Articles | Volume 12, issue 3
https://doi.org/10.5194/esd-12-899-2021
© Author(s) 2021. 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-12-899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review
| 
23 Aug 2021
Review |
| 23 Aug 2021
| 23 Aug 2021
The potential for structural errors in emergent constraints
Benjamin M. Sanderson
CORRESPONDING AUTHOR
Climate Modeling and Global Change, CERFACS, Toulouse, France
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA
Angeline G. Pendergrass
Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA
Charles D. Koven
Earth and Environmental Sciences, Lawrence Berkeley National Lab, Berkeley CA, USA
Florent Brient
Dynamical Meteorology Department, LMD/IPSL, Sorbonne Université, Paris, France
Ben B. B. Booth
Hadley Centre for Climate Prediction and Research, Met Office, Exeter, UK
Rosie A. Fisher
Climate Modeling and Global Change, CERFACS, Toulouse, France
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA
Reto Knutti
Dep. of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
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Detlef van Vuuren, Brian O'Neill, Claudia Tebaldi, Louise Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Benjamin Sanderson, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh Fall, Katja Frieler, Matthew Gidden, Laila Gohar, Andrew Jones, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camila Mathison, Shahbaz Mehmood, Luciana Prado, Qiang Zhang, Steven Rose, Alexander Ruane, Carl-Friederich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha Vishwanathan, Tokuta Yokohata, and Tilo Ziehn
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Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
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Solar radiation modification (SRM) artificially cools global temperature without acting on the cause of climate change. This study looks at how long SRM would have to be deployed to limit warming to 1.5 °C and how this timeframe is affected by different levels of mitigation, negative emissions and climate uncertainty. None of the three factors alone can guarantee short SRM deployment. Due to their uncertainty at the time of SRM initialization, any deployment risks may be several centuries long.
Benjamin M. Sanderson and Maria Rugenstein
Earth Syst. Dynam., 13, 1715–1736, https://doi.org/10.5194/esd-13-1715-2022, https://doi.org/10.5194/esd-13-1715-2022, 2022
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Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
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This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
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.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025, https://doi.org/10.5194/gmd-18-4399-2025, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
Mara Y. McPartland, Tomas Lovato, Charles D. Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
EGUsphere, https://doi.org/10.5194/egusphere-2025-3246, https://doi.org/10.5194/egusphere-2025-3246, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
Victor Brovkin, Benjamin M. Sanderson, Noel G. Brizuela, Tomohiro Hajima, Tatiana Ilyina, Chris D. Jones, Charles Koven, David Lawrence, Peter Lawrence, Hongmei Li, Spencer Liddcoat, Anastasia Romanou, Roland Séférian, Lori T. Sentman, Abigail L. S. Swann, Jerry Tjiputra, Tilo Ziehn, and Alexander J. Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-3270, https://doi.org/10.5194/egusphere-2025-3270, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Idealized experiments with Earth system models provide a basis for understanding the response of the carbon cycle to emissions. We show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean and hypothesize that this relationship does not depend on emission pathways. We reduce the coupled system to only one differential equation, which represents a powerful simplification of the Earth system dynamics as a function of fossil fuel emissions.
Susanne Baur, Benjamin M. Sanderson, Roland Séférian, and Laurent Terray
Earth Syst. Dynam., 16, 667–681, https://doi.org/10.5194/esd-16-667-2025, https://doi.org/10.5194/esd-16-667-2025, 2025
Short summary
Short summary
Stratospheric aerosol injection (SAI) could be used alongside mitigation to reduce global warming. Previous studies suggest that more atmospheric CO2 is taken up when SAI is deployed. Here, we look at the entire SAI deployment from start to after termination. We show how the initial CO2 uptake benefit, and hence lower mitigation burden, is reduced in later stages of SAI, where the reduction in natural CO2 uptake turns into an additional mitigation burden.
Junyan Ding, Nate McDowell, Vanessa Bailey, Nate Conroy, Donnie J. Day, Yilin Fang, Kenneth M. Kemner, Matthew L. Kirwan, Charlie D. Koven, Matthew Kovach, Patrick Megonigal, Kendalynn A. Morris, Teri O’Meara, Stephanie C. Pennington, Roberta B. Peixoto, Peter Thornton, Mike Weintraub, Peter Regier, Leticia Sandoval, Fausto Machado-Silva, Alice Stearns, Nick Ward, and Stephanie J. Wilson
EGUsphere, https://doi.org/10.5194/egusphere-2025-1544, https://doi.org/10.5194/egusphere-2025-1544, 2025
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We used a vegetation model to study why coastal forests are dying due to rising water levels and what happens to the ecosystem when marshes take over. We found that tree death is mainly caused by water-damaged roots, leading to major changes in the environment, such as reduced water use and carbon storage. Our study helps explain how coastal ecosystems are shifting and offers new ideas to explore in future field research.
Norman J. Steinert and Benjamin M. Sanderson
EGUsphere, https://doi.org/10.5194/egusphere-2025-1714, https://doi.org/10.5194/egusphere-2025-1714, 2025
Short summary
Short summary
In this study, we explore how carbon emissions from thawing permafrost, known as the permafrost carbon feedback, affect two important climate metrics: how much the Earth warms per amount of carbon we emit, and how much warming continues after we stop emitting carbon. Our study tackles a major gap in how we estimate future climate change. Using simplified climate models, we find a generalizable relationship between the permafrost carbon feedback and its additional warming impact on climate.
Marit Sandstad, Norman Julius Steinert, Susanne Baur, and Benjamin Mark Sanderson
EGUsphere, https://doi.org/10.5194/egusphere-2025-1038, https://doi.org/10.5194/egusphere-2025-1038, 2025
Short summary
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In this article we present METEORv1.0.0, a climate model emulator, that can be trained on full spacially resolved and widely available climate model data to reproduce climate variables, and make predictions from unseen emission trajectories. The methodology which consists of identifying patterns associated with various timescales of impact for one or more forcers using idealised experiments and anomaly calculations. Results for precipitation and temperature show good model performance.
Luna Bloin-Wibe, Robin Noyelle, Vincent Humphrey, Urs Beyerle, Reto Knutti, and Erich Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2025-525, https://doi.org/10.5194/egusphere-2025-525, 2025
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Weather extremes have become more frequent due to climate change. It is therefore crucial to understand them, but since they are rarer than average weather, they are challenging to study. Ensemble Boosting (EB) is a tool that generates extreme climate model events efficiently, but without directly estimating their probability. Here, we present a method to recover these probabilities for a global climate model. EB can thus now be used to find extremes with meaningful statistical information.
Detlef van Vuuren, Brian O'Neill, Claudia Tebaldi, Louise Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Benjamin Sanderson, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh Fall, Katja Frieler, Matthew Gidden, Laila Gohar, Andrew Jones, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camila Mathison, Shahbaz Mehmood, Luciana Prado, Qiang Zhang, Steven Rose, Alexander Ruane, Carl-Friederich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha Vishwanathan, Tokuta Yokohata, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3765, https://doi.org/10.5194/egusphere-2024-3765, 2025
Short summary
Short summary
We propose a set of six plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
John Patrick Dunne, Helene T. Hewitt, Julie Arblaster, Frédéric Bonou, Olivier Boucher, Tereza Cavazos, Paul J. Durack, Birgit Hassler, Martin Juckes, Tomoki Miyakawa, Matthew Mizielinski, Vaishali Naik, Zebedee Nicholls, Eleanor O’Rourke, Robert Pincus, Benjamin M. Sanderson, Isla R. Simpson, and Karl E. Taylor
EGUsphere, https://doi.org/10.5194/egusphere-2024-3874, https://doi.org/10.5194/egusphere-2024-3874, 2024
Short summary
Short summary
This manuscript provides the motivation and experimental design for the seventh phase of the Coupled Model Intercomparison Project (CMIP7) to coordinate community based efforts to answer key and timely climate science questions and facilitate delivery of relevant multi-model simulations for: prediction and projection, characterization, attribution and process understanding; vulnerability, impacts and adaptations analysis; national and international climate assessments; and society at large.
