Articles | Volume 21, issue 2
https://doi.org/10.5194/cp-21-381-2025
© Author(s) 2025. 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-21-381-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Testing the reliability of global surface temperature reconstructions of the Last Glacial Cycle with pseudo-proxy experiments
Jean-Philippe Baudouin
CORRESPONDING AUTHOR
Department of Geosciences, University of Tübingen, Tübingen, Germany
Nils Weitzel
Department of Geosciences, University of Tübingen, Tübingen, Germany
Maximilian May
Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany
Lukas Jonkers
MARUM Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
Andrew M. Dolman
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Potsdam, Germany
Kira Rehfeld
Department of Geosciences, University of Tübingen, Tübingen, Germany
Department of Physics, University of Tübingen, Tübingen, Germany
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Western disturbances (WDs) are storms that predominantly affect north India and Pakistan during the winter months, where they play an important role in regional water security, but can also bring a range of natural hazards. In this review, we summarise recent literature across a range of topics: their structure and lifecycle, precipitation and impacts, interactions with large-scale weather patterns, representation in models, how well they are forecast, and their response to changes in climate.
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Clim. Past, 19, 1043–1060, https://doi.org/10.5194/cp-19-1043-2023, https://doi.org/10.5194/cp-19-1043-2023, 2023
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Data–data and data–model vegetation comparisons are commonly based on comparing single vegetation estimates. While this approach generates good results on average, reducing pollen assemblages to single single plant functional type (PFT) or biome estimates can oversimplify the vegetation signal. We propose using a multivariate metric, the Earth mover's distance (EMD), to include more details about the vegetation structure when performing such comparisons.
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Elisa Ziegler, Nils Weitzel, Jean-Philippe Baudouin, Marie-Luise Kapsch, Uwe Mikolajewicz, Lauren Gregoire, Ruza Ivanovic, Paul J. Valdes, Christian Wirths, and Kira Rehfeld
Clim. Past, 21, 627–659, https://doi.org/10.5194/cp-21-627-2025, https://doi.org/10.5194/cp-21-627-2025, 2025
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During the Last Deglaciation, global surface temperature rose by about 4–7 °C over several millennia. We show that changes in year-to-year up to century-to-century fluctuations of temperature and precipitation during the Deglaciation were mostly larger than during either the preceding or succeeding more stable periods in 15 climate model simulations. The analysis demonstrates how ice sheets, meltwater, and volcanism influence simulated variability to inform future simulation protocols.
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Mara Y. McPartland, Thomas Münch, Andrew M. Dolman, Raphaël Hébert, and Thomas Laepple
Clim. Past Discuss., https://doi.org/10.5194/cp-2024-73, https://doi.org/10.5194/cp-2024-73, 2024
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Paleoclimate proxy records contain a combination of climate signals and non-climatic noise. This noise can affect year-to-year variations, or introduce uncertainty on medium and long timescales. Proxies contain different types, or "colors" of noise stemming from the diverse physical and biological processes that go into their creation. We show how non-climatic noise affects tree rings, corals and ice cores. We aim to improve representations of noise in paleoclimate research activities.
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Speleothems are a popular, multi-proxy climate archive that provide regional to global insights into past hydroclimate trends with precise chronologies. We present an update to the SISAL (Speleothem Isotopes
Synthesis and AnaLysis) database, SISALv3, which, for the first time, contains speleothem trace element records, in addition to an update to the stable isotope records available in previous versions of the database, cumulatively providing data from 365 globally distributed sites.
Synthesis and AnaLysis) database, SISALv3, which, for the first time, contains speleothem trace element records, in addition to an update to the stable isotope records available in previous versions of the database, cumulatively providing data from 365 globally distributed sites.
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Julie Christin Schindlbeck-Belo, Matthew Toohey, Marion Jegen, Steffen Kutterolf, and Kira Rehfeld
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Volcanic forcing of climate resulting from major explosive eruptions is a dominant natural driver of past climate variability. To support model studies of the potential impacts of explosive volcanism on climate variability across timescales, we present an ensemble reconstruction of volcanic stratospheric sulfur injection over the last 140 000 years that is based primarily on tephra records.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
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We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Manuel Chevalier, Anne Dallmeyer, Nils Weitzel, Chenzhi Li, Jean-Philippe Baudouin, Ulrike Herzschuh, Xianyong Cao, and Andreas Hense
Clim. Past, 19, 1043–1060, https://doi.org/10.5194/cp-19-1043-2023, https://doi.org/10.5194/cp-19-1043-2023, 2023
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Data–data and data–model vegetation comparisons are commonly based on comparing single vegetation estimates. While this approach generates good results on average, reducing pollen assemblages to single single plant functional type (PFT) or biome estimates can oversimplify the vegetation signal. We propose using a multivariate metric, the Earth mover's distance (EMD), to include more details about the vegetation structure when performing such comparisons.
Christian Wirths, Elisa Ziegler, and Kira Rehfeld
EGUsphere, https://doi.org/10.5194/egusphere-2023-86, https://doi.org/10.5194/egusphere-2023-86, 2023
Preprint archived
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We compare Holocene temperature trends from reconstructions and global climate models of different complexities. We find that models of all complexities disagree with mid-Holocene trends in reconstructions, and we show that this disagreement is largely independent of the type of reconstruction. From our results we conclude that a seasonal bias in the reconstructions is unlikely as a full explanation for the disagreement.
