Advertisement

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe

  • Robert Vautard
  • Nikolaos Christidis
  • Andrew Ciavarella
  • Carmen Alvarez-Castro
  • Omar Bellprat
  • Bo Christiansen
  • Ioana Colfescu
  • Tim Cowan
  • Francisco Doblas-Reyes
  • Jonathan Eden
  • Mathias Hauser
  • Gabriele Hegerl
  • Nils Hempelmann
  • Katharina Klehmet
  • Fraser Lott
  • Cathy Nangini
  • René Orth
  • Sabine Radanovics
  • Sonia I. Seneviratne
  • Geert Jan van Oldenborgh
  • Peter Stott
  • Simon Tett
  • Laura Wilcox
  • Pascal Yiou
Article

Abstract

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed sea surface temperature of the 54 year period 1960–2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land–atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North-Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution.

Notes

Acknowledgements

This study was part of the European Climate and weather Events: Interpretation and Attribution (EUCLEIA) FP7 SPACE project, Grant agreement no 607085, and concerned principally its Work Package 6 (Evaluation and diagnostics). NC and AC, FL and PS were also supported by the Joint BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101).

Supplementary material

382_2018_4183_MOESM1_ESM.docx (4.8 mb)
Supplementary material 1 (DOCX 4887 KB)

