Advertisement

Seasonal predictability of European summer climate re-assessed

  • Nele-Charlotte NeddermannEmail author
  • Wolfgang A. Müller
  • Mikhail Dobrynin
  • André Düsterhus
  • Johanna Baehr
Article

Abstract

We improve seasonal hindcast skill of European summer climate in an ensemble based coupled seasonal prediction system by selecting individual ensemble members based on their respective consistent chain of processes that describe a physical mechanism. This mechanism is associated with the second mode of seasonal climate variability in the North-Atlantic-European sector and is contrary to the summer North Atlantic Oscillation. We initially analyse the mechanism in the ERA-Interim reanalysis and then test the influence of the mechanism on European hindcast skill in an initialised coupled seasonal climate model. We show that the mechanism originates in the tropical North Atlantic in spring, where either warm or cold sea surface temperature anomalies (SSTs) are connected with the European climate by an upper-level wave-train. This wave-train is accompanied by a zonal pressure gradient, that in turn influences the climate over central Europe in the following summer. We analyse the seasonal summer hindcast skill in a mixed resolution hindcast ensemble simulation generated by MPI-ESM, with 30 members starting every year in May. While the mean over the full ensemble shows no seasonal hindcast skill in summer, we achieve significant hindcast skill through forming a new mean over subselected ensemble members. For this selection, we test every ensemble member for the proposed consistent chain of connections between the wave-train, the zonal pressure gradient and their impact on European summer temperatures, and find that the processes that describe the mechanism are not represented in every ensemble member. Due to its influence on European summer climate, we use the condition of the persistent spring SSTs to anticipate the phase of the mechanism in each considered year. We thus use statistical relations to select ensemble members generated by a dynamical prediction system. With this approach, we significantly enhance the seasonal hindcast skill and the reliability of the hindcasts in the North-Atlantic-European sector, especially in the areas where the mechanism is showing a prominent signal. Since we only use knowledge that would be available in a real forecast set-up, this approach can potentially be applied in operational ensemble prediction systems.

Notes

Acknowledgements

The authors would like to thank the two anonymous reviewers for their helpful remarks. Many thanks go also to the Climate Modelling group at the University Hamburg for the discussions and their feedback on the findings of this paper. This work was funded by the German Federal Ministry for Education and Research (BMBF) through the second Regional Atlantic Circulation and Global Change Project (RACE II; NCN, JB) and through the MiKlip project FLEXFORDEC (grant number 01LP1519A; WM), by the Copernicus Climate Change Service (contract number C3S 433 DWD; MD, JB). It was further funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the University Hamburg’s Cluster of Excellence Integrated Climate System Analysis and Prediction (CliSAP; AD, JB), and under Germany’s Excellence Strategy – EXC 2037 ‘Climate, Climatic Change, and Society’ (CliCCS) – Project Number: 390683824, as contribution to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg (AD, JB). The model simulations were performed using the high-performance computer at the German Climate Computing Center (DKRZ).

