Articles | Volume 19, issue 2
https://doi.org/10.5194/cp-19-323-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-19-323-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Feb 2023
Research article |
| 02 Feb 2023
| 02 Feb 2023
Causes of the weak emergent constraint on climate sensitivity at the Last Glacial Maximum
Department of Meteorology, Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Navjit Sagoo
Department of Meteorology, Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Jiang Zhu
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, USA
Thorsten Mauritsen
Department of Meteorology, Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Related authors
Raphael Grodofzig, Martin Renoult, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2734, https://doi.org/10.5194/egusphere-2023-2734, 2023
Short summary
Short summary
We investigate whether the Amazon rain forest has lost substantial resilience since 1990. This assertion is based on trends in the observational record of vegetation density. We calculate the same metrics in a large number of climate model simulations and find that several models behave indistinguishably from the observations. This suggests that the observed trend could be caused by internal variability and that the cause of the ongoing rapid loss of Amazon rain forest is due to local actors.
Martin Renoult, James Douglas Annan, Julia Catherine Hargreaves, Navjit Sagoo, Clare Flynn, Marie-Luise Kapsch, Qiang Li, Gerrit Lohmann, Uwe Mikolajewicz, Rumi Ohgaito, Xiaoxu Shi, Qiong Zhang, and Thorsten Mauritsen
Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, https://doi.org/10.5194/cp-16-1715-2020, 2020
Short summary
Short summary
Interest in past climates as sources of information for the climate system has grown in recent years. In particular, studies of the warm mid-Pliocene and cold Last Glacial Maximum showed relationships between the tropical surface temperature of the Earth and its sensitivity to an abrupt doubling of atmospheric CO2. In this study, we develop a new and promising statistical method and obtain similar results as previously observed, wherein the sensitivity does not seem to exceed extreme values.
Alejandro Uribe, Frida Bender, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1559, https://doi.org/10.5194/egusphere-2024-1559, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Our study explores climate feedbacks, vital for understanding global warming. It links them to shifts in Earth's energy balance at the atmosphere's top due to natural temperature variations. It takes roughly 50-years to establish this connection. Combined satellite observations and reanalysis suggest that Earth cools more than expected under carbon dioxide influence. However, continuous satellite data until at least the mid-2030s are crucial for refining our understanding of climate feedbacks.
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
EGUsphere, https://doi.org/10.5194/egusphere-2024-356, https://doi.org/10.5194/egusphere-2024-356, 2024
Short summary
Short summary
The imbalance between the energy the Earth absorbs from the Sun and the energy the Earth emits back to space gives rise to climate change, but measuring the small imbalance is challenging. We simulate satellites in various orbits to investigate how well they sample the imbalance, and find that the best option is to combine at least two satellites that see complementary parts of the Earth and cover the daily and annual cycles. This information is useful when planning future satellite missions.
Julia Campbell, Christopher J. Poulsen, Jiang Zhu, Jessica E. Tierney, and Jeremy Keeler
Clim. Past, 20, 495–522, https://doi.org/10.5194/cp-20-495-2024, https://doi.org/10.5194/cp-20-495-2024, 2024
Short summary
Short summary
In this study, we use climate modeling to investigate the relative impact of CO2 and orbit on Early Eocene (~ 55 million years ago) climate and compare our modeled results to fossil records to determine the context for the Paleocene–Eocene Thermal Maximum, the most extreme hyperthermal in the Cenozoic. Our conclusions consider limitations and illustrate the importance of climate models when interpreting paleoclimate records in times of extreme warmth.
Andrea Mosso, Thomas Hocking, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2024-618, https://doi.org/10.5194/egusphere-2024-618, 2024
Short summary
Short summary
Clouds play a crucial role in the energy balance of the earth, as they can either warm up or cool down the area they cover depending on their height and depth. It is expected that they will alter their behaviour under climate change, which will affect the warming generated by greenhouse gases. This paper proposes a new method to estimate their overall effect by simulating a climate where clouds are transparent. Results show that, with the model used, clouds have a stabilising effect on climate.
Antoine Hermant, Linnea Huusko, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.22541/essoar.170158317.78990757/v1, https://doi.org/10.22541/essoar.170158317.78990757/v1, 2024
Short summary
Short summary
Aerosol particles, from natural and human sources, have a cooling effect on the climate, partially offsetting global warming. They do this through direct (sunlight reflection) and indirect (cloud property alteration) mechanisms. Using a global climate model, we found that despite declining emissions, the direct effect of human-made aerosols has increased, while the indirect effect has decreased, attributed to the shift in emissions from North America and Europe to Southeast Asia.
Sarah L. Bradley, Raymond Sellevold, Michele Petrini, Miren Vizcaino, Sotiria Georgiou, Jiang Zhu, Bette L. Otto-Bliesner, and Marcus Lofverstrom
Clim. Past, 20, 211–235, https://doi.org/10.5194/cp-20-211-2024, https://doi.org/10.5194/cp-20-211-2024, 2024
Short summary
Short summary
The Last Glacial Maximum (LGM) was the most recent period with large ice sheets in Europe and North America. We provide a detailed analysis of surface mass and energy components for two time periods that bracket the LGM: 26 and 21 ka BP. We use an earth system model which has been adopted for modern ice sheets. We find that all Northern Hemisphere ice sheets have a positive surface mass balance apart from the British and Irish ice sheets and the North American ice sheet complex.
Clare Marie Flynn, Linnea Huusko, Angshuman Modak, and Thorsten Mauritsen
Atmos. Chem. Phys., 23, 15121–15133, https://doi.org/10.5194/acp-23-15121-2023, https://doi.org/10.5194/acp-23-15121-2023, 2023
Short summary
Short summary
The latest-generation climate models show surprisingly cold mid-20th century global-mean temperatures, often despite exhibiting more realistic late 20th/early 21st century temperatures. A too-strong aerosol forcing in many models was thought to the be primary cause of these too-cold mid-century temperatures, but this was found to only be a partial explanation. This also partly undermines the hope to construct a strong relationship between the mid-century temperatures and aerosol forcing.
Raphael Grodofzig, Martin Renoult, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2734, https://doi.org/10.5194/egusphere-2023-2734, 2023
Short summary
Short summary
We investigate whether the Amazon rain forest has lost substantial resilience since 1990. This assertion is based on trends in the observational record of vegetation density. We calculate the same metrics in a large number of climate model simulations and find that several models behave indistinguishably from the observations. This suggests that the observed trend could be caused by internal variability and that the cause of the ongoing rapid loss of Amazon rain forest is due to local actors.
Xiaodong Zhang, Brett J. Tipple, Jiang Zhu, William D. Rush, Christian A. Shields, Joseph B. Novak, and James C. Zachos
Clim. Past Discuss., https://doi.org/10.5194/cp-2023-89, https://doi.org/10.5194/cp-2023-89, 2023
Revised manuscript accepted for CP
Short summary
Short summary
This study is motivated by the current anthropogenic warming forced transition in regional hydroclimate. We use observations and model simulations during PETM, an anomalous greenhouse global warming event in the past, to constrain regional/local hydroclimate response. Our findings based on multi-proxy evidence within the context of model output suggest a transition to an overall drier climate punctuated by increased precipitation during summer in coastal California during the PETM.
James Douglas Annan, Julia Catherine Hargreaves, Thorsten Mauritsen, Erin McClymont, and Sze Ling Ho
EGUsphere, https://doi.org/10.5194/egusphere-2023-1941, https://doi.org/10.5194/egusphere-2023-1941, 2023
Short summary
Short summary
We have created a new global surface temperature reconstruction of the climate of the mid-Pliocene Warm Period, representing the period roughly 3.2 million years before the present day. We estimate that the globally averaged mean temperature was around 3.6 °C warmer than it was in pre-industrial times, but there is significant uncertainty on this value.
Sushant Das, Frida Bender, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1605, https://doi.org/10.5194/egusphere-2023-1605, 2023
Preprint archived
Short summary
Short summary
Quantifying global and Indian precipitation responses to anthropogenic aerosol and CO2 forcings using multiple models is needed for reducing climate uncertainty. The response to global warming from CO2 increases precipitation both globally and over India, whereas the cooling response to sulfate aerosol leads to a reduction in precipitation in both cases. An opposite response to black carbon is noted i.e., a global decrease but an increase of precipitation over India implying changes in dynamics.
