Articles | Volume 15, issue 5
https://doi.org/10.5194/esd-15-1301-2024
© Author(s) 2024. 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-15-1301-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
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15 Oct 2024
Research article | Highlight paper |
| 15 Oct 2024
| 15 Oct 2024
Uncertainty-informed selection of CMIP6 Earth system model subsets for use in multisectoral and impact models
Abigail Snyder
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD 20740, USA
Noah Prime
Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD 20740, USA
Claudia Tebaldi
Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD 20740, USA
Kalyn Dorheim
Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD 20740, USA
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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
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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.
Claudia Tebaldi, Abigail Snyder, and Kalyn Dorheim
Earth Syst. Dynam., 13, 1557–1609, https://doi.org/10.5194/esd-13-1557-2022, https://doi.org/10.5194/esd-13-1557-2022, 2022
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Impact modelers need many future scenarios to characterize the consequences of climate change. The climate modeling community cannot fully meet this need because of the computational cost of climate models. Emulators have fallen short of providing the entire range of inputs that modern impact models require. Our proposal, STITCHES, meets these demands in a comprehensive way and may thus support a fully integrated impact research effort and save resources for the climate modeling enterprise.
Katherine V. Calvin, Abigail Snyder, Xin Zhao, and Marshall Wise
Geosci. Model Dev., 15, 429–447, https://doi.org/10.5194/gmd-15-429-2022, https://doi.org/10.5194/gmd-15-429-2022, 2022
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Future changes in land use and cover have important implications for agriculture, energy, water use, and climate. In this study, we demonstrate a more systematic and empirically based approach to estimating a few key parameters for an economic model of land use and land cover change, gcamland. We identify parameter combinations that best replicate historical land use in the United States.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, https://doi.org/10.5194/gmd-13-3995-2020, 2020
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Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Robert Link, Abigail Snyder, Cary Lynch, Corinne Hartin, Ben Kravitz, and Ben Bond-Lamberty
Geosci. Model Dev., 12, 1477–1489, https://doi.org/10.5194/gmd-12-1477-2019, https://doi.org/10.5194/gmd-12-1477-2019, 2019
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Earth system models (ESMs) produce the highest-quality future climate data available, but they are costly to run, so only a few runs from each model are publicly available. What is needed are emulators that tell us what would have happened, if we had been able to perform as many ESM runs as we might have liked. Much of the existing work on emulators has focused on deterministic projections of average values. Here we present a way to imbue emulators with the variability seen in ESM runs.
Abigail Snyder, Katherine V. Calvin, Meridel Phillips, and Alex C. Ruane
Geosci. Model Dev., 12, 1319–1350, https://doi.org/10.5194/gmd-12-1319-2019, https://doi.org/10.5194/gmd-12-1319-2019, 2019
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Future changes in Earth system state will impact agricultural yields and therefore the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields, but are often too computationally intensive to dynamically couple into global multi-sector economic models, such as GCAM and other similar-in-scale models. This work describes a new crop yield change emulator, Persephone, that can capture yield changes in a computationally efficient way.
Katherine Calvin, Pralit Patel, Leon Clarke, Ghassem Asrar, Ben Bond-Lamberty, Ryna Yiyun Cui, Alan Di Vittorio, Kalyn Dorheim, Jae Edmonds, Corinne Hartin, Mohamad Hejazi, Russell Horowitz, Gokul Iyer, Page Kyle, Sonny Kim, Robert Link, Haewon McJeon, Steven J. Smith, Abigail Snyder, Stephanie Waldhoff, and Marshall Wise
Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, https://doi.org/10.5194/gmd-12-677-2019, 2019
Short summary
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This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM examines the future evolution of these systems through the end of the 21st century. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region’s demand for energy affect energy, water, and land in other regions.
Abigail C. Snyder, Robert P. Link, and Katherine V. Calvin
Geosci. Model Dev., 10, 4307–4319, https://doi.org/10.5194/gmd-10-4307-2017, https://doi.org/10.5194/gmd-10-4307-2017, 2017
Short summary
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Experiments conducting a model forecast for a period in which observational data are available are rarely undertaken in the integrated assessment model (IAM) community. When undertaken, results are often evaluated using global aggregates that mask deficiencies. Comparing land allocation simulations in GCAM with FAO observational data from 1990 to 2010, we find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs with global supply constraints similar to GCAM.
