{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T06:10:11Z","timestamp":1756447811711,"version":"3.44.0"},"reference-count":79,"publisher":"Copernicus GmbH","issue":"8","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["MR\/W009641\/1"],"award-info":[{"award-number":["MR\/W009641\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024934","name":"SFI Research Centre for Energy, Climate and Marine","doi-asserted-by":"publisher","award":["22\/CC\/11103"],"award-info":[{"award-number":["22\/CC\/11103"]}],"id":[{"id":"10.13039\/501100024934","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021525","name":"Insight SFI Research Centre for Data Analytics","doi-asserted-by":"publisher","award":["12\/RC\/2289_P2"],"award-info":[{"award-number":["12\/RC\/2289_P2"]}],"id":[{"id":"10.13039\/501100021525","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RYC2020-029253-I"],"award-info":[{"award-number":["RYC2020-029253-I"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012620","name":"Royal Holloway, University of London","doi-asserted-by":"publisher","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}],"id":[{"id":"10.13039\/501100012620","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clim. Past"],"abstract":"<jats:p>Abstract. Quantification of proxy records obtained from geological archives is key for extending the observational record to estimate the rate, strength, and impact of past climate changes but also for validating climate model simulations, improving future climate predictions. SCUBIDO (Simulating Climate Using Bayesian Inference with proxy Data Observations) is a new statistical model for reconstructing palaeoclimate variability and its uncertainty using Bayesian inference on multivariate non-biological proxy data.\u00a0We have developed the model for annually laminated (varved) lake sediments, as they provide a high temporal resolution to reconstructions with precise chronologies. This model uses non-destructive X-ray fluorescence core scanning (XRF-CS) data (chemical elemental composition of the sediments) because it can provide multivariate proxy information at a near-continuous, sub-millimetre resolution, and, when applied to annually laminated (varved) lake sediments or sediments with high accumulation rates, the reconstructions can be of an annual resolution. Whilst this model has been built for this proxy type, its flexibility means that the model could be applied to other multivariate proxy datasets. SCUBIDO uses a calibration period of instrumental climate data and overlapping \u00b5XRF-CS data to learn about the direct relationship between each geochemical element (reflecting different depositional processes) and climate but also the covariant response between the elements and climate. The understanding of these relationships is then applied to the rest of the record to transform the proxy values into a posterior distribution of palaeoclimate with quantified uncertainties. In this paper, we describe the mathematical details of this Bayesian approach and show detailed walk-through examples that reconstruct Holocene annual mean temperature from two varved lake records from central England and southern Finland. We choose to use varved sediments to demonstrate this approach, as SCUBIDO does not include a chronological module; thus the tight chronology associated with varved sediments is important. The out-of-sample validation for both sites shows a good agreement between the reconstructed and instrumental temperatures, emphasising the validity of this approach. The mathematical details and code have been synthesised into the R package, SCUBIDO, for simplification and to encourage others to use this modelling approach and produce their own reconstructions. Whilst the model has been designed and tested on varved sediments, \u00b5XRF-CS data from other types of sediment records that record a climate signal could also benefit from this approach.