Articles | Volume 21, issue 8
https://doi.org/10.5194/cp-21-1465-2025
© 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-21-1465-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Aug 2025
Research article |
| 29 Aug 2025
| 29 Aug 2025
SCUBIDO: a Bayesian modelling approach to reconstruct palaeoclimate from multivariate lake sediment data
Laura Boyall
CORRESPONDING AUTHOR
Department of Geography, Royal Holloway University of London, Egham, TW20 0EX, UK
Andrew C. Parnell
School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
Paul Lincoln
Department of Geography, Royal Holloway University of London, Egham, TW20 0EX, UK
Antti Ojala
Department of Geography and Geology, University of Turku, 20014 Turku, Finland
Geological Survey of Finland, Vuorimiehentie 5, 02151 Espoo, Finland
Armand Hernández
GRICA Group, Centro Interdisciplinar de Química e Bioloxía (CICA), Faculty of Sciences, Universidade de Coruña, Coruña, Spain
Celia Martin-Puertas
Department of Geography, Royal Holloway University of London, Egham, TW20 0EX, UK
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We provide a new global data set of charcoal preserved in sediments that can be used to examine how fire regimes have changed during past millennia and to investigate what caused these changes. The individual records have been standardised, and new age models have been constructed to allow better comparison across sites. The data set contains 1681 records from 1477 sites worldwide.
Cited articles
Aitchison, J.: The statistical analysis of compositional data, Chapman & Hall, London, https://doi.org/10.1002/bimj.4710300705, 1986.
Anchukaitis, K. J. and Smerdon, J. E.: Progress and uncertainties in global and hemispheric temperature reconstructions of the Common Era, Quaternary Sci. Rev., 286, 107537, https://doi.org/10.1016/j.quascirev.2022.107537, 2022.
Bader, J., Jungclaus, J., Krivova, N., Lorenz, S., Maycock, A., Raddatz, T., Schmidt, H., Toohey, M., Wu, C.-J., and Claussen, M.: Global temperature modes shed light on the Holocene temperature conundrum, Nat. Commun., 11, 4726, https://doi.org/10.1038/s41467-020-18478-6, 2020.
Bertrand, S., Tjallingii, R., Kylander, M .E., Wilhelm, B., Roberts, S. J., Arnaud, R., Brown, E., and Bindler, R.: Inorganic geochemistry of lake sediments: A review of analytical techniques and guidelines for data interpretation, Earth-Sci. Rev., 245, 104639, https://doi.org/10.1016/j.earscirev.2023.104639, 2024.
Birks, H. J. B.: Overview of numerical methods in palaeolimnology, in: Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques, edited by: Birks, H. J. B., Lotter, A. F., Juggins, S., and Smol, J. P., Springer, Dordrecht, https://doi.org/10.1007/978-94-007-2745-8_2, 19–92, 2012.
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.
Boyall, L. and Martin-Puertas, C.: Diss Mere XRF Data, Version v1, Zenodo [data set], https://doi.org/10.5281/zenodo.15168266, 2025.
Boyall, L., Valcárcel, J. I., Harding, P., Hernández, A., and Martin-Puertas, C.: Disentangling the environmental signals recorded in Holocene calcite varves based on modern lake observations and annual sedimentary processes in Diss Mere, England, J. Paleolimnol., 70, 39–56, https://doi.org/10.1007/s10933-023-00282-z, 2023.
Boyall, L., Martin-Puertas, C., Tjallingii, R., Milner, A. M., and Blockley, S. P. E.: Holocene climate evolution and human activity as recorded by the sediment record of lake Diss Mere, England, J. Quaternary Sci., 39, 6, https://doi.org/10.1002/jqs.3646, 2024.
Boyall, L., Parnell, A., Lincoln, P., and Martin-Puertas, C.: LauraBoyall/SCUBIDO: v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.16883480, 2025.
Brooks, S. P. and Gelman, A.: General methods for monitoring convergence of iterative simulations, J. Comput. Graph. Stat., 7, 434–455, https://doi.org/10.1080/10618600.1998.10474787, 1998.