Angshuman Modak and Thorsten Mauritsen
Atmos. Chem. Phys., 23, 7535–7549, https://doi.org/10.5194/acp-23-7535-2023, https://doi.org/10.5194/acp-23-7535-2023, 2023
Short summary
Short summary
We provide an improved estimate of equilibrium climate sensitivity (ECS) constrained based on the instrumental temperature record including the corrections for the pattern effect. The improved estimate factors in the uncertainty caused by the underlying sea-surface temperature datasets used in the estimates of pattern effect. This together with the inter-model spread lifts the corresponding IPCC AR6 estimate to 3.2 K [1.8 to 11.0], which is lower and better constrained than in past studies.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
Short summary
Short summary
Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth system models. These updates include the ability to run the scheme on graphics processing units (GPUs), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Cathy Hohenegger, Peter Korn, Leonidas Linardakis, René Redler, Reiner Schnur, Panagiotis Adamidis, Jiawei Bao, Swantje Bastin, Milad Behravesh, Martin Bergemann, Joachim Biercamp, Hendryk Bockelmann, Renate Brokopf, Nils Brüggemann, Lucas Casaroli, Fatemeh Chegini, George Datseris, Monika Esch, Geet George, Marco Giorgetta, Oliver Gutjahr, Helmuth Haak, Moritz Hanke, Tatiana Ilyina, Thomas Jahns, Johann Jungclaus, Marcel Kern, Daniel Klocke, Lukas Kluft, Tobias Kölling, Luis Kornblueh, Sergey Kosukhin, Clarissa Kroll, Junhong Lee, Thorsten Mauritsen, Carolin Mehlmann, Theresa Mieslinger, Ann Kristin Naumann, Laura Paccini, Angel Peinado, Divya Sri Praturi, Dian Putrasahan, Sebastian Rast, Thomas Riddick, Niklas Roeber, Hauke Schmidt, Uwe Schulzweida, Florian Schütte, Hans Segura, Radomyra Shevchenko, Vikram Singh, Mia Specht, Claudia Christine Stephan, Jin-Song von Storch, Raphaela Vogel, Christian Wengel, Marius Winkler, Florian Ziemen, Jochem Marotzke, and Bjorn Stevens
Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, https://doi.org/10.5194/gmd-16-779-2023, 2023
Short summary
Short summary
Models of the Earth system used to understand climate and predict its change typically employ a grid spacing of about 100 km. Yet, many atmospheric and oceanic processes occur on much smaller scales. In this study, we present a new model configuration designed for the simulation of the components of the Earth system and their interactions at kilometer and smaller scales, allowing an explicit representation of the main drivers of the flow of energy and matter by solving the underlying equations.
James D. Annan, Julia C. Hargreaves, and Thorsten Mauritsen
Clim. Past, 18, 1883–1896, https://doi.org/10.5194/cp-18-1883-2022, https://doi.org/10.5194/cp-18-1883-2022, 2022
Short summary
Short summary
We have created a new global surface temperature reconstruction of the climate of the Last Glacial Maximum, representing the period 19–23 000 years before the present day. We find that the globally averaged mean temperature was roughly 4.5 °C colder than it was in pre-industrial times, albeit there is significant uncertainty on this value.
Ryan A. Green, Laurie Menviel, Katrin J. Meissner, Xavier Crosta, Deepak Chandan, Gerrit Lohmann, W. Richard Peltier, Xiaoxu Shi, and Jiang Zhu
Clim. Past, 18, 845–862, https://doi.org/10.5194/cp-18-845-2022, https://doi.org/10.5194/cp-18-845-2022, 2022
Short summary
Short summary
Climate models are used to predict future climate changes and as such, it is important to assess their performance in simulating past climate changes. We analyze seasonal sea-ice cover over the Southern Ocean simulated from numerical PMIP3, PMIP4 and LOVECLIM simulations during the Last Glacial Maximum (LGM). Comparing these simulations to proxy data, we provide improved estimates of LGM seasonal sea-ice cover. Our estimate of summer sea-ice extent is 20 %–30 % larger than previous estimates.
Masa Kageyama, Sandy P. Harrison, Marie-L. Kapsch, Marcus Lofverstrom, Juan M. Lora, Uwe Mikolajewicz, Sam Sherriff-Tadano, Tristan Vadsaria, Ayako Abe-Ouchi, Nathaelle Bouttes, Deepak Chandan, Lauren J. Gregoire, Ruza F. Ivanovic, Kenji Izumi, Allegra N. LeGrande, Fanny Lhardy, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, André Paul, W. Richard Peltier, Christopher J. Poulsen, Aurélien Quiquet, Didier M. Roche, Xiaoxu Shi, Jessica E. Tierney, Paul J. Valdes, Evgeny Volodin, and Jiang Zhu
Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, https://doi.org/10.5194/cp-17-1065-2021, 2021
Short summary
Short summary
The Last Glacial Maximum (LGM; ~21 000 years ago) is a major focus for evaluating how well climate models simulate climate changes as large as those expected in the future. Here, we compare the latest climate model (CMIP6-PMIP4) to the previous one (CMIP5-PMIP3) and to reconstructions. Large-scale climate features (e.g. land–sea contrast, polar amplification) are well captured by all models, while regional changes (e.g. winter extratropical cooling, precipitations) are still poorly represented.
Jule Radtke, Thorsten Mauritsen, and Cathy Hohenegger
Atmos. Chem. Phys., 21, 3275–3288, https://doi.org/10.5194/acp-21-3275-2021, https://doi.org/10.5194/acp-21-3275-2021, 2021
Short summary
Short summary
Shallow trade wind clouds are a key source of uncertainty to projections of the Earth's changing climate. We perform high-resolution simulations of trade cumulus and investigate how the representation and climate feedback of these clouds depend on the specific grid spacing. We find that the cloud feedback is positive when simulated with kilometre but near zero when simulated with hectometre grid spacing. These findings suggest that storm-resolving models may exaggerate the trade cloud feedback.
Jiang Zhu and Christopher J. Poulsen
Clim. Past, 17, 253–267, https://doi.org/10.5194/cp-17-253-2021, https://doi.org/10.5194/cp-17-253-2021, 2021
Short summary
Short summary
Climate sensitivity has been directly calculated from paleoclimate data. This approach relies on good understandings of climate forcings and interactions within the Earth system. We conduct Last Glacial Maximum simulations using a climate model to quantify the forcing and efficacy of ice sheets and greenhouse gases and to directly estimate climate sensitivity in the model. Results suggest that the direct calculation overestimates the truth by 25 % due to neglecting ocean dynamical feedback.
Daniel J. Lunt, Fran Bragg, Wing-Le Chan, David K. Hutchinson, Jean-Baptiste Ladant, Polina Morozova, Igor Niezgodzki, Sebastian Steinig, Zhongshi Zhang, Jiang Zhu, Ayako Abe-Ouchi, Eleni Anagnostou, Agatha M. de Boer, Helen K. Coxall, Yannick Donnadieu, Gavin Foster, Gordon N. Inglis, Gregor Knorr, Petra M. Langebroek, Caroline H. Lear, Gerrit Lohmann, Christopher J. Poulsen, Pierre Sepulchre, Jessica E. Tierney, Paul J. Valdes, Evgeny M. Volodin, Tom Dunkley Jones, Christopher J. Hollis, Matthew Huber, and Bette L. Otto-Bliesner
Clim. Past, 17, 203–227, https://doi.org/10.5194/cp-17-203-2021, https://doi.org/10.5194/cp-17-203-2021, 2021
Short summary
Short summary
This paper presents the first modelling results from the Deep-Time Model Intercomparison Project (DeepMIP), in which we focus on the early Eocene climatic optimum (EECO, 50 million years ago). We show that, in contrast to previous work, at least three models (CESM, GFDL, and NorESM) produce climate states that are consistent with proxy indicators of global mean temperature and polar amplification, and they achieve this at a CO2 concentration that is consistent with the CO2 proxy record.
Martin Renoult, James Douglas Annan, Julia Catherine Hargreaves, Navjit Sagoo, Clare Flynn, Marie-Luise Kapsch, Qiang Li, Gerrit Lohmann, Uwe Mikolajewicz, Rumi Ohgaito, Xiaoxu Shi, Qiong Zhang, and Thorsten Mauritsen
Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, https://doi.org/10.5194/cp-16-1715-2020, 2020
Short summary
Short summary
Interest in past climates as sources of information for the climate system has grown in recent years. In particular, studies of the warm mid-Pliocene and cold Last Glacial Maximum showed relationships between the tropical surface temperature of the Earth and its sensitivity to an abrupt doubling of atmospheric CO2. In this study, we develop a new and promising statistical method and obtain similar results as previously observed, wherein the sensitivity does not seem to exceed extreme values.