Franziska Tell, Lukas Jonkers, Julie Meilland, and Michal Kucera
Biogeosciences, 19, 4903–4927, https://doi.org/10.5194/bg-19-4903-2022, https://doi.org/10.5194/bg-19-4903-2022, 2022
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This study analyses the production of calcite shells formed by one of the main Arctic pelagic calcifiers, the foraminifera N. pachyderma. Using vertically resolved profiles of shell concentration, size and weight, we show that calcification occurs throughout the upper 300 m with an average production flux below the calcification zone of 8 mg CaCO3 m−2 d−1 representing 23 % of the total pelagic biogenic carbonate production. The production flux is attenuated in the twilight zone by dissolution.
Janica C. Bühler, Josefine Axelsson, Franziska A. Lechleitner, Jens Fohlmeister, Allegra N. LeGrande, Madhavan Midhun, Jesper Sjolte, Martin Werner, Kei Yoshimura, and Kira Rehfeld
Clim. Past, 18, 1625–1654, https://doi.org/10.5194/cp-18-1625-2022, https://doi.org/10.5194/cp-18-1625-2022, 2022
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We collected and standardized the output of five isotope-enabled simulations for the last millennium and assess differences and similarities to records from a global speleothem database. Modeled isotope variations mostly arise from temperature differences. While lower-resolution speleothems do not capture extreme changes to the extent of models, they show higher variability on multi-decadal timescales. As no model excels in all comparisons, we advise a multi-model approach where possible.
Chenzhi Li, Alexander K. Postl, Thomas Böhmer, Xianyong Cao, Andrew M. Dolman, and Ulrike Herzschuh
Earth Syst. Sci. Data, 14, 1331–1343, https://doi.org/10.5194/essd-14-1331-2022, https://doi.org/10.5194/essd-14-1331-2022, 2022
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Here we present a global chronology framework of 2831 palynological records, including globally harmonized chronologies covering up to 273 000 years. A comparison with the original chronologies reveals a major improvement according to our assessment. Our chronology framework and revised chronologies will interest a broad geoscientific community, as it provides the opportunity to make use in synthesis studies of, for example, pollen-based vegetation and climate change.
Lukas Jonkers, Geert-Jan A. Brummer, Julie Meilland, Jeroen Groeneveld, and Michal Kucera
Clim. Past, 18, 89–101, https://doi.org/10.5194/cp-18-89-2022, https://doi.org/10.5194/cp-18-89-2022, 2022
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The variability in the geochemistry among individual foraminifera is used to reconstruct seasonal to interannual climate variability. This method requires that each foraminifera shell accurately records environmental conditions, which we test here using a sediment trap time series. Even in the absence of environmental variability, planktonic foraminifera display variability in their stable isotope ratios that needs to be considered in the interpretation of individual foraminifera data.
Lukas Jonkers, Oliver Bothe, and Michal Kucera
Clim. Past, 17, 2577–2581, https://doi.org/10.5194/cp-17-2577-2021, https://doi.org/10.5194/cp-17-2577-2021, 2021
Jean-Philippe Baudouin, Michael Herzog, and Cameron A. Petrie
Weather Clim. Dynam., 2, 1187–1207, https://doi.org/10.5194/wcd-2-1187-2021, https://doi.org/10.5194/wcd-2-1187-2021, 2021
Short summary
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Western disturbances are mid-latitude, high-altitude, low-pressure areas that bring orographic precipitation into the Upper Indus Basin. Using statistical tools, we show that the interaction between western disturbances and relief explains the near-surface, cross-barrier wind activity. We also reveal the existence of a moisture pathway from the nearby seas. Overall, we offer a conceptual framework for western-disturbance activity, particularly in terms of precipitation.
Raphaël Hébert, Kira Rehfeld, and Thomas Laepple
Nonlin. Processes Geophys., 28, 311–328, https://doi.org/10.5194/npg-28-311-2021, https://doi.org/10.5194/npg-28-311-2021, 2021
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Paleoclimate proxy data are essential for broadening our understanding of climate variability. There remain, however, challenges for traditional methods of variability analysis to be applied to such data, which are usually irregular. We perform a comparative analysis of different methods of scaling analysis, which provide variability estimates as a function of timescales, applied to irregular paleoclimate proxy data.
Elisa Ziegler and Kira Rehfeld
Geosci. Model Dev., 14, 2843–2866, https://doi.org/10.5194/gmd-14-2843-2021, https://doi.org/10.5194/gmd-14-2843-2021, 2021
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Past climate changes are the only record of how the climate responds to changes in conditions on Earth, but simulations with complex climate models are challenging. We extended a simple climate model such that it simulates the development of temperatures over time. In the model, changes in carbon dioxide and ice distribution affect the simulated temperatures the most. The model is very efficient and can therefore be used to examine past climate changes happening over long periods of time.
Janica C. Bühler, Carla Roesch, Moritz Kirschner, Louise Sime, Max D. Holloway, and Kira Rehfeld
Clim. Past, 17, 985–1004, https://doi.org/10.5194/cp-17-985-2021, https://doi.org/10.5194/cp-17-985-2021, 2021
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We present three new isotope-enabled simulations for the last millennium (850–1850 CE) and compare them to records from a global speleothem database. Offsets between the simulated and measured oxygen isotope ratios are fairly small. While modeled oxygen isotope ratios are more variable on decadal timescales, proxy records are more variable on (multi-)centennial timescales. This could be due to a lack of long-term variability in complex model simulations, but proxy biases cannot be excluded.
