Articles | Volume 11, issue 4
https://doi.org/10.5194/esd-11-1233-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-11-1233-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
21 Dec 2020
Research article | Highlight paper |
| 21 Dec 2020
| 21 Dec 2020
Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Axel Lauer
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Pierre Gentine
Department of Earth and Environmental Engineering, Columbia
University, New York, NY 10027, USA
Earth Institute and Data Science Institute, Columbia University, New York, NY 10027, USA
Steven C. Sherwood
Climate Change Research Centre and ARC Centre of Excellence for
Climate System Science, University of New South Wales, Sydney 2052, Australia
Veronika Eyring
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Institute of Environmental Physics (IUP), University of Bremen,
Bremen, Germany
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This study explores Atlantic–Pacific interactions and their response to external factors. Causal analysis of 1950–2014 data reveals a shift from a Pacific- to an Atlantic-driven regime. Contrasting impacts between El Niño and tropical Atlantic temperatures are highlighted, along with different pathways connecting the two oceans. The findings also suggest increasing remote contributions of forced Atlantic responses in modulating local Pacific responses during the most recent analyzed decades.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Steven C. Sherwood and Chris E. Forest
Atmos. Chem. Phys., 24, 2679–2686, https://doi.org/10.5194/acp-24-2679-2024, https://doi.org/10.5194/acp-24-2679-2024, 2024
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The most fundamental parameter used to gauge the severity of future climate change is the so-called equilibrium climate sensitivity, which measures the warming that would ultimately occur due to a doubling of atmospheric carbon dioxide levels. Due to recent advances it is now thought to probably lie in the range 2.5–4 °C. We discuss this and the issues involved in evaluating and using the number, pointing to some pitfalls in current efforts but also possibilities for further progress.
Lisa Bock and Axel Lauer
Atmos. Chem. Phys., 24, 1587–1605, https://doi.org/10.5194/acp-24-1587-2024, https://doi.org/10.5194/acp-24-1587-2024, 2024
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Climate model simulations still show a large range of effective climate sensitivity (ECS) with high uncertainties. An important contribution to ECS is cloud climate feedback. We investigate the representation of cloud physical and radiative properties from Coupled Model Intercomparison Project models grouped by ECS. We compare the simulated cloud properties of today’s climate from three ECS groups and quantify how the projected changes in cloud properties and cloud radiative effects differ.
Sina Loriani, Yevgeny Aksenov, David Armstrong McKay, Govindasamy Bala, Andreas Born, Cristiano M. Chiessi, Henk Dijkstra, Jonathan F. Donges, Sybren Drijfhout, Matthew H. England, Alexey V. Fedorov, Laura Jackson, Kai Kornhuber, Gabriele Messori, Francesco Pausata, Stefanie Rynders, Jean-Baptiste Salée, Bablu Sinha, Steven Sherwood, Didier Swingedouw, and Thejna Tharammal
EGUsphere, https://doi.org/10.5194/egusphere-2023-2589, https://doi.org/10.5194/egusphere-2023-2589, 2023
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In this work, we draw on paleoreords, observations and modelling studies to review tipping points in the ocean overturning circulations, monsoon systems and global atmospheric circulations. We find indications for tipping in the ocean overturning circulations and the West African monsoon, with potentially severe impacts on the Earth system and humans. Tipping in the other considered systems is considered conceivable but currently not sufficiently supported by evidence.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, https://doi.org/10.5194/esd-14-309-2023, 2023
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This study uses a causal discovery method to evaluate the ability of climate models to represent the interactions between the Atlantic multidecadal variability (AMV) and the Pacific decadal variability (PDV). The approach and findings in this study present a powerful methodology that can be applied to a number of environment-related topics, offering tremendous insights to improve the understanding of the complex Earth system and the state of the art of climate modeling.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
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Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Ren Wang, Pierre Gentine, Jiabo Yin, Lijuan Chen, Jianyao Chen, and Longhui Li
Hydrol. Earth Syst. Sci., 25, 3805–3818, https://doi.org/10.5194/hess-25-3805-2021, https://doi.org/10.5194/hess-25-3805-2021, 2021
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Assessment of changes in the global water cycle has been a challenge. This study estimated long-term global latent heat and sensible heat fluxes for recent decades using machine learning and ground observations. The results found that the decline in evaporative fraction was typically accompanied by an increase in long-term runoff in over 27.06 % of the global land areas. The observation-driven findings emphasized that surface vegetation has great impacts in regulating water and energy cycles.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
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This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
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Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
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We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs 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. Comparisons with CMIP5 are also discussed.
Andrew F. Feldman, Daniel J. Short Gianotti, Alexandra G. Konings, Pierre Gentine, and Dara Entekhabi
Biogeosciences, 18, 831–847, https://doi.org/10.5194/bg-18-831-2021, https://doi.org/10.5194/bg-18-831-2021, 2021
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We quantify global plant water uptake durations after rainfall using satellite-based plant water content measurements. In wetter regions, plant water uptake occurs within a day due to rapid coupling between soil and plant water content. Drylands show multi-day plant water uptake after rain pulses, providing widespread evidence for slow rehydration responses and pulse-driven growth responses. Our results suggest that drylands are sensitive to projected shifts in rainfall intensity and frequency.