Masaru Yoshioka, Daniel P. Grosvenor, Ben B. B. Booth, Colin P. Morice, and Ken S. Carslaw
Atmos. Chem. Phys., 24, 13681–13692, https://doi.org/10.5194/acp-24-13681-2024, https://doi.org/10.5194/acp-24-13681-2024, 2024
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A 2020 regulation has reduced sulfur emissions from shipping by about 80 %, leading to a decrease in atmospheric aerosols that have a cooling effect primarily by affecting cloud properties and amounts. Our climate model simulations predict a global temperature increase of 0.04 K over the next 3 decades as a result, which could contribute to surpassing the Paris Agreement's 1.5 °C target. Reduced aerosols may have also contributed to the recent temperature spikes.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Benjamin Mark Sanderson, Victor Brovkin, Rosie Fisher, David Hohn, Tatiana Ilyina, Chris Jones, Torben Koenigk, Charles Koven, Hongmei Li, David Lawrence, Peter Lawrence, Spencer Liddicoat, Andrew Macdougall, Nadine Mengis, Zebedee Nicholls, Eleanor O'Rourke, Anastasia Romanou, Marit Sandstad, Jörg Schwinger, Roland Seferian, Lori Sentman, Isla Simpson, Chris Smith, Norman Steinert, Abigail Swann, Jerry Tjiputra, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3356, https://doi.org/10.5194/egusphere-2024-3356, 2024
Short summary
Short summary
This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated zero emissions commitment due to emissions rates exceeding historical levels.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
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We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Saloua Peatier, Benjamin M. Sanderson, and Laurent Terray
Earth Syst. Dynam., 15, 987–1014, https://doi.org/10.5194/esd-15-987-2024, https://doi.org/10.5194/esd-15-987-2024, 2024
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The calibration of Earth system model parameters is a high-dimensionality problem subject to data, time, and computational constraints. In this study, we propose a practical solution for finding diverse near-optimal solutions. We argue that the effective degrees of freedom in the model performance response to parameter input is relatively small. Comparably performing parameter configurations exist and showcase different trade-offs in model errors, providing insights for model development.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Susanne Baur, Benjamin M. Sanderson, Roland Séférian, and Laurent Terray
Earth Syst. Dynam., 15, 307–322, https://doi.org/10.5194/esd-15-307-2024, https://doi.org/10.5194/esd-15-307-2024, 2024
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Most solar radiation modification (SRM) simulations assume no physical coupling between mitigation and SRM. We analyze the impact of SRM on photovoltaic (PV) and concentrated solar power (CSP) and find that almost all regions have reduced PV and CSP potential compared to a mitigated or unmitigated scenario, especially in the middle and high latitudes. This suggests that SRM could pose challenges for meeting energy demands with solar renewable resources.
Junyan Ding, Polly Buotte, Roger Bales, Bradley Christoffersen, Rosie A. Fisher, Michael Goulden, Ryan Knox, Lara Kueppers, Jacquelyn Shuman, Chonggang Xu, and Charles D. Koven
Biogeosciences, 20, 4491–4510, https://doi.org/10.5194/bg-20-4491-2023, https://doi.org/10.5194/bg-20-4491-2023, 2023
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We used a vegetation model to investigate how the different combinations of plant rooting depths and the sensitivity of leaves and stems to drying lead to differential responses of a pine forest to drought conditions in California, USA. We found that rooting depths are the strongest control in that ecosystem. Deep roots allow trees to fully utilize the soil water during a normal year but result in prolonged depletion of soil moisture during a severe drought and hence a high tree mortality risk.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Nina Raoult, Tim Jupp, Ben Booth, and Peter Cox
Earth Syst. Dynam., 14, 723–731, https://doi.org/10.5194/esd-14-723-2023, https://doi.org/10.5194/esd-14-723-2023, 2023
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Climate models are used to predict the impact of climate change. However, poorly constrained parameters used in the physics of the models mean that we simulate a large spread of possible future outcomes. We can use real-world observations to reduce the uncertainty of parameter values, but we do not have observations to reduce the spread of possible future outcomes directly. We present a method for translating the reduction in parameter uncertainty into a reduction in possible model projections.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
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We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483, https://doi.org/10.5194/esd-14-457-2023, https://doi.org/10.5194/esd-14-457-2023, 2023
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We carry out an assessment of an ensemble of general climate models (CMIP6) based on the ability of the models to represent the key physical processes that are important for representing European climate. Filtering the models with the assessment leads to more models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.
Susanne Baur, Alexander Nauels, Zebedee Nicholls, Benjamin M. Sanderson, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 14, 367–381, https://doi.org/10.5194/esd-14-367-2023, https://doi.org/10.5194/esd-14-367-2023, 2023
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Solar radiation modification (SRM) artificially cools global temperature without acting on the cause of climate change. This study looks at how long SRM would have to be deployed to limit warming to 1.5 °C and how this timeframe is affected by different levels of mitigation, negative emissions and climate uncertainty. None of the three factors alone can guarantee short SRM deployment. Due to their uncertainty at the time of SRM initialization, any deployment risks may be several centuries long.
Iris Elisabeth de Vries, Sebastian Sippel, Angeline Greene Pendergrass, and Reto Knutti
Earth Syst. Dynam., 14, 81–100, https://doi.org/10.5194/esd-14-81-2023, https://doi.org/10.5194/esd-14-81-2023, 2023
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Precipitation change is an important consequence of climate change, but it is hard to detect and quantify. Our intuitive method yields robust and interpretable detection of forced precipitation change in three observational datasets for global mean and extreme precipitation, but the different observational datasets show different magnitudes of forced change. Assessment and reduction of uncertainties surrounding forced precipitation change are important for future projections and adaptation.
Benjamin M. Sanderson and Maria Rugenstein
Earth Syst. Dynam., 13, 1715–1736, https://doi.org/10.5194/esd-13-1715-2022, https://doi.org/10.5194/esd-13-1715-2022, 2022
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Equilibrium climate sensitivity (ECS) is a measure of how much long-term warming should be expected in response to a change in greenhouse gas concentrations. It is generally calculated in climate models by extrapolating global average temperatures to a point of where the planet is no longer a net absorber of energy. Here we show that some climate models experience energy leaks which change as the planet warms, undermining the standard approach and biasing some existing model estimates of ECS.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
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We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Amy H. Peace, Ben B. B. Booth, Leighton A. Regayre, Ken S. Carslaw, David M. H. Sexton, Céline J. W. Bonfils, and John W. Rostron
Earth Syst. Dynam., 13, 1215–1232, https://doi.org/10.5194/esd-13-1215-2022, https://doi.org/10.5194/esd-13-1215-2022, 2022
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Anthropogenic aerosol emissions have been linked to driving climate responses such as shifts in the location of tropical rainfall. However, the interaction of aerosols with climate remains one of the most uncertain aspects of climate modelling and limits our ability to predict future climate change. We use an ensemble of climate model simulations to investigate what impact the large uncertainty in how aerosols interact with climate has on predicting future tropical rainfall shifts.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
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We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Jie Zhang, Kalli Furtado, Steven T. Turnock, Jane P. Mulcahy, Laura J. Wilcox, Ben B. Booth, David Sexton, Tongwen Wu, Fang Zhang, and Qianxia Liu
Atmos. Chem. Phys., 21, 18609–18627, https://doi.org/10.5194/acp-21-18609-2021, https://doi.org/10.5194/acp-21-18609-2021, 2021
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The CMIP6 ESMs systematically underestimate TAS anomalies in the NH midlatitudes, especially from 1960 to 1990. The anomalous cooling is concurrent in time and space with anthropogenic SO2 emissions. The spurious drop in TAS is attributed to the overestimated aerosol concentrations. The aerosol forcing sensitivity cannot well explain the inter-model spread of PHC biases. And the cloud-amount term accounts for most of the inter-model spread in aerosol forcing sensitivity.