References

  1. Alexander L (2016) Global observed long-term changes in temperature and precipitation extremes: a review of progress and limitations in IPCC assessments and beyond. Weather Clim Extremes 11:4–16CrossRefGoogle Scholar
  2. Angelil O, Perkins-Kirkpatrick S, Alexander LV et al (2016) Comparing regional precipitation and temperature extremes in climate model and reanalysis products. Weather Clim Extremes 13:35–43CrossRefGoogle Scholar
  3. Baldwin MP, Dunkerton TJ (1999) Propagation of the Arctic oscillation from the stratosphere to the troposphere. J Geophys Res Atmos 104(D24):30937–30946CrossRefGoogle Scholar
  4. Bellprat O, Doblas-Reyes F (2016) Attribution of extreme weather and climate events overestimated by unreliable climate simulations. Geophys Res Lett.  https://doi.org/10.1002/2015GL067189 Google Scholar
  5. Bellprat O, Kotlarski S, Lüthi D, Schär C (2014) Physical constraints for temperature biases in climate models. Geophys Res Lett 40(15):4042–4047CrossRefGoogle Scholar
  6. Bindoff NL, Stott PA, AchutaRao KM, Allen MR, Gillett N, Gutzler D, Hansingo K, Hegerl G et al (2013) Chapter 10—detection and attribution of climate change: from global to regional. In: Climate change 2013: The physical science basis. IPCC working group I contribution to AR5. Cambridge University Press, CambridgeGoogle Scholar
  7. Blauhut V, Gudmundsson L, Stahl K (2015) Towards pan-European drought risk maps: quantifying the link between drought indices and reported drought impacts. Environ Res Lett 10:014008CrossRefGoogle Scholar
  8. Burke C, Stott PA, Sun Y, Ciavarella A (2016) Attribution of extreme rainfall in southeast china during, May 2015, In “explaining extremes of 2014 from a climate perspective”. Bull Am Meteor Soc 97(12):S92–S96CrossRefGoogle Scholar
  9. Cassou C, Terray L, Phillips AS (2015) Tropical Atlantic influence on European heat waves. J Clim 18:2805–2811Google Scholar
  10. Cattiaux J, Vautard R, Cassou C, Yiou P, Masson-Delmotte V, Codron F (2010) Winter 2010 in Europe: a cold extreme in a warming climate. Geophys Res Lett 37:L20704.  https://doi.org/10.1029/2010GL044613 CrossRefGoogle Scholar
  11. Ciavarella A, Christidis N, Andrews M, Groenendijk M, Rostron J, Elkington M, Burke C, Lott FC, Stott PA (2018) Upgrade of the HadGEM3-A based attribution system to high resolution and a new validation framework for probabilistic event attribution. Weather Clim Extremes, Special Issue: First results from the C20C+ Detection and Attribution Project (in press)Google Scholar
  12. Christiansen B (2001) Downward propagation of zonal mean zonal wind anomalies from the stratosphere to the troposphere: model and reanalysis. J Geophys Res Atmos 106(D21):27307–27322CrossRefGoogle Scholar
  13. Christiansen B, Alvarez-Castro C, Christidis N, Ciavarella A, Colfescu I, Cowan T, Edenf J, Hauserg M, Hempelmannb N, Klehmeth K, Lott F, Nangini C, Jan van Oldenborgh G, Orth R, Stott P, Tett S, Vautard R, Wilcox L, Yioub P (2018) Was the Cold European Winter of 2009/10 Modified by Anthropogenic Climate Change? An Attribution Study. J Clim 31(9):3387–3410Google Scholar
  14. Christidis N, Stott PA, Scaife A, Arribas A, Jones GS, Copsey D, Knight JR, Tennant WJ (2013a) A new HadGEM3-A based system for attribution of weather and climate-related extreme events. J Clim 26:2756–2783CrossRefGoogle Scholar
  15. Christidis N, Stott PA, Karoly DJ, Ciavarella A (2013b) An attribution study of the heavy rainfall over eastern Australia in March 2012, In “explaining extremes of 2012 from a climate perspective”. Bull Am Meteor SocGoogle Scholar
  16. Christidis N, Stott PA, Ciavarella A (2014) The effect of anthropogenic climate change on the cold spring of 2013 in the UK. In “explaining extremes of 2013 from a climate perspective”. Bull Am Meteor Soc 95(9):S79–S82Google Scholar
  17. Christidis N, McCarthy M, Ciavarella A, Stott PA (2016) Human contribution to the record sunshine of 2014/15 in the United Kingdom, in “explaining extremes of 2015 from a climate perspective”. Bull Am Meteor SocGoogle Scholar
  18. Coles S, Bawa J, Trenner L, Dorazio P (2001) An introduction to statistical modeling of extreme values, vol 208. Springer, LondonCrossRefGoogle Scholar
  19. Corti T, Muccione V, Köllner-Heck P, Bresch D, Seneviratne SI (2009) Simulating past droughts and associated building damages in France. Hydrol Earth Syst Sci 13:1739–1747CrossRefGoogle Scholar