Bettina K. Gier, Michael Buchwitz, Maximilian Reuter, Peter M. Cox, Pierre Friedlingstein, and Veronika Eyring
Biogeosciences, 17, 6115–6144, https://doi.org/10.5194/bg-17-6115-2020, https://doi.org/10.5194/bg-17-6115-2020, 2020
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Models from Coupled Model Intercomparison Project (CMIP) phases 5 and 6 are compared to a satellite data product of column-averaged CO2 mole fractions (XCO2). The previously believed discrepancy of the negative trend in seasonal cycle amplitude in the satellite product, which is not seen in in situ data nor in the models, is attributed to a sampling characteristic. Furthermore, CMIP6 models are shown to have made progress in reproducing the observed XCO2 time series compared to CMIP5.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 224–232, 2002.
AMSR-E: AMSR-E Level 3 Sea Surface Temperature for Climate Model Comparison,
Ver. 1. PO.DAAC, CA, USA, Remote Sensing Systems, https://doi.org/10.5067/SST00-1D1M1, 2011.
Andela, B., Broetz, B., de Mora, L., Drost, N., Eyring, V., Koldunov, N., Lauer, A., Predoi, V., Righi, M., Schlund, M., Vegas-Regidor, J., Zimmermann, K., Bock, L., Diblen, F., Dreyer, L., Earnshaw, P., Hassler, B., Little, B., Loosveldt-Tomas, S., Smeets, S., Camphuijsen, J., Gier, B. K., Weigel, K., Hauser, M., Kalverla, P., Galytska, E., Cos-Espuña, P., Pelupessy, I., Koirala, S., Stacke, T., Alidoost, S., and Jury, M.: ESMValCore, Zenodo, https://doi.org/10.5281/zenodo.3387139, 2020b.
Andrews, T., Gregory, J. M., Webb, M. J., and Taylor, K. E.: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate
models, Geophys. Res. Lett., 39, L09712, https://doi.org/10.1029/2012GL051607, 2012.
Annan, J. D., Hargreaves, J. C., Mauritsen, T., and Stevens, B.: What could we learn about climate sensitivity from variability in the surface temperature record?, Earth Syst. Dynam., 11, 709–719, https://doi.org/10.5194/esd-11-709-2020, 2020.
Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L.,
Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon emission limits required to satisfy future representative concentration
pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805, https://doi.org/10.1029/2010GL046270, 2011.
Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E.,
McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D.
H., Strow, L. L., and Susskind, J.: AIRS/AMSU/HSB on the aqua mission: Design, science objectives, data products, and processing systems, IEEE T.
Geosci. Remote, 41, 253–264, 2003.
Beer, R.: TES on the Aura mission: Scientific objectives, measurements, and
analysis overview, IEEE T. Geosci. Remote, 44, 1102–1105, 2006.
Bellprat, O., Massonnet, F., Siegert, S., Prodhomme, C., Macias-Gomez, D.,
Guemas, V., and Doblas-Reyes, F.: Uncertainty propagation in observational
references to climate model scales, Remote Sens. Environ., 203, 101–108, 2017.
Bellucci, A., Gualdi, S., and Navarra, A.: The Double-ITCZ Syndrome in Coupled General Circulation Models: The Role of Large-Scale Vertical
Circulation Regimes, J. Climate, 23, 1127–1145, 2010.
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013.
Bi, D. H., Dix, M., Marsland, S. J., O'Farrell, S., Rashid, H. A., Uotila, P., Hirst, A. C., Kowalczyk, E., Golebiewski, M., Sullivan, A., Yan, H. L.,
Hannah, N., Franklin, C., Sun, Z. A., Vohralik, P., Watterson, I., Zhou, X.
B., Fiedler, R., Collier, M., Ma, Y. M., Noonan, J., Stevens, L., Uhe, P.,
Zhu, H. Y., Griffies, S. M., Hill, R., Harris, C., and Puri, K.: The ACCESS
coupled model: description, control climate and evaluation, Aust. Meteorol.
Ocean, 63, 41–64, 2013.
Bodas-Salcedo, A., Mulcahy, J. P., Andrews, T., Williams, K. D., Ringer, M.
A., Field, P. R., and Elsaesser, G. S.: Strong Dependence of Atmospheric
Feedbacks on Mixed-Phase Microphysics and Aerosol-Cloud Interactions in
HadGEM3, J. Adv. Model. Earth Syst., 11, 1735–1758, 2019.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., and Lohmann, U.: Clouds and aerosols, in: 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, UK and New York, NY, 2013.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J. L., Dupont, E., Ethe, C.,
Fairhead, L., Falletti, L., Flavoni, S., Foujols, M. A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J. Y., Guenet, B., Guez, L. E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Levy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J. B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottle, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thieblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Syst., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020.
Bretherton, C. S. and Caldwell, P. M.: Combining Emergent Constraints for
Climate Sensitivity, J. Climate, 33, 7413–7430, 2020.