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
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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.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Kanishka B. Narayan, Brian C. O'Neill, Stephanie Waldhoff, and Claudia Tebaldi
Earth Syst. Sci. Data, 16, 2333–2349, https://doi.org/10.5194/essd-16-2333-2024, https://doi.org/10.5194/essd-16-2333-2024, 2024
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Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. We also present another version of this dataset aggregated from the country level to 32 geographical regions.
Claudia Tebaldi, Abigail Snyder, and Kalyn Dorheim
Earth Syst. Dynam., 13, 1557–1609, https://doi.org/10.5194/esd-13-1557-2022, https://doi.org/10.5194/esd-13-1557-2022, 2022
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Impact modelers need many future scenarios to characterize the consequences of climate change. The climate modeling community cannot fully meet this need because of the computational cost of climate models. Emulators have fallen short of providing the entire range of inputs that modern impact models require. Our proposal, STITCHES, meets these demands in a comprehensive way and may thus support a fully integrated impact research effort and save resources for the climate modeling enterprise.
Mari R. Tye, Katherine Dagon, Maria J. Molina, Jadwiga H. Richter, Daniele Visioni, Ben Kravitz, and Simone Tilmes
Earth Syst. Dynam., 13, 1233–1257, https://doi.org/10.5194/esd-13-1233-2022, https://doi.org/10.5194/esd-13-1233-2022, 2022
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We examined the potential effect of stratospheric aerosol injection (SAI) on extreme temperature and precipitation. SAI may cause daytime temperatures to cool but nighttime to warm. Daytime cooling may occur in all seasons across the globe, with the largest decreases in summer. In contrast, nighttime warming may be greatest at high latitudes in winter. SAI may reduce the frequency and intensity of extreme rainfall. The combined changes may exacerbate drying over parts of the global south.
Katherine V. Calvin, Abigail Snyder, Xin Zhao, and Marshall Wise
Geosci. Model Dev., 15, 429–447, https://doi.org/10.5194/gmd-15-429-2022, https://doi.org/10.5194/gmd-15-429-2022, 2022
Short summary
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Future changes in land use and cover have important implications for agriculture, energy, water use, and climate. In this study, we demonstrate a more systematic and empirically based approach to estimating a few key parameters for an economic model of land use and land cover change, gcamland. We identify parameter combinations that best replicate historical land use in the United States.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
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We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Jeff W. Atkins, Elizabeth Agee, Alexandra Barry, Kyla M. Dahlin, Kalyn Dorheim, Maxim S. Grigri, Lisa T. Haber, Laura J. Hickey, Aaron G. Kamoske, Kayla Mathes, Catherine McGuigan, Evan Paris, Stephanie C. Pennington, Carly Rodriguez, Autym Shafer, Alexey Shiklomanov, Jason Tallant, Christopher M. Gough, and Ben Bond-Lamberty
Earth Syst. Sci. Data, 13, 943–952, https://doi.org/10.5194/essd-13-943-2021, https://doi.org/10.5194/essd-13-943-2021, 2021
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The fortedata R package is an open data notebook from the Forest Resilience Threshold Experiment (FoRTE) – a modeling and manipulative field experiment that tests the effects of disturbance severity and disturbance type on carbon cycling dynamics in a temperate forest. The data included help to interpret how carbon cycling processes respond over time to disturbance.
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.
Kalyn Dorheim, Steven J. Smith, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 365–375, https://doi.org/10.5194/gmd-14-365-2021, https://doi.org/10.5194/gmd-14-365-2021, 2021
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Simple climate models are frequently used in research and decision-making communities because of their tractability and low computational cost. Simple climate models are diverse, including highly idealized and process-based models. Here we present a hybrid approach that combines the strength of two types of simple climate models in a flexible framework. This hybrid approach has provided insights into the climate system and opens an avenue for investigating radiative forcing uncertainties.