James D. Annan, Julia C. Hargreaves, Thorsten Mauritsen, and Bjorn Stevens
Earth Syst. Dynam., 11, 709–719, https://doi.org/10.5194/esd-11-709-2020, https://doi.org/10.5194/esd-11-709-2020, 2020
Short summary
Short summary
In this paper we explore the potential of variability for constraining the equilibrium response of the climate system to external forcing. We show that the constraint is inherently skewed, with a long tail to high sensitivity, and that while the variability may contain some useful information, it is unlikely to generate a tight constraint.
Clare Marie Flynn and Thorsten Mauritsen
Atmos. Chem. Phys., 20, 7829–7842, https://doi.org/10.5194/acp-20-7829-2020, https://doi.org/10.5194/acp-20-7829-2020, 2020
Short summary
Short summary
The range of climate sensitivity of models participating in CMIP6 has increased relative to models participating in CMIP5 due to decreases in the total feedback parameter. This is caused by increases in the shortwave all-sky and clear-sky feedbacks, particularly over the Southern Ocean. These shifts between CMIP6 and CMIP5 did not arise by chance. Both CMIP5 and CMIP6 models are found to exhibit aerosol forcing that is too strong, causing too much cooling relative to observations.
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
Short summary
Short summary
The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Andrew E. Dessler, Thorsten Mauritsen, and Bjorn Stevens
Atmos. Chem. Phys., 18, 5147–5155, https://doi.org/10.5194/acp-18-5147-2018, https://doi.org/10.5194/acp-18-5147-2018, 2018
Short summary
Short summary
One of the most important parameters in climate science is the equilibrium climate sensitivity (ECS). Estimates of this quantity based on 20th-century observations suggest low values of ECS (below 2 °C). We show that these calculations may be significantly in error. Together with other recent work on this problem, it seems probable that the ECS is larger than suggested by the 20th-century observations.
Bjorn Stevens, Stephanie Fiedler, Stefan Kinne, Karsten Peters, Sebastian Rast, Jobst Müsse, Steven J. Smith, and Thorsten Mauritsen
Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, https://doi.org/10.5194/gmd-10-433-2017, 2017
Short summary
Short summary
A simple analytic description of aerosol optical properties and their main effects on clouds is developed and described. The analytic description is easy to use and easy to modify and should aid experimentation to help understand how aerosol radiative and cloud interactions effect climate and circulation. The climatology is recommended for adoption by models participating in the sixth phase of the Coupled Model Intercomparison Project.
Matthew J. Carmichael, Daniel J. Lunt, Matthew Huber, Malte Heinemann, Jeffrey Kiehl, Allegra LeGrande, Claire A. Loptson, Chris D. Roberts, Navjit Sagoo, Christine Shields, Paul J. Valdes, Arne Winguth, Cornelia Winguth, and Richard D. Pancost
Clim. Past, 12, 455–481, https://doi.org/10.5194/cp-12-455-2016, https://doi.org/10.5194/cp-12-455-2016, 2016
Short summary
Short summary
In this paper, we assess how well model-simulated precipitation rates compare to those indicated by geological data for the early Eocene, a warm interval 56–49 million years ago. Our results show that a number of models struggle to produce sufficient precipitation at high latitudes, which likely relates to cool simulated temperatures in these regions. However, calculating precipitation rates from plant fossils is highly uncertain, and further data are now required.
M. Tjernström, C. Leck, C. E. Birch, J. W. Bottenheim, B. J. Brooks, I. M. Brooks, L. Bäcklin, R. Y.-W. Chang, G. de Leeuw, L. Di Liberto, S. de la Rosa, E. Granath, M. Graus, A. Hansel, J. Heintzenberg, A. Held, A. Hind, P. Johnston, J. Knulst, M. Martin, P. A. Matrai, T. Mauritsen, M. Müller, S. J. Norris, M. V. Orellana, D. A. Orsini, J. Paatero, P. O. G. Persson, Q. Gao, C. Rauschenberg, Z. Ristovski, J. Sedlar, M. D. Shupe, B. Sierau, A. Sirevaag, S. Sjogren, O. Stetzer, E. Swietlicki, M. Szczodrak, P. Vaattovaara, N. Wahlberg, M. Westberg, and C. R. Wheeler
Atmos. Chem. Phys., 14, 2823–2869, https://doi.org/10.5194/acp-14-2823-2014, https://doi.org/10.5194/acp-14-2823-2014, 2014
E. Gasson, D. J. Lunt, R. DeConto, A. Goldner, M. Heinemann, M. Huber, A. N. LeGrande, D. Pollard, N. Sagoo, M. Siddall, A. Winguth, and P. J. Valdes
Clim. Past, 10, 451–466, https://doi.org/10.5194/cp-10-451-2014, https://doi.org/10.5194/cp-10-451-2014, 2014
M. D. Shupe, P. O. G. Persson, I. M. Brooks, M. Tjernström, J. Sedlar, T. Mauritsen, S. Sjogren, and C. Leck
Atmos. Chem. Phys., 13, 9379–9399, https://doi.org/10.5194/acp-13-9379-2013, https://doi.org/10.5194/acp-13-9379-2013, 2013
Related subject area
Subject: Climate Modelling | Archive: Modelling only | Timescale: Millenial/D-O
High-resolution LGM climate of Europe and the Alpine region using the regional climate model WRF
Does a difference in ice sheets between Marine Isotope Stages 3 and 5a affect the duration of stadials? Implications from hosing experiments
Impact of mid-glacial ice sheets on deep ocean circulation and global climate
A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP
Equilibrium simulations of Marine Isotope Stage 3 climate
Heinrich events show two-stage climate response in transient glacial simulations
Hosed vs. unhosed: interruptions of the Atlantic Meridional Overturning Circulation in a global coupled model, with and without freshwater forcing
The climate reconstruction in Shandong Peninsula, northern China, during the last millennium based on stalagmite laminae together with a comparison to δ18O
Variability of daily winter wind speed distribution over Northern Europe during the past millennium in regional and global climate simulations
Last interglacial model–data mismatch of thermal maximum temperatures partially explained
Hindcasting the continuum of Dansgaard–Oeschger variability: mechanisms, patterns and timing
Climatic impacts of fresh water hosing under Last Glacial Maximum conditions: a multi-model study
A mechanism for dust-induced destabilization of glacial climates
The climate in the Baltic Sea region during the last millennium simulated with a regional climate model
Role of CO2 and Southern Ocean winds in glacial abrupt climate change
Heinrich event 1: an example of dynamical ice-sheet reaction to oceanic changes
Weakened atmospheric energy transport feedback in cold glacial climates
Water vapour source impacts on oxygen isotope variability in tropical precipitation during Heinrich events
Glacial climate sensitivity to different states of the Atlantic Meridional Overturning Circulation: results from the IPSL model
Emmanuele Russo, Jonathan Buzan, Sebastian Lienert, Guillaume Jouvet, Patricio Velasquez Alvarez, Basil Davis, Patrick Ludwig, Fortunat Joos, and Christoph C. Raible
Clim. Past, 20, 449–465, https://doi.org/10.5194/cp-20-449-2024, https://doi.org/10.5194/cp-20-449-2024, 2024
Short summary
Short summary
We present a series of experiments conducted for the Last Glacial Maximum (~21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF) at convection-permitting resolutions. The model, with new developments better suited to paleo-studies, agrees well with pollen-based climate reconstructions. This agreement is improved when considering different sources of uncertainty. The effect of convection-permitting resolutions is also assessed.
Sam Sherriff-Tadano, Ayako Abe-Ouchi, Akira Oka, Takahito Mitsui, and Fuyuki Saito
Clim. Past, 17, 1919–1936, https://doi.org/10.5194/cp-17-1919-2021, https://doi.org/10.5194/cp-17-1919-2021, 2021
Short summary
Short summary
Glacial periods underwent climate shifts between warm states and cold states on a millennial timescale. Frequency of these climate shifts varied along time: it was shorter during mid-glacial period compared to early glacial period. Here, from climate simulations of early and mid-glacial periods with a comprehensive climate model, we show that the larger ice sheet in the mid-glacial compared to early glacial periods could contribute to the frequent climate shifts during the mid-glacial period.
Sam Sherriff-Tadano, Ayako Abe-Ouchi, and Akira Oka
Clim. Past, 17, 95–110, https://doi.org/10.5194/cp-17-95-2021, https://doi.org/10.5194/cp-17-95-2021, 2021
Short summary
Short summary
We perform simulations of Marine Isotope Stage 3 and 5a with an atmosphere–ocean general circulation model to explore the effect of the southward expansion of mid-glacial ice sheets on the Atlantic Meridional Overturning Circulation (AMOC) and climate. We find that the southward expansion of the mid-glacial ice sheet causes a surface cooling over the North Atlantic and Southern Ocean, but it exerts a small impact on the AMOC due to the competing effects of surface wind and surface cooling.