Christina Heinze-Deml, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen
Geosci. Model Dev., 14, 4977–4999, https://doi.org/10.5194/gmd-14-4977-2021, https://doi.org/10.5194/gmd-14-4977-2021, 2021
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Quantifying dynamical and thermodynamical components of regional precipitation change is a key challenge in climate science. We introduce a novel statistical model (Latent Linear Adjustment Autoencoder) that combines the flexibility of deep neural networks with the robustness advantages of linear regression. The method enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner.
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
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This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Polly C. Buotte, Charles D. Koven, Chonggang Xu, Jacquelyn K. Shuman, Michael L. Goulden, Samuel Levis, Jessica Katz, Junyan Ding, Wu Ma, Zachary Robbins, and Lara M. Kueppers
Biogeosciences, 18, 4473–4490, https://doi.org/10.5194/bg-18-4473-2021, https://doi.org/10.5194/bg-18-4473-2021, 2021
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We present an approach for ensuring the definitions of plant types in dynamic vegetation models are connected to the underlying ecological processes controlling community composition. Our approach can be applied regionally or globally. Robust resolution of community composition will allow us to use these models to address important questions related to future climate and management effects on plant community composition, structure, carbon storage, and feedbacks within the Earth system.
Wu Ma, Lu Zhai, Alexandria Pivovaroff, Jacquelyn Shuman, Polly Buotte, Junyan Ding, Bradley Christoffersen, Ryan Knox, Max Moritz, Rosie A. Fisher, Charles D. Koven, Lara Kueppers, and Chonggang Xu
Biogeosciences, 18, 4005–4020, https://doi.org/10.5194/bg-18-4005-2021, https://doi.org/10.5194/bg-18-4005-2021, 2021
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We use a hydrodynamic demographic vegetation model to estimate live fuel moisture dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. Our results suggest that multivariate climate change could cause a significant net reduction in live fuel moisture and thus exacerbate future wildfire danger in chaparral shrub systems.
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.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Robinson I. Negrón-Juárez, Jennifer A. Holm, Boris Faybishenko, Daniel Magnabosco-Marra, Rosie A. Fisher, Jacquelyn K. Shuman, Alessandro C. de Araujo, William J. Riley, and Jeffrey Q. Chambers
Biogeosciences, 17, 6185–6205, https://doi.org/10.5194/bg-17-6185-2020, https://doi.org/10.5194/bg-17-6185-2020, 2020
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The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
Lukas Brunner, Angeline G. Pendergrass, Flavio Lehner, Anna L. Merrifield, Ruth Lorenz, and Reto Knutti
Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, https://doi.org/10.5194/esd-11-995-2020, 2020
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In this study, we weight climate models by their performance with respect to simulating aspects of historical climate and their degree of interdependence. Our method is found to increase projection skill and to correct for structurally similar models. The weighted end-of-century mean warming (2081–2100 relative to 1995–2014) is 3.7 °C with a likely (66 %) range of 3.1 to 4.6 °C for the strong climate change scenario SSP5-8.5; this is a reduction of 0.4 °C compared with the unweighted mean.
Maoyi Huang, Yi Xu, Marcos Longo, Michael Keller, Ryan G. Knox, Charles D. Koven, and Rosie A. Fisher
Biogeosciences, 17, 4999–5023, https://doi.org/10.5194/bg-17-4999-2020, https://doi.org/10.5194/bg-17-4999-2020, 2020
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The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is enhanced to mimic the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events; determine the survivorship of cohorts in the disturbed forest; and modifying the biomass, coarse woody debris, and litter pools. This study lays the foundation to simulate land use change and forest degradation in FATES as part of an Earth system model.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti
Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, https://doi.org/10.5194/esd-11-807-2020, 2020
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Justifiable uncertainty estimates of future change in northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections from the same climate model. Weights reduce the influence of model biases and handle dependence by identifying a projection's model of origin from historical characteristics; contributions from the same model are scaled by the number of members.
Cited articles
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 224–232, https://doi.org/10.1038/nature01092,
2002.
Allen, M. R. and Stott, P. A.: Estimating signal amplitudes in optimal
fingerprinting, part I: theory, Clim. Dynam., 21, 477–491,
https://doi.org/10.1007/s00382-003-0313-9, 2003.
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., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020.
Baker, N. C. and Taylor, P. C.: A Framework for Evaluating Climate Model
Performance Metrics, J. Climate, 29, 1773–1782,
https://doi.org/10.1175/JCLI-D-15-0114.1, 2016.
Boé, J., Hall, A., and Qu, X.: September sea-ice cover in the Arctic
Ocean projected to vanish by 2100, Nat. Geosci., 2, 341–343,
https://doi.org/10.1038/ngeo467, 2009.
Boer, G. J., Stowasser, M., and Hamilton, K.: Inferring climate sensitivity
from volcanic events, Clim. Dynam., 28, 481–502,
https://doi.org/10.1007/s00382-006-0193-x, 2007.
Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018.
Bony, S., Schulz, H., Vial, J., and Stevens, B.: Sugar, Gravel, Fish, and
Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on
Environmental Conditions, Geophys. Res. Lett., 47, e2019GL085988,
https://doi.org/10.1029/2019GL085988, 2020.
Bretherton, C. and Caldwell, P.: Combining Emergent Constraints for Climate
Sensitivity, J. Climate, 33, 7413–7430,
https://doi.org/10.1175/JCLI-D-19-0911.1, 2020.
Brienen, R. J. W., Caldwell, L., Duchesne, L., Voelker, S., Barichivich, J.,
Baliva, M., Ceccantini, G., Di Filippo, A., Helama, S., Locosselli, G. M.,
Lopez, L., Piovesan, G., Schöngart, J., Villalba, R., and Gloor, E.:
Forest carbon sink neutralized by pervasive growth-lifespan trade-offs, Nat.
Commun., 11, 1–10, https://doi.org/10.1038/s41467-020-17966-z, 2020.
Brient, F.: Reducing uncertainties in climate projections with emergent
constraints: Concepts, Examples and Prospects, Adv. Atmos.
Sci., 37, 1–15,
https://doi.org/10.1007/s00376-019-9140-8, 2019.
Brient, F. and Schneider, T.: Constraints on Climate Sensitivity from
Space-Based Measurements of Low-Cloud Reflection, J. Climate,
29, 5821–5835, https://doi.org/10.1175/jcli-d-15-0897.1, 2016.
Brient, F., Schneider, T., Tan, Z., Bony, S., Qu, X., and Hall, A.:
Shallowness of tropical low clouds as a predictor of climate models'
response to warming, Clim. Dynam., 47, 433–449,
https://doi.org/10.1007/s00382-015-2846-0, 2016.
Brown, P. T., Stolpe, M. B., and Caldeira, K.: Assumptions for emergent
constraints, Nature, 563, E1–E3, https://doi.org/10.1038/s41586-018-0638-5, 2018.
