEMBLES regional projections. Clim Change 120(4):859–869. https://doi.org/10.1007/s10584-013-0844-y
Vial J, Osborn T (2012) Assessment of atmosphere-ocean general circulation model simulations of winter northern hemisphere atmospheric blocking. Clim Dyn 39:95–112. https://doi.org/10.1007/s00382-011-1177-z
Wang XL, Swail VR, Zwiers FW (2006) Climatology and Changes of Extratropical Cyclone Activity: Comparison of ERA-40 with NCEP-NCAR Reanalysis for 1958–2001. J Clim 19(13):3145–3166. https://doi.org/10.1175/JCLI3781.1
Weijs SV, van Nooijen R, van de Giesen N (2010) Kullback–Leibler divergence as a forecast skill score with classic reliability-resolution-uncertainty decomposition. Mon Weather Rev 138(9):3387–3399. https://doi.org/10.1175/2010MWR3229.1
WMO (2017) WMO Guidelines on the Calculation of Climate Normals. Tech. Rep. WMO No. 1203, World Meteorological Organization, ISBN:978-92-63-11203-3
Woollings T, Hoskins B, Blackburn M, Berrisford P (2008) A New Rossby Wave-Breaking Interpretation of the North Atlantic Oscillation. J Atmos Sci 65(2):609–626. https://doi.org/10.1175/2007JAS2347.1
Zappa G, Shaffrey LC, Hodges KI (2013) The Ability of CMIP5 Models to Simulate North Atlantic Extratropical Cyclones*. J Clim 26(15):5379–5396. https://doi.org/10.1175/JCLI-D-12-00501.1
Acknowledgements
We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. J.A.F., A.C and J.B. acknowledge funding from the Project INDECIS, part of European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the European Union Grant 690462. J.F. acknowledges support from the Spanish R&D Program through project INSIGNIA (CGL2016-79210-R), co-funded by the European Regional Development Fund (ERDF/ FEDER). We also thank the Santander Climate Data Service (http://scds.es) and our colleagues Antonio Cofiño and Ezequiel Cimadevilla for their support. Sixto Herrera and José M. Gutiérrez provided useful comments on earlier stages of this study. Finally, we thank two anonymous referees for their insightful comments that helped to improve the original manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Fernandez-Granja, J.A., Casanueva, A., Bedia, J. et al. Improved atmospheric circulation over Europe by the new generation of CMIP6 earth system models. Clim Dyn 56, 3527–3540 (2021). https://doi.org/10.1007/s00382-021-05652-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-021-05652-9
Keywords
- Lamb weather type classification
- Comparison CMIP5-CMIP6
- Process-based evaluation