Brient, F. and Schneider, T.: Constraints on Climate Sensitivity from
Space-Based Measurements of Low-Cloud Reflection, J. Climate, 29, 5821–5835,
2016.
Brown, P. T., Stolpe, M. B., and Caldeira, K.: Assumptions for emergent
constraints, Nature, 563, E1–E3, 2018.
Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B.
D., and Sanderson, B. M.: Statistical significance of climate sensitivity
predictors obtained by data mining, Geophys. Res. Lett., 41, 1803–1808, 2014.
Caldwell, P. M., Zelinka, M. D., and Klein, S. A.: Evaluating Emergent
Constraints on Equilibrium Climate Sensitivity, J. Climate, 31, 3921–3942,
2018.
Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., Bao, Y., He, J., Zhou, X., and Wu, L.: The NUIST Earth System Model (NESM) version 3: description and preliminary evaluation, Geosci. Model Dev., 11, 2975–2993, https://doi.org/10.5194/gmd-11-2975-2018, 2018.
Ceppi, P., Brient, F., Zelinka, M. D., and Hartmann, D. L.: Cloud feedback
mechanisms and their representation in global climate models, Wires Clim.
Change, 8, e465, https://doi.org/10.1002/wcc.465, 2017.
Charney, J. G., Arakawa, A., Baker, D. J., Bolin, B., Dickinson, R. E., Goody, R. M., Leith, C. E., Stommel, H. M., and Wunsch, C. I.: Carbon dioxide and climate: a scientific assessment, National Academy of Sciences, Washington, D.C., 1979.
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S.,
Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.:
Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled
Model, J. Adv. Model. Earth Syst., 11, 185–209, 2019.
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T.,
Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., and Krinner, G.:
Long-term climate change: projections, commitments and irreversibility, in:
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, UK and New York, NY, 2013.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011.
Counillon, F., Keenlyside, N., Bethke, I., Wang, Y. G., Billeau, S., Shen, M. L., and Bentsen, M.: Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model, Tellus A, 68, 32437, https://doi.org/10.3402/tellusa.v68.32437, 2016.
Cox, P. M., Huntingford, C., and Williamson, M. S.: Emergent constraint on
equilibrium climate sensitivity from global temperature variability, Nature,
553, 319–322, 2018.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S.,
van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C.,
Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E.,
Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System
Model Version 2 (CESM2), J. Adv. Model. Earth Syst., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, 2011.
Delworth, T. L., Stouffer, R. J., Dixon, K. W., Spelman, M. J., Knutson, T.
R., Broccoli, A. J., Kushner, P. J., and Wetherald, R. T.: Review of simulations of climate variability and change with the GFDL R30 coupled
climate model, Clim. Dynam., 19, 555–574, 2002.
Dix, M., Vohralik, P., Bi, D. H., Rashid, H., Marsland, S., O'Farrell, S.,
Uotila, P., Hirst, T., Kowalczyk, E., Sullivan, A., Yan, H. L., Franklin,
C., Sun, Z. A., Watterson, I., Collier, M., Noonan, J., Rotstayn, L., Stevens, L., Uhe, P., and Puri, K.: The ACCESS coupled model: documentation of core CMIP5 simulations and initial results, Aust. Meteorol. Ocean, 63,
83–99, 2013.
Donner, L. J., Wyman, B. L., Hemler, R. S., Horowitz, L. W., Ming, Y., Zhao,
M., Golaz, J. C., Ginoux, P., Lin, S. J., Schwarzkopf, M. D., Austin, J.,
Alaka, G., Cooke, W. F., Delworth, T. L., Freidenreich, S. M., Gordon, C. T., Griffies, S. M., Held, I. M., Hurlin, W. J., Klein, S. A., Knutson, T. R., Langenhorst, A. R., Lee, H. C., Lin, Y. L., Magi, B. I., Malyshev, S. L., Milly, P. C. D., Naik, V., Nath, M. J., Pincus, R., Ploshay, J. J., Ramaswamy, V., Seman, C. J., Shevliakova, E., Sirutis, J. J., Stern, W. F., Stouffer, R. J., Wilson, R. J., Winton, M., Wittenberg, A. T., and Zeng, F.
R.: The Dynamical Core, Physical Parameterizations, and Basic Simulation
Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled
Model CM3, J. Climate, 24, 3484–3519, 2011.
Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont,
O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp,
L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., de Noblet, N., Duvel, J. P., Ethe, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J. Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M. P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate
change projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 40, 2123–2165, 2013.
Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W.,
Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J.,
Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Phillipps, P. J., Sentman, L. T., Samuels, B. L., Spelman, M. J., Winton, M., Wittenberg, A. T., and Zadeh, N.: GFDL's ESM2 Global Coupled Climate-Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics, J. Climate, 25, 6646–6665, 2012.
ESGF: ESGF Node at DKRZ, available at: https://esgf-data.dkrz.de/projects/esgf-dkrz/, last access: 18 December 2020.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A. G.,
Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D.,
Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.:
Taking climate model evaluation to the next level, Nat. Clim. Change, 9,
102–110, 2019.
Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP, Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020.
Fasullo, J. T. and Trenberth, K. E.: A Less Cloudy Future: The Role of
Subtropical Subsidence in Climate Sensitivity, Science, 338, 792–794, 2012.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M.,
Worley, P. H., Yang, Z. L., and Zhang, M. H.: The Community Climate System
Model Version 4, J. Climate, 24, 4973–4991, 2011.
Gettelman, A., Hannay, C., Bacmeister, J. T., Neale, R. B., Pendergrass, A.
G., Danabasoglu, G., Lamarque, J. F., Fasullo, J. T., Bailey, D. A., Lawrence, D. M., and Mills, M. J.: High Climate Sensitivity in the Community
Earth System Model Version 2 (CESM2), Geophys. Res. Lett., 46, 8329–8337,
2019a.
Gettelman, A., Mills, M. J., Kinnison, D. E., Garcia, R. R., Smith, A. K.,
Marsh, D. R., Tilmes, S., Vitt, F., Bardeen, C. G., McInerny, J., Liu, H.
L., Solomon, S. C., Polvani, L. M., Emmons, L. K., Lamarque, J. F., Richter,
J. H., Glanville, A. S., Bacmeister, J. T., Phillips, A. S., Neale, R. B.,
Simpson, I. R., DuVivier, A. K., Hodzic, A., and Randel, W. J.: The Whole
Atmosphere Community Climate Model Version 6 (WACCM6), J. Geophys. Res.-Atmos., 124, 12380–12403, https://doi.org/10.1029/2019jd030943, 2019b.
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J.,
Bottinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K.,
Gayler, V., Haak, H., Hollweg, H. D., Ilyina, T., Kinne, S., Kornblueh, L.,
Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan,
F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur,
R., Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J.,
Widmann, H., Wieners, K. H., Claussen, M., Marotzke, J., and Stevens, B.:
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, 572–597, 2013.
Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M.
A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J.
G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J.,
Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H. Y., Lin, W. Y., Lipscomb, W. H., Ma, P. L., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X. Y., Singh, B., Tang,
J. Y., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan,
H., Wang, H. L., Wang, S. L., Williams, D. N., Wolfram, P. J., Worley, P.
H., Xie, S. C., Yang, Y., Yoon, J. H., Zelinka, M. D., Zender, C. S., Zeng,
X. B., Zhang, C. Z., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.:
The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard
Resolution, J. Adv. Model. Earth Syst., 11, 2089–2129, 2019.
Gregory, J. M., Ingram, W. J., Palmer, M. A., Jones, G. S., Stott, P. A.,
Thorpe, R. B., Lowe, J. A., Johns, T. C., and Williams, K. D.: A new method
for diagnosing radiative forcing and climate sensitivity, Geophys. Res. Lett., 31, L03205, https://doi.org/10.1029/2003GL018747, 2004.
Guo, Y. Y., Yu, Y. Q., Lin, P. F., Liu, H. L., He, B., Bao, Q., Zhao, S. W.,
and Wang, X. W.: Overview of the CMIP6 Historical Experiment Datasets with
the Climate System Model CAS FGOALS-f3-L, Adv. Atmos. Sci., 37, 1057–1066, https://doi.org/10.1007/s00376-020-2004-4, 2020.
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020.
Hall, A. and Qu, X.: Using the current seasonal cycle to constrain snow
albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502, https://doi.org/10.1029/2005GL025127, 2006.
Hargreaves, J. C. and Annan, J. D.: Could the Pliocene constrain the equilibrium climate sensitivity?, Clim. Past, 12, 1591–1599, https://doi.org/10.5194/cp-12-1591-2016, 2016.
Hargreaves, J. C., Annan, J. D., Yoshimori, M., and Abe-Ouchi, A.: Can the
Last Glacial Maximum constrain climate sensitivity?, Geophys. Res. Lett., 39,
L24702, https://doi.org/10.1029/2012GL053872, 2012.
He, B., Bao, Q., Wang, X. C., Zhou, L. J., Wu, X. F., Liu, Y. M., Wu, G. X.,
Chen, K. J., He, S. C., Hu, W. T., Li, J. D., Li, J. X., Nian, G. K., Wang,
L., Yang, J., Zhang, M. H., and Zhang, X. Q.: CAS FGOALS-f3-L Model Datasets
for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation,
Adv. Atmos. Sci., 36, 771–778, 2019.
He, B., Liu, Y. M., Wu, G. X., Bao, Q., Zhou, T. J., Wu, X. F., Wang, L.,
Li, J. D., Wang, X. C., Li, J. X., Hu, W. T., Zhang, X. Q., Sheng, C., and
Tang, Y. Q.: CAS FGOALS-f3-L Model Datasets for CMIP6 GMMIP Tier-1 and
Tier-3 Experiments, Adv. Atmos. Sci., 37, 18–28, 2020.