Brunner, L., Lorenz, R., Zumwald, M., and Knutti, R.: Quantifying uncertainty
in European climate projections using combined performance-independence
weighting, Environ. Res. Lett., 14, 124010,
https://doi.org/10.1088/1748-9326/ab492f, 2019.
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced global warming from CMIP6 projections when weighting models by performance and independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020.
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, https://doi.org/10.1002/2014gl059205, 2014.
Caldwell, P. M., Zelinka, M. D., and Klein, S. A.: Evaluating Emergent
Constraints on Equilibrium Climate Sensitivity, J. Climate, 31,
3921–3942, https://doi.org/10.1175/jcli-d-17-0631.1, 2018.
Chadburn, S. E., Burke, E. J., Cox, P. M., Friedlingstein, P., Hugelius, G.,
and Westermann, S.: An observation-based constraint on permafrost loss as a
function of global warming, Nat. Clim. Change, 7, 340–344,
https://doi.org/10.1038/nclimate3262, 2017.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C.
D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model
Benchmarking (ILAMB) System: Design, Theory, and Implementation, J.
Adv. Model. Earth Sy., 10, 2731–2754,
https://doi.org/10.1029/2018ms001354, 2018.
Covey, C., Abe-Ouchi, A., Boer, G. J., Boville, B. A., Cubasch, U.,
Fairhead, L., Flato, G. M., Gordon, H., Guilyardi, E., Jiang, X., Johns, T.
C., Le Treut, H., Madec, G., Meehl, G. A., Miller, R., Noda, A., Power, S.
B., Roeckner, E., Russell, G., Schneider, E. K., Stouffer, R. J., Terray, L.,
and von Storch, J.-S.: The seasonal cycle in coupled ocean-atmosphere
general circulation models, Clim. Dynam., 16, 775–787,
https://doi.org/10.1007/s003820000081, 2000.
Cox, P. M.: Emergent Constraints on Climate-Carbon Cycle Feedbacks, Curr.
Clim. Change Rep., 5, 275–281, https://doi.org/10.1007/s40641-019-00141-y, 2019.
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C.,
Jones, C. D., and Luke, C. M.: Sensitivity of tropical carbon to climate
change constrained by carbon dioxide variability, Nature, 494,
341–344, https://doi.org/10.1038/nature11882, 2013.
Cox, P. M., Williamson, M. S., Nijsse, F. J. M. M., and Huntingford, C.: Cox
et al. reply, Nature, 563, E10–E15, https://doi.org/10.1038/s41586-018-0641-x,
2018a.
Cox, P. M., Huntingford, C., and Williamson, M. S.: Emergent constraint on
equilibrium climate sensitivity from global temperature variability, Nature,
553, 319–322, https://doi.org/10.1038/nature25450, 2018b.
Davies-Barnard, T., Meyerholt, J., Zaehle, S., Friedlingstein, P., Brovkin, V., Fan, Y., Fisher, R. A., Jones, C. D., Lee, H., Peano, D., Smith, B., Wårlind, D., and Wiltshire, A. J.: Nitrogen cycling in CMIP6 land surface models: progress and limitations, Biogeosciences, 17, 5129–5148, https://doi.org/10.5194/bg-17-5129-2020, 2020.
de Wilde, P. and Tian, W.: : Towards probabilistic performance
metrics for climate change impact studies, Energy and Buildings, 43,
3013–3018, https://doi.org/10.1016/j.enbuild.2011.07.014, 2011.
Douville, H. and Plazzotta, M.: Midlatitude Summer Drying: An Underestimated
Threat in CMIP5 Models?, Geophys. Res. Lett., 44, 9967–9975,
https://doi.org/10.1002/2017gl075353, 2017.
Edwards, J. M., Beljaars, A. C. M., Holtslag, A. A. M., and Lock, A. P.:
Representation of Boundary-Layer Processes in Numerical Weather Prediction
and Climate Models, Bound.-Lay. Meteorol., 177, 511–539,
https://doi.org/10.1007/s10546-020-00530-z, 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, https://doi.org/10.1038/s41558-018-0355-y, 2019.
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P., Huntingford, C., Meir, P., and Ian Woodward, F.:
Assessing uncertainties in a second-generation dynamic vegetation model caused by ecological
scale limitations, New Phytol., 187, 666–681, https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010.
Fleischer, K., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues, T. F.,
Fuchslueger, L., Garcia, S., Goll, D. S., Grandis, A., Jiang, M., Haverd,
V., Hofhansl, F., Holm, J. A., Kruijt, B., Leung, F., Medlyn, B. E.,
Mercado, L. M., Norby, R. J., Pak, B., von Randow, C., Quesada, C. A.,
Schaap, K. J., Valverde-Barrantes, O. J., Wang, Y.-P., Yang, X., Zaehle, S.,
Zhu, Q., and Lapola, D. M.: Amazon forest response to CO2 fertilization
dependent on plant phosphorus acquisition, Nat. Geosci., 12,
736–741, https://doi.org/10.1038/s41561-019-0404-9, 2019.
Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., and Webster, M.
D.: Quantifying uncertainties in climate system properties with the use of
recent climate observations, Science, 295, 113–117,
https://doi.org/10.1126/science.1064419, 2002.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections
due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/jcli-d-12-00579.1, 2014.
Geoffroy, O., Saint-Martin, D., Bellon, G., Voldoire, A., Olivié, D. J.
L., and Tytéca, S.: Transient Climate Response in a Two-Layer
Energy-Balance Model. Part II: Representation of the Efficacy of Deep-Ocean
Heat Uptake and Validation for CMIP5 AOGCMs, J. Climate, 26,
1859–1876, https://doi.org/10.1175/jcli-d-12-00196.1, 2013.
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for
climate models, J. Geophys. Res., 113, D06104,
https://doi.org/10.1029/2007jd008972, 2008.
Golaz, J., 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., Lin, W.,
Lipscomb, W. H., Ma, P., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F.,
Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J.,
Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang,
H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang,
Y., Yoon, J., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., 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 Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Gordon, N. D. and Klein, S. A.: Low-cloud optical depth feedback in climate
models, J. Geophys. Res.-Atmos., 119, 6052–6065,
https://doi.org/10.1002/2013JD021052, 2014.
Goris, N., Tjiputra, J. F., Olsen, A., Schwinger, J., Lauvset, S. K., and
Jeansson, E.: Constraining projection-based estimates of the future North
Atlantic carbon uptake, J. Climate, 31, 3959–3978,
https://doi.org/10.1175/jcli-d-17-0564.1, 2018.
Gregory, J. M., Andrews, T., and Good, P.: The inconstancy of the transient
climate response parameter under increasing CO2, Philos. T. R. Soc. A, 373, 20140417, https://doi.org/10.1098/rsta.2014.0417, 2015.
Hall, A., Cox, P., Huntingford, C., and Klein, S.: Progressing emergent
constraints on future climate change, Nat. Clim. Change, 9, 269–278,
https://doi.org/10.1038/s41558-019-0436-6, 2019.
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.
Hegerl, G. and Zwiers, F.: Use of models in detection and attribution of
climate change, WIRES Clim. Change, 2,
570–591, https://doi.org/10.1002/wcc.121, 2011.
Hegerl, G. C., Stott, P. A., Allen, M. R., Mitchell, J. F. B., Tett, S. F.
B., and Cubasch, U.: Optimal detection and attribution of climate change:
sensitivity of results to climate model differences, Clim. Dynam., 16,
737–754, https://doi.org/10.1007/s003820000071, 2000.