Andrew M. Dolman, Torben Kunz, Jeroen Groeneveld, and Thomas Laepple
Clim. Past, 17, 825–841, https://doi.org/10.5194/cp-17-825-2021, https://doi.org/10.5194/cp-17-825-2021, 2021
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Uncertainties in climate proxy records are temporally autocorrelated. By deriving expressions for the power spectra of errors in proxy records, we can estimate appropriate uncertainties for any timescale, for example, for temporally smoothed records or for time slices. Here we outline and demonstrate this approach for climate proxies recovered from marine sediment cores.
Laia Comas-Bru, Kira Rehfeld, Carla Roesch, Sahar Amirnezhad-Mozhdehi, Sandy P. Harrison, Kamolphat Atsawawaranunt, Syed Masood Ahmad, Yassine Ait Brahim, Andy Baker, Matthew Bosomworth, Sebastian F. M. Breitenbach, Yuval Burstyn, Andrea Columbu, Michael Deininger, Attila Demény, Bronwyn Dixon, Jens Fohlmeister, István Gábor Hatvani, Jun Hu, Nikita Kaushal, Zoltán Kern, Inga Labuhn, Franziska A. Lechleitner, Andrew Lorrey, Belen Martrat, Valdir Felipe Novello, Jessica Oster, Carlos Pérez-Mejías, Denis Scholz, Nick Scroxton, Nitesh Sinha, Brittany Marie Ward, Sophie Warken, Haiwei Zhang, and SISAL Working Group members
Earth Syst. Sci. Data, 12, 2579–2606, https://doi.org/10.5194/essd-12-2579-2020, https://doi.org/10.5194/essd-12-2579-2020, 2020
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This paper presents an updated version of the SISAL (Speleothem Isotope Synthesis and Analysis) database. This new version contains isotopic data from 691 speleothem records from 294 cave sites and new age–depth models, including their uncertainties, for 512 speleothems.
Bronwen L. Konecky, Nicholas P. McKay, Olga V. Churakova (Sidorova), Laia Comas-Bru, Emilie P. Dassié, Kristine L. DeLong, Georgina M. Falster, Matt J. Fischer, Matthew D. Jones, Lukas Jonkers, Darrell S. Kaufman, Guillaume Leduc, Shreyas R. Managave, Belen Martrat, Thomas Opel, Anais J. Orsi, Judson W. Partin, Hussein R. Sayani, Elizabeth K. Thomas, Diane M. Thompson, Jonathan J. Tyler, Nerilie J. Abram, Alyssa R. Atwood, Olivier Cartapanis, Jessica L. Conroy, Mark A. Curran, Sylvia G. Dee, Michael Deininger, Dmitry V. Divine, Zoltán Kern, Trevor J. Porter, Samantha L. Stevenson, Lucien von Gunten, and Iso2k Project Members
Earth Syst. Sci. Data, 12, 2261–2288, https://doi.org/10.5194/essd-12-2261-2020, https://doi.org/10.5194/essd-12-2261-2020, 2020
Cited articles
Anand, P., Elderfield, H., and Conte, M. H.: Calibration of Mg/Ca thermometry in planktonic foraminifera from a sediment trap time series, Paleoceanography, 18, 2, https://doi.org/10.1029/2002PA000846, 2003. 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
Argus, D. F., Peltier, W. R., Drummond, R., and Moore, A. W.: The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based on GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories, Geophys. J. Int., 198, 537–563, https://doi.org/10.1093/gji/ggu140, 2014. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020. a
Baudouin, J.-P.: Code and data in support of “Testing the reliability of global surface temperature reconstructions of the last glacial cycle”, Zenodo, https://doi.org/10.5281/zenodo.14025763, 2024.