Zebedee R. J. Nicholls, Malte Meinshausen, Jared Lewis, Robert Gieseke, Dietmar Dommenget, Kalyn Dorheim, Chen-Shuo Fan, Jan S. Fuglestvedt, Thomas Gasser, Ulrich Golüke, Philip Goodwin, Corinne Hartin, Austin P. Hope, Elmar Kriegler, Nicholas J. Leach, Davide Marchegiani, Laura A. McBride, Yann Quilcaille, Joeri Rogelj, Ross J. Salawitch, Bjørn H. Samset, Marit Sandstad, Alexey N. Shiklomanov, Ragnhild B. Skeie, Christopher J. Smith, Steve Smith, Katsumasa Tanaka, Junichi Tsutsui, and Zhiang Xie
Geosci. Model Dev., 13, 5175–5190, https://doi.org/10.5194/gmd-13-5175-2020, https://doi.org/10.5194/gmd-13-5175-2020, 2020
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Computational limits mean that we cannot run our most comprehensive climate models for all applications of interest. In such cases, reduced complexity models (RCMs) are used. Here, researchers working on 15 different models present the first systematic community effort to evaluate and compare RCMs: the Reduced Complexity Model Intercomparison Project (RCMIP). Our research ensures that users of RCMs can more easily evaluate the strengths, weaknesses and limitations of their tools.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, https://doi.org/10.5194/gmd-13-3995-2020, 2020
Short summary
Short summary
Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Robert Vautard, Geert Jan van Oldenborgh, Friederike E. L. Otto, Pascal Yiou, Hylke de Vries, Erik van Meijgaard, Andrew Stepek, Jean-Michel Soubeyroux, Sjoukje Philip, Sarah F. Kew, Cecilia Costella, Roop Singh, and Claudia Tebaldi
Earth Syst. Dynam., 10, 271–286, https://doi.org/10.5194/esd-10-271-2019, https://doi.org/10.5194/esd-10-271-2019, 2019
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The effect of human activities on the probability of winter wind storms like the ones that occurred in Western Europe in January 2018 is analysed using multiple model ensembles. Despite a significant probability decline in observations, we find no significant change in probabilities due to human influence on climate so far. However, such extreme events are likely to be slightly more frequent in the future. The observed decrease in storminess is likely to be due to increasing roughness.
Robert Link, Abigail Snyder, Cary Lynch, Corinne Hartin, Ben Kravitz, and Ben Bond-Lamberty
Geosci. Model Dev., 12, 1477–1489, https://doi.org/10.5194/gmd-12-1477-2019, https://doi.org/10.5194/gmd-12-1477-2019, 2019
Short summary
Short summary
Earth system models (ESMs) produce the highest-quality future climate data available, but they are costly to run, so only a few runs from each model are publicly available. What is needed are emulators that tell us what would have happened, if we had been able to perform as many ESM runs as we might have liked. Much of the existing work on emulators has focused on deterministic projections of average values. Here we present a way to imbue emulators with the variability seen in ESM runs.
Abigail Snyder, Katherine V. Calvin, Meridel Phillips, and Alex C. Ruane
Geosci. Model Dev., 12, 1319–1350, https://doi.org/10.5194/gmd-12-1319-2019, https://doi.org/10.5194/gmd-12-1319-2019, 2019
Short summary
Short summary
Future changes in Earth system state will impact agricultural yields and therefore the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields, but are often too computationally intensive to dynamically couple into global multi-sector economic models, such as GCAM and other similar-in-scale models. This work describes a new crop yield change emulator, Persephone, that can capture yield changes in a computationally efficient way.
Katherine Calvin, Pralit Patel, Leon Clarke, Ghassem Asrar, Ben Bond-Lamberty, Ryna Yiyun Cui, Alan Di Vittorio, Kalyn Dorheim, Jae Edmonds, Corinne Hartin, Mohamad Hejazi, Russell Horowitz, Gokul Iyer, Page Kyle, Sonny Kim, Robert Link, Haewon McJeon, Steven J. Smith, Abigail Snyder, Stephanie Waldhoff, and Marshall Wise
Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, https://doi.org/10.5194/gmd-12-677-2019, 2019
Short summary
Short summary
This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM examines the future evolution of these systems through the end of the 21st century. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region’s demand for energy affect energy, water, and land in other regions.
Abigail C. Snyder, Robert P. Link, and Katherine V. Calvin
Geosci. Model Dev., 10, 4307–4319, https://doi.org/10.5194/gmd-10-4307-2017, https://doi.org/10.5194/gmd-10-4307-2017, 2017
Short summary
Short summary
Experiments conducting a model forecast for a period in which observational data are available are rarely undertaken in the integrated assessment model (IAM) community. When undertaken, results are often evaluated using global aggregates that mask deficiencies. Comparing land allocation simulations in GCAM with FAO observational data from 1990 to 2010, we find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs with global supply constraints similar to GCAM.