Hirota, N., Takayabu, Y. N., Watanabe, M., and Kimoto, M.: Precipitation
Reproducibility over Tropical Oceans and Its Relationship to the Double ITCZ
Problem in CMIP3 and MIROC5 Climate Models, J. Climate, 24, 4859–4873, 2011.
Iversen, T., Bentsen, M., Bethke, I., Debernard, J. B., Kirkevåg, A., Seland, Ø., Drange, H., Kristjansson, J. E., Medhaug, I., Sand, M., and Seierstad, I. A.: The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections, Geosci. Model Dev., 6, 389–415, https://doi.org/10.5194/gmd-6-389-2013, 2013.
Ji, D., Wang, L., Feng, J., Wu, Q., Cheng, H., Zhang, Q., Yang, J., Dong, W., Dai, Y., Gong, D., Zhang, R.-H., Wang, X., Liu, J., Moore, J. C., Chen, D., and Zhou, M.: Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1, Geosci. Model Dev., 7, 2039–2064, https://doi.org/10.5194/gmd-7-2039-2014, 2014.
Jimenez-de-la-Cuesta, D. and Mauritsen, T.: Emergent constraints on Earth's
transient and equilibrium response to doubled CO2 from post-1970s global warming, Nat. Geosci., 12, 902–905, 2019.
Knutti, R., Rugenstein, M. A. A., and Hegerl, G. C.: Beyond equilibrium
climate sensitivity, Nat Geosci, 10, 727–736, 2017a.
Knutti, R., Sedlacek, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and
Eyring, V.: A climate model projection weighting scheme accounting for
performance and interdependence, Geophys. Res. Lett., 44, 1909–1918, 2017b.
Kuhlbrodt, T., Jones, C. G., Sellar, A., Storkey, D., Blockley, E., Stringer, M., Hill, R., Graham, T., Ridley, J., Blaker, A., Calvert, D., Copsey, D., Ellis, R., Hewitt, H., Hyder, P., Ineson, S., Mulcahy, J., Siahaan, A., and Walton, J.: The Low-Resolution Version of HadGEM3 GC3.1: Development and Evaluation for Global Climate, J. Adv. Model. Earth Syst., 10, 2865–2888, 2018.
Lauer, A., Jones, C., Eyring, V., Evaldsson, M., Hagemann, S., Mäkelä, J., Martin, G., Roehrig, R., and Wang, S.: Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean, and monsoons, Earth Syst. Dynam., 9, 33–67, https://doi.org/10.5194/esd-9-33-2018, 2018.
Lauer, A., Eyring, V., Bellprat, O., Bock, L., Gier, B. K., Hunter, A., Lorenz, R., Pérez-Zanón, N., Righi, M., Schlund, M., Senftleben, D., Weigel, K., and Zechlau, S.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP, Geosci. Model Dev., 13, 4205–4228, https://doi.org/10.5194/gmd-13-4205-2020, 2020.
Law, R. M., Ziehn, T., Matear, R. J., Lenton, A., Chamberlain, M. A., Stevens, L. E., Wang, Y.-P., Srbinovsky, J., Bi, D., Yan, H., and Vohralik, P. F.: The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 1: Model description and pre-industrial simulation, Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017, 2017.
Lee, J., Kim, J., Sun, M. A., Kim, B. H., Moon, H., Sung, H. M., Kim, J., and Byun, Y. H.: Evaluation of the Korea Meteorological Administration Advanced Community Earth-System model (K-ACE), Asia-Pac. J. Atmos. Sci., 56, 381–395, 2020a.
Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., Chen, J.-P., Pan, H.-L., and Hsu, H.-H.: Taiwan Earth System Model Version 1: description and evaluation of mean state, Geosci. Model Dev., 13, 3887–3904, https://doi.org/10.5194/gmd-13-3887-2020, 2020b.
Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., Dong, L., Zhou, T., Liu, L., Wang, L., Pu, Y., Chen, X., Chen, L., Xie, Z., Liu, H., Zhang, L.,
Huang, X., Feng, T., Zheng, W., Xia, K., Liu, H., Liu, J., Wang, Y., Wang,
L., Jia, B., Xie, F., Wang, B., Zhao, S., Yu, Z., Zhao, B., and Wei, J.: The
Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3 (FGOALS-g3): Description and Evaluation, J. Adv. Model. Earth Syst., 12, e2019MS002012, https://doi.org/10.1029/2019MS002012, 2020.
Li, L. J., Lin, P. F., Yu, Y. Q., Wang, B., Zhou, T. J., Liu, L., Liu, J. P., Bao, Q., Xu, S. M., Huang, W. Y., Xia, K., Pu, Y., Dong, L., Shen, S., Liu, Y. M., Hu, N., Liu, M. M., Sun, W. Q., Shi, X. J., Zheng, W. P., Wu, B., Song, M. R., Liu, H. L., Zhang, X. H., Wu, G. X., Xue, W., Huang, X. M., Yang, G. W., Song, Z. Y., and Qiao, F. L.: The flexible global ocean-atmosphere-land system model, Grid-point Version 2: FGOALS-g2, Adv.
Atmos. Sci., 30, 543–560, 2013.