Bereiter, B., Shackleton, S., Baggenstos, D., Kawamura, K., and Severinghaus, J.: Mean global ocean temperatures during the last glacial transition, Nature, 553, 39–44, https://doi.org/10.1038/nature25152, 2018. a
Berger, A. L.: Long-Term Variations of Daily Insolation and Quaternary Climatic Changes, J. Atmos. Sci., 35, 2362–2367, https://doi.org/10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2, 1978. a, b, c, d
Berger, W. H. and Heath, G. R.: Vertical mixing in pelagic sediments, J. Mar. Res., 26, https://elischolar.library.yale.edu/journal_of_marine_research/1122 (last access: 16 January 2025), 1968. a
Blaauw, M. and Christen, J. A.: Flexible paleoclimate age-depth models using an autoregressive gamma process, Bayesian Anal., 6, 457–474, https://doi.org/10.1214/11-BA618, 2011. a
Boudreau, B. P.: Mean mixed depth of sediments: The wherefore and the why, Limnol. Oceanogr., 43, 524–526, https://doi.org/10.4319/lo.1998.43.3.0524, 1998. a
Bova, S., Rosenthal, Y., Liu, Z., Godad, S. P., and Yan, M.: Seasonal origin of the thermal maxima at the Holocene and the last interglacial, Nature, 589, 548–553, https://doi.org/10.1038/s41586-020-03155-x, 2021. a, b
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., Loutre, M.-F., Marti, O., Merkel, U., Ramstein, G., Valdes, P., Weber, L., Yu, Y., and Zhao, Y.: Results of PMIP2 coupled simulations of the Mid-Holocene and Last Glacial Maximum – Part 2: feedbacks with emphasis on the location of the ITCZ and mid- and high latitudes heat budget, Clim. Past, 3, 279–296, https://doi.org/10.5194/cp-3-279-2007, 2007. a
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
Brierley, C. M., Zhao, A., Harrison, S. P., Braconnot, P., Williams, C. J. R., Thornalley, D. J. R., Shi, X., Peterschmitt, J.-Y., Ohgaito, R., Kaufman, D. S., Kageyama, M., Hargreaves, J. C., Erb, M. P., Emile-Geay, J., D'Agostino, R., Chandan, D., Carré, M., Bartlein, P. J., Zheng, W., Zhang, Z., Zhang, Q., Yang, H., Volodin, E. M., Tomas, R. A., Routson, C., Peltier, W. R., Otto-Bliesner, B., Morozova, P. A., McKay, N. P., Lohmann, G., Legrande, A. N., Guo, C., Cao, J., Brady, E., Annan, J. D., and Abe-Ouchi, A.: Large-scale features and evaluation of the PMIP4-CMIP6 midHolocene simulations, Clim. Past, 16, 1847–1872, https://doi.org/10.5194/cp-16-1847-2020, 2020. a
Briggs, R. D., Pollard, D., and Tarasov, L.: A data-constrained large ensemble analysis of Antarctic evolution since the Eemian, Quaternary Sci. Rev., 103, 91–115, https://doi.org/10.1016/j.quascirev.2014.09.003, 2014. a
Brovkin, V., Ganopolski, A., Archer, D., and Munhoven, G.: Glacial CO2 cycle as a succession of key physical and biogeochemical processes, Clim. Past, 8, 251–264, https://doi.org/10.5194/cp-8-251-2012, 2012. a
Clark, P. U., Shakun, J. D., Rosenthal, Y., Köhler, P., and Bartlein, P. J.: Global and regional temperature change over the past 4.5 million years, Science, 383, 884–890, https://doi.org/10.1126/science.adi1908, 2024. a, b, c, d
Dolman, A. and Laepple, T.: sedproxy: Simulation of Sediment Archived Climate Proxy Records, r package, https://cran.r-project.org/web/packages/sedproxy/sedproxy.pdf (last access: 16 January 2025), 2023. a
Dolman, A. M., Kunz, T., Groeneveld, J., and Laepple, T.: A spectral approach to estimating the timescale-dependent uncertainty of paleoclimate records – Part 2: Application and interpretation, Clim. Past, 17, 825–841, https://doi.org/10.5194/cp-17-825-2021, 2021. a
Erokhina, O. and Mikolajewicz, U.: A New Eulerian Iceberg Module for Climate Studies, J. Adv. Model. Earth Syst., 16, e2023MS003807, https://doi.org/10.1029/2023MS003807, 2024. a
Fieg, K., Latif, M., Ilyina, T., and Schulz, M.: From the last interglacial to the future – new insights into climate change from the PalMod Earth System modelling framework , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10048, https://doi.org/10.5194/egusphere-egu23-10048, 2023. a
Friedrich, T. and Timmermann, A.: Using Late Pleistocene sea surface temperature reconstructions to constrain future greenhouse warming, Earth Planet. Sci. Lett., 530, 115911, https://doi.org/10.1016/j.epsl.2019.115911, 2020. a, b, c
Ganopolski, A. and Calov, R.: The role of orbital forcing, carbon dioxide and regolith in 100 kyr glacial cycles, Clim. Past, 7, 1415–1425, https://doi.org/10.5194/cp-7-1415-2011, 2011. a
Goosse, H., Driesschaert, E., Fichefet, T., and Loutre, M.-F.: Information on the early Holocene climate constrains the summer sea ice projections for the 21st century, Clim. Past, 3, 683–692, https://doi.org/10.5194/cp-3-683-2007, 2007. a