References

  1. Alexander MA, Bladé I, Newman M, Lanzante JR, Lau NC, Scott JD (2002) The atmospheric bridge: the influence of ENSO teleconnections on air-sea interaction over the global oceans. J Clim 15(16):2205–2231CrossRefGoogle Scholar
  2. Arribas A, Glover M, Maidens A, Peterson K, Gordon M, MacLachlan C, Graham R, Fereday D, Camp J, Scaife A et al (2011) The GloSea4 ensemble prediction system for seasonal forecasting. Mon Weather Rev 139(6):1891–1910CrossRefGoogle Scholar
  3. Baehr J, Piontek R (2014) Ensemble initialization of the oceanic component of a coupled model through bred vectors at seasonal-to-interannual timescales. Geosci Model Dev 7(1):453–461CrossRefGoogle Scholar
  4. Baehr J, Fröhlich K, Botzet M, Domeisen DI, Kornblueh L, Notz D, Piontek R, Pohlmann H, Tietsche S, Mueller WA (2015) The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model. Clim Dyn 44(9–10):2723–2735CrossRefGoogle Scholar
  5. Baker L, Shaffrey L, Sutton R, Weisheimer A, Scaife A (2018) An intercomparison of skill and overconfidence/underconfidence of the wintertime North Atlantic Oscillation in multimodel seasonal forecasts. Geophys Res Lett 45:7808–7817CrossRefGoogle Scholar
  6. Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 139(674):1132–1161CrossRefGoogle Scholar
  7. Barnston AG, Livezey RE (1987) Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mont Weather Rev 115(6):1083–1126CrossRefGoogle Scholar
  8. Bjerknes J (1966) A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature. Tellus 18(4):820–829CrossRefGoogle Scholar
  9. Bladé I, Liebmann B, Fortuny D, van Oldenborgh GJ (2012) Observed and simulated impacts of the summer NAO in Europe: implications for projected drying in the Mediterranean region. Clim Dyn 39(3–4):709–727CrossRefGoogle Scholar
  10. Branstator G (2002) Circumglobal teleconnections, the jet stream waveguide, and the North Atlantic Oscillation. J Clim 15(14):1893–1910CrossRefGoogle Scholar
  11. Branstator G, Teng H (2017) Tropospheric waveguide teleconnections and their seasonality. J Atmos Sci 74(5):1513–1532CrossRefGoogle Scholar
  12. Bretherton CS, Smith C, Wallace JM (1992) An intercomparison of methods for finding coupled patterns in climate data. J Clim 5(6):541–560CrossRefGoogle Scholar
  13. Cassou C, Terray L, Phillips AS (2005) Tropical Atlantic influence on European heat waves. J Clim 18(15):2805–2811CrossRefGoogle Scholar
  14. Collins M (2002) Climate predictability on interannual to decadal time scales: the initial value problem. Clim Dyn 19(8):671–692CrossRefGoogle Scholar
  15. Dee DP, Uppala S, Simmons A, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M, Balsamo G, Bauer P et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  16. Ding Q, Wang B (2005) Circumglobal teleconnection in the Northern Hemisphere summer. J Clim 18(17):3483–3505CrossRefGoogle Scholar
  17. Doblas-Reyes FJ, García-Serrano J, Lienert F, Biescas AP, Rodrigues LR (2013) Seasonal climate predictability and forecasting: status and prospects. Wiley Interdiscip Rev Clim Change 4(4):245–268CrossRefGoogle Scholar
  18. Dobrynin M, Domeisen DI, Müller WA, Bell L, Brune S, Bunzel F, Düsterhus A, Fröhlich K, Pohlmann H, Baehr J (2018) Improved teleconnection-based dynamical seasonal predictions of boreal winter. Geophys Res Lett 48:3605–3614CrossRefGoogle Scholar
  19. Domeisen DI, Butler AH, Fröhlich K, Bittner M, Müller WA, Baehr J (2015) Seasonal predictability over Europe arising from El Nino and stratospheric variability in the MPI-ESM seasonal prediction system. J Clim 28(1):256–271CrossRefGoogle Scholar
  20. Duchez A, Frajka-Williams E, Josey SA, Evans DG, Grist JP, Marsh R, McCarthy GD, Sinha B, Berry DI, Hirschi JJ (2016) Drivers of exceptionally cold North Atlantic Ocean temperatures and their link to the 2015 European heat wave. Environ Res Lett 11(7):074004CrossRefGoogle Scholar
  21. Düsterhus A, Dobrynin M, Domeisen DI, Pohlmann H, Baehr J (2017) A statistical-dynamical seasonal prediction of the summer North Atlantic Oscillation. In: Proceedings of the19th EGU General Assembly, EGU2017. Vienna, pp 7153Google Scholar
  22. Eade R, Smith D, Scaife A, Wallace E, Dunstone N, Hermanson L, Robinson N (2014) Do seasonal-to-decadal climate predictions underestimate the predictability of the real world? Geophys Res Lett 41(15):5620–5628CrossRefGoogle Scholar
  23. Fetterer F, Knowles K, Meier W, Savoie M (2002) Sea ice index. National Snow and Ice Data Center, BoulderGoogle Scholar
  24. 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):1082–1103CrossRefGoogle Scholar
  25. Gastineau G, Frankignoul C (2015) Influence of the North Atlantic SST variability on the atmospheric circulation during the twentieth century. J Clim 28(4):1396–1416CrossRefGoogle Scholar