Benjamin M. Sanderson, Yangyang Xu, Claudia Tebaldi, Michael Wehner, Brian O'Neill, Alexandra Jahn, Angeline G. Pendergrass, Flavio Lehner, Warren G. Strand, Lei Lin, Reto Knutti, and Jean Francois Lamarque
Earth Syst. Dynam., 8, 827–847, https://doi.org/10.5194/esd-8-827-2017, https://doi.org/10.5194/esd-8-827-2017, 2017
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We present the results of a set of climate simulations designed to simulate futures in which the Earth's temperature is stabilized at the levels referred to in the 2015 Paris Agreement. We consider the necessary future emissions reductions and the aspects of extreme weather which differ significantly between the 2 and 1.5 °C climate in the simulations.
Nathan P. Gillett, Hideo Shiogama, Bernd Funke, Gabriele Hegerl, Reto Knutti, Katja Matthes, Benjamin D. Santer, Daithi Stone, and Claudia Tebaldi
Geosci. Model Dev., 9, 3685–3697, https://doi.org/10.5194/gmd-9-3685-2016, https://doi.org/10.5194/gmd-9-3685-2016, 2016
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Detection and attribution of climate change is the process of determining the causes of observed climate changes, which has underpinned key conclusions on the role of human influence on climate in the reports of the Intergovernmental Panel on Climate Change (IPCC). This paper describes a coordinated set of climate model experiments that will form part of the Sixth Coupled Model Intercomparison Project and will support improved attribution of climate change in the next IPCC report.
Brian C. O'Neill, Claudia Tebaldi, Detlef P. van Vuuren, Veronika Eyring, Pierre Friedlingstein, George Hurtt, Reto Knutti, Elmar Kriegler, Jean-Francois Lamarque, Jason Lowe, Gerald A. Meehl, Richard Moss, Keywan Riahi, and Benjamin M. Sanderson
Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, https://doi.org/10.5194/gmd-9-3461-2016, 2016
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The Scenario Model Intercomparison Project (ScenarioMIP) will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. The design consists of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions. Climate model projections will facilitate integrated studies of climate change as well as address targeted scientific questions.
Related subject area
Topics: Earth system | Interactions: Other interactions | Methods: Model and data diagnostics
Chaotic oceanic excitation of low-frequency polar motion variability
Lara Börger, Michael Schindelegger, Mengnan Zhao, Rui M. Ponte, Anno Löcher, Bernd Uebbing, Jean-Marc Molines, and Thierry Penduff
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-21, https://doi.org/10.5194/esd-2024-21, 2024
Revised manuscript accepted for ESD
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Flows in the ocean are driven either by atmospheric forces or by small-scale internal disturbances that are inherently chaotic. We use computer simulation results to show that these chaotic oceanic disturbances can attain spatial scales large enough to alter the motion of Earth’s pole of rotation. Given their size and unpredictable nature, the chaotic signals are a source of uncertainty when interpreting observed year-to-year polar motion changes in terms of other processes in the Earth system.
Cited articles
Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019.
Allstadt, A. J., Vavrus, S. J., Heglund, P. J., Pidgeon, A. M., Thogmartin, W. E., and Radeloff, V. C.: Spring plant phenology and false springs in the conterminous US during the 21st century, Environ. Res. Lett., 10, 104008, https://doi.org/10.1088/1748-9326/10/10/104008, 2015.
Barsugli, J. J., Guentchev, G., Horton, R. M., Wood, A., Mearns, L. O., Liang, X.-Z., Winkler, J. A., Dixon, K., Hayhoe, K., Rood, R. B., Goddard, L., Ray, A., Buja, L., and Ammann, C.: The practitioner's dilemma: How to assess the credibility of downscaled climate projections, Eos T. Am. Geophys. Un., 94, 424–425, https://doi.org/10.1002/2013eo460005, 2013.
Beusch, L., Gudmundsson, L., and Seneviratne, S. I.: Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land, Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, 2020.
Brands, S.: A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to-high latitudes, Geosci. Model Dev., 15, 1375–1411, https://doi.org/10.5194/gmd-15-1375-2022, 2022.
Core Writing Team, Lee, H., and Romero, J. (Eds.): Summary for Policymakers, in: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, https://doi.org/10.59327/IPCC/AR6-9789291691647.001, 2023.
Dolan, F., Lamontagne, J., Link, R., Hejazi, M., Reed, P., and Edmonds, J.: Evaluating the economic impact of water scarcity in a changing world, Nat. Commun., 12, 1915, https://doi.org/10.1038/s41467-021-22194-0, 2021.