Burls, N. and Sagoo, N.: Increasingly sophisticated climate models need the out-of-sample tests paleoclimates provide, J. Adv. Model. Earth Sy., 14, e2022MS003389, https://doi.org/10.1029/2022MS003389, 2022.
Cahill, N., Croke, J., Campbell, M., Hughes, K., Vitkovsky, J., Kilgallen, J. E., and Parnell, A.: A Bayesian time series model for reconstructing hydroclimate from multiple proxies, Environmetrics, 34, e2786, https://doi.org/10.1002/env.2786, 2023.
Cartapanis, O., Jonkers, L., Moffa-Sanchez, P., Jaccard, S. L., and de Vernal, A.: Complex spatio-temporal structure of the Holocene Thermal Maximum, Nat. Commun., 13, 5662, https://doi.org/10.1038/s41467-022-33362-1, 2022.
Cassou, C., Kushnir, Y., Hawkins, E., Pirani, A., Kucharski, F., Kand, I.-S., and Caltabiano, N.: Decadal climate variability and predictability: Challenges and opportunities, B. Am. Meteorol. Soc., 99, 479–490, https://doi.org/10.1175/BAMS-D-16-0286.1, 2018.
Chamberlain, S., Hocking, D., Anderson, B., Salmon, M., Erickson, A., Potter, N., Stachelek, J., Simmons, A., Ram, K., and Edmund, H.: rnoaa: NOAA weather data, R. R package version 1.4.0, https://github.com/ropensci/rnoaa (last access: 12 December 2024), 2024.
Chevalier, M., Davis, B. A. S., Heiri, O., Seppä, H., Chase, B. M., Gajewski, K., Lacourse, T., Telford, R., Finsinger, W., Guiot, J., Kühl, N., Maezumi, S. Y., Tipton, J., Carter, V., Brussel, T., Phelps, L., Dawson, A., Zanon, M., Vallé, F., Nolan, C., Mauri, A., de Vernal, A., Izumi, K., Holmström, L., Marsicek, J., Goring, S., Sommer, P., Chaput, M., and Kupriyanov, D.: Pollen-based climate reconstruction techniques for late Quaternary studies, Earth Sci. Rev., 210, 1–33, https://doi.org/10.1016/j.earscirev.2020.103384, 2020.
Chu, P.-S. and Zhao, X.: Bayesian analysis for extreme climatic events: A review, Atmos. Res., 102, 243–262, https://doi.org/10.1016/j.atmosres.2011.07.001, 2011.
Davis, B. A. S., Brewer, S., Stevenson, A. C., and Guiot, J.: The temperature of Europe during the Holocene reconstructed from pollen data, Quaternary Sci. Rev., 22, 1701–1716, https://doi.org/10.1016/S0277-3791(03)00173-2, 2003.
Davies, S. J., Lamb, H. F., and Roberts, S. J.: Micro-XRF core scanning in palaeolimnology: recent developments, in: Micro-XRF Studies of Sediment Cores: Applications of a Non-Destructive Tool for the Environmental Sciences, edited by: Croudace, I. W. and Rothwell, R. G., Developments in Paleoenvironmental Research, Springer, Dordrecht, 189–226, https://doi.org/10.1007/978-94-017-9849-5_7, 2015.
Dunlea, A. G., Murray, R. W., Tada, R., Alvarez-Zarikian, C. A., Anderson, C. H., Gilli, A., Giosan, L., Gorgas, T., Hennekam, R., Irino, T., Murayama, M., Peterson, L. C., Reichart, G.-J., Seki, A., Zheng, H., and Ziegler, M.: Intercomparison of XRF core scanning results from seven labs and approaches to practical calibration, Geochem. Geophys. Geosyst., 21, e2020GC009248, https://doi.org/10.1029/2020GC009248, 2020.
Erb, M. P., McKay, N. P., Steiger, N., Dee, S., Hancock, C., Ivanovic, R. F., Gregoire, L. J., and Valdes, P.: Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation, Clim. Past, 18, 2599–2629, https://doi.org/10.5194/cp-18-2599-2022, 2022a.