Goosse, H., Brovkin, V., Fichefet, T., Haarsma, R., Huybrechts, P., Jongma, J., Mouchet, A., Selten, F., Barriat, P.-Y., Campin, J.-M., Deleersnijder, E., Driesschaert, E., Goelzer, H., Janssens, I., Loutre, M.-F., Morales Maqueda, M. A., Opsteegh, T., Mathieu, P.-P., Munhoven, G., Pettersson, E. J., Renssen, H., Roche, D. M., Schaeffer, M., Tartinville, B., Timmermann, A., and Weber, S. L.: Description of the Earth system model of intermediate complexity LOVECLIM version 1.2, Geosci. Model Dev., 3, 603–633, https://doi.org/10.5194/gmd-3-603-2010, 2010. a
Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns, T. C., Mitchell, J. F. B., and Wood, R. A.: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dynam., 16, 147–168, https://doi.org/10.1007/s003820050010, 2000. a
Ho, S. L. and Laepple, T.: Glacial cooling as inferred from marine temperature proxies TEXH86 and UK′37, Earth Planet. Sci. Lett., 409, 15–22, https://doi.org/10.1016/j.epsl.2014.10.033, 2015. a
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013. a
ICCP: Loveclim 784K, ICCP [data set], http://climatedata.ibs.re.kr:9090/dods/public-data/loveclim-784k, 2018. a
ICCP: 3Ma transient climate simulation, ICCP [data set], https://doi.org/10.22741/iccp.20230001, 2023. a
Imbrie, J.: A new micropaleontological method for quantitative paleoclimatology: application to a late Pleistocene Caribbean core, The late Cenozoic glacial ages, 71–181 pp., 1971. a
Ivanovic, R. F., Gregoire, L. J., Kageyama, M., Roche, D. M., Valdes, P. J., Burke, A., Drummond, R., Peltier, W. R., and Tarasov, L.: Transient climate simulations of the deglaciation 21–9 thousand years before present (version 1) – PMIP4 Core experiment design and boundary conditions, Geosci. Model Dev., 9, 2563–2587, https://doi.org/10.5194/gmd-9-2563-2016, 2016. a
Jaume-Santero, F., Barriopedro, D., García-Herrera, R., Calvo, N., and Salcedo-Sanz, S.: Selection of optimal proxy locations for temperature field reconstructions using evolutionary algorithms, Sci. Rep., 10, 7900, https://doi.org/10.1038/s41598-020-64459-6, 2020. a
Jonkers, L. and Kučera, M.: Quantifying the effect of seasonal and vertical habitat tracking on planktonic foraminifera proxies, Clim. Past, 13, 573–586, https://doi.org/10.5194/cp-13-573-2017, 2017. a
Jonkers, L., Cartapanis, O., Langner, M., McKay, N., Mulitza, S., Strack, A., and Kucera, M.: Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis, Earth Syst. Sci. Data, 12, 1053–1081, https://doi.org/10.5194/essd-12-1053-2020, 2020. a, b, c
Jonkers, L., Laepple, T., Rillo, M. C., Shi, X., Dolman, A. M., Lohmann, G., Paul, A., Mix, A., and Kucera, M.: Strong temperature gradients in the ice age North Atlantic Ocean revealed by plankton biogeography, Nat. Geosci., 16, 1114–1119, https://doi.org/10.1038/s41561-023-01328-7, 2023. a
Judd, E. J., Bhattacharya, T., and Ivany, L. C.: A Dynamical Framework for Interpreting Ancient Sea Surface Temperatures, Geophys. Res. Lett., 47, e2020GL089044, https://doi.org/10.1029/2020GL089044, 2020. a
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., Chandan, D., Gregoire, L. J., Ivanovic, R. F., Izumi, K., LeGrande, A. N., Lhardy, F., Lohmann, G., Morozova, P. A., Ohgaito, R., Paul, A., Peltier, W. R., 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
Kapsch, M.-L., Mikolajewicz, U., Ziemen, F., and Schannwell, C.: Ocean Response in Transient Simulations of the Last Deglaciation Dominated by Underlying Ice-Sheet Reconstruction and Method of Meltwater Distribution, Geophys. Res. Lett., 49, e2021GL096767, https://doi.org/10.1029/2021GL096767, 2022. a, b, c
Kleinen, T., Gromov, S., Steil, B., and Brovkin, V.: Atmospheric methane underestimated in future climate projections, Environ. Res. Lett., 16, 094006, https://doi.org/10.1088/1748-9326/ac1814, 2021. a
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F., and Fischer, H.: A 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4, and N2O and their radiative forcing, Earth Syst. Sci. Data, 9, 363–387, https://doi.org/10.5194/essd-9-363-2017, 2017. a
Laepple, T., Shakun, J., He, F., and Marcott, S.: Concerns of assuming linearity in the reconstruction of thermal maxima, Nature, 607, E12–E14, https://doi.org/10.1038/s41586-022-04831-w, 2022. a
Laepple, T., Ziegler, E., Weitzel, N., Hébert, R., Ellerhoff, B., Schoch, P., Martrat, B., Bothe, O., Moreno-Chamarro, E., Chevalier, M., Herbert, A., and Rehfeld, K.: Regional but not global temperature variability underestimated by climate models at supradecadal timescales, Nat. Geosci., 16, 958–966, https://doi.org/10.1038/s41561-023-01299-9, 2023. a
Latif, M., Claussen, M., Schulz, M., and Brücher, T.: Comprehensive Earth system models of the last glacial cycle, Eos, 97, https://doi.org/10.1029/2016EO059587, 2016. a