  20. Cowan T, Purich A, Perkins S, Pezza A, Boschat G, Sadler K (2014) More frequent, longer, and hotter heat waves for australia in the twenty-first century. J Clim 27:5851–5871.  https://doi.org/10.1175/JCLI-D-14-00092.1 CrossRefGoogle Scholar
  21. Cowan T, Hegerl G, Colfescu I, Purich A, Boshcat G (2017) Factors contributing to record-breaking heat waves over the Great Plains during the 1930s Dust Bowl. J Clim.  https://doi.org/10.1175/JCLI-D-16-0436.1 (in press) Google Scholar
  22. Deser C, Hurrell JW, Phillips AS (2016) The role of the North Atlantic oscillation in European climate projections. Clim Dyn.  https://doi.org/10.1007/s00382-016-3502-z Google Scholar
  23. Eden JM, Wolter K, Otto FEL, Oldenborgh GJ van (2016) Multi-method attribution analysis of extreme precipitation in Boulder, Colorado. Env Res Lett 11:124009.  https://doi.org/10.1088/1748-9326/11/12/124009 CrossRefGoogle Scholar
  24. Eden JM, Bellprat O, Kew S, Lenderink G, Manola I, Omrani H, Oldenborgh GJ van (2017) Extreme precipitation in the Netherlands: an event attribution case study. Clim Dyn (submitted) Google Scholar
  25. Fischer EM, Rajczak J, Schär C (2012) Changes in European summer temperature variability revisited. Geophys Res Lett 39(19).  https://doi.org/10.1029/2012GL052730
  26. Geyer B (2014) High-resolution atmospheric reconstruction for Europe 1948–2012: coastDat2. Earth Syst Sci Data 6:147–164CrossRefGoogle Scholar
  27. Hauser M, Orth R, Seneviratne SI (2016) Role of soil moisture versus recent climate change for the 2010 heat wave in Russia. Geophys Res Lett 43:2819–2826.  https://doi.org/10.1002/2016GL068036 CrossRefGoogle Scholar
  28. Hauser M, Gudmundsson L, Orth R, Jézéquel A, Haustein K, Vautard R, van Oldenborgh GJ, Seneviratne SI (2017) Methods and model dependency of extreme event attribution: the 2015 European drought. Earth’s Future 5(10):1034–1043CrossRefGoogle Scholar
  29. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation. J Geophys Res 113:D20119CrossRefGoogle Scholar
  30. Hegerl G, Zwiers F (2011) Use of models in detection and attribution of climate change. Wiley Interdiscip Rev Clim Change 2(4):570–591CrossRefGoogle Scholar
  31. Hirschi M, Seneviratne SI, Alexandrov V, Boberg F, Boroneant C, Christensen OB, Formayer H, Orlowsky B, Stepanek P (2011) Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nat Geosci 4(1):17–21CrossRefGoogle Scholar
  32. Hollander M, Wolfe DA (1999) Nonparametric statistical methods. John Wiley, Hoboken, p 787Google Scholar
  33. Hünicke B, Zorita E et al (2015) The BACC II author team, second assessment of climate change for the Baltic Sea basin, regional climate studies.  https://doi.org/10.1007/978-3-319-16006-1$49
  34. Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (2003) An overview of the North Atlantic oscillation. American Geophysical Union, Washington, DC, pp 1–35Google Scholar
  35. Jones CD, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O’Connor FM, Andres RJ, Bell C, Boo K-O, Bozzo A, Butchart N, Cadule P, Corbin KD, Doutriaux-Boucher M, Friedlingstein P, Gornall J, Gray L, Halloran PR, Hurtt G, Ingram WJ, Lamarque J-F, Law RM, Meinshausen M, Osprey S, Palin EJ, Parsons Chin L, Raddatz T, Sanderson MG, Sellar AA, Schurer A, Valdes P, Wood N, Woodward S, Yoshioka M, Zerroukat M (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570CrossRefGoogle Scholar
  36. 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. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  37. Kapitza H (2008) Mops—a morphodynamical prediction system on cluster computers. In: Laginha JM, Palma M, Amestoy PR, Dayde M, Mattoso M, Lopez J (eds) High performance computing for computational science—VECPAR 2008. Springer, Berlin, pp 63–68 (Lecture Notes in Computer Science) CrossRefGoogle Scholar
  38. Kirtman B, Power SB, Adedoyin JA, Boer GJ, Bojariu R, Camilloni I, Doblas-Reyes FJ, Fiore AM, Kimoto M, Meehl GA, Prather M, Sarr A, Schär C, Sutton R, van Oldenborgh GJ, Vecchi G, Wang HJ (2013) Near-term climate change: projections and predictability. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 953–1028.  https://doi.org/10.1017/CBO9781107415324.023 Google Scholar
  39. Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82:247–267CrossRefGoogle Scholar
  40. Krueger O, Hegerl GC, Tett SF (2015) Evaluation of mechanisms of hot and cold days in climate models over Central Europe. Environ Res Lett 10(1):014002CrossRefGoogle Scholar