Martin Renoult, James Douglas Annan, Julia Catherine Hargreaves, Navjit Sagoo, Clare Flynn, Marie-Luise Kapsch, Qiang Li, Gerrit Lohmann, Uwe Mikolajewicz, Rumi Ohgaito, Xiaoxu Shi, Qiong Zhang, and Thorsten Mauritsen
Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, https://doi.org/10.5194/cp-16-1715-2020, 2020
Short summary
Short summary
Interest in past climates as sources of information for the climate system has grown in recent years. In particular, studies of the warm mid-Pliocene and cold Last Glacial Maximum showed relationships between the tropical surface temperature of the Earth and its sensitivity to an abrupt doubling of atmospheric CO2. In this study, we develop a new and promising statistical method and obtain similar results as previously observed, wherein the sensitivity does not seem to exceed extreme values.
Chuncheng Guo, Kerim H. Nisancioglu, Mats Bentsen, Ingo Bethke, and Zhongshi Zhang
Clim. Past, 15, 1133–1151, https://doi.org/10.5194/cp-15-1133-2019, https://doi.org/10.5194/cp-15-1133-2019, 2019
Short summary
Short summary
We present an equilibrium simulation of the climate of Marine Isotope Stage 3, with an IPCC-class model with a relatively high model resolution and a long integration. The simulated climate resembles a warm interstadial state, as indicated by reconstructions of Greenland temperature, sea ice extent, and AMOC. Sensitivity experiments to changes in atmospheric CO2 levels and ice sheet size show that the model is in a relatively stable climate state without multiple equilibria.
Florian Andreas Ziemen, Marie-Luise Kapsch, Marlene Klockmann, and Uwe Mikolajewicz
Clim. Past, 15, 153–168, https://doi.org/10.5194/cp-15-153-2019, https://doi.org/10.5194/cp-15-153-2019, 2019
Short summary
Short summary
Heinrich events are among the dominant modes of glacial climate variability. They are caused by massive ice discharges from the Laurentide Ice Sheet into the North Atlantic. In previous studies, the climate changes were either seen as resulting from freshwater released from the melt of the discharged icebergs or by ice sheet elevation changes. With a coupled ice sheet–climate model, we show that both effects are relevant with the freshwater effects preceding the ice sheet elevation effects.
Nicolas Brown and Eric D. Galbraith
Clim. Past, 12, 1663–1679, https://doi.org/10.5194/cp-12-1663-2016, https://doi.org/10.5194/cp-12-1663-2016, 2016
Short summary
Short summary
An Earth system model is used to explore variability in the global impacts of AMOC disruptions. The model exhibits spontaneous AMOC oscillations under particular boundary conditions, which we compare with freshwater-forced disruptions. We find that the global impacts are similar whether the AMOC disruptions are spontaneous or forced. Freshwater forcing generally amplifies the global impacts, with tropical precipitation and the stability of polar haloclines showing particular sensitivity.
Qing Wang, Houyun Zhou, Ke Cheng, Hong Chi, Chuan-Chou Shen, Changshan Wang, and Qianqian Ma
Clim. Past, 12, 871–881, https://doi.org/10.5194/cp-12-871-2016, https://doi.org/10.5194/cp-12-871-2016, 2016
Short summary
Short summary
The upper part of stalagmite ky1 (from top to 42.769 mm depth), consisting of 678 laminae, was collected from a cave in northern China, located in the East Asia monsoon area. The time of deposition ranges from AD 1217±20 to 1894±20. The analysis shows that both the variations in the thickness of the laminae themselves and the fluctuating degree of variation in the thickness of the laminae of stalagmite ky1 have obviously staged characteristics and synchronized with climate.
Svenja E. Bierstedt, Birgit Hünicke, Eduardo Zorita, Sebastian Wagner, and Juan José Gómez-Navarro
Clim. Past, 12, 317–338, https://doi.org/10.5194/cp-12-317-2016, https://doi.org/10.5194/cp-12-317-2016, 2016
P. Bakker and H. Renssen
Clim. Past, 10, 1633–1644, https://doi.org/10.5194/cp-10-1633-2014, https://doi.org/10.5194/cp-10-1633-2014, 2014
L. Menviel, A. Timmermann, T. Friedrich, and M. H. England
Clim. Past, 10, 63–77, https://doi.org/10.5194/cp-10-63-2014, https://doi.org/10.5194/cp-10-63-2014, 2014
M. Kageyama, U. Merkel, B. Otto-Bliesner, M. Prange, A. Abe-Ouchi, G. Lohmann, R. Ohgaito, D. M. Roche, J. Singarayer, D. Swingedouw, and X Zhang
Clim. Past, 9, 935–953, https://doi.org/10.5194/cp-9-935-2013, https://doi.org/10.5194/cp-9-935-2013, 2013
B. F. Farrell and D. S. Abbot
Clim. Past, 8, 2061–2067, https://doi.org/10.5194/cp-8-2061-2012, https://doi.org/10.5194/cp-8-2061-2012, 2012
S. Schimanke, H. E. M. Meier, E. Kjellström, G. Strandberg, and R. Hordoir
Clim. Past, 8, 1419–1433, https://doi.org/10.5194/cp-8-1419-2012, https://doi.org/10.5194/cp-8-1419-2012, 2012
R. Banderas, J. Álvarez-Solas, and M. Montoya
Clim. Past, 8, 1011–1021, https://doi.org/10.5194/cp-8-1011-2012, https://doi.org/10.5194/cp-8-1011-2012, 2012
J. Álvarez-Solas, M. Montoya, C. Ritz, G. Ramstein, S. Charbit, C. Dumas, K. Nisancioglu, T. Dokken, and A. Ganopolski
Clim. Past, 7, 1297–1306, https://doi.org/10.5194/cp-7-1297-2011, https://doi.org/10.5194/cp-7-1297-2011, 2011
I. Cvijanovic, P. L. Langen, and E. Kaas
Clim. Past, 7, 1061–1073, https://doi.org/10.5194/cp-7-1061-2011, https://doi.org/10.5194/cp-7-1061-2011, 2011
S. C. Lewis, A. N. LeGrande, M. Kelley, and G. A. Schmidt
Clim. Past, 6, 325–343, https://doi.org/10.5194/cp-6-325-2010, https://doi.org/10.5194/cp-6-325-2010, 2010
M. Kageyama, J. Mignot, D. Swingedouw, C. Marzin, R. Alkama, and O. Marti
Clim. Past, 5, 551–570, https://doi.org/10.5194/cp-5-551-2009, https://doi.org/10.5194/cp-5-551-2009, 2009
Cited articles
Abe-Ouchi, A., Saito, F., Kageyama, M., Braconnot, P., Harrison, S. P.,
Lambeck, K., Otto-Bliesner, B. L., Peltier, W., Tarasov, L., Peterschmitt,
J.-Y., and Takahashi, K.: Ice-sheet configuration in the CMIP5/PMIP3 Last
Glacial Maximum experiments, Geosci.Model Dev., 8, 3621–3637,
https://doi.org/10.5194/gmd-8-3621-2015, 2015. a, b
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. a
Annan, J. D., Hargreaves, J. C., and Mauritsen, T.: A new global surface temperature reconstruction for the Last Glacial Maximum, Clim. Past, 18, 1883–1896, https://doi.org/10.5194/cp-18-1883-2022, 2022. a, b, c, d
Armour, K. C., Bitz, C. M., and Roe, G. H.: Time-varying climate sensitivity
from regional feedbacks, J. Climate, 26, 4518–4534, 2013. a
Arrhenius, S.: XXXI. On the influence of carbonic acid in the air upon the
temperature of the ground, The London, Edinburgh, and Dublin Philosophical
Magazine and Journal of Science, 41, 237–276, 1896. a
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. a
Böhm, E., Lippold, J., Gutjahr, M., Frank, M., Blaser, P., Antz, B.,
Fohlmeister, J., Frank, N., Andersen, M., and Deininger, M.: Strong and deep
Atlantic meridional overturning circulation during the last glacial cycle,
Nature, 517, 73–76, https://doi.org/10.1038/nature14059, 2015. a, b
Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S.,
Dufresne, J.-L., Hall, A., Hallegatte, S., Holland, M. M., Ingram, W.,
Randall, D., Soden, B., Tselioudis, G., and Webb, M.: How well do we