Lenton, T.: QUEST Quaternary: FAMOUS glacial cycle model data, NCAS British Atmospheric Data Centre [data set], https://catalogue.ceda.ac.uk/uuid/a43dcfaccfae4824ab9ab2b572703e72/ (Last access: 16 January 2025), 2008. a
Lisiecki, L. E. and Stern, J. V.: Regional and global benthic δ18O stacks for the last glacial cycle, Paleoceanography, 31, 1368–1394, https://doi.org/10.1002/2016PA003002, 2016. a
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
Lougheed, B. C.: Using sedimentological priors to improve 14C calibration of bioturbated sediment archives, Radiocarbon, 64, 135–151, https://doi.org/10.1017/RDC.2021.116, 2022. a
Lunt, D. J., Bragg, F., Chan, W.-L., Hutchinson, D. K., Ladant, J.-B., Morozova, P., Niezgodzki, I., Steinig, S., Zhang, Z., Zhu, J., Abe-Ouchi, A., Anagnostou, E., de Boer, A. M., Coxall, H. K., Donnadieu, Y., Foster, G., Inglis, G. N., Knorr, G., Langebroek, P. M., Lear, C. H., Lohmann, G., Poulsen, C. J., Sepulchre, P., Tierney, J. E., Valdes, P. J., Volodin, E. M., Dunkley Jones, T., Hollis, C. J., Huber, M., and Otto-Bliesner, B. L.: DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data, Clim. Past, 17, 203–227, https://doi.org/10.5194/cp-17-203-2021, 2021. a
Lüthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J.-M., Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K., and Stocker, T. F.: High-resolution carbon dioxide concentration record 650,000–800,000 years before present, Nature, 453, 379–382, https://doi.org/10.1038/nature06949, 2008. a, b, c
Marsicek, J., Shuman, B. N., Bartlein, P. J., Shafer, S. L., and Brewer, S.: Reconciling divergent trends and millennial variations in Holocene temperatures, Nature, 554, 92–96, https://doi.org/10.1038/nature25464, 2018. 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
Mikolajewicz, U., Kapsch, M.-L., Gayler, V., Meccia, V. L., Riddick, T., Ziemen, F. A., and Schannwell, C.: PalMod2 MPI-M MPI-ESM1-2-CR Transient Simulations of the Last Deglaciation with prescribed ice sheets from GLAC-1D reconstructions (r1i1p2f2), World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/PMMXMCRTDGP122, 2023a. a
Mikolajewicz, U., Kapsch, M.-L., Gayler, V., Meccia, V. L., Riddick, T., Ziemen, F. A., and Schannwell, C.: PalMod2 MPI-M MPI-ESM1-2-CR Transient Simulations of the Last Deglaciation with prescribed ice sheets from ICE-6G reconstructions (r1i1p2f2), World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/PMMXMCRTDIP122, 2023. a
Muntjewerf, L., Petrini, M., Vizcaino, M., Ernani da Silva, C., Sellevold, R., Scherrenberg, M. D. W., Thayer-Calder, K., Bradley, S. L., Lenaerts, J. T. M., Lipscomb, W. H., and Lofverstrom, M.: Greenland Ice Sheet contribution to 21st century sea level rise as simulated by the coupled CESM2.1-CISM2.1, Geophys. Res. Lett., 47, e2019GL086836, https://doi.org/10.1029/2019GL086836, 2020. a
Müller, P. J., Kirst, G., Ruhland, G., von Storch, I., and Rosell-Melé, A.: Calibration of the alkenone paleotemperature index U37K′ based on core-tops from the eastern South Atlantic and the global ocean (60° N–60° S), Geochim. Cosmochim. Acta, 62, 1757–1772, https://doi.org/10.1016/S0016-7037(98)00097-0, 1998. a
Nilsen, T., Talento, S., and Werner, J. P.: Constraining two climate field reconstruction methodologies over the North Atlantic realm using pseudo-proxy experiments, Quaternary Sci. Rev., 265, 107009, https://doi.org/10.1016/j.quascirev.2021.107009, 2021. a
Nowicki, S. M. J., Payne, A., Larour, E., Seroussi, H., Goelzer, H., Lipscomb, W., Gregory, J., Abe-Ouchi, A., and Shepherd, A.: Ice Sheet Model Intercomparison Project (ISMIP6) contribution to CMIP6, Geosci. Model Dev., 9, 4521–4545, https://doi.org/10.5194/gmd-9-4521-2016, 2016. a
Nürnberg, D., Bijma, J., and Hemleben, C.: Assessing the reliability of magnesium in foraminiferal calcite as a proxy for water mass temperatures, Geochim. Cosmochim. Acta, 60, 803–814, https://doi.org/10.1016/0016-7037(95)00446-7, 1996. a, b
Osman, M. B., Tierney, J. E., Zhu, J., Tardif, R., Hakim, G. J., King, J., and Poulsen, C. J.: Globally resolved surface temperatures since the Last Glacial Maximum, Nature, 599, 239–244, https://doi.org/10.1038/s41586-021-03984-4, 2021. a, b
PAGES 2k Consortium: Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era, Nat. Geosci., 12, 643–649, https://doi.org/10.1038/s41561-019-0400-0, 2019. a
Paul, A., Mulitza, S., Stein, R., and Werner, M.: A global climatology of the ocean surface during the Last Glacial Maximum mapped on a regular grid (GLOMAP), Clim. Past, 17, 805–824, https://doi.org/10.5194/cp-17-805-2021, 2021. a, b, c