Hegerl, G. C., Crowley, T. J., Hyde, W. T., and Frame, D. J.: Climate
sensitivity constrained by temperature reconstructions over the past seven
centuries, Nature, 440, 1029–1032, https://doi.org/10.1038/nature04679, 2006.
Hoffman, F. M., Randerson, J. T., Arora, V. K., Bao, Q., Cadule, P., Ji, D.,
Jones, C. D., Kawamiya, M., Khatiwala, S., Lindsay, K., Obata, A.,
Shevliakova, E., Six, K. D., Tjiputra, J. F., Volodin, E. M., and Wu, T.:
Causes and implications of persistent atmospheric carbon dioxide biases in
Earth System Models, J. Geophys. Res.-Biogeo.,
119, 141–162, https://doi.org/10.1002/2013jg002381, 2014.
Holtslag, A. A. M., Steeneveld, G. J., and van de Wiel, B. J. H.: Role of land-surface temperature
feedback on model performance for the stable boundary layer, in:
Atmospheric Boundary Layers, edited by: Baklanov, A. and Grisogono, B., Springer, New York, NY, 205–220,
https://doi.org/10.1007/978-0-387-74321-9_14, 2007.
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan,
Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C.,
Tomassini, L., Watanabe, M., and Williamson, D.: The Art and Science of
Climate Model Tuning, B. Am. Meteorol. Soc., 98, 589–602,
https://doi.org/10.1175/BAMS-D-15-00135.1, 2017.
Huber, M., Mahlstein, I., Wild, M., Fasullo, J., and Knutti, R.: Constraints
on Climate Sensitivity from Radiation Patterns in Climate Models, J. Climate,
24, 1034–1052, https://doi.org/10.1175/2010JCLI3403.1, 2011.
Jiménez-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, https://doi.org/10.1038/s41561-019-0463-y,
2019.
Kamae, Y., Shiogama, H., Watanabe, M., Ogura, T., Yokohata, T., and Kimoto,
M.: Lower-Tropospheric Mixing as a Constraint on Cloud Feedback in a
Multiparameter Multiphysics Ensemble, J. Climate, 29, 6259–6275,
https://doi.org/10.1175/JCLI-D-16-0042.1, 2016.
Karpechko, A. Y., Maraun, D., and Eyring, V.: Improving Antarctic total ozone
projections by a process-oriented multiple diagnostic ensemble regression,
J. Atmos. Sci., 70, 3959–3976, https://doi.org/10.1175/jas-d-13-071.1, 2013.
Kessler, A. and Tjiputra, J.: The Southern Ocean as a constraint to reduce uncertainty in future ocean carbon sinks, Earth Syst. Dynam., 7, 295–312, https://doi.org/10.5194/esd-7-295-2016, 2016.
Kettleborough, J. A., Booth, B. B. B., Stott, P. A., and Allen, M. R.:
Estimates of uncertainty in predictions of global mean surface temperature,
J. Climate, 20, 843–855, https://doi.org/10.1175/jcli4012.1, 2007.
Kiehl, J. T.: Twentieth century climate model response and climate
sensitivity, Geophys. Res. Lett., 34, L22710, https://doi.org/10.1029/2007gl031383, 2007.
Klein, S. A. and Hall, A.: Emergent Constraints for Cloud Feedbacks, Current
Climate Change Reports, 1, 276–287, https://doi.org/10.1007/s40641-015-0027-1, 2015.
Klein, S. A., Hall, A., Norris, J. R., and Pincus, R.: Low-cloud feedbacks
from cloud-controlling factors: A review, Surv. Geophys., 38, 1307–1329,
https://doi.org/10.1007/s10712-017-9433-3, 2017.
Knutti, R.: Why are climate models reproducing the observed global surface
warming so well?, Geophys. Res. Lett., 35, L18704, https://doi.org/10.1029/2008gl034932,
2008.
Knutti, R. and Tomassini, L.: Constraints on the transient climate response
from observed global temperature and ocean heat uptake, Geophys. Res.
Lett., 35, L09701, https://doi.org/10.1029/2007gl032904, 2008.
Knutti, R., Stocker, T. F., Joos, F., and Plattner, G.-K.: Constraints on
radiative forcing and future climate change from observations and climate
model ensembles, Nature, 416, 719–723, https://doi.org/10.1038/416719a, 2002.
Knutti, R., Meehl, G. A., Allen, M. R., and Stainforth, D. A.: Constraining
Climate Sensitivity from the Seasonal Cycle in Surface Temperature, J.
Climate, 19, 4224–4233, https://doi.org/10.1175/jcli3865.1, 2006.
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy:
Generation CMIP5 and how we got there, Geophys. Res. Lett., 40,
1194–1199, https://doi.org/10.1002/grl.50256, 2013.
Knutti, R., Sedláček, 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,
https://doi.org/10.1002/2016gl072012, 2017.
Koven, C., Arora, V. K., Cadule, P., Fisher, R. A., Jones, C. D., Lawrence, D. M., Lewis, J., Lindsey, K., Mathesius, S., Meinshausen, M., Mills, M., Nicholls, Z., Sanderson, B. M., Swart, N. C., Wieder, W. R., and Zickfeld, K.: 23rd Century surprises: Long-term dynamics of the climate and carbon cycle under both high and net negative emissions scenarios, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-23, in review, 2021.
Kubo, R.: The fluctuation-dissipation theorem, Rep. Prog. Phys., 29, 255,
https://doi.org/10.1088/0034-4885/29/1/306, 1966.
Kwiatkowski, L., Bopp, L., Aumont, O., Ciais, P., Cox, P. M.,
Laufkötter, C., Li, Y., and Séférian, R.: Emergent constraints on
projections of declining primary production in the tropical oceans, Nat.
Clim. Change, 7, 355–358, https://doi.org/10.1038/nclimate3265, 2017.
Leith, C. E.: Climate Response and Fluctuation Dissipation, J.
Atmos. Sci., 32, 2022–2026,
https://doi.org/10.1175/1520-0469(1975)032<2022:crafd>2.0.co;2,
1975.
Lenton, T. M., Rockström, J., Gaffney, O., Rahmstorf, S., Richardson,
K., Steffen, W., and Schellnhuber, H. J.: Climate tipping points – too
risky to bet against, Nature, 575, 592–595,
https://doi.org/10.1038/d41586-019-03595-0, 2019.
Levine, N. M., Zhang, K., Longo, M., Baccini, A., Phillips, O. L., Lewis, S.
L., Alvarez-Dávila, E., de Andrade, A. C. S., Brienen, R. J. W., Erwin,
T. L., Feldpausch, T. R., Mendoza, A. L. M., Vargas, P. N., Prieto, A.,
Silva-Espejo, J. E., Malhi, Y., and Moorcroft, P. R.: Ecosystem heterogeneity
determines the ecological resilience of the Amazon to climate change,
P. Natl. Acad. Sci. USA, 113, 793–797,
https://doi.org/10.1073/pnas.1511344112, 2016.
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, https://doi.org/10.1002/2017gl073151, 2017.
Longo, M., Knox, R. G., Levine, N. M., Alves, L. F., Bonal, D., Camargo, P.
B., Fitzjarrald, D. R., Hayek, M. N., Restrepo-Coupe, N., Saleska, S. R., da
Silva, R., Stark, S. C., Tapajós, R. P., Wiedemann, K. T., Zhang, K.,
Wofsy, S. C., and Moorcroft, P. R.: Ecosystem heterogeneity and diversity
mitigate Amazon forest resilience to frequent extreme droughts, New Phytol.,
219, 914–931, https://doi.org/10.1111/nph.15185, 2018.