Lipat, B. R., Tselioudis, G., Grise, K. M., and Polvani, L. M.: CMIP5 models' shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent, Geophys. Res. Lett., 44, 5739–5748, 2017.
Loeb, N. G., Doelling, D. R., Wang, H. L., Su, W. Y., Nguyen, C., Corbett, J. G., Liang, L. S., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31, 895–918,
2018.
Mace, G. G., Zhang, Q. Q., Vaughan, M., Marchand, R., Stephens, G., Trepte, C., and Winker, D.: A description of hydrometeor layer occurrence statistics
derived from the first year of merged Cloudsat and CALIPSO data, J. Geophys.
Res.-Atmos., 114, D00A26, https://doi.org/10.1029/2007JD009755, 2009.
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Flaeschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann,
S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenez-de-la-Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher,
D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei,
D., Meraner, K., Mikolajewicz, U., Modali, K., Mobis, B., Muller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S. S., Paulsen, H.,
Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J. S., Tian, F. X., Voigt, A., Vrese, P., Wieners, K. H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst., 11, 998–1038, 2019.
Meehl, G. A., Washington, W. M., Arblaster, J. M., Hu, A. X., Teng, H. Y.,
Tebaldi, C., Sanderson, B. N., Lamarque, J. F., Conley, A., Strand, W. G., and White, J. B.: Climate System Response to External Forcings and Climate
Change Projections in CCSM4, J. Climate, 25, 3661–3683, 2012.
Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J.-F., Stouffer, R. J., Taylor, K. E., and Schlund, M.: Context for interpreting
equilibrium climate sensitivity and transient climate response from the
CMIP6 Earth system models, Sci. Adv., 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981, 2020.
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set, J. Geophys. Res.-Atmos., 117,
D08101, https://doi.org/10.1029/2011JD017187, 2012.
Muller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine,
T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E.,
Stemmler, I., Tian, F., and Marotzke, J.: A Higher-resolution Version of the
Max Planck Institute Earth System Model (MPI-ESM1.2-HR), J. Adv. Model. Earth
Syst., 10, 1383–1413, 2018.
Park, S., Shin, J., Kim, S., Oh, E., and Kim, Y.: Global Climate Simulated by the Seoul National University Atmosphere Model Version 0 with a Unified Convection Scheme (SAM0-UNICON), J. Climate, 32, 2917–2949, 2019.
Po-Chedley, S., Proistosescu, C., Armour, K. C., and Santer, B. D.: Climate
constraint reflects forced signal, Nature, 563, E6–E9, 2018.
Rackow, T., Goessling, H. F., Jung, T., Sidorenko, D., Semmler, T., Barbi, D., and Handorf, D.: Towards multi-resolution global climate modeling with
ECHAM6-FESOM. Part II: climate variability, Clim. Dynam., 50, 2369–2394, 2018.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L.
V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204, 2019.
Renoult, M., Annan, J. D., Hargreaves, J. C., Sagoo, N., Flynn, C., Kapsch, M.-L., Li, Q., Lohmann, G., Mikolajewicz, U., Ohgaito, R., Shi, X., Zhang, Q., and Mauritsen, T.: A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP, Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, 2020.
Riahi, K., Rao, S., Krey, V., Cho, C. H., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafaj, P.: RCP 8.5-A scenario of
comparatively high greenhouse gas emissions, Climatic Change, 109, 33–57,
2011.
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020.
Rind, D., Orbe, C., Jonas, J., Nazarenko, L., Zhou, T., Kelley, M., Lacis, A., Shindell, D., Faluvegi, G., Romanou, A., Russell, G., Tausnev, N., Bauer, M., and Schmidt, G.: GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere – Model Structure, Climatology, Variability, and Climate Sensitivity, J. Geophys. Res.-Atmos., 125, e2019JD032204, https://doi.org/10.1029/2019JD032204, 2020.
Rong, X. Y., Li, J., Chen, H. M., Xin, Y. F., Su, J. Z., Hua, L. J., Zhou, T. J., Qi, Y. J., Zhang, Z. Q., Zhang, G., and Li, J. D.: The CAMS Climate System Model and a Basic Evaluation of Its Climatology and Climate Variability Simulation, J. Meteorol. Res.-Prc., 32, 839–861, 2018.
Rossow, W. B. and Schiffer, R. A.: Isccp Cloud Data Products, B. Am. Meteorol. Soc., 72, 2–20, 1991.
Rotstayn, L. D., Jeffrey, S. J., Collier, M. A., Dravitzki, S. M., Hirst, A. C., Syktus, J. I., and Wong, K. K.: Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations, Atmos. Chem. Phys., 12, 6377–6404, https://doi.org/10.5194/acp-12-6377-2012, 2012.
Rugenstein, M., Bloch-Johnson, J., Abe-Ouchi, A., Andrews, T., Beyerle, U.,
Cao, L., Chadha, T., Danabasoglu, G., Dufresne, J. L., Duan, L., Foujols, M.