  26. Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Böttinger M, Brovkin V, Crueger T, Esch M, Fieg K et al (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J Adv Model Earth Syst 5(3):572–597CrossRefGoogle Scholar
  27. Ho CK, Hawkins E, Shaffrey L, Bröcker J, Hermanson L, Murphy JM, Smith DM, Eade R (2013) Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion. Geophys Res Lett 40(21):5770–5775CrossRefGoogle Scholar
  28. Hodson DL, Sutton RT, Cassou C, Keenlyside N, Okumura Y, Zhou T (2010) Climate impacts of recent multidecadal changes in Atlantic Ocean sea surface temperature: a multimodel comparison. Clim Dyn 34(7–8):1041–1058CrossRefGoogle Scholar
  29. Hoskins BJ, Ambrizzi T (1993) Rossby wave propagation on a realistic longitudinally varying flow. J Atmos Sci 50(12):1661–1671CrossRefGoogle Scholar
  30. Hoskins BJ, Karoly DJ (1981) The steady linear response of a spherical atmosphere to thermal and orographic forcing. J Atmos Sci 38(6):1179–1196CrossRefGoogle Scholar
  31. Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science 269(5224):676–679CrossRefGoogle Scholar
  32. Hurrell JW (1996) Influence of variations in extratropical wintertime teleconnections on Northern Hemisphere temperature. Geophys Res Lett 23(6):665–668CrossRefGoogle Scholar
  33. Iglesias I, Lorenzo MN, Taboada JJ (2014) Seasonal predictability of the East Atlantic pattern from sea surface temperatures. PloS One 9(1):86439CrossRefGoogle Scholar
  34. Jungclaus J, Keenlyside N, Botzet M, Haak H, Luo JJ, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J Clim 19(16):3952–3972CrossRefGoogle Scholar
  35. Lau NC, Nath MJ (2001) Impact of ENSO on SST variability in the North Pacific and North Atlantic: seasonal dependence and role of extratropical sea-air coupling. J Clim 14(13):2846–2866CrossRefGoogle Scholar
  36. Li J, Ruan C (2018) The North Atlantic-Eurasian teleconnection in summer and its effects on Eurasian climates. Environ Res Lett 13(2):024007CrossRefGoogle Scholar
  37. Marshall J, Kushnir Y, Battisti D, Chang P, Czaja A, Dickson R, Hurrell J, McCARTNEY M, Saravanan R, Visbeck M (2001) North Atlantic climate variability: phenomena, impacts and mechanisms. Int J Climatol 21(15):1863–1898CrossRefGoogle Scholar
  38. Michelangeli PA, Vautard R, Legras B (1995) Weather regimes: recurrence and quasi stationarity. J Atmos Sci 52(8):1237–1256CrossRefGoogle Scholar
  39. Müller W, Jungclaus J, Mauritsen T, Baehr J, Bittner M, Budich R, Bunzel F, Esch M, Ghosh R, Haak H et al (2018) A higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1. 2-HR). J Adv Model Earth Syst 10(7):1383–1413CrossRefGoogle Scholar
  40. North GR, Bell TL, Cahalan RF, Moeng FJ (1982) Sampling errors in the estimation of Empirical Orthogonal Functions. Mon Weather Rev 110(7):699–706CrossRefGoogle Scholar
  41. Palmer TN, Anderson DL (1994) The prospects for seasonal forecasting—a review paper. Q J R Meteorol Soc 120(518):755–793Google Scholar
  42. Saeed S, Van Lipzig N, Müller WA, Saeed F, Zanchettin D (2014) Influence of the circumglobal wave-train on European summer precipitation. Clim Dyn 43(1–2):503–515CrossRefGoogle Scholar
  43. Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, Salzmann M, Schmidt H, Bader J, Block K et al (2013) Atmospheric component of the MPI-M Earth System Model: ECHAM6. J Adv Model Earth Syst 5(2):146–172CrossRefGoogle Scholar
  44. Tietsche S, Notz D, Jungclaus J, Marotzke J (2013) Assimilation of sea-ice concentration in a global climate model-physical and statistical aspects. Ocean Sci 9(1):19CrossRefGoogle Scholar
  45. Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon Weather Rev 109(4):784–812CrossRefGoogle Scholar
  46. Wilks DS (2011) Statistical methods in the atmospheric sciences, vol 100. Academic, New YorkGoogle Scholar
  47. Wu B, Lin J, Zhou T (2016) Interdecadal circumglobal teleconnection pattern during boreal summer. Atmos Sci Lett 17(8):446–452CrossRefGoogle Scholar
  48. Wulff CO, Greatbatch RJ, Domeisen DI, Gollan G, Hansen F (2017) Tropical forcing of the Summer East Atlantic pattern. Geophys Res Lett 44:21CrossRefGoogle Scholar
  49. Yasui S, Watanabe M (2010) Forcing processes of the summertime circumglobal teleconnection pattern in a dry AGCM. J Clim 23(8):2093–2114CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Oceanography, CENUniversität HamburgHamburgGermany
  2. 2.International Max Planck Research School on Earth System ModellingMax Planck Institute for MeteorologyHamburgGermany
  3. 3.Max Planck Institute for MeteorologyHamburgGermany

Personalised recommendations