Europe

Last modified by S2S_regionact on 2022/01/14 19:18

Introduction

In Europe several institutions, operational weather services, research groups and individual researchers from various European countries organise regional activities on S2S research in a decentralised manner. Key research foci are on European high impact weather, S2S forecasts for the European energy sector, and the prediction of European weather regimes, and the role of teleconnection in S2S predictability for Europe. Furthermore various European projects do research on S2S prediction in other world regions.

The WMO S2S prediction project and its community as well as S2S activities of the European Centre for Medium-Range Weather Forecasts (ECMWF) provide platforms for collaboration and coordination of the European S2S community.

Information on regional workshops

  • Next Generation Energy and Climate Modelling (NextGenEC) annual workshop series: NextGenEC workshops aim to bring together an international group of researchers working at the interface between climate science and energy applications. The goal is to stimulate an active and ongoing discussion around the use of both historic and future climate datasets in energy system analysis https://research.reading.ac.uk/met-energy/next-generation-energy-climate-modelling-2021/

  • European Meteorological Society (EMS) annual meeting: The EMS annual meeting brings together experts from applied meteorology and weather services and from academia. Several sessions focus on high impact weather events, remote influences on predictability in Europe, and novel forecasting techniques. Next Meeting 5-9 September 2022 in Bonn, Germany https://www.ems2022.eu/

  • European Geophysical Union (EGU) annual meeting: The EGU annual meeting provides a platform for the international research community. The dedicated session AS1.20 “Subseasonal-to-Seasonal Prediction: Processes and Impacts” brings the S2S community regularly together. Next meeting 3-8 April 2022 in Vienna and virtually https://www.egu22.eu/

  • 6th WGNE workshop on systematic errors in weather and climate models: The workshop brings together a wide range of experts on simulating the Earth System including atmosphere, ocean, waves, land-surface, atmospheric composition, and associated disciplines to advance the understanding of systematic simulation errors at all timescales. A particular emphasis is given to identifying errors in complex coupled systems and to understand their root causes. ECMWF, Reading 31 October – 4 November 2022 https://events.ecmwf.int/event/241/

  • Fourth workshop of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project: ECMWF, Reading 29-31 March 2022 https://events.ecmwf.int/event/293/

  • Atmospheric Blocking Virtual Workshop 2021: The workshop offerec an opportunity to bring together specialists in the fields of atmospheric and climate science, to review the current state of research, and to discuss the most important open questions regarding blocking. The focus was set on the understanding of dynamics and physical processes in atmospheric blocking, with special focus on information the role of dry and moist dynamics in the formation, maintenance and decay of blocking, (ii) teleconnections and external forcing associated with blocking, and (iii) model representation and predictability of blocking dynamics and physical processes. https://blocking-workshop-2021.wavestoweather.de/

Key regionally-relevant S2S research questions & activities being pursued in the region

  • How predictable are (large-scale) high impact weather events in the European region, and how can we improve forecasts of extremes?

  • How can we make better use of S2S weather forecasts for the energy sector and strengthen the European power system through knowledge of S2S weather variability?

  • How can we exploit potential sources for S2S predictability of European surface weather emerging from teleconnections (stratosphere, MJO, ENSO, annular modes, …)?

  • How can we improve S2S predictions of weather regimes?

Institutions/Projects in the region working on S2S

European projects with a S2S focus or S2S component

  • Climate Advanced Forecasting of sub-seasonal Extremes (CAFES) project: A European Union Horizon 2020 Innovative Training Network to improve the sub-seasonal predictability of extreme weather events through the interdisciplinary training of 12 ESRs in aspects such as climate science, complex networks and data analysis. http://www.cafes2se-itn.eu/

  • S2S4E Climate Services for Energy project: aiming to make the European energy sector more resilient to climate variability and high impact events.  https://s2s4e.eu/

  • ECMWF hosting the S2S database: https://confluence.ecmwf.int/display/S2S

  • Waves to Weather collaborative research centre: a consortium to address the great challenge of identifying the limits of weather predictability in different situations and to produce the best forecasts that are physically possible https://www.wavestoweather.de/

  • African SWIFT project (UK) part of the real-time pilot and has a strong S2S component https://africanswift.org/