Dolan, F., Lamontagne, J., Calvin, K., Snyder, A., Narayan, K. B., Di Vittorio, A. V., and Vernon, C. R.: Modeling the economic and environmental impacts of land scarcity under deep uncertainty, Earths Future, 10, e2021EF002466, https://doi.org/10.1029/2021EF002466, 2022.
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.
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017.
Gerst, K. L., Crimmins, T. M., Posthumus, E. E., Rosemartin, A. H., and Schwartz, M. D.: How Well Do the Spring Indices Predict Phenological Activity across Plant Species?, Int. J. Biometeorol., 64, 889–901, https://doi.org/10.1007/s00484-020-01879-z, 2020.
Graham, N. T., Hejazi, M. I., Chen, M., Davies, E. G. R., Edmonds, J. A., Kim, S. H., Turner, S. W. D., Li, X., Vernon, C. R., Calvin, K., Miralles-Wilhelm, F., Clarke, L., Kyle, P., Link, R., Patel, P., Snyder, A., and Wise, M.: Humans drive future water scarcity changes across all Shared Socioeconomic Pathways, Environ. Res. Lett., 15, 014007, https://doi.org/10.1088/1748-9326/ab639b, 2020.
Guivarch, C., Le Gallic, T., Bauer, N., Fragkos, P., Huppmann, D., Jaxa-Rozen, M., Keppo, I., Kriegler, E., Krisztin, T., Marangoni, G., Pye, S., Riahi, K., Schaeffer, R., Tavoni, M., Trutnevyte, E., van Vuuren, D., and Wagner, F.: Using large ensembles of climate change mitigation scenarios for robust insights, Nat. Clim. Change, 12, 428–435, https://doi.org/10.1038/s41558-022-01349-x, 2022.
Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., and Zelinka, M.: Climate Simulations: Recognize the `hot Model' Problem, Nature Publishing Group UK, https://doi.org/10.1038/d41586-022-01192-2, 2022.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional climate predictions, B. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in projections of regional precipitation change, Clim. Dynam., 37, 407–418, https://doi.org/10.1007/s00382-010-0810-6, 2011.
Iturbide, M., Fernández, J., Gutiérrez, J. M., Pirani, A., Huard, D., Al Khourdajie, A., Baño-Medina, J., Bedia, J., Casanueva, A., Cimadevilla, E., Cofiño, A. S., De Felice, M., Diez-Sierra, J., García-Díaz, M., Goldie, J., Herrera, D. A., Herrera, S., Manzanas, R., Milovac, J., Radhakrishnan, A., San-Martín, D., Spinuso, A., Thyng, K. M., Trenham, C., and Yelekçi, Ö.: Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository, Scientific Data, 9, 629, https://doi.org/10.1038/s41597-022-01739-y, 2022.
Kendon, E. J., Jones, R. G., Kjellström, E., and Murphy, J. M.: Using and Designing GCM–RCM Ensemble Regional Climate Projections, J. Climate, 23, 6485–6503, https://doi.org/10.1175/2010JCLI3502.1, 2010.
Lafferty, D. C. and Sriver, R. L.: Downscaling and Bias-Correction Contribute Considerable Uncertainty to Local Climate Projections in CMIP6, Npj Climate and Atmospheric Science, 6, 1–13, https://doi.org/10.1038/s41612-023-00486-0, 2023.
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019.
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020.
Lovato, T., Peano, D., Butenschön, M., Materia, S., Iovino, D., Scoccimarro, E., Fogli, P. G., Cherchi, A., Bellucci, A., Gualdi, S., Masina, S., and Navarra, A.: CMIP6 simulations with the CMCC Earth system model (CMCC-ESM2), J. Adv. Model. Earth Sy., 14, e2021MS002814, https://doi.org/10.1029/2021MS002814, 2022.
Mearns, L. O., Sain, S., Leung, L. R., Bukovsky, M. S., McGinnis, S., Biner, S., Caya, D., Arritt, R. W., Gutowski, W., Takle, E., Snyder, M., Jones, R. G., Nunes, A. M. B., Tucker, S., Herzmann, D., McDaniel, L., and Sloan, L.: Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP), Climatic Change, 120, 965–975, https://doi.org/10.1007/s10584-013-0831-3, 2013.
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, Science Advances, 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981, 2020.
Merrifield, A. L., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications, Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, 2023.