understand and evaluate climate change feedback processes?, J. Climate, 19, 3445–3482, https://doi.org/10.1175/JCLI3819.1, 2006. a
Braconnot, P., Otto-Bliesner, B., Harrison, S., Joussaume, S., Peterchmitt, J.-Y., Abe-Ouchi, A., Crucifix, M., Driesschaert, E., Fichefet, Th., Hewitt, C. D., Kageyama, M., Kitoh, A., Laîné, A., Loutre, M.-F., Marti, O., Merkel, U., Ramstein, G., Valdes, P., Weber, S. L., Yu, Y., and Zhao, Y.: Results of PMIP2 coupled simulations of the Mid-Holocene and Last Glacial Maximum – Part 1: experiments and large-scale features, Clim. Past, 3, 261–277, https://doi.org/10.5194/cp-3-261-2007, 2007. a, b, c
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J., Masson-Delmotte, V., Abe-Ouchi, A., Otto-Bliesner, B., and Zhao, Y.: Evaluation of climate models using palaeoclimatic data, Nat. Clim. Change, 2, 417–424, https://doi.org/10.1038/nclimate1456, 2012. a, b
Brady, E. C., Otto-Bliesner, B. L., Kay, J. E., and Rosenbloom, N.: Sensitivity to glacial forcing in the CCSM4, J. Climate, 26, 1901–1925,
https://doi.org/10.1175/JCLI-D-11-00416.1, 2013. a, b
Bryan, K.: Accelerating the convergence to equilibrium of ocean-climate models, J. Phys. Oceanogr., 14, 666–673,
https://doi.org/10.1175/1520-0485(1984)014<0666:ATCTEO>2.0.CO;2, 1984. a
Burls, N. and Fedorov, A.: What controls the mean east–west sea surface
temperature gradient in the equatorial Pacific: The role of cloud albedo, J. Climate, 27, 2757–2778, 2014. a
Burls, N. J., Bradshaw, C., De Boer, A. M., Herold, N., Huber, M., Pound, M.,
Donnadieu, Y., Farnsworth, A., Frigola, A., Gasson, E., von der Heydt, A. S.,
Hutchinson, D. K., Knorr, G., Lawrence, K. T., Lear, C. H., Li, X., Lohmann,
G., Lunt, D. J., Marzocchi, A., Prange, M., Riihimaki, C. A., Sarr, A.-C.,
Siler, N., and Zhang, Z.: Simulating Miocene warmth: insights from an opportunistic Multi-Model ensemble (MioMIP1), Paleoceanogr. Paleocl., 36, e2020PA004054, https://doi.org/10.1029/2020PA004054, 2021. a
Burton, L. E., Haywood, A. M., Tindall, J. C., Dolan, A. M., Hill, D. J., Abe-Ouchi, A., Chan, W.-L., Chandan, D., Feng, R., Hunter, S. J., Li, X., Peltier, W. R., Tan, N., Stepanek, C., and Zhang, Z.: On the climatic influence of CO2 forcing in the Pliocene, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-90, in review, 2022. a, b
Caballero, R. and Huber, M.: State-dependent climate sensitivity in past warm
climates and its implications for future climate projections, P. Natl. Acad. Sci. USA, 110, 14162–14167, 2013. a
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. a
Ceppi, P. and Gregory, J. M.: Relationship of tropospheric stability to climate sensitivity and Earth’s observed radiation budget, P. Natl. Acad. Sci. USA, 114, 13126–13131, 2017. a
Cesana, G., Waliser, D., Jiang, X., and Li, J.-L.: Multimodel evaluation of
cloud phase transition using satellite and reanalysis data, J. Geophys. Res.-Atmos., 120, 7871–7892, https://doi.org/10.1002/2014JD022932, 2015. a
Chalmers, J., Kay, J. E., Middlemas, E. A., Maroon, E. A., and DiNezio, P.:
Does disabling cloud radiative feedbacks change spatial patterns of surface
greenhouse warming and cooling?, J. Climate, 35, 1787–1807, https://doi.org/10.1175/JCLI-D-21-0391.1, 2022. a
Clark, P. and Mix, A.: Ice sheets and sea level of the Last Glacial Maximum,
Quaternary Sci. Rev., 21, 1–7, https://doi.org/10.1016/S0277-3791(01)00118-4, 2002. a
Cleator, S. F., Harrison, S. P., Nichols, N. K., Prentice, I. C., and Roulstone, I.: A new multivariable benchmark for Last Glacial Maximum climate simulations, Clim. Past, 16, 699–712, https://doi.org/10.5194/cp-16-699-2020, 2020. a
Colman, R. and McAvaney, B.: A study of general circulation model climate
feedbacks determined from perturbed sea surface temperature experiments, J. Geophys. Res.-Atmos., 102, 19383–19402, https://doi.org/10.1029/97JD00206, 1997. a
Covey, C., Abe-Ouchi, A., Boer, G., Boville, B., Cubasch, U., Fairhead, L.,
Flato, G., 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. a
Cubasch, U., Meehl, G. A., Boer, G. J., Stouffer, R. J., Diz, M., Noda, A., Senior, C. A., Raper, S., and Yap, K. S.: Projections of future climate change, in: Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K., and Johnson, C. A., Cambridge University Press, UK, 944 pp., ISBN 0521014956, 2001. a
Cuffey, K. M. and Paterson, W. S. B.: The physics of glaciers, Academic Press, ISBN 978-0-12-369461-4, 2010. a
Dowsett, H. J., Robinson, M. M., and Foley, K. M.: Pliocene three-dimensional
global ocean temperature reconstruction, Clim. Past, 5, 769–783,
https://doi.org/10.5194/cp-5-769-2009, 2009. a
ESGF: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data, Future Generat. Comput. Syst., 36, 400–417, https://doi.org/10.1016/j.future.2013.07.002, 2014 a
Essery, R., Rutter, N., Pomeroy, J., Baxter, R., Stähli, M., Gustafsson,
D., Barr, A., Bartlett, P., and Elder, K.: SNOWMIP2: An evaluation of forest
snow process simulations, B. Am. Meteorol. Soc., 90, 1120–1136, https://doi.org/10.1175/2009BAMS2629.1, 2009. a
Etminan, M., Myhre, G., Highwood, E., and Shine, K.: Radiative forcing of
carbon dioxide, methane, and nitrous oxide: A significant revision of the
methane radiative forcing, Geophys. Res. Lett., 43, 614–623, https://doi.org/10.1002/2016GL071930, 2016. a
Fletcher, C. G., Thackeray, C. W., and Burgers, T. M.: Evaluating biases in
simulated snow albedo feedback in two generations of climate models, J. Geophys. Res.-Atmos., 120, 12–26, https://doi.org/10.1002/2014JD022546, 2015. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J., Frame, D.,
Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.:
The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, in:
Climate Change 2021: The Physical Science Basis, Contribution of Working
Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, Cambridge University Press, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf (last access: 31 January 2023), 2021. a, b, c
Gallée, H., Van Yperselb, J., Fichefet, T., Marsiat, I., Tricot, C., and
Berger, A.: Simulation of the last glacial cycle by a coupled, sectorially
averaged climate-ice sheet model: 2. Response to insolation and CO2
variations, J. Geophys. Res.-Atmos., 97, 15713–15740, 1992. a
Ganopolski, A., Petoukhov, V., Rahmstorf, S., Brovkin, V., Claussen, M.,
Eliseev, A., and Kubatzki, C.: CLIMBER-2: a climate system model of
intermediate complexity. Part II: model sensitivity, Clim. Dynam., 17,
735–751, 2001. a
Gettelman, A., Mills, M., Kinnison, D., Garcia, R., Smith, A., Marsh, D.,
Tilmes, S., Vitt, F., Bardeen, C., McInerny, J., Liu, H.-L., Solomon, S.,
Polvani, L., Emmons, L., Lamarque, J.-F., Richter, J., Glanville, A.,
Bacmeister, J., Philips, A., Neale, R., Simpson, I., DuVivier, A., Hodzic,
A., and Randel, W.: The whole atmosphere community climate model version 6 (WACCM6), J. Geophys. Res.-Atmos., 124, 12380–12403, https://doi.org/10.1029/2019JD030943, 2019. a
Gregoire, L. J., Valdes, P. J., Payne, A. J., and Kahana, R.: Optimal tuning of a GCM using modern and glacial constraints, Clim. Dynam., 37, 705–719,