  • Science for Humanitarian Emergencies and Resilience (SHEAR) project (UK): SHEAR is carrying out innovative research in the most hazard-prone parts of the world to better understand and predict disasters, and minimise the risk they pose to vulnerable communities.  http://www.shear.org.uk/

  • Improving Subseasonal and Seasonal sUmmer forecast over southern Europe through machine Learning project (ISSUL): https://cordis.europa.eu/project/id/101024255

  • S2S real time pilots project 8 (S2S4E) and project 10 (Intesa Operativa fra DPC e CNR-ISAC (Operational Agreement between Italian Civil Protection Agency and CNR-ISAC)) http://www.s2sprediction.net/resources/documents/SharedProjectList_FINAL_ForSharing.pdf

  • Engagement in Sparc/SNAP: role of the stratosphere in S2S prediction  http://www.s2sprediction.net/xwiki/bin/view/Phase2/Stratosphere

Host institutions of researchers and research groups working on S2S and operational centres with a focus on S2S prediction and/or model development

Publications

incomplete list

since 2022

2017-2021 (alphabetically)

  • Ardilouze, C., D. Specq, L. Batté, and C. Cassou, 2021: Flow dependence of wintertime subseasonal prediction skill over Europe. Weather and Climate Dynamics, 2, 1033–1049, doi:https://doi.org/10.5194/wcd-2-1033-2021.
  • Bloomfield, H. C., D. J. Brayshaw, and A. J. Charlton‐Perez, 2020: Characterizing the winter meteorological drivers of the European electricity system using targeted circulation types. Meteorological Applications, 27, e1858, doi:10.1002/met.1858.
  • Bloomfield, H. C.,P. L. M. Gonzalez, and A. Charlton-Perez, 2021a: Pattern-based conditioning enhances sub-seasonal prediction skill of European national energy variables. Meteorological Applications, 28, e2018, doi:10.1002/met.2018.
  • ——, ——, ——, and ——, 2021b: Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries. Earth System Science Data, 13, 2259–2274, doi:10.5194/essd-13-2259-2021.
  • Brayshaw, D. J., A. Troccoli, R. Fordham, and J. Methven, 2011: The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK. Renewable Energy, 36, 2087–2096, doi:10.1016/j.renene.2011.01.025.
  • Büeler, D., R. Beerli, H. Wernli, and C. M. Grams, 2020: Stratospheric influence on ECMWF sub-seasonal forecast skill for energy-industry-relevant surface weather in European countries. Q.J.R. Meteorol. Soc., 146, 3675–3694, doi:https://doi.org/10.1002/qj.3866.
  • ——, L. Ferranti, L. Magnusson, J. F. Quinting, and C. M. Grams, 2021: Year-round sub-seasonal forecast skill for Atlantic–European weather regimes. Q.J.R. Meteorol. Soc., 147, 4283–4309, doi:https://doi.org/10.1002/qj.4178.
  • Butler, A. H., A. Charlton‐Perez, D. I. V. Domeisen, I. R. Simpson, and J. Sjoberg, 2019: Predictability of Northern Hemisphere Final Stratospheric Warmings and Their Surface Impacts. Geophysical Research Letters, 46, 10578–10588, doi:10.1029/2019GL083346.
  • Charlton-Perez, A. J., R. W. Aldridge, C. M. Grams, and R. Lee, 2019: Winter pressures on the UK health system dominated by the Greenland blocking weather regime. Weather and Climate Extremes, 25, 100218, doi:10.1016/j.wace.2019.100218.
  • ——, J. Bröcker, A. Y. Karpechko, S. H. Lee, M. Sigmond, and I. R. Simpson, 2021: A Minimal Model to Diagnose the Contribution of the Stratosphere to Tropospheric Forecast Skill. Journal of Geophysical Research: Atmospheres, 126, e2021JD035504, doi:https://doi.org/10.1029/2021JD035504.