\n                    <\/jats:p>","DOI":"10.5194\/cp-21-1465-2025","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T05:42:37Z","timestamp":1756446157000},"page":"1465-1480","source":"Crossref","is-referenced-by-count":0,"title":["SCUBIDO: a Bayesian modelling approach to reconstruct palaeoclimate from multivariate lake sediment data"],"prefix":"10.5194","volume":"21","author":[{"given":"Laura","family":"Boyall","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7956-7939","authenticated-orcid":false,"given":"Andrew C.","family":"Parnell","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0566-2970","authenticated-orcid":false,"given":"Paul","family":"Lincoln","sequence":"additional","affiliation":[]},{"given":"Antti","family":"Ojala","sequence":"additional","affiliation":[]},{"given":"Armand","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[]},{"given":"Celia","family":"Martin-Puertas","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Aitchison,\u00a0J.: The statistical analysis of compositional data, Chapman &amp;amp; Hall, London, https:\/\/doi.org\/10.1002\/bimj.4710300705, 1986.","DOI":"10.1002\/bimj.4710300705"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Anchukaitis,\u00a0K.\u00a0J. and Smerdon,\u00a0J.\u00a0E.: 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.","DOI":"10.1016\/j.quascirev.2022.107537"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Bader,\u00a0J., Jungclaus,\u00a0J., Krivova,\u00a0N., Lorenz,\u00a0S., Maycock,\u00a0A., Raddatz,\u00a0T., Schmidt,\u00a0H., Toohey,\u00a0M., Wu,\u00a0C.-J., and Claussen,\u00a0M.: Global temperature modes shed light on the Holocene temperature conundrum, Nat. Commun., 11, 4726, https:\/\/doi.org\/10.1038\/s41467-020-18478-6, 2020.","DOI":"10.1038\/s41467-020-18478-6"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1016\/j.earscirev.2023.104639"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Birks,\u00a0H.\u00a0J.\u00a0B.: Overview of numerical methods in palaeolimnology, in: Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques, edited by: Birks,\u00a0H.\u00a0J.\u00a0B., Lotter,\u00a0A.\u00a0F., Juggins,\u00a0S., and Smol,\u00a0J.\u00a0P., Springer, Dordrecht, https:\/\/doi.org\/10.1007\/978-94-007-2745-8_2, 19\u201392, 2012.","DOI":"10.1007\/978-94-007-2745-8_2"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Bova,\u00a0S., Rosenthal,\u00a0Y., Liu,\u00a0Z., Godad,\u00a0S.\u00a0P., and Yan,\u00a0M.: Seasonal origin of the thermal maxima at the Holocene and the last interglacial, Nature, 589, 548\u2013553, https:\/\/doi.org\/10.1038\/s41586-020-03155-x, 2021.","DOI":"10.1038\/s41586-020-03155-x"},{"key":"ref7","unstructured":"Boyall, L. and Martin-Puertas, C.: Diss Mere XRF Data, Version v1, Zenodo [data set], https:\/\/doi.org\/10.5281\/zenodo.15168266, 2025."},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Boyall,\u00a0L., Valc\u00e1rcel,\u00a0J.\u00a0I., Harding,\u00a0P., Hern\u00e1ndez,\u00a0A., and Martin-Puertas,\u00a0C.: Disentangling the environmental signals recorded in Holocene calcite varves based on modern lake observations and annual sedimentary processes in Diss Mere, England,\u00a0J. Paleolimnol., 70, 39\u201356, https:\/\/doi.org\/10.1007\/s10933-023-00282-z, 2023.","DOI":"10.1007\/s10933-023-00282-z"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Boyall,\u00a0L., Martin-Puertas,\u00a0C., Tjallingii,\u00a0R., Milner,\u00a0A.