https://doi.org/10.1007/s00382-010-0934-8, 2011. a
Gregory, J., Bloch-Johnson, J., Couldrey, M., Exarchou, E., Griffies, S., Kuhlbrodt, T., Saenko, O., Suzuki, T., Wu, Q., and Zanna, L.: A new conceptual model of global ocean heat uptake and transient climate response, in preparation, 2023. a
Gregory, J. M., Ingram, W., Palmer, M., Jones, G., Stott, P., Thorpe, R., Lowe, J., Johns, T., and Williams, K.: A new method for diagnosing radiative
forcing and climate sensitivity, Geophys. Res. Lett., 31, L03205, https://doi.org/10.1029/2003GL018747, 2004. a, b
Gulev, S., Thorne, P., Ahn, J., Dentener, F., Domingues, C., Gerland, S., Gong, D., Kaufman, D., Nnamchi, H., Quaas, J., Rivera, J., Sathyendranath, S., Smith, S., Trewin, B., von Shuckmann, K., and Vose, R.: Changing State of the Climate System, in: Climate Change 2021: The Physical Science Basis,
Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter01.pdf (last access: 31 January 2023), 2021. a
Hall, A. and Qu, X.: Using the current seasonal cycle to constrain snow albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502, https://doi.org/10.1029/2005GL025127, 2006. a
Haney, R. L.: Surface thermal boundary condition for ocean circulation models, J. Phys. Oceanogr., 1, 241–248,
https://doi.org/10.1175/1520-0485(1971)001<0241:STBCFO>2.0.CO;2, 1971. a
Hargreaves, J. and Annan, J.: On the importance of paleoclimate modelling for
improving predictions of future climate change, Clim. Past, 5, 803–814, https://doi.org/10.5194/cp-5-803-2009, 2009. a
Hargreaves, J. C., Abe-Ouchi, A., and Annan, J. D.: Linking glacial and future climates through an ensemble of GCM simulations, Clim. Past, 3,
77–87, https://doi.org/10.5194/cp-3-77-2007, 2007. a, b
Haywood, A. M., Dowsett, H. J., Robinson, M. M., Stoll, D. K., Dolan, A. M.,
Lunt, D. J., Otto-Bliesner, B., and Chandler, M. A.: Pliocene Model
Intercomparison Project (PlioMIP): experimental design and boundary conditions (Experiment 2), Geosci. Model Dev., 4, 571–577,
https://doi.org/10.5194/gmd-4-571-2011, 2011. a
Haywood, A. M., Tindall, J. C., Dowsett, H. J., Dolan, A. M., Foley, K. M., Hunter, S. J., Hill, D. J., Chan, W.-L., Abe-Ouchi, A., Stepanek, C., Lohmann, G., Chandan, D., Peltier, W. R., Tan, N., Contoux, C., Ramstein, G., Li, X., Zhang, Z., Guo, C., Nisancioglu, K. H., Zhang, Q., Li, Q., Kamae, Y., Chandler, M. A., Sohl, L. E., Otto-Bliesner, B. L., Feng, R., Brady, E. C., von der Heydt, A. S., Baatsen, M. L. J., and Lunt, D. J.: The Pliocene Model Intercomparison Project Phase 2: large-scale climate features and climate sensitivity, Clim. Past, 16, 2095–2123, https://doi.org/10.5194/cp-16-2095-2020, 2020. a, b
Hewitt, C., Stouffer, R., Broccoli, A., Mitchell, J., and Valdes, P. J.: The
effect of ocean dynamics in a coupled GCM simulation of the Last Glacial
Maximum, Clim. Dynam., 20, 203–218, https://doi.org/10.1007/s00382-002-0272-6, 2003. a
Huusko, L. L., Bender, F. A., Ekman, A. M., and Storelvmo, T.: Climate
sensitivity indices and their relation with projected temperature change in
CMIP6 models, Environ. Res. Lett., 16, 064095, https://doi.org/10.1088/1748-9326/ac0748, 2021. a
IPSL: Index of /pmip1db, IPSL [data set], http://dods.lsce.ipsl.fr/pmip1db/ (last access: 31 January 2023), 2023a. a
IPSL: Index of /pmip2_dbext, IPSL [data set], http://dods.lsce.ipsl.fr/pmip2_dbext/ (last access: 31 January 2023), 2023b. a
IPSL: Index of /pmip4/db, IPSL [data set], http://dods.lsce.ipsl.fr/pmip4/db/ (last access: 31 January 2023), 2023c. a
Jackson, L., Kahana, R., Graham, T., Ringer, M., Woollings, T., Mecking, J.,
and Wood, R.: Global and European climate impacts of a slowdown of the AMOC
in a high resolution GCM, Clim. Dynam., 45, 3299–3316,
https://doi.org/10.1007/s00382-015-2540-2, 2015. a, b
Jansen, M. F., Nadeau, L.-P., and Merlis, T. M.: Transient versus equilibrium
response of the ocean’s overturning circulation to warming, J. Climate, 31, 5147–5163, 2018. a
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. a, b
Kageyama, M., Albani, S., Braconnot, P., Harrison, S. P., Hopcroft, P. O.,
Ivanovic, R. F., Lambert, F., Marti, O., Peltier, W. R., Peterschmitt, J.-Y.,
Roche, D. M., Tarasov, L., Zhang, X., Brady, E. C., Haywood, A. M., LeGrande,
A. N., Lunt, D. J., Mahowald, N. M., Mikolajewicz, U., Nisancioglu, K. H.,
Otto-Bliesner, B. L., Renssen, H., Tomas, R. A., Zhang, Q., Abe-Ouchi, A.,
Bartlein, P. J., Cao, J., Li, Q., Lohmann, G., Ohgaito, R., Shi, X., Volodin,
E., Yoshida, K., Zhang, X., and Zheng, W.: The PMIP4 contribution to
CMIP6 – Part 4: scientific objectives and experimental design of the
PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity
experiments, Geosci. Model Dev., 10, 4035–4055,
https://doi.org/10.5194/gmd-10-4035-2017, 2017. a, b, c
Kageyama, M., Harrison, S. P., Kapsch, M.-L., Lofverstrom, M., Lora, J. M.,
Mikolajewicz, U., Sherriff-Tadano, S., Vadsaria, T., Abe-Ouchi, A., Bouttes,
N., Deepak, C., Gregoire, L. J., Ivanovic, R. F., Izumi, K., LeGrande, A. N.,
Lhardy, F., Lohmann, G., Morozova, P. A., Ohgaito, R., Paul, A., Peltier, W.,
Poulsen, C. J., Quiquet, A., Roche, D. M., Shi, X., Tierney, J. E., Valdes,
P. J., Volodin, E., and Zhu, J.: The PMIP4 Last Glacial Maximum experiments:
preliminary results and comparison with the PMIP3 simulations, Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z
Kleman, J., Fastook, J., Ebert, K., Nilsson, J., and Caballero, R.: Pre-LGM
Northern Hemisphere ice sheet topography, Clim. Past, 9, 2365–2378,
https://doi.org/10.5194/cp-9-2365-2013, 2013. a
Köhler, P., Bintanja, R., Fischer, H., Joos, F., Knutti, R., Lohmann, G.,
and Masson-Delmotte, V.: What caused Earth's temperature variations during
the last 800,000 years? Data-based evidence on radiative forcing and
constraints on climate sensitivity, Quaternary Sci. Rev., 29, 129–145,
https://doi.org/10.1016/j.quascirev.2009.09.026, 2010. a
Komurcu, M., Storelvmo, T., Tan, I., Lohmann, U., Yun, Y., Penner, J. E., Wang, Y., Liu, X., and Takemura, T.: Intercomparison of the cloud water phase among global climate models, J. Geophys. Res.-Atmospheres, 119, 3372–3400, https://doi.org/10.1002/2013JD021119, 2014. a, b
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-phase clouds: Progress and challenges, Meteorol. Monogr., 58, 5–1, 2017. a
Lee, J., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J., Engelbrecht,
F., Fischer, E., Fyfe, J., Jones, C., Maycock, A., Mutemi, J., Ndiaye, O.,
Panickal, S., and Zhou, T.: Scenario-Based Projections and Near-Term
Information, in: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter04.pdf (last access: 31 January 2023), 2021. a, b
Liakka, J. and Löfverström, M.: Arctic warming induced by the Laurentide Ice Sheet topography, Clim. Past, 14, 887–900,
https://doi.org/10.5194/cp-14-887-2018, 2018. a
Lippold, J., Luo, Y., Francois, R., Allen, S. E., Gherardi, J., Pichat, S.,
Hickey, B., and Schulz, H.: Strength and geometry of the glacial Atlantic
Meridional Overturning Circulation, Nat. Geosci., 5, 813–816,
https://doi.org/10.1038/ngeo1608, 2012. a, b
Lippold, J., Gutjahr, M., Blaser, P., Christner, E., de Carvalho Ferreira,