Müller, C., Franke, J., Jägermeyr, J., Ruane, A. C., Elliott, J., Moyer, E., Heinke, J., Falloon, P. D., Folberth, C., Francois, L., Hank, T., Izaurralde, R. C., Jacquemin, I., Liu, W., Olin, S., Pugh, T. A. M., Williams, K., and Zabel, F.: Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios, Environ. Res. Lett., 16, 034040, https://doi.org/ 10.1088/1748-9326/abd8fc, 2021.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nath, S., Lejeune, Q., Beusch, L., Seneviratne, S. I., and Schleussner, C.-F.: MESMER-M: an Earth system model emulator for spatially resolved monthly temperature, Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, 2022.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Parding, K. M., Dobler, A., McSweeney, C. F., Landgren, O. A., Benestad, R., Erlandsen, H. B., Mezghani, A., Gregow, H., Räty, O., Viktor, E., El Zohbi, J., Christensen, O. B., and Loukos, H.: GCMeval – An interactive tool for evaluation and selection of climate model ensembles, Clim. Serv., 18, 100167, https://doi.org/10.1016/j.cliser.2020.100167, 2020.
Peterson, A. G. and Abatzoglou, J. T.: Observed changes in false springs over the contiguous United States, Geophys. Res. Lett., 41, 2156–2162, https://doi.org/10.1002/2014gl059266, 2014.
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., Fekete, B. M., Franssen, W., Gerten, D., Gosling, S. N., Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262–3267, https://doi.org/10.1073/pnas.1222473110, 2014.
Quilcaille, Y., Gudmundsson, L., Beusch, L., Hauser, M., and Seneviratne, S. I.: Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models, Geophys. Res. Lett., 49, e2022GL099012, https://doi.org/10.1029/2022GL099012, 2022.
Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., Asseng, S., Basso, B. B., Ewert, F. A., Wallach, D., Baigorria, G. A., and Winter, J. M.: The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies, Agr. Forest Meteorol., 170, 166–182, https://doi.org/10.1016/j.agrformet.2012.09.011, 2013.
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., and Jones, J. W.: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison, P. Natl. Acad. Sci. USA, 111, 3268–3273, https://doi.org/10.1073/pnas.1222463110, 2014.
Sinha, E., Bond-Lamberty, B., Calvin, K. V., Drewniak, B. A., Bisht, G., Bernacchi, C., Blakely, B. J., and Moore, C. E.: The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2), J. Geophys. Res.-Biogeo., 128, e2022JG007187, https://doi.org/10.1029/2022jg007187, 2023a.
Sinha, E., Calvin, K. V., and Bond-Lamberty, B.: Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2), J. Adv. Model. Earth Sy., 15, e2022MS003171, https://doi.org/10.1029/2022MS003171, 2023b.
Snyder, A. and Prime, N.: code and data for Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models (Version v1), MSD-LIVE Data Repository [data set], https://doi.org/10.57931/2223040, 2023.
Tebaldi, C., Armbruster, A., Engler, H. P., and Link, R.: Emulating climate extreme indices, Environ. Res. Lett., 15, 074006, https://doi.org/10.1088/1748-9326/ab8332, 2020.
Tebaldi, C., Dorheim, K., Wehner, M., and Leung, R.: Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates, Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, 2021.
Tebaldi, C., Snyder, A., and Dorheim, K.: STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations, Earth Syst. Dynam., 13, 1557–1609, https://doi.org/10.5194/esd-13-1557-2022, 2022.
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The inter-sectoral impact model intercomparison project (ISI–MIP): project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232, https://doi.org/10.1073/pnas.1312330110, 2014.
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.
Chief editor
Earth System Models (ESMs) are used as inputs in impact models to estimate future climate risks. Hence, accurately representing the entire spectrum of uncertainty in ESMs is vital for comprehending the future co-evolution of the coupled human-natural system. Numerous ESMs are part of the CMIP6 suite and it is impossible to incorporate all of them in impact modeling. This study will help in selecting a suitable subset of ESMs that will reduce computing costs while preserving the range of uncertainty.
Earth System Models (ESMs) are used as inputs in impact models to estimate future climate risks....
Short summary
From running climate models to using their outputs to identify impacts, modeling the integrated human–Earth system is expensive. This work presents a method to identify a smaller subset of models from the full set that preserves the uncertainty characteristics of the full set. This results in a smaller number of runs that an impact modeler can use to assess how uncertainty propagates from the Earth to the human system, while still capturing the range of outcomes provided by climate models.
From running climate models to using their outputs to identify impacts, modeling the integrated...
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