\u00a0M., and Blockley,\u00a0S.\u00a0P.\u00a0E.: 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.","DOI":"10.1002\/jqs.3646"},{"key":"ref10","unstructured":"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."},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Brooks,\u00a0S.\u00a0P. and Gelman,\u00a0A.: General methods for monitoring convergence of iterative simulations,\u00a0J. Comput. Graph. Stat., 7, 434\u2013455, https:\/\/doi.org\/10.1080\/10618600.1998.10474787, 1998.","DOI":"10.1080\/10618600.1998.10474787"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Burls,\u00a0N. and Sagoo,\u00a0N.: Increasingly sophisticated climate models need the out-of-sample tests paleoclimates provide,\u00a0J. Adv. Model. Earth Sy., 14, e2022MS003389, https:\/\/doi.org\/10.1029\/2022MS003389, 2022.","DOI":"10.1029\/2022MS003389"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Cahill,\u00a0N., Croke,\u00a0J., Campbell,\u00a0M., Hughes,\u00a0K., Vitkovsky,\u00a0J., Kilgallen,\u00a0J.\u00a0E., and Parnell,\u00a0A.: A Bayesian time series model for reconstructing hydroclimate from multiple proxies, Environmetrics, 34, e2786, https:\/\/doi.org\/10.1002\/env.2786, 2023.","DOI":"10.1002\/env.2786"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Cartapanis,\u00a0O., Jonkers,\u00a0L., Moffa-Sanchez,\u00a0P., Jaccard,\u00a0S.\u00a0L., and de Vernal,\u00a0A.: Complex spatio-temporal structure of the Holocene Thermal Maximum, Nat. Commun., 13, 5662, https:\/\/doi.org\/10.1038\/s41467-022-33362-1, 2022.","DOI":"10.1038\/s41467-022-33362-1"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Cassou,\u00a0C., Kushnir,\u00a0Y., Hawkins,\u00a0E., Pirani,\u00a0A., Kucharski,\u00a0F., Kand,\u00a0I.-S., and Caltabiano,\u00a0N.: Decadal climate variability and predictability: Challenges and opportunities, B. Am. Meteorol. Soc., 99, 479\u2013490, https:\/\/doi.org\/10.1175\/BAMS-D-16-0286.1, 2018.","DOI":"10.1175\/BAMS-D-16-0286.1"},{"key":"ref16","unstructured":"Chamberlain,\u00a0S., Hocking,\u00a0D., Anderson,\u00a0B., Salmon,\u00a0M., Erickson,\u00a0A., Potter,\u00a0N., Stachelek,\u00a0J., Simmons,\u00a0A., Ram,\u00a0K., and Edmund,\u00a0H.: rnoaa: NOAA weather data, R. R package version 1.4.0, https:\/\/github.com\/ropensci\/rnoaa (last access: 12 December 2024), 2024."},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Chevalier,\u00a0M., Davis,\u00a0B.\u00a0A.\u00a0S., Heiri,\u00a0O., Sepp\u00e4,\u00a0H., Chase,\u00a0B.\u00a0M., Gajewski,\u00a0K., Lacourse,\u00a0T., Telford,\u00a0R., Finsinger,\u00a0W., Guiot,\u00a0J., K\u00fchl,\u00a0N., Maezumi,\u00a0S.\u00a0Y., Tipton,\u00a0J., Carter,\u00a0V., Brussel,\u00a0T., Phelps,\u00a0L., Dawson,\u00a0A., Zanon,\u00a0M., Vall\u00e9,\u00a0F., Nolan,\u00a0C., Mauri,\u00a0A., de Vernal,\u00a0A., Izumi,\u00a0K., Holmstr\u00f6m,\u00a0L., Marsicek,\u00a0J., Goring,\u00a0S., Sommer,\u00a0P., Chaput,\u00a0M., and Kupriyanov,\u00a0D.: Pollen-based climate reconstruction techniques for late Quaternary studies, Earth Sci. Rev., 210, 1\u201333, https:\/\/doi.org\/10.1016\/j.earscirev.2020.103384, 2020.","DOI":"10.1016\/j.earscirev.2020.103384"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Chu,\u00a0P.-S. and Zhao,\u00a0X.: Bayesian analysis for extreme climatic events: A review, Atmos. Res., 102, 243\u2013262, https:\/\/doi.org\/10.1016\/j.atmosres.2011.07.001, 2011.","DOI":"10.1016\/j.atmosres.2011.07.001"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Davis,\u00a0B.\u00a0A.\u00a0S., Brewer,\u00a0S., Stevenson,\u00a0A.