Erb, M. P., McKay, N. P., Steiger, N., Dee, S., Hancock, C., Ivanovic, R. F., Gregoire, L. J., and Valdes, P.: Holocene temperature reconstruction using paleoclimate data assimilation, 1.0.0-beta, Zenodo [data set], https://doi.org/10.5281/zenodo.6426332, 2022b.
Gelman, A. and Rubin, D. B.: Inference from iterative simulation using multiple sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B.: Bayesian data analysis, 1st edn., Chapman and Hall/CRC, https://doi.org/10.1201/9780429258411, 1995.
Haslett, J., Whiley, M., Bhattacharya, S., Salter-Townshend, M., Wilson, S. P., Allen, J. R. M., Huntley, B., and Mitchell, F. J. G.: Bayesian palaeoclimate reconstruction, J. R. Stat. Soc. A Stat., 169, 395–438, 2006.
Hernández, A., Sánchez-López, G., Pla-Rabes, S., Comas-Bru, L., Parnell, A., Cahill, N., Geyer, A., Trigo, R. M., and Giralt, S.: A 2000 year Bayesian NAO reconstruction from the Iberian Peninsula, Sci. Rep., 10, 14961, https://doi.org/10.1038/s41598-020-71372-5, 2020.
Holmström, L., Ilvonen, L., Seppä, H., and Veski, S.: A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records, Ann. Appl. Stat., 9, 1194–1225, https://doi.org/10.1214/15-AOAS832, 2015.
Imbrie, J. and Kipp, N. G.: A new micropaleontological method for quantitative paleoclimatology: application to a late Pleistocene Caribbean core, in: The Late Cenozoic Glacial Ages, edited by: Turekian, K. K., Yale University Press, New Haven, 71–181, ISBN-10: 0300014201, 1971.
IPCC: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Lee, H., and Romero, J., IPCC, Geneva, Switzerland, 35–115, https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023.
Jiang, W., Guiot, J., Chu, G., Wu, H., Yuan, B., Hatté, C., and Guo, Z.: An improved methodology of the modern analogues technique for palaeoclimate reconstruction in arid and semi-arid regions, Boreas, 39, 145–153, https://doi.org/10.1111/j.1502-3885.2009.00115.x, 2010.
Jones, P. D., Briffa, K. R., Osborn, T. J., Lough, J. M., van Ommen, T. D., Vinther, B. M., Luterbacher, J., Wahl, E. R., Zwiers, F. W., Mann, M. E., Schmidt, G. A., Ammann, C. M., Buckley, B. M., Cobb, K. M., Esper, J., Goosse, H., Graham, N., Jansen, E., Kiefer, T., Kull, C., Küttel, M., Mosley-Thompson, E., Overpeck, J. T., Riedwyl, N., Schulz, M., Tudhope, A. W., Villalba, R., Wanner, H., Wolff, E., and Xoplaki, E.: High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects, Holocene, 19, 3–49, https://doi.org/10.1177/0959683608098952, 2009.
Juggins, S. and Birks, H. J. B.: Quantitative environmental reconstructions from biological data, in: Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques, edited by: Birks, H. J. B., Lotter, A. F., Juggins, S., and Smol, J. P., Springer Netherlands, Dordrecht, 431–494, https://doi.org/10.1007/978-94-007-2745-8_14, 2012.
Kageyama, M., Braconnot, P., Harrison, S. P., Haywood, A. M., Jungclaus, J. H., Otto-Bliesner, B. L., Peterschmitt, J.-Y., Abe-Ouchi, A., Albani, S., Bartlein, P. J., Brierley, C., Crucifix, M., Dolan, A., Fernandez-Donado, L., Fischer, H., Hopcroft, P. O., Ivanovic, R. F., Lambert, F., Lunt, D. J., Mahowald, N. M., Peltier, W. R., Phipps, S. J., Roche, D. M., Schmidt, G. A., Tarasov, L., Valdes, P. J., Zhang, Q., and Zhou, T.: The PMIP4 contribution to CMIP6 – Part 1: Overview and over-arching analysis plan, Geosci. Model Dev., 11, 1033–1057, https://doi.org/10.5194/gmd-11-1033-2018, 2018.