  41. Legras B, Ghil M (1985) Persistent anomalies, blocking and variations in atmospheric predictability. J Atmos Sci 42:433–471CrossRefGoogle Scholar
  42. Lott FC, Stott PA (2016) Evaluating simulated fraction of attributable risk using climate observations. J Clim 29(12):4565–4575CrossRefGoogle Scholar
  43. Lott F, Christidis N, Stott PA (2013) Can the 2011 East African drought be attributed to human-induced climate change? Geophys Res Lett 40:1177–1181CrossRefGoogle Scholar
  44. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology, vol 17. American Meteorological Society, Boston, pp 179–183Google Scholar
  45. Michelangeli PA, Vautard R, Legras B (1995) Weather regimes: recurrence and quasi-stationarity. J Atmos Sci 52:1237–1256CrossRefGoogle Scholar
  46. Mueller B, Hirschi M, Jimenez C, Ciais P, Dirmeyer PA, Dolman AJ, Fisher JB, Jung M, Ludwig F, Maignan F, Miralles D, McCabe MF, Reichstein M, Sheffield J, Wang KC, Wood EF, Zhang Y, Seneviratne SI (2013) Benchmark products for land evapotranspiration: LandFlux-EVAL multi-dataset synthesis. Hydrol Earth Syst Sci 17:3707–3720CrossRefGoogle Scholar
  47. National Academies of Sciences, Engineering, and Medicine (2016) Attribution of extreme weather events in the context of climate change. National Academies PressGoogle Scholar
  48. Orsolini YJ, Senan R, Balsamo G, Doblas-Reyes FJ, Vitart F, Weisheimer A, Carrasco A, Benestad RE (2013) Impact of snow initialization on sub-seasonal forecasts. Clim Dyn 41:1969–1982.  https://doi.org/10.1007/s00382-013-1782-0 CrossRefGoogle Scholar
  49. Orth R, Seneviratne SI (2015) Introduction of a simple-model-based land surface dataset for Europe. Environ Res Lett 10:044,012CrossRefGoogle Scholar
  50. Pall P, Aina T, Stone DA, Stott PA, Nozawa T, Hilberts AGJ, Lohmann D, Allen MR (2011) Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470(7334):382–385CrossRefGoogle Scholar
  51. Perkins SE, Alexander LV, Nairn JR (2012) Increasing frequency, intensity and duration of observed global heatwaves and warm spells. Geophys Res Lett 39:L20714.  https://doi.org/10.1029/2012GL053361 CrossRefGoogle Scholar
  52. Pezza AB, van Rensch P, Cai W (2012) Severe heat waves in Southern Australia: synoptic climatology and large scale connections. Clim Dyn 38:209–224.  https://doi.org/10.1007/s00382-011-1016-2 CrossRefGoogle Scholar
  53. Philip S, Kew SF, van Oldenborgh GJ, Aalbers E, Vautard R, Otto F, Haustein K, Habets F, Singh R, Cullen H (2017) Validation of a rapid attribution of the May/June 2016 flood-inducing precipitation in France to climate change. Clim Dyn (submitted) Google Scholar
  54. Quesada B, Vautard R, Yiou P, Hirschi M, Seneviratne SI (2012) Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat Clim Change 2:736–741CrossRefGoogle Scholar
  55. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res.  https://doi.org/10.1029/2002JD002670 Google Scholar
  56. Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM), editorial. Meteorol Z 12(4):347–348CrossRefGoogle Scholar
  57. Rosenzweig C, Iglesias A, Yang X (2001) Climate change and extreme weather events; implications for food production, plant diseases, and pests. Glob Change Hum Health 2:90–104CrossRefGoogle Scholar
  58. Schaller N, Kay AL, Lamb R, Massey NR, van Oldenborgh G-J, Otto FEL, Sparrow SN, Vautard R, Yiou P, Bowery A, Crooks SM, Huntingford C, Ingram W, Jones R, Legg T, Miller J, Skeggs J, Wallom D, Wilson S, Allen MR (2015) Human influence on climate in the 2014 Southern England winter floods and their impacts. Nat Clim Change.  https://doi.org/10.1038/nclimate2927 Google Scholar
  59. Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Orlowsky B, Teuling AJ (2010) Investigating soil moisture-climate interactions in a changing climate: a review. Earth Sci Rev 99:3–4, 125–161CrossRefGoogle Scholar
  60. Seneviratne SI et al (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge Univ. Press, Cambridge, pp 109–230CrossRefGoogle Scholar
  61. Seneviratne SI, Donat M, Pitman AJ, Knutti R, Wilby RL (2016) Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529:477–483CrossRefGoogle Scholar
  62. Sippel S, Otto FEL, Forkel M, Allen MR, Guillod BP, Heimann M, Reichstein M, Seneviratne SI, Thonicke K, Mahecha MD (2016) A novel bias correction methodology for climate impact simulations. Earth Syst Dyn 7:71–88.  https://doi.org/10.5194/esd-7-71-2016 CrossRefGoogle Scholar