\u00a0C., and Guiot,\u00a0J.: The temperature of Europe during the Holocene reconstructed from pollen data, Quaternary Sci. Rev., 22, 1701\u20131716, https:\/\/doi.org\/10.1016\/S0277-3791(03)00173-2, 2003.","DOI":"10.1016\/S0277-3791(03)00173-2"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Davies,\u00a0S.\u00a0J., Lamb,\u00a0H.\u00a0F., and Roberts,\u00a0S.\u00a0J.: 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,\u00a0I.\u00a0W. and Rothwell,\u00a0R.\u00a0G., Developments in Paleoenvironmental Research, Springer, Dordrecht, 189\u2013226, https:\/\/doi.org\/10.1007\/978-94-017-9849-5_7, 2015.","DOI":"10.1007\/978-94-017-9849-5_7"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1029\/2020GC009248"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Erb,\u00a0M.\u00a0P., McKay,\u00a0N.\u00a0P., Steiger,\u00a0N., Dee,\u00a0S., Hancock,\u00a0C., Ivanovic,\u00a0R.\u00a0F., Gregoire,\u00a0L.\u00a0J., and Valdes,\u00a0P.: Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation, Clim. Past, 18, 2599\u20132629, https:\/\/doi.org\/10.5194\/cp-18-2599-2022, 2022a.","DOI":"10.5194\/cp-18-2599-2022"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.5194\/egusphere-2022-184"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Gelman,\u00a0A. and Rubin,\u00a0D.\u00a0B.: Inference from iterative simulation using multiple sequences, Stat. Sci., 7, 457\u2013472, https:\/\/doi.org\/10.1214\/ss\/1177011136, 1992.","DOI":"10.1214\/ss\/1177011136"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Gelman,\u00a0A., Carlin,\u00a0J.\u00a0B., Stern,\u00a0H.\u00a0S., and Rubin,\u00a0D.\u00a0B.: Bayesian data analysis, 1st edn., Chapman and Hall\/CRC, https:\/\/doi.org\/10.1201\/9780429258411, 1995.","DOI":"10.1201\/9780429258411"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Haslett,\u00a0J., Whiley,\u00a0M., Bhattacharya,\u00a0S., Salter-Townshend,\u00a0M., Wilson,\u00a0S.\u00a0P., Allen,\u00a0J.\u00a0R.\u00a0M., Huntley,\u00a0B., and Mitchell,\u00a0F.\u00a0J.\u00a0G.: Bayesian palaeoclimate reconstruction, J.\u00a0R. Stat. Soc. A Stat., 169, 395\u2013438, 2006.","DOI":"10.1111\/j.1467-985X.2006.00429.x"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez,\u00a0A., S\u00e1nchez-L\u00f3pez,\u00a0G., Pla-Rabes,\u00a0S., Comas-Bru,\u00a0L., Parnell,\u00a0A., Cahill,\u00a0N., Geyer,\u00a0A., Trigo,\u00a0R.\u00a0M., and Giralt,\u00a0S.: A 2000\u00a0year Bayesian NAO reconstruction from the Iberian Peninsula, Sci. Rep., 10, 14961, https:\/\/doi.org\/10.1038\/s41598-020-71372-5, 2020.","DOI":"10.1038\/s41598-020-71372-5"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Holmstr\u00f6m,\u00a0L., Ilvonen,\u00a0L., Sepp\u00e4,\u00a0H., and Veski,\u00a0S.: A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records, Ann. Appl. Stat., 9, 1194\u20131225, https:\/\/doi.org\/10.1214\/15-AOAS832, 2015.","DOI":"10.1214\/15-AOAS832"},{"key":"ref29","unstructured":"Imbrie,\u00a0J. and Kipp,\u00a0N.\u00a0G.: A new micropaleontological method for quantitative paleoclimatology: application to a late Pleistocene Caribbean core, in: The Late Cenozoic Glacial Ages, edited by: Turekian,\u00a0K.\u00a0K., Yale University Press, New Haven, 71\u2013181, ISBN-10: 0300014201, 1971."},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"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\u2013115, https:\/\/doi.org\/10.59327\/IPCC\/AR6-9789291691647, 2023.","DOI":"10.59327\/IPCC\/AR6-9789291691647"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Jiang,\u00a0W., Guiot,\u00a0J., Chu,\u00a0G., Wu,\u00a0H., Yuan,\u00a0B., Hatt\u00e9,\u00a0C., and Guo,\u00a0Z.: An improved methodology of the modern analogues technique for palaeoclimate reconstruction in arid and semi-arid regions, Boreas, 39, 145\u2013153, https:\/\/doi.org\/10.1111\/j.1502-3885.2009.00115.x, 2010.","DOI":"10.1111\/j.1502-3885.2009.00115.x"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Jones,\u00a0P.