Kaufman, D., Mckay, N., Routson, N., Erb, M., Dätwyler, C., Sommer, P. S., and Davis, D.: Holocene global mean surface temperature, a multi-method reconstruction approach, Sci. Data, 7, 201, https://doi.org/10.1038/s41597-020-0530-7. 2020a.
Kaufman, D. S., McKay, N. P., Routson, C., Erb, M. P., Dätwyler, C., Sommer, P., Heiri, O., and Davis, B. A. S.: NOAA/WDS Paleoclimatology – Global Holocene Mean Surface Temperature Reconstructions, NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/vzys-1280, 2020c.
Kaufman, D. S. and McKay, N. P.: Technical Note: Past and future warming – direct comparison on multi-century timescales, Clim. Past, 18, 911–917, https://doi.org/10.5194/cp-18-911-2022, 2022.
Lincoln, P.: µ-XRF and varve data from Lake Nautajärvi (NAU-23), Version v1, Zenodo [data set], https://doi.org/10.5281/zenodo.14645779, 2025.
Lincoln, P., Tjallingii, R., Kosonen, E., Ojala, A., Abrook, A. M., and Martin-Puertas, C.: Disruption of boreal lake circulation in response to mid-Holocene warmth; Evidence from the varved sediments of Lake Nautajärvi, southern Finland, Sci. Total Environ., 964, 178519, https://doi.org/10.1016/j.scitotenv.2025.178519, 2025.
Liu, M., Prentice, I. C., ter Braak, C. J. F., and Harrison, S. P.: An improved statistical approach for reconstructing past climates from biotic assemblages, Proc. Math. Phys. Eng. Sci., 476, 20200346, https://doi.org/10.1098/rspa.2020.0346, 2020.
Liu, Z., Zhu, J., Rosenthal, Y., Zhang, X., Otto-Bliesner, B. L., Timmermann, A., Smith, R. S., Lohmann, G., Zheng, W., and Elison Timm, O.: The Holocene temperature conundrum, P. Natl. Acad. Sci. USA, 111, E3501–E3505, https://doi.org/10.1073/pnas.1407229111, 2014.
Martin-Puertas, C., Walsh, A. A., Blockley, S. P. E., Harding, P., Biddulph, G. E., Palmer, A., Ramisch, A., and Brauer, A.: The first Holocene varve chronology for the UK: based on the integration of varve counting, radiocarbon dating and tephrostratigraphy from Diss Mere (UK), Quat. Geochronol., 61, 101134, https://doi.org/10.1016/j.quageo.2020.101134, 2021.
Martin-Puertas, C., Hernandez, A., Pardo-Igúzquiza, E., Boyall, L., Brierley, C., Jiang, Z., Tjallingii, R., Blockley, S. P. E., and Rodríguez-Tovar, F. J.: Dampened predictable decadal North Atlantic climate fluctuations due to ice melting, Nat. Geosci., 16, 357–362, https://doi.org/10.1038/s41561-023-01145-y, 2023.
Mauri, A., Davis, B. A. S., Collins, P. M., and Kaplan, J. O. The climate of Europe during the Holocene: a gridded pollen-based reconstruction and its multi-proxy evaluation, Quaternary Sci. Rev., 112, 109–127, https://doi.org/10.1016/j.quascirev.2015.01.013, 2015.
Met Office: Monthly Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0, CEDA Archive [data set], https://doi.org/10.5285/35fb8318798e437ba5b108e5eca6e92d, 2025.
Met Office Hadley Centre: HadCET: Central England Temperature Data, https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html, last access: 4 November 2024.
Ojala, A. E. K. and Alenius, T.: 10 000 years of interannual sedimentation recorded in the Lake Nautajärvi (Finland) clastic–organic varves, Palaeogeogr. Palaeocl., 219, 285–302, https://doi.org/10.1016/j.palaeo.2005.01.002, 2005.