  63. Stegehuis A, Vautard R, Ciais P, Teuling R, Jung M, Yiou P (2013) Summer temperatures in Europe and land heat fluxes in observation-based data and regional climate model simulations. Clim Dyn 41:455–477CrossRefGoogle Scholar
  64. Stott PA, Christidis N, Otto F, Sun Y, Vanderlinden J-P, van Oldenborgh G-J, Vautard R, von Storch H, Walton P, Yiou P, Zwiers FW (2016) Attribution of extreme weather and climate-related events. WIREs Clim Change 7:23–41CrossRefGoogle Scholar
  65. Sztobryn M, Stigge H-J, Wiebliński D, Weidig B, Stanislawczyk I, Kańska A, Krzysztofik B, Kowalska B, Letkiewicz B, Mykita M (2005) Storm surges in the Southern Baltic Sea (Western and Central Parts), Berichte des Bundesamtes für Seeschifffahrt und Hydrographie Nr. 39Google Scholar
  66. University of East Anglia Climatic Research Unit, Harris IC, Jones PD (2015) CRU TS3.23: climatic research unit (CRU) time-series (TS) version 3.23 of high resolution gridded data of month-by-month variation in climate (Jan 1901–Dec 2014). Centre Environ Data Anal.  https://doi.org/10.5285/4c7fdfa6-f176-4c58-acee-683d5e9d2ed5 Google Scholar
  67. van Oldenborgh GJ, Drijfhout S, Ulden AV, Haarsma R, Sterl A, Severijns C, Hazeleger W, Dijkstra H (2009) Western Europe is warming much faster than expected. Clim Past 5(1):1–12CrossRefGoogle Scholar
  68. van Haren R, van Oldenborgh GJ, Lenderink G, Collins M, Hazeleger W (2013) SST and circulation trend biases cause an underestimation of European precipitation trends. Clim Dyn 40(1–2):1–20CrossRefGoogle Scholar
  69. van Oldenborgh GJ, Reyes FD, Drijfhout SS, Hawkins E (2013) Reliability of regional climate model trends. Environ Res Lett 8(1):014055CrossRefGoogle Scholar
  70. Vautard R, Legras B (1988) On the source of midlatitude low-frequency variability. Part II: nonlinear equilibration of weather regimes. J Atmos Sci 45:2845–2867CrossRefGoogle Scholar
  71. Vautard R, Yiou P, D’Andrea F, de Noblet N, Viovy N, Cassou C, Polcher J, Ciais P, Kageyama M, Fan Y (2007) Summertime European heat and drought waves induced by wintertime Mediterranean rainfall deficit. Geophys Res Lett 34:L07711.  https://doi.org/10.1029/2006GL028001 CrossRefGoogle Scholar
  72. Wilcox LJ, Yiou P, Hauser M, Lott FC, van Oldenborgh GJ, Colfescu I, Dong B, Hegerl G, Shaffrey L, Sutton R (2017) Multiple perspectives on the attribution of the extreme European summer of 2012 to climate change. Clim Dyn.  https://doi.org/10.1007/s00382-017-3822-7 (First Online) Google Scholar
  73. Williams KD, Harris CM, Bodas-Salcedo A, Camp J, Comer RE, Copsey D, Fereday D, Graham T, Hill R, Hinton T, Hyder P, Ineson S, Masato G, Milton SF, Roberts MJ, Rowell DP, Sanchez C, Shelly A, Sinha B, Walters DN, West A, Woollings T, Xavier PK (2015) The met office global coupled model 2.0 (GC2) configuration. Geosci Model Dev 8:1509–1524CrossRefGoogle Scholar
  74. Zhao Y, Sultan B, Vautard R, Braconnot P, Wang HJ, Ducharne A (2016) Potential escalation of heat-related working costs with climate and socio-economic changes in China. Proc Natl Acad Sci 113:4640–4645CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Robert Vautard
    • 1
  • Nikolaos Christidis
    • 2
  • Andrew Ciavarella
    • 2
  • Carmen Alvarez-Castro
    • 1
  • Omar Bellprat
    • 3
  • Bo Christiansen
    • 4
  • Ioana Colfescu
    • 5
    • 10
  • Tim Cowan
    • 5
  • Francisco Doblas-Reyes
    • 3
  • Jonathan Eden
    • 6
  • Mathias Hauser
    • 7
  • Gabriele Hegerl
    • 5
  • Nils Hempelmann
    • 1
  • Katharina Klehmet
    • 8
  • Fraser Lott
    • 2
  • Cathy Nangini
    • 1
  • René Orth
    • 7
  • Sabine Radanovics
    • 1
  • Sonia I. Seneviratne
    • 7
  • Geert Jan van Oldenborgh
    • 6
  • Peter Stott
    • 2
  • Simon Tett
    • 5
  • Laura Wilcox
    • 9
  • Pascal Yiou
    • 1
  1. 1.Laboratoire des Sciences du Climat et de l’Environnement, Institut Pierre-Simon LaplaceUniversité Paris-SaclayGif sur YvetteFrance
  2. 2.UK Met Office Hadley CentreExeterUK
  3. 3.Barcelona Supercomputing CenterBarcelonaSpain
  4. 4.Danish Meteorological InstituteCopenhagenDenmark
  5. 5.School of GeoSciencesUniversity of EdinburghEdinburghUK
  6. 6.Royal Netherlands Meteorological Institute (KNMI)De BiltNetherlands
  7. 7.Institute for Atmospheric and Climate ScienceETH ZurichZurichSwitzerland
  8. 8.Institute of Coastal ResearchHelmholtz-Zentrum GeesthachtGeesthachtGermany
  9. 9.Department of MeteorologyUniversity of ReadingReadingUK
  10. 10.National Centre for Atmospheric Science, School of Earth and EnvironmentUniversity of LeedsLeedsUK

Personalised recommendations