\u00a0D., Briffa,\u00a0K.\u00a0R., Osborn,\u00a0T.\u00a0J., Lough,\u00a0J.\u00a0M., van Ommen,\u00a0T.\u00a0D., Vinther,\u00a0B.\u00a0M., Luterbacher,\u00a0J., Wahl,\u00a0E.\u00a0R., Zwiers,\u00a0F.\u00a0W., Mann,\u00a0M.\u00a0E., Schmidt,\u00a0G.\u00a0A., Ammann,\u00a0C.\u00a0M., Buckley,\u00a0B.\u00a0M., Cobb,\u00a0K.\u00a0M., Esper,\u00a0J., Goosse,\u00a0H., Graham,\u00a0N., Jansen,\u00a0E., Kiefer,\u00a0T., Kull,\u00a0C., K\u00fcttel,\u00a0M., Mosley-Thompson,\u00a0E., Overpeck,\u00a0J.\u00a0T., Riedwyl,\u00a0N., Schulz,\u00a0M., Tudhope,\u00a0A.\u00a0W., Villalba,\u00a0R., Wanner,\u00a0H., Wolff,\u00a0E., and Xoplaki,\u00a0E.: High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects, Holocene, 19, 3\u201349, https:\/\/doi.org\/10.1177\/0959683608098952, 2009.","DOI":"10.1177\/0959683608098952"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Juggins,\u00a0S. and Birks,\u00a0H.\u00a0J.\u00a0B.: Quantitative environmental reconstructions from biological data, in: Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques, edited by: Birks,\u00a0H.\u00a0J.\u00a0B., Lotter,\u00a0A.\u00a0F., Juggins,\u00a0S., and Smol,\u00a0J.\u00a0P., Springer Netherlands, Dordrecht, 431\u2013494, https:\/\/doi.org\/10.1007\/978-94-007-2745-8_14, 2012.","DOI":"10.1007\/978-94-007-2745-8_14"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"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\u00a0\u2013 Part 1: Overview and over-arching analysis plan, Geosci. Model Dev., 11, 1033\u20131057, https:\/\/doi.org\/10.5194\/gmd-11-1033-2018, 2018.","DOI":"10.5194\/gmd-11-1033-2018"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Kaufman,\u00a0D., Mckay,\u00a0N., Routson,\u00a0N., Erb,\u00a0M., D\u00e4twyler,\u00a0C., Sommer,\u00a0P.\u00a0S., and Davis,\u00a0D.: Holocene global mean surface temperature, a multi-method reconstruction approach, Sci. Data, 7, 201, https:\/\/doi.org\/10.1038\/s41597-020-0530-7. 2020a.","DOI":"10.1038\/s41597-020-0530-7"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Kaufman,\u00a0D., McKay,\u00a0N., Routson,\u00a0C., Erb,\u00a0M., Davis,\u00a0B., Heiri,\u00a0O., Jaccard,\u00a0S., Tierney,\u00a0J., D\u00e4twyler,\u00a0C., Axford,\u00a0Y., Brussel,\u00a0T., Cartapanis,\u00a0O., Chase,\u00a0B., Dawson,\u00a0A., de Vernal,\u00a0A., Engels,\u00a0S., Jonkers,\u00a0L., Marsicek,\u00a0J., Moffa-S\u00e1nchez,\u00a0P., Morrill,\u00a0C., Orsi,\u00a0A., Rehfeld,\u00a0K., Saunders,\u00a0K., Sommer,\u00a0P.\u00a0S., Thomas,\u00a0E., Tonello,\u00a0M., T\u00f3th,\u00a0M., Vachula,\u00a0R., Andreev,\u00a0A., Bertrand,\u00a0S., Biskaborn,\u00a0B., Bringu\u00e9,\u00a0M., Brooks,\u00a0S., Caniup\u00e1n,\u00a0M., Chevalier,\u00a0M., Cwynar,\u00a0L., Emile-Geay,\u00a0J., Fegyveresi,\u00a0J., Feurdean,\u00a0A., Finsinger,\u00a0W., Fortin,\u00a0M.-C., Foster,\u00a0L., Fox,\u00a0M., Gajewski,\u00a0K., Grosjean,\u00a0M., Hausmann,\u00a0S., Heinrichs,\u00a0M., Holmes,\u00a0N., Ilyashuk,\u00a0B., Ilyashuk,\u00a0E., Juggins,\u00a0S., Khider,\u00a0D., Koinig,\u00a0K., Langdon,\u00a0P., Larocque-Tobler,\u00a0I., Li,\u00a0J., Lotter,\u00a0A., Luoto,\u00a0T., Mackay,\u00a0A., Magyari,\u00a0E., Malevich,\u00a0S., Mark,\u00a0B., Massaferro,\u00a0J., Montade,\u00a0V., Nazarova,\u00a0L., Novenko,\u00a0E., Pa\u0159il,\u00a0P., Pearson,\u00a0E., Peros,\u00a0M., Pienitz,\u00a0R., P\u0142\u00f3ciennik,\u00a0M., Porinchu,\u00a0D., Potito,\u00a0A., Rees,\u00a0A., Reinemann,\u00a0S., Roberts,\u00a0S., Rolland,\u00a0N., Salonen,\u00a0S., Self,\u00a0A., Sepp\u00e4,\u00a0H., Shala,\u00a0S., St-Jacques,\u00a0J.