Articles | Volume 16, issue 6
https://doi.org/10.5194/cp-16-2599-2020
https://doi.org/10.5194/cp-16-2599-2020
Research article
 | 
23 Dec 2020
Research article |  | 23 Dec 2020

OPTiMAL: a new machine learning approach for GDGT-based palaeothermometry

Tom Dunkley Jones, Yvette L. Eley, William Thomson, Sarah E. Greene, Ilya Mandel, Kirsty Edgar, and James A. Bendle

Related authors

Late Neogene evolution of modern deep-dwelling plankton
Flavia Boscolo-Galazzo, Amy Jones, Tom Dunkley Jones, Katherine A. Crichton, Bridget S. Wade, and Paul N. Pearson
Biogeosciences, 19, 743–762, https://doi.org/10.5194/bg-19-743-2022,https://doi.org/10.5194/bg-19-743-2022, 2022
Short summary
DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data
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
Global mean surface temperature and climate sensitivity of the early Eocene Climatic Optimum (EECO), Paleocene–Eocene Thermal Maximum (PETM), and latest Paleocene
Gordon N. Inglis, Fran Bragg, Natalie J. Burls, Marlow Julius Cramwinckel, David Evans, Gavin L. Foster, Matthew Huber, Daniel J. Lunt, Nicholas Siler, Sebastian Steinig, Jessica E. Tierney, Richard Wilkinson, Eleni Anagnostou, Agatha M. de Boer, Tom Dunkley Jones, Kirsty M. Edgar, Christopher J. Hollis, David K. Hutchinson, and Richard D. Pancost
Clim. Past, 16, 1953–1968, https://doi.org/10.5194/cp-16-1953-2020,https://doi.org/10.5194/cp-16-1953-2020, 2020
Short summary
Organic-walled dinoflagellate cyst biostratigraphy of the upper Eocene to lower Oligocene Yazoo Formation, US Gulf Coast
Marcelo Augusto De Lira Mota, Guy Harrington, and Tom Dunkley Jones
J. Micropalaeontol., 39, 1–26, https://doi.org/10.5194/jm-39-1-2020,https://doi.org/10.5194/jm-39-1-2020, 2020
Short summary
The DeepMIP contribution to PMIP4: methodologies for selection, compilation and analysis of latest Paleocene and early Eocene climate proxy data, incorporating version 0.1 of the DeepMIP database
Christopher J. Hollis, Tom Dunkley Jones, Eleni Anagnostou, Peter K. Bijl, Marlow Julius Cramwinckel, Ying Cui, Gerald R. Dickens, Kirsty M. Edgar, Yvette Eley, David Evans, Gavin L. Foster, Joost Frieling, Gordon N. Inglis, Elizabeth M. Kennedy, Reinhard Kozdon, Vittoria Lauretano, Caroline H. Lear, Kate Littler, Lucas Lourens, A. Nele Meckler, B. David A. Naafs, Heiko Pälike, Richard D. Pancost, Paul N. Pearson, Ursula Röhl, Dana L. Royer, Ulrich Salzmann, Brian A. Schubert, Hannu Seebeck, Appy Sluijs, Robert P. Speijer, Peter Stassen, Jessica Tierney, Aradhna Tripati, Bridget Wade, Thomas Westerhold, Caitlyn Witkowski, James C. Zachos, Yi Ge Zhang, Matthew Huber, and Daniel J. Lunt
Geosci. Model Dev., 12, 3149–3206, https://doi.org/10.5194/gmd-12-3149-2019,https://doi.org/10.5194/gmd-12-3149-2019, 2019
Short summary

Related subject area

Subject: Proxy Use-Development-Validation | Archive: Marine Archives | Timescale: Cenozoic
Paleocene–Eocene age glendonites from the Mid-Norwegian Margin – indicators of cold snaps in the hothouse?
Madeleine L. Vickers, Morgan T. Jones, Jack Longman, David Evans, Clemens V. Ullmann, Ella Wulfsberg Stokke, Martin Vickers, Joost Frieling, Dustin T. Harper, Vincent J. Clementi, and IODP Expedition 396 Scientists
Clim. Past, 20, 1–23, https://doi.org/10.5194/cp-20-1-2024,https://doi.org/10.5194/cp-20-1-2024, 2024
Short summary
Coccolithophorids precipitate carbonate in clumped isotope equilibrium with seawater
Alexander J. Clark, Ismael Torres-Romero, Madalina Jaggi, Stefano M. Bernasconi, and Heather M. Stoll
EGUsphere, https://doi.org/10.5194/egusphere-2023-2581,https://doi.org/10.5194/egusphere-2023-2581, 2023
Short summary
Can we reliably reconstruct the mid-Pliocene Warm Period with sparse data and uncertain models?
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
Assessing environmental change associated with early Eocene hyperthermals in the Atlantic Coastal Plain, USA
William Rush, Jean Self-Trail, Yang Zhang, Appy Sluijs, Henk Brinkhuis, James Zachos, James G. Ogg, and Marci Robinson
Clim. Past, 19, 1677–1698, https://doi.org/10.5194/cp-19-1677-2023,https://doi.org/10.5194/cp-19-1677-2023, 2023
Short summary
Technical note: A new online tool for δ18O–temperature conversions
Daniel E. Gaskell and Pincelli M. Hull
Clim. Past, 19, 1265–1274, https://doi.org/10.5194/cp-19-1265-2023,https://doi.org/10.5194/cp-19-1265-2023, 2023
Short summary

Cited articles

Aitchison, J.: The Statistical Analysis of Compositional Data, J. R. Stat. Soc. Series B Stat. Methodol., 44, 139–160, 1982. 
Aitchison, J.: Principal component analysis of compositional data, Biometrika, 70, 57–65, 1983. 
Aitchison, J. and Greenacre, M.: Biplots of compositional data, J. R. Stat. Soc. Ser. C Appl. Stat., 51, 375–392, 2002. 
Álvarez, M. A., Rosasco, L., and Lawrence, N. D.: Kernels for Vector-Valued Functions: A Review, Foundations and Trends® in Machine Learning, 4, 195–266, 2012. 
Bale, N. J., Palatinszky, M., Rijpstra, I. C., Herbold, C. W., Wagner, M., and Sinnighe Damste, J. S.: Membrane lipid composition of the moderately thermophilic ammonia-oxidizing Archaeon “Candidatus Nitrosotenius uzonensis” at different growth temperatures, Appl. Environ. Microb., 85, e01332-19, https://doi.org/10.1128/AEM.01332-19, 2019. 
Download
Short summary
We explore the utiliity of the composition of fossil lipid biomarkers, which are commonly preserved in ancient marine sediments, in providing estimates of past ocean temperatures. The group of lipids concerned show compositional changes across the modern oceans that are correlated, to some extent, with local surface ocean temperatures. Here we present new machine learning approaches to improve our understanding of this temperature sensitivity and its application to reconstructing past climates.