  • Christopher J. White; Daniela I.V. Domeisen; Nachiketa Acharya; Elijah A. Adefisan; Michael L. Anderson; Stella Aura; Ahmed A. Balogun; Douglas Bertram; Sonia Bluhm; David J. Brayshaw; Jethro Browell; Dominik Büeler; Andrew Charlton-Perez; Xandre Chourio; Isadora Christel; Caio A.S. Coelho; Michael J. DeFlorio; Luca Delle Monache; Francesca Di Giuseppe; Ana María García-Solórzano; Peter B. Gibson; Lisa Goddard; Carmen González Romero; Richard J. Graham; Robert M. Graham; Christian M. Grams; Alan Halford; W.T. Katty Huang; Kjeld Jensen; Mary Kilavi; Kamoru A. Lawal; Robert W. Lee; David MacLeod; Andrea Manrique-Suñén; Eduardo S.P.R. Martins; Carolyn J. Maxwell; William J. Merryfield; Ángel G. Muñoz; Eniola Olaniyan; George Otieno; John A. Oyedepo; Lluís Palma; Ilias G. Pechlivanidis; Diego Pons; F. Martin Ralph; Dirceu S. Reis Jr.; Tomas A. Remenyi; James S. Risbey; Donald J.C. Robertson; Andrew W. Robertson; Stefan Smith; Albert Soret; Ting Sun; Martin C. Todd; Carly R. Tozer; Francisco C. Vasconcelos Jr.; Ilaria Vigo; Duane E. Waliser; Fredrik Wetterhall; Robert G. Wilson, 2021: Advances in the application and utility of subseasonal-to-seasonal predictions. Bull. Amer. Meteor. Soc., n/a, n/a.
  • Cortesi, N., V. Torralba, N. González-Reviriego, A. Soret, and F. J. Doblas-Reyes, 2019: Characterization of European wind speed variability using weather regimes. Climate Dynamics, doi:10.1007/s00382-019-04839-5.
  • ——, ——, L. Lledó, A. Manrique-Suñén, N. Gonzalez-Reviriego, A. Soret, and F. J. Doblas-Reyes, 2021: Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability. Clim Dyn, doi:10.1007/s00382-021-05679-y.
  • Dalelane, C., M. Dobrynin, and K. Fröhlich, 2020: Seasonal Forecasts of Winter Temperature Improved by Higher-Order Modes of Mean Sea Level Pressure Variability in the North Atlantic Sector. Geophysical Research Letters, 47, e2020GL088717, doi:https://doi.org/10.1029/2020GL088717.
  • Day, J. J., I. Sandu, L. Magnusson, M. J. Rodwell, H. Lawrence, N. Bormann, and T. Jung, 2019: Increased Arctic influence on the midlatitude flow during Scandinavian Blocking episodes. Quarterly Journal of the Royal Meteorological Society, 145, 3846–3862, doi:10.1002/qj.3673.
  • Dobrynin, M., and Coauthors, 2018: Improved Teleconnection-Based Dynamical Seasonal Predictions of Boreal Winter. Geophysical Research Letters, 45, 3605–3614, doi:10.1002/2018GL077209.
  • Domeisen, D. I. V., and A. H. Butler, 2020: Stratospheric drivers of extreme events at the Earth’s surface. Commun Earth Environ, 1, 1–8, doi:10.1038/s43247-020-00060-z.
  • ——, C. M. Grams, and L. Papritz, 2020: The role of North Atlantic–European weather regimes in the surface impact of sudden stratospheric warming events. Weather and Climate Dynamics, 1, 373–388, doi:https://doi.org/10.5194/wcd-1-373-2020.
  • ——, and Coauthors, The role of the stratosphere in subseasonal to seasonal prediction Part I: Predictability of the stratosphere. Journal of Geophysical Research: Atmospheres, n/a, doi:10.1029/2019JD030920.
  • ——, and Coauthors, The role of the stratosphere in subseasonal to seasonal prediction Part II: Predictability arising from stratosphere - troposphere coupling. Journal of Geophysical Research: Atmospheres, n/a, doi:10.1029/2019JD030923.
  • Ferranti, L., S. Corti, and M. Janousek, 2015: Flow-dependent verification of the ECMWF ensemble over the Euro-Atlantic sector. Q.J.R. Meteorol. Soc., 141, 916–924, doi:10.1002/qj.2411.
  • ——, L. Magnusson, F. Vitart, and D. S. Richardson, 2018: How far in advance can we predict changes in large-scale flow leading to severe cold conditions over Europe? Q.J.R. Meteorol. Soc., 144, 1788–1802, doi:10.1002/qj.3341.