-M., Stenni,\u00a0B., Syrykh,\u00a0L., Tarrats,\u00a0P., Taylor,\u00a0K., van den Bos,\u00a0V., Velle,\u00a0G., Wahl,\u00a0E., Walker,\u00a0I., Wilmshurst,\u00a0J., Zhang,\u00a0E., and Zhilich,\u00a0S.: A global database of Holocene paleotemperature records, Sci. Data, 7, 115, https:\/\/doi.org\/10.1038\/s41597-020-0445-3, 2020b.","DOI":"10.1038\/s41597-020-0515-6"},{"key":"ref37","unstructured":"Kaufman, D. S., McKay, N. P., Routson, C., Erb, M. P., D\u00e4twyler, C., Sommer, P., Heiri, O., and Davis, B. A. S.: NOAA\/WDS Paleoclimatology \u2013 Global Holocene Mean Surface Temperature Reconstructions, NOAA National Centers for Environmental Information [data set], https:\/\/doi.org\/10.25921\/vzys-1280, 2020c."},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Kaufman,\u00a0D.\u00a0S. and McKay,\u00a0N.\u00a0P.: Technical Note: Past and future warming\u00a0\u2013 direct comparison on multi-century timescales, Clim. Past, 18, 911\u2013917, https:\/\/doi.org\/10.5194\/cp-18-911-2022, 2022.","DOI":"10.5194\/cp-18-911-2022"},{"key":"ref39","unstructured":"Lincoln, P.: \u00b5-XRF and varve data from Lake Nautaj\u00e4rvi (NAU-23), Version\u00a0v1, Zenodo [data set], https:\/\/doi.org\/10.5281\/zenodo.14645779, 2025."},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Lincoln,\u00a0P., Tjallingii,\u00a0R., Kosonen,\u00a0E., Ojala,\u00a0A., Abrook,\u00a0A.\u00a0M., and Martin-Puertas,\u00a0C.: Disruption of boreal lake circulation in response to mid-Holocene warmth; Evidence from the varved sediments of Lake Nautaj\u00e4rvi, southern Finland, Sci. Total Environ., 964, 178519, https:\/\/doi.org\/10.1016\/j.scitotenv.2025.178519, 2025.","DOI":"10.1016\/j.scitotenv.2025.178519"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Liu,\u00a0M., Prentice,\u00a0I.\u00a0C., ter Braak,\u00a0C.\u00a0J.\u00a0F., and Harrison,\u00a0S.\u00a0P.: 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.","DOI":"10.1098\/rspa.2020.0346"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"Liu,\u00a0Z., Zhu,\u00a0J., Rosenthal,\u00a0Y., Zhang,\u00a0X., Otto-Bliesner,\u00a0B.\u00a0L., Timmermann,\u00a0A., Smith,\u00a0R.\u00a0S., Lohmann,\u00a0G., Zheng,\u00a0W., and Elison Timm,\u00a0O.: The Holocene temperature conundrum, P. Natl. Acad. Sci. USA, 111, E3501\u2013E3505, https:\/\/doi.org\/10.1073\/pnas.1407229111, 2014.","DOI":"10.1073\/pnas.1407229111"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Martin-Puertas,\u00a0C., Walsh,\u00a0A.\u00a0A., Blockley,\u00a0S.\u00a0P.\u00a0E., Harding,\u00a0P., Biddulph,\u00a0G.\u00a0E., Palmer,\u00a0A., Ramisch,\u00a0A., and Brauer,\u00a0A.: 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.","DOI":"10.1016\/j.quageo.2020.101134"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Martin-Puertas,\u00a0C., Hernandez,\u00a0A., Pardo-Ig\u00fazquiza,\u00a0E., Boyall,\u00a0L., Brierley,\u00a0C., Jiang,\u00a0Z., Tjallingii,\u00a0R., Blockley,\u00a0S.\u00a0P.\u00a0E., and Rodr\u00edguez-Tovar,\u00a0F.\u00a0J.: Dampened predictable decadal North Atlantic climate fluctuations due to ice melting, Nat. Geosci., 16, 357\u2013362, https:\/\/doi.org\/10.1038\/s41561-023-01145-y, 2023.","DOI":"10.1038\/s41561-023-01145-y"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"Mauri,\u00a0A., Davis,\u00a0B.\u00a0A.\u00a0S., Collins,\u00a0P.\u00a0M., and Kaplan,\u00a0J.