Peeters, F. J. C., van der Lubbe, H. J. L., and Scussolini, P.: Age-depth models for tropical marine hemipelagic deposits improve significantly when proxy-based information on sediment composition is included, Paleoceanogr. Paleoclimatol., 38, e2022PA004476, https://doi.org/10.1029/2022PA004476, 2023. a
Peltier, W.: global glacial isostasy and the surface of the ice-age earth: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111–149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004. a
Peltier, W. R., Argus, D. F., and Drummond, R.: Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model, J. Geophys. Res.-Solid Earth, 120, 450–487, https://doi.org/10.1002/2014JB011176, 2015. a, b, c
Prahl, F. G., Muehlhausen, L. A., and Zahnle, D. L.: Further evaluation of long-chain alkenones as indicators of paleoceanographic conditions, Geochim. Cosmochim. Acta, 52, 2303–2310, https://doi.org/0.1016/0016-7037(88)90132-9, 1988. a
Rackow, T., Wesche, C., Timmermann, R., Hellmer, H. H., Juricke, S., and Jung, T.: A simulation of small to giant Antarctic iceberg evolution: Differential impact on climatology estimates, J. Geophys. Res.-Oceans, 122, 3170–3190, https://doi.org/10.1002/2016JC012513, 2017. a
Rampen, S. W., Willmott, V., Kim, J.-H., Uliana, E., Mollenhauer, G., Schefuß, E., Sinninghe Damsté, J. S., and Schouten, S.: Long chain 1,13- and 1,15-diols as a potential proxy for palaeotemperature reconstruction, Geochim. Cosmochim. Acta, 84, 204–216, https://doi.org/10.1016/j.gca.2012.01.024, 2012. a
Roche, D. M., Waelbroeck, C., Metcalfe, B., and Caley, T.: FAME (v1.0): a simple module to simulate the effect of planktonic foraminifer species-specific habitat on their oxygen isotopic content, Geosci. Model Dev., 11, 3587–3603, https://doi.org/10.5194/gmd-11-3587-2018, 2018. a
Rohling, E. J., Medina-Elizalde, M., Shepherd, J. G., Siddall, M., and Stanford, J. D.: Sea surface and high-latitude temperature sensitivity to radiative forcing of climate over several glacial cycles, J. Climate, 25, 1635–1656, https://doi.org/10.1175/2011JCLI4078.1, 2012. a
Schouten, S., Hopmans, E. C., Schefuß, E., and Sinninghe Damsté, J. S.: Distributional variations in marine crenarchaeotal membrane lipids: a new tool for reconstructing ancient sea water temperatures?, Earth Planet. Sci. Lett., 204, 265–274, https://doi.org/10.1016/S0012-821X(02)00979-2, 2002. a
Shakun, J. D., Clark, P. U., He, F., Marcott, S. A., Mix, A. C., Liu, Z., Otto-Bliesner, B., Schmittner, A., and Bard, E.: Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation, Nature, 484, 49–54, https://doi.org/10.1038/nature10915, 2012. a, b
Smith, R. S. and Gregory, J.: The last glacial cycle: transient simulations with an AOGCM, Clim. Dynam., 38, 1545–1559, https://doi.org/10.1007/s00382-011-1283-y, 2012. a, b
Smith, R. S., Gregory, J. M., and Osprey, A.: A description of the FAMOUS (version XDBUA) climate model and control run, Geosci. Model Dev., 1, 53–68, https://doi.org/10.5194/gmd-1-53-2008, 2008. a
Snyder, C. W.: Evolution of global temperature over the past two million years, Nature, 538, 226, https://doi.org/10.1038/nature19798, 2016. a, b, c, d
Song, X., Wang, D.-Y., Li, F., and Zeng, X.-D.: Evaluating the performance of CMIP6 Earth system models in simulating global vegetation structure and distribution, Adv. Clim. Change Res., 12, 584–595, https://doi.org/10.1016/j.accre.2021.06.008, 2021. a
Spahni, R., Chappellaz, J., Stocker, T. F., Loulergue, L., Hausammann, G., Kawamura, K., Flückiger, J., Schwander, J., Raynaud, D., Masson-Delmotte, V., and Jouzel, J.: Atmospheric methane and nitrous oxide of the late pleistocene from Antarctic ice cores, Science, 310, 1317–1321, https://doi.org/10.1126/science.1120132, 2005. a
Steinke, S., Kienast, M., Groeneveld, J., Lin, L.-C., Chen, M.-T., and Rendle-Bühring, R.: Proxy dependence of the temporal pattern of deglacial warming in the tropical South China Sea: toward resolving seasonality, Quaternary Sci. Rev., 27, 688–700, https://doi.org/10.1016/j.quascirev.2007.12.003, 2008. a
Tarasov, L., Dyke, A. S., Neal, R. M., and Peltier, W.: A data-calibrated distribution of deglacial chronologies for the North American ice complex from glaciological modeling, sea Level and Ice Sheet Evolution: A PALSEA Special Edition, Earth Planet. Sci. Lett., 315–316, 30–40, https://doi.org/10.1016/j.epsl.2011.09.010, 2012. a
Teal, L. R., Bulling, M. T., Parker, E. R., and Solan, M.: Global patterns of bioturbation intensity and mixed depth of marine soft sediments, Aqua. Biol., 2, 207–218, https://doi.org/10.3354/ab00052, 2008. a
Telford, R. J., Li, C., and Kucera, M.: Mismatch between the depth habitat of planktonic foraminifera and the calibration depth of SST transfer functions may bias reconstructions, Clim. Past, 9, 859–870, https://doi.org/10.5194/cp-9-859-2013, 2013. a