A., Frolicher, T., Geoffroy, O., Gregory, J., Knutti, R., Li, C., Marzocchi,
A., Mauritsen, T., Menary, M., Moyer, E., Nazarenko, L., Paynter, D., Saint-Martin, D., Schmidt, G. A., Yamamoto, A., and Yang, S. T.: LongRunMIP:
Motivation and Design for a Large Collection of Millennial-Length AOGCM
Simulations, B. Am. Meteorol. Soc., 100, 2551–2570, 2019.
Rugenstein, M., Bloch-Johnson, J., Gregory, J., Andrews, T., Mauritsen, T.,
Li, C., Frolicher, T. L., Paynter, D., Danabasoglu, G., Yang, S. T., Dufresne, J. L., Cao, L., Schmidt, G. A., Abe-Ouchi, A., Geoffroy, O., and
Knutti, R.: Equilibrium Climate Sensitivity Estimated by Equilibrating
Climate Models, Geophys. Res. Lett., 47, e2019GL083898, https://doi.org/10.1029/2019GL083898, 2020.
Rypdal, M., Fredriksen, H. B., Rypdal, K., and Steene, R. J.: Emergent
constraints on climate sensitivity, Nature, 563, E4–E5, 2018.
Sanderson, B. M., Knutti, R., and Caldwell, P.: A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble, J. Climate, 28, 5171–5194, 2015.
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017.
Schlund, M., Eyring, V., Camps-Valls, G., Friedlingstein, P., Gentine, P., and Reichstein, M.: Constraining uncertainty in projected gross primary
production with machine learning, J. Geophys. Res.-Biogeo., 125, e2019JG005619, https://doi.org/10.1029/2019JG005619, 2020.
Schmidt, G. A., Ruedy, R., Hansen, J. E., Aleinov, I., Bell, N., Bauer, M.,
Bauer, S., Cairns, B., Canuto, V., Cheng, Y., Del Genio, A., Faluvegi, G.,
Friend, A. D., Hall, T. M., Hu, Y. Y., Kelley, M., Kiang, N. Y., Koch, D.,
Lacis, A. A., Lerner, J., Lo, K. K., Miller, R. L., Nazarenko, L., Oinas, V., Perlwitz, J., Perlwitz, J., Rind, D., Romanou, A., Russell, G. L., Sato, M., Shindell, D. T., Stone, P. H., Sun, S., Tausnev, N., Thresher, D., and Yao, M. S.: Present-day atmospheric simulations using GISS ModelE: Comparison to in situ, satellite, and reanalysis data, J. Climate, 19, 153–192, 2006.
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A.,
Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E.,
Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A.,
Rocher, M., Roehrig, R., Salas-y-Mélia, D., Sanchez, E., Terray, L.,
Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role
of Earth System Processes in Present-Day and Future Climate, J. Adv. Model. Earth Syst., 11, 4182–4227, 2019.
Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen, A., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Schancke Aas, K., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Hafsahl Karset, I. H., Landgren, O., Liakka, J., Onsum Moseid, K., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iverson, T., and Schulz, M.: The Norwegian Earth System Model, NorESM2 – Evaluation of theCMIP6 DECK and historical simulations, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-378, in review, 2020.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y. M., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the UK Earth System Model, J. Adv. Model. Earth Syst., 11, 4513–4558, 2019.
Sherwood, S., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L.,
Hausfather, Z., Heydt, A. S. v. d., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An assessment of Earth's climate sensitivity using multiple
lines of evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020.
Sherwood, S. C., Bony, S., and Dufresne, J. L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42, 2014.
Sidorenko, D., Rackow, T., Jung, T., Semmler, T., Barbi, D., Danilov, S.,
Dethloff, K., Dorn, W., Fieg, K., Goessling, H., Handorf, D., Harig, S., Hiller, W., Juricke, S., Losch, M., Schroter, J., Sein, D. V., and Wang, Q.:
Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part I:
model formulation and mean climate, Clim. Dynam., 44, 757–780, 2015.
Smith, T. M. and Reynolds, R. W.: Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997), J. Climate, 16, 1495–1510, 2003.
Su, H., Jiang, J. H., Zhai, C. X., Shen, T. J., Neelin, J. D., Stephens, G. L., and Yung, Y. L.: Weakening and strengthening structures in the Hadley
Circulation change under global warming and implications for cloud response
and climate sensitivity, J. Geophys. Res.-Atmos., 119, 5787–5805, 2014.
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019.
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of Cmip5 and
the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Tian, B. J.: Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias, Geophys. Res. Lett., 42, 4133–4141, 2015.
Tokarska, K. B., Stolpe, M. B., Sippel, S., Fischer, E. M., Smith, C. J., Lehner, F., and Knutti, R.: Past warming trend constrains future warming in
CMIP6 models, Sci. Adv., 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549, 2020.
Vial, J., Dufresne, J. L., and Bony, S.: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates, Clim. Dynam., 41, 3339–3362, 2013.
Voldoire, A., Sanchez-Gomez, E., Melia, D. S. Y., Decharme, B., Cassou, C.,
Senesi, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Deque, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine,
M. P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R.,
Belamari, S., Braun, A., Coquart, L., and Chauvin, F.: The CNRM-CM5.1 global
climate model: description and basic evaluation, Clim. Dynam., 40, 2091–2121, 2013.