Lorenz, R., Herger, N., Sedláček, J., Eyring, V., Fischer, E. M., and
Knutti, R.: Prospects and Caveats of Weighting Climate Models for Summer
Maximum Temperature Projections Over North America, J. Geophys. Res.-Atmos., 123, 4509–4526, https://doi.org/10.1029/2017JD027992, 2018.
Mahlstein, I. and Knutti, R.: September Arctic sea ice predicted to
disappear near 2 ∘C global warming above present, J. Geophys.
Res., 117, D06104, https://doi.org/10.1029/2011jd016709, 2012.
Masson, D. and Knutti, R.: Climate model genealogy, Geophys. Res. Lett.,
38, L08703, https://doi.org/10.1029/2011gl046864, 2011.
Masson, D. and Knutti, R.: Predictor screening, calibration, and
observational constraints in climate model ensembles: An illustration using
climate sensitivity, J. Climate, 26, 887–898,
https://doi.org/10.1175/jcli-d-11-00540.1, 2013a.
Masson, D. and Knutti, R.: Predictor Screening, Calibration, and
Observational Constraints in Climate Model Ensembles: An Illustration Using
Climate Sensitivity, J. Climate, 26, 887–898,
https://doi.org/10.1175/JCLI-D-11-00540.1, 2013b.
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta,
M., Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz,
D., Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a
global model, J. Adv. Model. Earth Sy., 4, M00A01, https://doi.org/10.1029/2012MS000154,
2012.
McDowell, N., Allen, C. D., Anderson-Teixeira, K., Brando, P., Brienen, R.,
Chambers, J., Christoffersen, B., Davies, S., Doughty, C., Duque, A.,
Espirito-Santo, F., Fisher, R., Fontes, C. G., Galbraith, D., Goodsman, D.,
Grossiord, C., Hartmann, H., Holm, J., Johnson, D. J., Kassim, A. R.,
Keller, M., Koven, C., Kueppers, L., Kumagai, T., Malhi, Y., McMahon, S. M.,
Mencuccini, M., Meir, P., Moorcroft, P., Muller-Landau, H. C., Phillips, O.
L., Powell, T., Sierra, C. A., Sperry, J., Warren, J., Xu, C., and Xu, X.:
Drivers and mechanisms of tree mortality in moist tropical forests, New
Phytol., 219, 851–869, https://doi.org/10.1111/nph.15027, 2018.
McKiver, W. J., Vichi, M., Lovato, T., Storto, A., and Masina, S.: Impact of
increased grid resolution on global marine biogeochemistry, J. Marine Syst.,
147, 153–168, https://doi.org/10.1016/j.jmarsys.2014.10.003, 2015.
McNeall, D., Williams, J., Booth, B., Betts, R., Challenor, P., Wiltshire, A., and Sexton, D.: The impact of structural error on parameter constraint in a climate model, Earth Syst. Dynam., 7, 917–935, https://doi.org/10.5194/esd-7-917-2016, 2016.
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.
Mongwe, N. P., Chang, N., and Monteiro, P. M. S.: The seasonal cycle as a
mode to diagnose biases in modelled CO2 fluxes in the Southern Ocean, Ocean Model. (Oxf.), 106, 90–103, https://doi.org/10.1016/j.ocemod.2016.09.006, 2016.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the
atmosphere, Tr. Akad. Nauk. SSSR Geophiz. Inst., 24, 163–187, 1954.
Needham, J. F., Chambers, J., Fisher, R., Knox, R., and Koven, C. D.: Forest
responses to simulated elevated CO under alternate hypotheses of size- and
age-dependent mortality, Glob. Change Biol., 26, 5734–5753, https://doi.org/10.1111/gcb.15254, 2020.
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11, 737–750, https://doi.org/10.5194/esd-11-737-2020, 2020.
O'Gorman, P. A.: Sensitivity of tropical precipitation extremes to climate
change, Nat. Geosci., 5, 697–700, https://doi.org/10.1038/ngeo1568, 2012.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W.,
Christ, R., Church, J. A., Clarke, L., Dahe, Q., Dasgupta, P., Dubash, N.
K., Edenhofer, O., Elgizouli, I., Field, C. B., Forster, P., Friedlingstein,
P., Fuglestvedt, J., Gomez-Echeverri, L., Hallegatte, S., Hegerl, G.,
Howden, M., Jiang, K., Jimenez Cisneroz, B., Kattsov, V., Lee, H., Mach, K.
J., Marotzke, J., Mastrandrea, M. D., Meyer, L., Minx, J., Mulugetta, Y.,
O'Brien, K., Oppenheimer, M., Pereira, J. J., Pichs-Madruga, R., Plattner,
G.-K., Pörtner, H.-O., Power, S. B., Preston, B., Ravindranath, N. H.,
Reisinger, A., Riahi, K., Rusticucci, M., Scholes, R., Seyboth, K., Sokona,
Y., Stavins, R., Stocker, T. F., Tschakert, P., van Vuuren, D., and van
Ypserle, J.-P.: Climate Change 2014: Synthesis Report. Contribution of
Working Groups I, II and III to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Pachauri, R. K., and
Meyer, L., IPCC, Geneva, Switzerland, available at:
https://epic.awi.de/id/eprint/37530/ (last access: 30 January 2020), 2014.
Palmer, T.: Short-term tests validate long-term estimates of climate change,
Nature, 582, 185–186, https://doi.org/10.1038/d41586-020-01484-5, 2020.
Piani, C., Frame, D. J., Stainforth, D. A., and Allen, M. R.: Constraints on
climate change from a multi-thousand member ensemble of simulations,
Geophys. Res. Lett., 32, L23825, https://doi.org/10.1029/2005gl024452, 2005.
Pincus, R., Winker, D., Bony, S., and Stevens, B. (Eds.): Shallow Clouds, Water Vapor,
Circulation, and Climate Sensitivity, Springer International Publishing,
https://doi.org/10.1007/978-3-319-77273-8, 2018.
Plazzotta, M., Séférian, R., Douville, H., Kravitz, B., and Tjiputra,
J.: Land Surface Cooling Induced by Sulfate Geoengineering Constrained by
Major Volcanic Eruptions, Geophys. Res. Lett., 45, 5663–5671,
https://doi.org/10.1029/2018GL077583, 2018.
Po-Chedley, S., Proistosescu, C., Armour, K. C., and Santer, B. D.: Climate
constraint reflects forced signal, Nature, 563, E6–E9,
https://doi.org/10.1038/s41586-018-0640-y, 2018.
Qu, X. and Hall, A.: What Controls the Strength of Snow-Albedo Feedback?,
J. Climate, 20, 3971–3981, https://doi.org/10.1175/jcli4186.1, 2007.
Qu, X. and Hall, A.: On the persistent spread in snow-albedo feedback,
Clim. Dynam., 42, 69–81, https://doi.org/10.1007/s00382-013-1774-0, 2014.
Qu, X., Hall, A., Klein, S. A., and Caldwell, P. M.: On the spread of changes
in marine low cloud cover in climate model simulations of the 21st century,
Clim. Dynam., 42, 2603–2626, https://doi.org/10.1007/s00382-013-1945-z,
2014.
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.
Ribes, A., Zwiers, F. W., Azaïs, J.-M., and Naveau, P.: A new
statistical approach to climate change detection and attribution, Clim.
Dynam., 48, 367–386, https://doi.org/10.1007/s00382-016-3079-6, 2017.
Rodwell, M. J. and Palmer, T. N.: Using numerical weather prediction to
assess climate models, Q. J. Roy. Meteor.