M. L., Mulitza, S., Christl, M., Wombacher, F., Böhm, E., Antz, B.,
Cartapanis, O., Vogel, H., and Jaccard, S. L.: Deep water provenance and
dynamics of the (de)glacial Atlantic meridional overturning circulation,
Earth Planet. Sc. Lett., 445, 68–78, https://doi.org/10.1016/j.epsl.2016.04.013, 2016. a, b
Liu, Z., Zhu, J., Rosenthal, Y., Zhang, X., Otto-Bliesner, B. L., Timmermann,
A., Smith, R. S., Lohmann, G., Zheng, W., and Timm, O. E.: The Holocene
temperature conundrum, P. Natl. Acad. Sci. USA, 111, E3501–E3505, https://doi.org/10.1073/pnas.1407229111, 2014. a
Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018. a
Loulergue, L., Schilt, A., Spahni, R., Masson-Delmotte, V., Blunier, T.,
Lemieux, B., Barnola, J.-M., Raynaud, D., Stocker, T. F., and Chappellaz, J.:
Orbital and millennial-scale features of atmospheric CH4 over the past 800,000 years, Nature, 453, 383–386, https://doi.org/10.1038/nature06950, 2008. a
Lunt, D. J., Haywood, A. M., Schmidt, G. A., Salzmann, U., Valdes, P. J.,
Dowsett, H. J., and Loptson, C. A.: On the causes of mid-Pliocene warmth and
polar amplification, Earth Planet. Sc. Lett., 321, 128–138, 2012. a
Lynch-Stieglitz, J.: The Atlantic meridional overturning circulation and abrupt climate change, Annu. Rev. Mar. Sci., 9, 83–104, 2017. a
Lynch-Stieglitz, J., Adkins, J. F., Curry, W. B., Dokken, T., Hall, I. R.,
Herguera, J. C., Hirschi, J. J.-M., Ivanova, E. V., Kissel, C., Marchal, O.,
Marchitto, T. M., McCave, I. N., McManus, J. F., Mulitza, S., Ninnemann, U., Peeters, F., Yu, E.-F., and Zahn, R.: Atlantic meridional overturning circulation during the Last Glacial Maximum, Science, 316, 66–69, 2007. a
Marzocchi, A. and Jansen, M. F.: Connecting Antarctic sea ice to deep-ocean
circulation in modern and glacial climate simulations, Geophys. Res. Lett., 44, 6286–6295, https://doi.org/10.1002/2017GL073936, 2017. a, b, c, d
Mauritsen, T.: Clouds cooled the Earth, Nat. Geosci., 9, 865–867, 2016. a
Mauritsen, T. and Roeckner, E.: Tuning the MPI-ESM1.2 global climate model to improve the match with instrumental record warming by lowering its climate
sensitivity, J. Adv. Model. Earth Syst., 12, e2019MS002037, https://doi.org/10.1029/2019MS002037, 2020. a
Mauritsen, T. and Stevens, B.: Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models, Nat.
Geosci., 8, 346–351, https://doi.org/10.1038/ngeo2414, 2015. a
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 Syst., 4, M00A01, https://doi.org/10.1029/2012MS000154, 2012. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S. S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1. 2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst., 11, 998–1038,
https://doi.org/10.1029/2018MS001400, 2019. a, b, c, d, e
McManus, J. F., Francois, R., Gherardi, J.-M., Keigwin, L. D., and Brown-Leger, S.: Collapse and rapid resumption of Atlantic meridional circulation linked to deglacial climate changes, Nature, 428, 834–837,
https://doi.org/10.1038/nature02494, 2004. a
Meehl, G. A., Collins, W. D., Boville, B. A., Kiehl, J. T., Wigley, T., and
Arblaster, J. M.: Response of the NCAR Climate System Model to increased CO2 and the role of physical processes, J. Climate, 13, 1879–1898, 2000. a
Meraner, K., Mauritsen, T., and Voigt, A.: Robust increase in equilibrium
climate sensitivity under global warming, Geophys. Res. Lett., 40, 5944–5948, https://doi.org/10.1002/2013GL058118, 2013. a
Muglia, J. and Schmittner, A.: Glacial Atlantic overturning increased by wind
stress in climate models, Geophys. Res. Lett., 42, 9862–9868,
https://doi.org/10.1002/2015GL064583, 2015. a, b, c
Noda, S., Kodera, K., Adachi, Y., Deushi, M., Kitoh, A., Mizuta, R., Murakami, S., Yoshida, K., and Yoden, S.: Mitigation of global cooling by stratospheric chemistry feedbacks in a simulation of the Last Glacial Maximum, J. Geophys. Res.-Atmos., 123, 9378–9390, 2018. a
Ohgaito, R., Yamamoto, A., Hajima, T., O'ishi, R., Abe, M., Tatebe, H.,
Abe-Ouchi, A., and Kawamiya, M.: PMIP4 experiments using MIROC-ES2L Earth
system model, Geosci. Model Dev., 14, 1195–1217, https://doi.org/10.5194/gmd-14-1195-2021, 2021. a
Otto-Bliesner, B., Hewitt, C., Marchitto, T., Brady, E., Abe-Ouchi, A.,
Crucifix, M., Murakami, S., and Weber, S.: Last Glacial Maximum ocean
thermohaline circulation: PMIP2 model intercomparisons and data constraints,
Geophys. Res. Lett., 34, L12706, https://doi.org/10.1029/2007GL029475, 2007. a, b, c
PALAEOSENS Project Members: Making sense of palaeoclimate sensitivity, Nature, 491, 683–691, https://doi.org/10.1038/nature11574, 2012. a, b, c
Peltier, W. R.: Ice age paleotopography, Science, 265, 195–201,
https://doi.org/10.1126/science.265.5169.195, 1994. a
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. a
Rahmstorf, S.: Ocean circulation and climate during the past 120,000 years,
Nature, 419, 207–214, https://doi.org/10.1038/nature01090, 2002. a, b
Ray, N. and Adams, J.: A GIS-based vegetation map of the world at the last
glacial maximum (25,000–15,000 BP), Internet Archaeol., 11, https://doi.org/10.11141/ia.11.2, 2001. a
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. a, b, c, d, e, f, g, h, i, j, k, l, m
Renoult, M., Sagoo, N., and Mauritsen, T.: High-biased climate sensitivity estimates from mid-Pliocene Warm Period temperatures, in preparation, 2023. a
Sagoo, N.: Using the Last Glacial Maximum to rule out high sensitivity models, in preparation, 2023. a
Sagoo, N. and Storelvmo, T.: Testing the sensitivity of past climates to the
indirect effects of dust, Geophys. Res. Lett., 44, 5807–5817,
https://doi.org/10.1002/2017GL072584, 2017. a
Schmidt, G., Annan, J., Bartlein, P., Cook, B., Guilyardi, É., Hargreaves, J., Harrison, S., Kageyama, M., LeGrande, A., 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. a, b, c, d, e
Shakun, J. D.: Modest global-scale cooling despite extensive early Pleistocene ice sheets, Quaternary Sci. Rev., 165, 25–30, https://doi.org/10.1016/j.quascirev.2017.04.010, 2017. a, b, c
Sherriff-Tadano, S., Abe-Ouchi, A., Yoshimori, M., Oka, A., and Chan, W.-L.:
Influence of glacial ice sheets on the Atlantic meridional overturning
circulation through surface wind change, Clim. Dynam., 50, 2881–2903,
https://doi.org/10.1007/s00382-017-3780-0, 2018. a, b
Sherwood, S., Webb, M., Annan, J., Armour, K., Forster, P., Hargreaves, J.,
Hegerl, G., Klein, S., Marvel, K., Rohling, E., Watanabe, M., Andrews, T.,
Braconnot, P., Bretherton, C., Foster, G., Hausfather, Z., von der Heydt, A.,
Knutti, R., Mauritsen, T., Norris, J., Proistosescu, C., Rugenstein, M.,
Schmidt, G., Tokarska, K., and Zelinka, M.: An assessment of Earth's climate
sensitivity using multiple lines of evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a, b, c
Shin, S.-I., Liu, Z., Otto-Bliesner, B., Brady, E., Kutzbach, J., and Harrison, S.: A simulation of the Last Glacial Maximum climate using the NCAR-CCSM, Clim. Dynam., 20, 127–151, https://doi.org/10.1007/s00382-002-0260-x, 2003. a
Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., and Shields, C. A.: Quantifying climate feedbacks using radiative kernels, J.