  • González-Alemán, J. J., C. M. Grams, B. Ayarzagüena, P. Zurita-Gotor, D. I. V. Domeisen, I. Gómara, B. Rodríguez-Fonseca, and F. Vitart, Tropospheric role in the predictability of the surface impact of the 2018 sudden stratospheric warming event. Geophysical Research Letters, n/a, e2021GL095464, doi:https://doi.org/10.1029/2021GL095464.
  • Grams, C. M., R. Beerli, S. Pfenninger, I. Staffell, and H. Wernli, 2017: Balancing Europe’s wind-power output through spatial deployment informed by weather regimes. Nature Climate Change, 7, 557–562, doi:10.1038/nclimate3338.
  • Grazzini, F., and F. Vitart, 2015: Atmospheric predictability and Rossby wave packets: Predictability and Rossby Wave Packets. Quarterly Journal of the Royal Meteorological Society, 141, 2793–2802, doi:10.1002/qj.2564.
  • Hochman, A., G. Messori, J. F. Quinting, J. G. Pinto, and C. M. Grams, 2021: Do Atlantic-European Weather Regimes Physically Exist? Geophysical Research Letters, 48, e2021GL095574, doi:https://doi.org/10.1029/2021GL095574.
  • Karpechko, A. Y., A. Charlton‐Perez, M. Balmaseda, N. Tyrrell, and F. Vitart, 2018: Predicting Sudden Stratospheric Warming 2018 and Its Climate Impacts With a Multimodel Ensemble. Geophysical Research Letters, 45, 13,538-13,546, doi:10.1029/2018GL081091.
  • Karpechko, A. Yu., P. Hitchcock, D. H. W. Peters, and A. Schneidereit, 2017: Predictability of downward propagation of major sudden stratospheric warmings. Q.J.R. Meteorol. Soc., 143, 1459–1470, doi:10.1002/qj.3017.
  • Kautz, L.-A., I. Polichtchouk, T. Birner, H. Garny, and J. G. Pinto, 2020: Enhanced extended-range predictability of the 2018 late-winter Eurasian cold spell due to the stratosphere. Q.J.R. Meteorol. Soc., 146, 1040–1055, doi:10.1002/qj.3724.
  • Kolstad, E. W., C. O. Wulff, D. I. V. Domeisen, and T. Woollings, Tracing North Atlantic Oscillation forecast errors to stratospheric origins. J. Climate, 1–40, doi:10.1175/JCLI-D-20-0270.1.
  • Lledó, L., J. Ramon, A. Soret, and F.-J. Doblas-Reyes, 2021: Seasonal prediction of renewable energy generation in Europe based on four teleconnection indices. Renewable Energy, doi:10.1016/j.renene.2021.12.130.
  • MacLeod, D., V. Torralba, M. Davis, and F. Doblas‐Reyes, 2018: Transforming climate model output to forecasts of wind power production: how much resolution is enough? Meteorological Applications, 25, 1–10, doi:10.1002/met.1660.
  • Maier-Gerber, M., A. H. Fink, M. Riemer, E. Schoemer, C. Fischer, and B. Schulz, 2021: Statistical-Dynamical Forecasting of Sub-Seasonal North Atlantic Tropical Cyclone Occurrence. Weather and Forecasting, 1, doi:10.1175/WAF-D-21-0020.1.
  • Manrique-Suñén, A., N. Gonzalez-Reviriego, V. Torralba, N. Cortesi, and F. J. Doblas-Reyes, Choices in the verification of S2S forecasts and their implications for climate services. Mon. Wea. Rev., 1–41, doi:10.1175/MWR-D-20-0067.1.
  • Mastrantonas, N., L. Magnusson, F. Pappenberger, and J. Matschullat, What do large-scale patterns teach us about extreme precipitation over the Mediterranean at medium- and extended-range forecasts? Quarterly Journal of the Royal Meteorological Society, n/a, doi:https://doi.org/10.1002/qj.4236.
  • Merryfield, W. J., and Coauthors, 2020: Current and emerging developments in subseasonal to decadal prediction. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-19-0037.1.
  • Polkova, I., and Coauthors, Predictors and prediction skill for marine cold air outbreaks over the Barents Sea. Quarterly Journal of the Royal Meteorological Society, n/a, doi:https://doi.org/10.1002/qj.4038.