Soc., 133, 129–146, https://doi.org/10.1002/qj.23, 2007.
Rose, B. E. J. and Rayborn, L.: The effects of ocean heat uptake on
transient climate sensitivity, Curr. Clim. Change Rep., 2, 190–201,
https://doi.org/10.1007/s40641-016-0048-4, 2016.
Rougier, J.: Probabilistic Inference for Future Climate Using an Ensemble of
Climate Model Evaluations, Climatic Change, 81, 247–264,
https://doi.org/10.1007/s10584-006-9156-9, 2007.
Royer, D. L., Berner, R. A., and Park, J.: Climate sensitivity constrained by
CO2 concentrations over the past 420 million years, Nature, 446,
530–532, https://doi.org/10.1038/nature05699, 2007.
Rugenstein, M., Bloch-Johnson, J., Gregory, J., Andrews, T., Mauritsen, T.,
Li, C., Frölicher, T. L., Paynter, D., Danabasoglu, G., Yang, S.,
Dufresne, J., 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, 1029, 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,
https://doi.org/10.1038/s41586-018-0639-4, 2018.
Sakschewski, B., von Bloh, W., Boit, A., Poorter, L., Peña-Claros, M.,
Heinke, J., Joshi, J., and Thonicke, K.: Resilience of Amazon forests emerges
from plant trait diversity, Nat. Clim. Change, 6, 1032–1036,
https://doi.org/10.1038/nclimate3109, 2016.
Sallée, J.-B., Shuckburgh, E., Bruneau, N., Meijers, A. J. S.,
Bracegirdle, T. J., Wang, Z., and Roy, T.: Assessment of Southern Ocean water
mass circulation and characteristics in CMIP5 models: Historical bias and
forcing response, J. Geophys. Res.-Oceans, 118, 1830–1844,
https://doi.org/10.1002/jgrc.20135, 2013.
Sanderson, B.: Relating climate sensitivity indices to projection uncertainty, Earth Syst. Dynam., 11, 721–735, https://doi.org/10.5194/esd-11-721-2020, 2020.
Sanderson, B.: benmsanderson/structure_ec: (1.1), Zenodo [code],
https://doi.org/10.5281/zenodo.5093130, 2021.
Sanderson, B. M.: A Multimodel Study of Parametric Uncertainty in
Predictions of Climate Response to Rising Greenhouse Gas Concentrations,
J. Climate, 24, 1362–1377, https://doi.org/10.1175/2010jcli3498.1, 2011.
Sanderson, B. M.: On the estimation of systematic error in regression-based
predictions of climate sensitivity, Climatic Change, 118, 757–770,
https://doi.org/10.1007/s10584-012-0671-6, 2013.
Sanderson, B. M., Knutti, R., Aina, T., Christensen, C., Faull, N., Frame,
D. J., Ingram, W. J., Piani, C., Stainforth, D. A., Stone, D. A., and Allen,
M. R.: Constraints on Model Response to Greenhouse Gas Forcing and the Role
of Subgrid-Scale Processes, J. Climate, 21, 2384–2400,
https://doi.org/10.1175/2008jcli1869.1, 2008.
Sanderson, B. M., Shell, K. M., and Ingram, W.: Climate feedbacks determined
using radiative kernels in a multi-thousand member ensemble of AOGCMs,
Clim. Dynam., 35, 1219–1236, https://doi.org/10.1007/s00382-009-0661-1, 2010.
Sanderson, B. M., Knutti, R., and Caldwell, P.: A Representative Democracy to
Reduce Interdependency in a Multimodel Ensemble, J. Climate, 28,
5171–5194, https://doi.org/10.1175/jcli-d-14-00362.1, 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.
Sandu, I., Beljaars, A., Bechtold, P., Mauritsen, T., and Balsamo, G.: Why is
it so difficult to represent stably stratified conditions in numerical
weather prediction (NWP) models?: STABLE CONDITIONS IN NWP MODELS, J. Adv.
Model. Earth Sy., 5, 117–133, https://doi.org/10.1002/jame.20013, 2013.
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., and Eyring, V.: Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?, Earth Syst. Dynam., 11, 1233–1258, https://doi.org/10.5194/esd-11-1233-2020, 2020.
Schmidt, G. A., Annan, J. D., Bartlein, P. J., Cook, B. I., Guilyardi, E., Hargreaves, J. C., Harrison, S. P., Kageyama, M., LeGrande, A. N., Konecky, B., Lovejoy, S., Mann, M. E., Masson-Delmotte, V., Risi, C., Thompson, D., Timmermann, A., Tremblay, L.-B., and Yiou, P.: Using palaeo-climate comparisons to constrain future projections in CMIP5, Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, 2014.
Schmidt, G. A., Bader, D., Donner, L. J., Elsaesser, G. S., Golaz, J.-C., Hannay, C., Molod, A., Neale, R. B., and Saha, S.: Practice and philosophy of climate model tuning across six US modeling centers, Geosci. Model Dev., 10, 3207–3223, https://doi.org/10.5194/gmd-10-3207-2017, 2017.
Schurer, A., Hegerl, G., Ribes, A., Polson, D., Morice, C., and Tett, S.:
Estimating the Transient Climate Response from Observed Warming, J.
Climate, 31, 8645–8663, https://doi.org/10.1175/jcli-d-17-0717.1, 2018.
Sexton, D. M. H. and Murphy, J. M.: Multivariate probabilistic projections
using imperfect climate models. Part II: robustness of methodological
choices and consequences for climate sensitivity, Clim. Dynam.,
38, 2543–2558, https://doi.org/10.1007/s00382-011-1209-8, 2012.
Shao, P., Zeng, X., Moore, D. J. P., and Zeng, X.: Soil microbial respiration
from observations and Earth System Models, Environ. Res. Lett.,
8, 034034, https://doi.org/10.1088/1748-9326/8/3/034034, 2013.
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505,
37–42, https://doi.org/10.1038/nature12829, 2014.
Sherwood, S. C., 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., von der Heydt, A. S., 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.
Shi, Z., Crowell, S., Luo, Y., and Moore, B., 3rd: Model structures amplify
uncertainty in predicted soil carbon responses to climate change, Nat.
Commun., 9, 2171, https://doi.org/10.1038/s41467-018-04526-9, 2018.
Shiogama, H., Watanabe, M., Yoshimori, M., Yokohata, T., Ogura, T., Annan,
J. D., Hargreaves, J. C., Abe, M., Kamae, Y., O'ishi, R., Nobui, R., Emori,
S., Nozawa, T., Abe-Ouchi, A., and Kimoto, M.: Perturbed physics ensemble
using the MIROC5 coupled atmosphere–ocean GCM without flux corrections:
experimental design and results, Clim. Dynam., 39, 3041–3056,
https://doi.org/10.1007/s00382-012-1441-x, 2012.
Siler, N., Po-Chedley, S., and Bretherton, C. S.: Variability in modeled
cloud feedback tied to differences in the climatological spatial pattern of
clouds, Clim. Dynam., 50, 1209–1220, https://doi.org/10.1007/s00382-017-3673-2,
2018.
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame,
D. J., Kettleborough, J. A., Knight, S., Martin, A., Murphy, J. M., Piani,
C., Sexton, D., Smith, L. A., Spicer, R. A., Thorpe, A. J., and Allen, M. R.:
Uncertainty in predictions of the climate response to rising levels of
greenhouse gases, Nature, 433, 403–406, https://doi.org/10.1038/nature03301,
2005.
Stock, C. A.: Comparing apples to oranges: Perspectives on satellite-based
primary production estimates drawn from a global biogeochemical model, J.