Climate, 21, 3504–3520, 2008. a
Steinthorsdottir, M., Coxall, H., De Boer, A., Huber, M., Barbolini, N.,
Bradshaw, C., Burls, N., Feakins, S., Gasson, E., Henderiks, J., Holbourn, A. E., Kiel, S., John, M. J., Knorr, G., Kürschner, W. M., Lear, C. H.,
Liebrand, D., Lunt, D. J., Mörs, T., Pearson, P. N., Pound, M. J., Stoll,
H., and Strömberg, C. A. E.: The Miocene: the future of the past,
Paleoceanogr. Paleocl., 36, e2020PA004037, https://doi.org/10.1029/2020PA004037, 2021. a
Sundqvist, H., Berge, E., and Kristjánsson, J. E.: Condensation and cloud
parameterization studies with a mesoscale numerical weather prediction model,
Mon. Weather Rev., 117, 1641–1657, 1989. a
Svendsen, J. I., Alexanderson, H., Astakhov, V. I., Demidov, I., Dowdeswell,
J. A., Funder, S., Gataullin, V., Henriksen, M., Hjort, C., Houmark-Nielsen,
M., Hubberten, H. W.,Ingólfsson, Ó., Jakobsson, M., Kjær, K. H.,
Larsen, E., Lokrantz, H., Lunkka, J. P., Lyså, A., Mangerud, J., Matiouchkov, A., Murray, A., Möller, P., Niessen, F., Nikolskaya, O.,
Polyak, L., Saarnisto, M., Siegert, C., Siegert, M. J., Spielhagen, R. F.,
and Stein, R.: Late Quaternary ice sheet history of northern Eurasia,
Quaternary Sci. Rev., 23, 1229–1271, https://doi.org/10.1016/j.quascirev.2003.12.008, 2004. a
Tan, I. and Storelvmo, T.: Sensitivity study on the influence of cloud
microphysical parameters on mixed-phase cloud thermodynamic phase partitioning in CAM5, J. Atmos. Sci., 73, 709–728, 2016. a
Thackeray, C. W., Qu, X., and Hall, A.: Why Do Models Produce Spread in Snow
Albedo Feedback?, Geophys. Res. Lett., 45, 6223–6231,
https://doi.org/10.1029/2018GL078493, 2018. a, b
Thompson, S. L. and Pollard, D.: A global climate model (GENESIS) with a
land-surface transfer scheme (LSX). Part II: CO2 sensitivity, J.
Climate, 8, 1104–1121, https://doi.org/10.1175/1520-0442(1995)008<1104:AGCMWA>2.0.CO;2, 1995. a
Thompson, S. L. and Pollard, D.: Greenland and Antarctic mass balances for
present and doubled atmospheric CO2 from the GENESIS version-2 global climate model, J. Climate, 10, 871–900,
https://doi.org/10.1175/1520-0442(1997)010<0871:GAAMBF>2.0.CO;2, 1997. a, b
Treut, H. L., Li, Z., and Forichon, M.: Sensitivity of the LMD general
circulation model to greenhouse forcing associated with two different cloud
water parameterizations, J. Climate, 7, 1827–1841, 1994. a
Valdes, P. J., Beerling, D. J., and Johnson, C. E.: The ice age methane budget, Geophys. Res. Lett., 32, L02704, https://doi.org/10.1029/2004GL021004, 2005. a
von der Heydt, A. S., Köhler, P., van de Wal, R. S., and Dijkstra, H. A.:
On the state dependency of fast feedback processes in (paleo) climate
sensitivity, Geophys. Res. Lett., 41, 6484–6492, https://doi.org/10.1002/2014GL061121, 2014. a
Webb, M. J., Senior, C., Sexton, D., Ingram, W., Williams, K., Ringer, M.,
McAvaney, B., Colman, R., Soden, B., Gudgel, R., Knutson, T., Emori, S.,
Ogura, T., Tsushima, Y., Andronova, N., Li, B., Musat, I., Bony, S., and
Taylor, K.: On the contribution of local feedback mechanisms to the range of
climate sensitivity in two GCM ensembles, Clim. Dynam., 27, 17–38,
https://doi.org/10.1007/s00382-006-0111-2, 2006. a
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S.,
Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S.,
Marchand, R., Medeiros, B., Siebesma, A., Skinner, C., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The cloud feedback model
intercomparison project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017. a
Weber, S. L., Drijfhout, S. S., Abe-Ouchi, A., Crucifix, M., Eby, M.,
Ganopolski, A., Murakami, S., Otto-Bliesner, B., and Peltier, W. R.: The
modern and glacial overturning circulation in the Atlantic ocean in PMIP
coupled model simulations, Clim. Past, 3, 51–64, https://doi.org/10.5194/cp-3-51-2007, 2007. a
Weiffenbach, J. E., Baatsen, M. L. J., Dijkstra, H. A., von der Heydt, A. S., Abe-Ouchi, A., Brady, E. C., Chan, W.-L., Chandan, D., Chandler, M. A., Contoux, C., Feng, R., Guo, C., Han, Z., Haywood, A. M., Li, Q., Li, X., Lohmann, G., Lunt, D. J., Nisancioglu, K. H., Otto-Bliesner, B. L., Peltier, W. R., Ramstein, G., Sohl, L. E., Stepanek, C., Tan, N., Tindall, J. C., Williams, C. J. R., Zhang, Q., and Zhang, Z.: Unraveling the mechanisms and implications of a stronger mid-Pliocene Atlantic Meridional Overturning Circulation (AMOC) in PlioMIP2, Clim. Past, 19, 61–85, https://doi.org/10.5194/cp-19-61-2023, 2023. a, b, c
Weijer, W., Cheng, W., Garuba, O. A., Hu, A., and Nadiga, B.: CMIP6 models
predict significant 21st century decline of the Atlantic Meridional
Overturning Circulation, Geophys. Res. Lett., 47, e2019GL086075, https://doi.org/10.1029/2019GL086075, 2020. a, b
Wetherald, R. and Manabe, S.: Cloud feedback processes in a general circulation model, J. Atmos. Sci., 45, 1397–1416,
https://doi.org/10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2, 1988. a
Williamson, D. L., Kiehl, J. T., Ramanathan, V., Dickinson, R. E., and Hack,
J. J.: Description of NCAR community climate model (CCM1), National Center
for Atmospheric Research, Boulder, Colorado, https://doi.org/10.5065/D6TB14WH, 1987. a
Yoshimori, M., Hargreaves, J. C., Annan, J. D., Yokohata, T., and Abe-Ouchi,
A.: Dependency of feedbacks on forcing and climate state in physics parameter
ensembles, J. Climate, 24, 6440–6455, https://doi.org/10.1175/2011JCLI3954.1, 2011. a, b
Yu, E.-F., Francois, R., and Bacon, M. P.: Similar rates of modern and
last-glacial ocean thermohaline circulation inferred from radiochemical data,
Nature, 379, 689–694, https://doi.org/10.1038/379689a0, 1996. a
Zhu, J., Liu, Z., Brady, E., Otto-Bliesner, B., Zhang, J., Noone, D., Tomas,
R., Nusbaumer, J., Wong, T., Jahn, A., and Tabor, C.: Reduced ENSO
variability at the LGM revealed by an isotope-enabled Earth system model,
Geophys. Res. Lett., 44, 6984–6992, https://doi.org/10.1002/2017GL073406, 2017. a
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E.,
Lofverstrom, M., and DiNezio, P.: Assessment of equilibrium climate
sensitivity of the Community Earth System Model version 2 through simulation
of the Last Glacial Maximum, Geophys. Res. Lett., 48, e2020GL091220, https://doi.org/10.1029/2020GL091220, 2021. a, b, c, d, e, f, g
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Gettelman, A., Bacmeister, J. T., Neale, R. B., Poulsen, C. J., Shaw, J. K., McGraw, Z. S., and Kay, J. E.: LGM paleoclimate constraints inform cloud parameterizations and equilibrium
climate sensitivity in CESM2, J. Adv. Model. Earth Syst., 14, e2021MS002776, https://doi.org/10.1029/2021MS002776, 2022a.
a, b, c, d, e
Zhu, J., Otto-Bliesner, B L., Garcia, R., Brady, E. C., Mills, M., Kinnison,
D., and Lamarque, J.-F.: Small impact of stratospheric dynamics and chemistry
on the surface temperature of the Last Glacial Maximum in CESM2 (WACCM6ma),
Geophys. Res. Lett., 49, e2022GL099875, https://doi.org/10.1029/2022GL099875, 2022b. a
Short summary
The relationship between the Last Glacial Maximum and the sensitivity of climate models to a doubling of CO2 can be used to estimate the true sensitivity of the Earth. However, this relationship has varied in successive model generations. In this study, we assess multiple processes at the Last Glacial Maximum which weaken this relationship. For example, how models respond to the presence of ice sheets is a large contributor of uncertainty.
The relationship between the Last Glacial Maximum and the sensitivity of climate models to a...