  • Quinting, J. F., and F. Vitart, 2019: Representation of Synoptic-Scale Rossby Wave Packets and Blocking in the S2S Prediction Project Database. Geophys. Res. Lett., 46, 1070–1078, doi:10.1029/2018GL081381.
  • Quinting, J. F., and C. M. Grams, 2021: EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part I: Development of deep learning model. Geoscientific Model Development Discussions, 1–29, doi:10.5194/gmd-2021-276.
  • ——, C. M. Grams, A. Oertel, and M. Pickl, 2021: EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets. Geoscientific Model Development Discussions, 1–24, doi:10.5194/gmd-2021-278.
  • Roberts, C. D., F. Vitart, M. A. Balmaseda, and F. Molteni, 2020: The Time-Scale-Dependent Response of the Wintertime North Atlantic to Increased Ocean Model Resolution in a Coupled Forecast Model. J. Climate, 33, 3663–3689, doi:10.1175/JCLI-D-19-0235.1.
  • ——, ——, and ——, 2021: Hemispheric Impact of North Atlantic SSTs in Subseasonal Forecasts. Geophysical Research Letters, 48, e2020GL0911446, doi:https://doi.org/10.1029/2020GL091446.
  • Santos-Alamillos, F. J., D. J. Brayshaw, J. Methven, N. S. Thomaidis, J. A. Ruiz-Arias, and D. Pozo-Vázquez, 2017: Exploring the meteorological potential for planning a high performance European electricity super-grid: optimal power capacity distribution among countries. Environ. Res. Lett., 12, 114030, doi:10.1088/1748-9326/aa8f18.
  • Thornton, H. E., A. A. Scaife, B. J. Hoskins, and D. J. Brayshaw, 2017: The relationship between wind power, electricity demand and winter weather patterns in Great Britain. Environ. Res. Lett., 12, 064017, doi:10.1088/1748-9326/aa69c6.
  • Torralba, V., N. Gonzalez‐Reviriego, N. Cortesi, A. Manrique‐Suñén, L. Lledó, R. Marcos, A. Soret, and F. J. Doblas‐Reyes, 2020: Challenges in the selection of atmospheric circulation patterns for the wind energy sector. International Journal of Climatology, doi:10.1002/joc.6881.
  • Vitart, F., 2014: Evolution of ECMWF sub-seasonal forecast skill scores. Quarterly Journal of the Royal Meteorological Society, 140, 1889–1899, doi:10.1002/qj.2256.
  • ——, 2017: Madden—Julian Oscillation prediction and teleconnections in the S2S database. Q.J.R. Meteorol. Soc, 143, 2210–2220, doi:10.1002/qj.3079.
  • ——, and A. W. Robertson, 2018: The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. npj Climate and Atmospheric Science, 1, 3, doi:10.1038/s41612-018-0013-0.
  • Vitart, F., and Coauthors, 2017: The Subseasonal to Seasonal (S2S) Prediction Project Database. Bull. Amer. Meteor. Soc., 98, 163–173, doi:10.1175/BAMS-D-16-0017.1.
  • Wandel, J., J. F. Quinting, and C. M. Grams, 2021: Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part II: Verification of Operational Reforecasts. Journal of the Atmospheric Sciences, 78, 3965–3982, doi:10.1175/JAS-D-20-0385.1.
  • White, C. J., and Coauthors, 2017: Potential applications of subseasonal-to-seasonal (S2S) predictions. Met. Apps, 24, 315–325, doi:10.1002/met.1654.
  • ——, and Coauthors, 2021: Advances in the application and utility of subseasonal-to-seasonal predictions. Bulletin of the American Meteorological Society, 1, 1–57, doi:10.1175/BAMS-D-20-0224.1.
  • Wu, R. W.-Y., Z. Wu, and D. I. V. Domeisen, 2022: Differences in the Sub-seasonal Predictability of Extreme Stratospheric Events. Weather and Climate Dynamics Discussions, 1–27, doi:10.5194/wcd-2021-84.


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