Ojala, A. E. K., Alenius, T., Seppä, H., and Giesecke, T.: Integrated varve and pollen-based temperature reconstruction from Finland: evidence for Holocene seasonal temperature patterns at high latitudes, Holocene, 18, 529–538, https://doi.org/10.1177/0959683608089207, 2008a.
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/10.31223/X5S31Z, 2021a.
Osman, M. B., Tierney, J. E., Tardif, R., Hakim, G. J., and Poulsen, C. J.: NOAA/WDS Paleoclimatology - Globally Resolved Surface Temperatures Since the Last Glacial Maximum, NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/njxd-hg08, 2021b.
PAGES2k Consortium: A global multiproxy database for temperature reconstruction of the Common Era, Sci. Data, 4, 170088, https://doi.org/10.1038/sdata.2017.88, 2017.
Parker, D. E., Legg, T. P. and Folland, C. K.: A new daily central England temperature series, 1772–1991, Int. J. Climatol., 12, 317–342, https://doi.org/10.1002/joc.3370120402, 1992.
Parnell, A. C., Sweeney, J., Doan, T. K., Salter-Townshend, M., Allen, J. R. M., Huntley, B., and Haslett, J.: Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility, J. R. Stat. Soc. C-Appl., 64, 115–138, 2015.
Parnell, A. C., Haslett, J., Sweeney, J., Doan, T. K., Allen, J. R. M., and Huntley, B.: Joint palaeoclimate reconstruction from pollen data via forward models and climate histories, Quaternary Sci. Rev., 151, 1, https://doi.org/10.1016/j.quascirev.2016.09.007, 2016.
Peti, L. and Augustinus, P. C.: Micro-XRF-inferred depositional history of the Orakei maar lake sediment sequence, Auckland, New Zealand, J. Paleolimnol., 67, 327–344, https://doi.org/10.1007/s10933-022-00235-y, 2022.
Plummer, M.: JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling, in: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, Austria, 20–22 March 2003, 1–10, https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf(last access: 1 November 2024), 2003.
Rasmussen, S. O., Vinther, B. M., Clausen, H. B., and Andersen, K. K.: Early Holocene climate oscillations recorded in three Greenland ice cores, Quaternary Sci. Rev., 26, 1907–1914, https://doi.org/10.1016/j.quascirev.2007.06.015, 2007.
Smerdon, J. E. and Pollack, H. N.: Reconstructing Earth's surface temperature over the past 2000 years: the science behind the headlines, WIREs Clim. Change, 7, 746–771, https://doi.org/10.1002/wcc.418, 2016.
Snyder, C. W.: The value of paleoclimate research in our changing climate, Clim. Change, 100, 407–418, https://doi.org/10.1007/s10584-010-9842-5, 2010.
Su, Y. S., Yajima, M., and Baio, G.: R2jags: Using R to run 'JAGS', R package version 0.8-9, CRAN [code], https://CRAN.R-project.org/package=R2jags (last access: 15 August 2025), 2024.
Sweeney, J., Salter-Townshend, M., Edwards, T., Buck, C. E., and Parnell, A. C.: Statistical challenges in estimating past climate changes, WIREs Comput. Stat., 10, e1437, https://doi.org/10.1002/wics.1437, 2018.
Tardif, R., Hakim, G. J., Perkins, W. A., Horlick, K. A., Erb, M. P., Emile-Geay, J., Anderson, D. M., Steig, E. J., and Noone, D.: Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling, Clim. Past, 15, 1251–1273, https://doi.org/10.5194/cp-15-1251-2019, 2019 (data available at: https://atmos.washington.edu/~hakim/lmr/, last access: 13 June 2025).
ter Braak, C. J. F., and Juggins, S.: Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages, Hydrobiologia 269, 485–502, https://doi.org/10.1007/BF00028046, 1993.
Tierney, J. E., Malevich, S. B., Gray, W., Vetter, L., and Thirumalai, K.: Bayesian calibration of the Mg/Ca paleothermometer in planktic foraminifera, Paleoceanogr. Paleoclimatol., 34, 2005–2030, https://doi.org/10.1029/2019PA003744, 2019.