Tierney, J. E. and Tingley, M. P.: BAYSPLINE: A new calibration for the alkenone paleothermometer, Paleoceanogr. Paleoclimatol., 33, 281–301, https://doi.org/10.1002/2017PA003201, 2018. a
Tierney, J. E., Zhu, J., King, J., Malevich, S. B., Hakim, G. J., and Poulsen, C. J.: Glacial cooling and climate sensitivity revisited, Nature, 584, 569–573, https://doi.org/10.1038/s41586-020-2617-x, 2020. a, b
Timmermann, A. and Friedrich, T.: Late Pleistocene climate drivers of early human migration, Nature, 538, 92–95, https://doi.org/10.1038/nature19365, 2016. a
Timmermann, A., Sachs, J., and Timm, O. E.: Assessing divergent SST behavior during the last 21 ka derived from alkenones and G. ruber-Mg/Ca in the equatorial Pacific, Paleoceanography, 29, 680–696, https://doi.org/10.1002/2013PA002598, 2014. a, b
UNFCCC: Paris Agreement, https://unfccc.int/sites/default/files/english_paris_agreement.pdf (last access: 16 January 2025), 2015. a
Von Storch, H. and Zwiers, F. W.: Statistical analysis in climate research, Statistical analysis in climate research, ISBN Statistical analysis in climate research, https://doi.org/10.1017/CBO9780511612336, 1999. a
Waelbroeck, C., Lougheed, B. C., Vazquez Riveiros, N., Missiaen, L., Pedro, J., Dokken, T., Hajdas, I., Wacker, L., Abbott, P., Dumoulin, J.-P., Thil, F., Eynaud, F., Rossignol, L., Fersi, W., Albuquerque, A. L., Arz, H., Austin, W. E. N., Came, R., Carlson, A. E., Collins, J. A., Dennielou, B., Desprat, S., Dickson, A., Elliot, M., Farmer, C., Giraudeau, J., Gottschalk, J., Henderiks, J., Hughen, K., Jung, S., Knutz, P., Lebreiro, S., Lund, D. C., Lynch-Stieglitz, J., Malaizé, B., Marchitto, T., Martínez-Méndez, G., Mollenhauer, G., Naughton, F., Nave, S., Nürnberg, D., Oppo, D., Peck, V., Peeters, F. J. C., Penaud, A., Portilho-Ramos, R. d. C., Repschläger, J., Roberts, J., Rühlemann, C., Salgueiro, E., Sanchez Goni, M. F., Schönfeld, J., Scussolini, P., Skinner, L. C., Skonieczny, C., Thornalley, D., Toucanne, S., Rooij, D. V., Vidal, L., Voelker, A. H. L., Wary, M., Weldeab, S., and Ziegler, M.: Consistently dated Atlantic sediment cores over the last 40 thousand years, Sci. Data, 6, 165, https://doi.org/10.1038/s41597-019-0173-8, 2019. a
Wang, J., Emile-Geay, J., Guillot, D., Smerdon, J. E., and Rajaratnam, B.: Evaluating climate field reconstruction techniques using improved emulations of real-world conditions, Clim. Past, 10, 1–19, https://doi.org/10.5194/cp-10-1-2014, 2014. a
Weitzel, N., Andres, H., Baudouin, J.-P., Kapsch, M.-L., Mikolajewicz, U., Jonkers, L., Bothe, O., Ziegler, E., Kleinen, T., Paul, A., and Rehfeld, K.: Towards spatio-temporal comparison of simulated and reconstructed sea surface temperatures for the last deglaciation, Clim. Past, 20, 865–890, https://doi.org/10.5194/cp-20-865-2024, 2024. a, b, c
Willeit, M., Ganopolski, A., Calov, R., and Brovkin, V.: Mid-Pleistocene transition in glacial cycles explained by declining CO2 and regolith removal, Sci. Adv., 5, eaav7337, https://doi.org/10.1126/sciadv.aav7337, 2019. a
Yun, K.-S., Timmermann, A., Lee, S.-S., Willeit, M., Ganopolski, A., and Jadhav, J.: A transient coupled general circulation model (CGCM) simulation of the past 3 million years, Clim. Past, 19, 1951–1974, https://doi.org/10.5194/cp-19-1951-2023, 2023. a
Zhang, S., Solan, M., and Tarhan, L.: Global distribution and environmental correlates of marine bioturbation, Current Biol., 34, 2580–2593, https://doi.org/10.1016/j.cub.2024.04.065, 2024. a
Ziemen, F. A., Kapsch, M.-L., Klockmann, M., and Mikolajewicz, U.: Heinrich events show two-stage climate response in transient glacial simulations, Clim. Past, 15, 153–168, https://doi.org/10.5194/cp-15-153-2019, 2019. a
Zuhr, A. M., Dolman, A. M., Ho, S. L., Groeneveld, J., Löwemark, L., Grotheer, H., Su, C.-C., and Laepple, T.: Age-Heterogeneity in Marine Sediments Revealed by Three-Dimensional High-Resolution Radiocarbon Measurements, Front. Earth Sci., 10, https://doi.org/10.3389/feart.2022.871902, 2022. a
Short summary
Earth's past temperature reconstructions are critical for understanding climate change. We test the ability of these reconstructions using climate simulations. Uncertainties, mainly from past temperature measurement methods and age determination, impact reconstructions over time. While more data enhance accuracy for long-term trends, high-quality data are more important for short-term precision. Our study lays the groundwork for better reconstructions and suggests avenues for improvement.
Earth's past temperature reconstructions are critical for understanding climate change. We test...