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A.,
Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P.,
Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke,
S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes,
J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK
Experiments With CNRM-CM6-1, J. Adv. Model. Earth Syst., 11, 2177–2213, 2019.
Volodin, E. M.: Relation between temperature sensitivity to doubled carbon
dioxide and the distribution of clouds in current climate models, Izv Atmos.
Ocean Phys., 44, 288–299, 2008.
Volodin, E. M., Dianskii, N. A., and Gusev, A. V.: Simulating present-day
climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations, Izv Atmos. Ocean Phys., 46, 414–431, 2010.
Volodin, E. M., Mortikov, E. V., Kostrykin, S. V., Galin, V. Y., Lykosov, V.
N., Gritsun, A. S., Diansky, N. A., Gusev, A. V., and Yakovlev, N. G.: Simulation of modern climate with the new version of the INM RAS climate model, Izv Atmos. Ocean Phys., 53, 142–155, 2017a.
Volodin, E. M., Mortikov, E. V., Kostrykin, S. V., Galin, V. Y., Lykossov, V. N., Gritsun, A. S., Diansky, N. A., Gusev, A. V., and Iakovlev, N. G.: Simulation of the present-day climate with the climate model INMCM5, Clim.
Dynam., 49, 3715–3734, 2017b.
Wang, T., Jiang, J., Zhang, M., Zhang, H., He, J., Hao, H., and Chi, X.:
Design and Research of CAS-CIG for Earth System Models, Earth Space Sci., 7,
e2019EA000965, https://doi.org/10.1029/2019EA000965, 2020.
Watanabe, M., Suzuki, T., O'ishi, R., Komuro, Y., Watanabe, S., Emori, S.,
Takemura, T., Chikira, M., Ogura, T., Sekiguchi, M., Takata, K., Yamazaki,
D., Yokohata, T., Nozawa, T., Hasumi, H., Tatebe, H., and Kimoto, M.: Improved Climate Simulation by MIROC5. Mean States, Variability, and Climate
Sensitivity, J. Climate, 23, 6312–6335, 2010.
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011.
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations, J. Adv. Model. Earth Syst., 10, 357–380, 2018.
Wu, T. W., Song, L. C., Li, W. P., Wang, Z. Z., Zhang, H., Xin, X. G., Zhang, Y. W., Zhang, L., Li, J. L., Wu, F. H., Liu, Y. M., Zhang, F., Shi, X. L., Chu, M., Zhang, J., Fang, Y. J., Wang, F., Lu, Y. X., Liu, X. W., Wei, M., Liu, Q. X., Zhou, W. Y., Dong, M., Zhao, Q. G., Ji, J. J., Li, L., and Zhou, M. Y.: An Overview of BCC Climate System Model Development and Application for Climate Change Studies, J. Meteorol. Res.-Prc., 28, 34–56, 2014.
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.: The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6, Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019.
Wyser, K., van Noije, T., Yang, S., von Hardenberg, J., O'Donnell, D., and Döscher, R.: On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6, Geosci. Model Dev., 13, 3465–3474, https://doi.org/10.5194/gmd-13-3465-2020, 2020.
Yukimoto, S., Adachi, Y., Hosaka, M., Sakami, T., Yoshimura, H., Hirabara, M., Tanaka, T. Y., Shindo, E., Tsujino, H., Deushi, M., Mizuta, R., Yabu, S., Obata, A., Nakano, H., Koshiro, T., Ose, T., and Kitoh, A.: A New Global Climate Model of the Meteorological Research Institute: MRI-CGCM3-Model
Description and Basic Performance, J. Meteorol. Soc. Jpn., 90a, 23–64, 2012.
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S.,
Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H.,
Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, J. Meteorol. Soc. Jpn., 97, 931–965, 2019.
Zelinka, M. D., Myers, T. A., Mccoy, D. T., Po-Chedley, S., Caldwell, P. M.,
Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020.
Zhai, C. X., Jiang, J. H., and Su, H.: Long-term cloud change imprinted in
seasonal cloud variation: More evidence of high climate sensitivity, Geophys.
Res. Lett., 42, 8729–8737, 2015.
Ziehn, T., Lenton, A., Law, R. M., Matear, R. J., and Chamberlain, M. A.: The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations, Geosci. Model Dev., 10, 2591–2614, https://doi.org/10.5194/gmd-10-2591-2017, 2017.
Short summary
As an important measure of climate change, the Equilibrium Climate Sensitivity (ECS) describes the change in surface temperature after a doubling of the atmospheric CO2 concentration. Climate models from the Coupled Model Intercomparison Project (CMIP) show a wide range in ECS. Emergent constraints are a technique to reduce uncertainties in ECS with observational data. Emergent constraints developed with data from CMIP phase 5 show reduced skill and higher ECS ranges when applied to CMIP6 data.
As an important measure of climate change, the Equilibrium Climate Sensitivity (ECS) describes...
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