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Preprints
https://doi.org/10.5194/esd-2020-68
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2020-68
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 Sep 2020

16 Sep 2020

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This preprint is currently under review for the journal ESD.

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Claudia Tebaldi1, Kevin Debeire2,3, Veronika Eyring2,4, Erich Fischer5, John Fyfe6, Pierre Friedlingstein7,8, Reto Knutti5, Jason Lowe9,10, Brian O'Neill11, Benjamin Sanderson12, Detlef van Vuuren13, Keywan Riahi14, Malte Meinshausen15, Zebedee Nicholls15, George Hurtt16, Elmar Kriegler17, Jean-Francois Lamarque18, Gerald Meehl18, Richard Moss1, Susanne E. Bauer19, Olivier Boucher20, Victor Brovkin21,a, Jean-Christophe Golaz22, Silvio Gualdi23, Huan Guo24, Jasmin G. John24, Slava Kharin6, Tsuyoshi Koshiro25, Libin Ma26, Dirk Olivié27, Swapna Panickal28, Fangli Qiao29, Nan Rosenbloom18, Martin Schupfner30, Roland Seferian31, Zhenya Song29, Christian Steger32, Alistair Sellar9, Neil Swart6, Kaoru Tachiiri33, Hiroaki Tatebe33, Aurore Voldoire31, Evgeny Volodin34, Klaus Wyser35, Xiaoge Xin36, Rong Xinyao37, Shuting Yang38, Yongqiang Yu39, and Tilo Ziehn40 Claudia Tebaldi et al.
  • 1Joint Global Change Research Institute (JGCRI), Pacific Northwest National Laboratory, College Park, MD, USA
  • 2Deutsches Zentrum für Luft-und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 3Deutsches Zentrum für Luft-und Raumfahrt (DLR), Institut für Datenwissenschaften, Jena, Germany
  • 4University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
  • 5ETH Zurich, Institute for Atmospheric and Climate Science, Zurich, Switzerland
  • 6Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC, Canada
  • 7College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QE, United Kingdom
  • 8LMD/IPSL, ENS, PSL Université, Ècole Polytechnique, Institut Polytechnique de Paris, Sorbonne Université, CNRS, Paris, France
  • 9Met Office Hadley Center, Exeter, UK
  • 10Priestley International Center for Climate, School of Earth and Environment, University of Leeds, Leeds, UK
  • 11Josef Korbel School of International Studies, University of Denver, Denver, USA
  • 12CNRS/Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Toulouse, France
  • 13PBL Netherlands Environmental Assessment Agency and Faculty of Geosciences, Utrecht University, Utrecht, The Nederlands
  • 14International Institute for Applied Systems Analysis, Laxenburg, Austria
  • 15Climate & Energy College, School of Earth Sciences, University of Melbourne, Australia
  • 16Department of Geographical Sciences, University of Maryland, College Park, MD, USA
  • 17Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
  • 18Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
  • 19NASA Goddard Institute for Space Studies, New York, NY, USA
  • 20Institut Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France
  • 21Max Planck Institute for Meteorology, Hamburg, Germany
  • 22Lawrence Livermore National Laboratory, Livermore, CA, USA
  • 23Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy
  • 24NOAA/OAR/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
  • 25Meteorological Research Institute, Tsukuba, Japan
  • 26Earth System Modeling Center, Nanjing University of Information Science and Technology, Jiangsu, China
  • 27Norwegian Meteorological Institute, Oslo, Norway
  • 28Indian Institute of Tropical Meteorology, India
  • 29First Institute of Oceanography (FIO), Ministry of Natural Resources (MNR), Qingdao, China
  • 30Deutsches Klimarechenzentrum, Hamburg, Germany
  • 31CNRM, Université de Toulouse, Météo‐France, CNRS, Toulouse, France
  • 32Deutscher Wetterdienst, Offenbach, Germany
  • 33Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
  • 34Institute of Numerical Mathematics, Moscow, Russian Federation
  • 35Swedish Meteorological and Hydrological Institute, Norrkoeping, Sweden
  • 36Beijing Climate Center, China Meteorological Administration, Beijing, China
  • 37State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
  • 38Danish Meteorological Institute, Copenhagen, Denmark
  • 39LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 40CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia
  • aalso at: Center for Earth System Research and Sustainability, University of Hamburg, Germany

Abstract. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.

Claudia Tebaldi et al.

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Short summary
We present a broad overview of CMIP6 ScenarioMIP outcomes from a multi-model ensemble according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. We also document results from large ensembles.
We present a broad overview of CMIP6 ScenarioMIP outcomes from a multi-model ensemble according...
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