Tingley, M. P. and Huybers, P.: A Bayesian algorithm for reconstructing climate anomalies in space and time. Part I: Development and applications to paleoclimate reconstruction problems, J. Climate, 23, 2759–2781, https://doi.org/10.1175/2009JCLI3015.1, 2010.
Tingley, M. P., Craigmile, P. F., Haran, M., Li, B., Mannshardt, E., and Rajaratnam, B.: Piecing together the past: statistical insights into paleoclimatic reconstructions, Quaternary Sci. Rev., 35, 1–22, https://doi.org/10.1016/j.quascirev.2012.01.012, 2012.
Tjallingii, R., Röhl, U., Kölling, M., and Bickert, T.: Influence of the water content on X-ray fluorescence core-scanning measurements in soft marine sediments, Geochem. Geophy. Geosy., 8, Q02004, https://doi.org/10.1029/2006GC001393, 2007.
van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., and Yau, C.: Bayesian statistics and modelling, Nat. Rev. Methods Primers, 1, 1–26, https://doi.org/10.1038/s43586-020-00001-2, 2021.
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P. C.: Rank-normalization, folding, and localization: an improved for assessing convergence of MCMC (with discussion), Bayesian Anal., 16, 667–718, https://doi.org/10.1214/20-BA1221, 2021.
Wastegård, S.: The Holocene of Sweden – a review, GFF, 144, 126–149 https://doi.org/10.1080/11035897.2022.2086290, 2022.
Wegmann, M. and Jaume-Santero, F.: Artificial intelligence achieves easy-to-adapt nonlinear global temperature reconstructions using minimal local data, Commun. Earth. Environ., 4, 217, https://doi.org/10.1038/s43247-023-00872-9, 2023.
Weltje, G. J. and Tjallingii, R.: Calibration of XRF core scanners for quantitative geochemical logging of sediment cores: theory and application, Earth Planet. Sc. Lett., 274, 423–438, https://doi.org/10.1016/j.epsl.2008.07.054, 2008.
Weltje, G. J., Bloemsma, M. R., Tjallingii, R., Heslop, D., Röhl, U., and Croudace, I. W.: Prediction of geochemical composition from XRF core scanner data: a new multivariate approach including automatic selection of calibration samples and quantification of uncertainties, in: Micro-XRF Studies of Sediment Cores: Applications of a Non-Destructive Tool for the Environmental Sciences, edited by: Croudace, I. W. and Rothwell, R. G., Springer Netherlands, Dordrecht, 507–534, https://doi.org/10.1007/978-94-017-9849-5_21, 2015.
Yu, G. and Harrison, S.: Holocene changes in atmospheric circulation patterns as shown by lake status changes in northern Europe, Boreas, 24, 260–258, https://doi.org/10.1111/j.1502-3885.1995.tb00778.x, 1995.
Zander, P. D., Żarczyński, M., Tylmann, W., Vogel, H., and Grosjean, M.: Subdecadal Holocene warm-season temperature variability in Central Europe recorded by biochemical varves, Geophys. Res. Lett., 51, e2024GL110871, https://doi.org/10.1029/2024GL110871, 2024.
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., Kay, J. E.: LGM paleoclimate constraints inform cloud parameterizations and equilibrium climate sensitivity in CESM2, J. Adv. Model. Earth Sy., 14, e2021MS002776, https://doi.org/10.1029/2021MS002776, 2022.
Zolitschka, B., Francus, P., Ojala, A. E. K., and Schimmelmann, A.: Varves in lake sediments – a review, Quaternary Sci. Rev., 117, 1–41, https://doi.org/10.1016/j.quascirev.2015.03.019, 2015.
Short summary
We present a new approach to reconstructing annual mean temperature using geochemical data from lake sediments. This paper uses Bayesian inference, a type of statistical approach, and creates a model called Simulating Climate Using Bayesian Inference with proxy Data Observations (SCUBIDO), which takes the high-resolution geochemical data and transforms them into quantitative climate information at an annual resolution. We show the results from two lakes in England and Finland to produce temperature reconstructions for the past 8000 years with data every year.
We present a new approach to reconstructing annual mean temperature using geochemical data from...