Mar. Res., 77, 259–282, https://doi.org/10.1357/002224019828474296, 2019.
Su, H., Jiang, J. H., Zhai, C., Shen, T. J., David Neelin, J., 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, https://doi.org/10.1002/2014jd021642, 2014.
Svensson, G. and Lindvall, J.: Evaluation of Near-Surface Variables and the
Vertical Structure of the Boundary Layer in CMIP5 Models, J.
Climate, 28, 5233–5253, https://doi.org/10.1175/jcli-d-14-00596.1, 2015.
Teckentrup, L., Harrison, S. P., Hantson, S., Heil, A., Melton, J. R., Forrest, M., Li, F., Yue, C., Arneth, A., Hickler, T., Sitch, S., and Lasslop, G.: Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models, Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, 2019.
Terhaar, J., Kwiatkowski, L., and Bopp, L.: Emergent constraint on Arctic
Ocean acidification in the twenty-first century, Nature, 582,
379–383, https://doi.org/10.1038/s41586-020-2360-3, 2020.
Terhaar, J., Frölicher, T. L., and Joos, F.: Southern Ocean anthropogenic
carbon sink constrained by sea surface salinity, Sci. Adv., 7, eabd5964,
https://doi.org/10.1126/sciadv.abd5964, 2021.
Teskey, R., Wertin, T., Bauweraerts, I., Ameye, M., McGuire, M. A., and
Steppe, K.: Responses of tree species to heat waves and extreme heat events,
Plant Cell Environ., 38, 1699–1712, https://doi.org/10.1111/pce.12417, 2015.
Tett, S. F. B., Yamazaki, K., Mineter, M. J., Cartis, C., and Eizenberg, N.: Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3, Geosci. Model Dev., 10, 3567–3589, https://doi.org/10.5194/gmd-10-3567-2017, 2017.
Thackeray, C. W. and Hall, A.: An emergent constraint on future Arctic
sea-ice albedo feedback, Nat. Clim. Change, 9, 972–978,
https://doi.org/10.1038/s41558-019-0619-1, 2019.
Tian, B.: Spread of model climate sensitivity linked to double-Intertropical
Convergence Zone bias, Geophys. Res. Lett., 42, 4133–4141,
https://doi.org/10.1002/2015gl064119, 2015.
Tjiputra, J. F., Schwinger, J., Bentsen, M., Morée, A. L., Gao, S., Bethke, I., Heinze, C., Goris, N., Gupta, A., He, Y.-C., Olivié, D., Seland, Ø., and Schulz, M.: Ocean biogeochemistry in the Norwegian Earth System Model version 2 (NorESM2), Geosci. Model Dev., 13, 2393–2431, https://doi.org/10.5194/gmd-13-2393-2020, 2020.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
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, Science Advances, 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549,
2020.
Trenberth, K. E. and Fasullo, J. T.: Simulation of Present-Day and
Twenty-First-Century Energy Budgets of the Southern Oceans, J. Climate, 23,
440–454, https://doi.org/10.1175/2009JCLI3152.1, 2010.
Varney, R. M., Chadburn, S. E., Friedlingstein, P., Burke, E. J., Koven, C.
D., Hugelius, G., and Cox, P. M.: A spatial emergent constraint on the
sensitivity of soil carbon turnover to global warming, Nat. Commun., 11,
1–8, https://doi.org/10.1038/s41467-020-19208-8, 2020.
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, https://doi.org/10.1134/S0001433808030043, 2008.
Wang, J., Zeng, N., Liu, Y., and Bao, Q.: To what extent can interannual CO2
variability constrain carbon cycle sensitivity to climate change in CMIP5
Earth System Models?, Geophys. Res. Lett., 41, 3535–3544,
https://doi.org/10.1002/2014GL060004, 2014.
Watanabe, M., Kamae, Y., Shiogama, H., DeAngelis, A. M., and Suzuki, K.: Low
clouds link equilibrium climate sensitivity to hydrological sensitivity,
Nat. Clim. Change, 8, 901–906, https://doi.org/10.1038/s41558-018-0272-0, 2018.
Wei, N., Zhou, L., and Dai, Y.: Evaluation of simulated climatological
diurnal temperature range in CMIP5 models from the perspective of planetary
boundary layer turbulent mixing, Clim. Dynam., 49, 1–22,
https://doi.org/10.1007/s00382-016-3323-0, 2017.
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Emergent
constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system
models, J. Geophys. Res.-Biogeo., 119, 794–807,
https://doi.org/10.1002/2013JG002591, 2014.
Wigley, T. M. L.: Effect of climate sensitivity on the response to volcanic
forcing, J. Geophys. Res., 110, D09107, https://doi.org/10.1029/2004jd005557,
2005.
Williams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D., Evans, B.,
Ferraro, R., Hansen, R., Lautenschlager, M., and Trenham, C.: A Global
Repository for Planet-Sized Experiments and Observations, B.
Am. Meteorol. Soc., 97, 803–816,
https://doi.org/10.1175/bams-d-15-00132.1, 2016.
Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P.,
Jackson, L., and Yamazaki, K.: History matching for exploring and reducing
climate model parameter space using observations and a large perturbed
physics ensemble, Clim. Dynam., 41, 1703–1729,
https://doi.org/10.1007/s00382-013-1896-4, 2013.
Williamson, D. B. and Sansom, P. G.: How Are Emergent Constraints
Quantifying Uncertainty and What Do They Leave Behind?, B.
Am. Meteorol. Soc., 100, 2571–2588,
https://doi.org/10.1175/bams-d-19-0131.1, 2019.
Williamson, M. S., Cox, P. M., and Nijsse, F. J. M. M.: Theoretical
foundations of emergent constraints: relationships between climate
sensitivity and global temperature variability in conceptual models, Dyn.
Stat. Clim. Syst., 3, dzy006, https://doi.org/10.1093/climsys/dzy006, 2019.
Yan, X., Zhang, R., and Knutson, T. R.: Underestimated AMOC variability and
implications for AMV and predictability in CMIP models, Geophys. Res. Lett.,
45, 4319–4328, https://doi.org/10.1029/2018gl077378, 2018.
Yokohata, T., Webb, M. J., Collins, M., Williams, K. D., Yoshimori, M.,
Hargreaves, J. C., and Annan, J. D.: Structural Similarities and Differences
in Climate Responses to CO2 Increase between Two Perturbed Physics
Ensembles, J. Climate, 23, 1392–1410,
https://doi.org/10.1175/2009jcli2917.1, 2010.
Zaehle, S., Jones, C. D., Houlton, B., Lamarque, J.-F., and Robertson, E.:
Nitrogen Availability Reduces CMIP5 Projections of Twenty-First-Century Land
Carbon Uptake, J. Climate, 28, 2494–2511,
https://doi.org/10.1175/jcli-d-13-00776.1, 2015.
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., 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, https://doi.org/10.1002/2015gl065911,
2015a.
Zhai, C., Jiang, J. H., and Su, H.: Long-term cloud change imprinted in
seasonal cloud variation: More evidence of high climate sensitivity: Cloud
Feedback and Seasonal Variation, Geophys. Res. Lett., 42, 8729–8737,
https://doi.org/10.1002/2015GL065911, 2015b.
Zhang, T., Zhang, M., Lin, W., Lin, Y., Xue, W., Yu, H., He, J., Xin, X., Ma, H.-Y., Xie, S., and Zheng, W.: Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method, Geosci. Model Dev., 11, 5189–5201, https://doi.org/10.5194/gmd-11-5189-2018, 2018.
Short summary
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases and common simplifications that may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints and a number of practical case studies.
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by...
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