\u00a0O. The climate of Europe during the Holocene: a gridded pollen-based reconstruction and its multi-proxy evaluation, Quaternary Sci. Rev., 112, 109\u2013127, https:\/\/doi.org\/10.1016\/j.quascirev.2015.01.013, 2015.","DOI":"10.1016\/j.quascirev.2015.01.013"},{"key":"ref46","unstructured":"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."},{"key":"ref47","unstructured":"Met Office Hadley Centre: HadCET: Central England Temperature Data, https:\/\/www.metoffice.gov.uk\/hadobs\/hadcet\/data\/download.html, last access: 4 November 2024."},{"key":"ref48","doi-asserted-by":"crossref","unstructured":"Ojala,\u00a0A.\u00a0E.\u00a0K. and Alenius,\u00a0T.: 10\u2009000\u00a0years of interannual sedimentation recorded in the Lake Nautaj\u00e4rvi (Finland) clastic\u2013organic varves, Palaeogeogr. Palaeocl., 219, 285\u2013302, https:\/\/doi.org\/10.1016\/j.palaeo.2005.01.002, 2005.","DOI":"10.1016\/j.palaeo.2005.01.002"},{"key":"ref49","doi-asserted-by":"crossref","unstructured":"Ojala,\u00a0A.\u00a0E.\u00a0K., Alenius,\u00a0T., Sepp\u00e4,\u00a0H., and Giesecke,\u00a0T.: Integrated varve and pollen-based temperature reconstruction from Finland: evidence for Holocene seasonal temperature patterns at high latitudes, Holocene, 18, 529\u2013538, https:\/\/doi.org\/10.1177\/0959683608089207, 2008a.","DOI":"10.1177\/0959683608089207"},{"key":"ref50","doi-asserted-by":"crossref","unstructured":"Ojala,\u00a0A.\u00a0E.\u00a0K. Heinsalu,\u00a0A., Kauppila,\u00a0T., Alenius,\u00a0T., and Saarnisto,\u00a0M. Characterising changes in the sedimentary environment of a varved lake sediment record in southern central Finland around 8000\u2009&lt;span class=&quot;inline-formula&quot;&gt;cal\u2009yr\u2009BP&lt;\/span&gt;, J. Quaternary Sci., 23, 765\u2013775, https:\/\/doi.org\/10.1002\/jqs.1157, 2008b.","DOI":"10.1002\/jqs.1157"},{"key":"ref51","doi-asserted-by":"crossref","unstructured":"Osman,\u00a0M.\u00a0B., Tierney,\u00a0J.\u00a0E., Zhu,\u00a0J., Tardif,\u00a0R., Hakim,\u00a0G.\u00a0J., King,\u00a0J., and Poulsen,\u00a0C.\u00a0J.: Globally resolved surface temperatures since the Last Glacial Maximum, Nature, 599, 239\u2013244, https:\/\/doi.org\/10.31223\/X5S31Z, 2021a.","DOI":"10.1038\/s41586-021-03984-4"},{"key":"ref52","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.31223\/X5S31Z"},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1038\/sdata.2017.88"},{"key":"ref54","doi-asserted-by":"crossref","unstructured":"Parker, D. E., Legg, T. P. and Folland, C. K.: A new daily central England temperature series, 1772\u20131991, Int. J. Climatol., 12, 317\u2013342, https:\/\/doi.org\/10.1002\/joc.3370120402, 1992.","DOI":"10.1002\/joc.3370120402"},{"key":"ref55","doi-asserted-by":"crossref","unstructured":"Parnell,\u00a0A.\u00a0C., Sweeney,\u00a0J., Doan,\u00a0T.\u00a0K., Salter-Townshend,\u00a0M., Allen,\u00a0J.\u00a0R.\u00a0M., Huntley,\u00a0B., and Haslett,\u00a0J.: Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility,\u00a0J.\u00a0R. Stat. Soc. C-Appl., 64, 115\u2013138, 2015.","DOI":"10.1111\/rssc.12065"},{"key":"ref56","doi-asserted-by":"crossref","unstructured":"Parnell,\u00a0A.\u00a0C., Haslett,\u00a0J., Sweeney,\u00a0J., Doan,\u00a0T.\u00a0K., Allen,\u00a0J.\u00a0R.\u00a0M., and Huntley,\u00a0B.: 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.","DOI":"10.1016\/j.quascirev.2016.09.007"},{"key":"ref57","doi-asserted-by":"crossref","unstructured":"Peti,\u00a0L. and Augustinus,\u00a0P.\u00a0C.: Micro-XRF-inferred depositional history of the Orakei maar lake sediment sequence, Auckland, New Zealand,\u00a0J. Paleolimnol., 67, 327\u2013344, https:\/\/doi.org\/10.1007\/s10933-022-00235-y, 2022.","DOI":"10.1007\/s10933-022-00235-y"},{"key":"ref58","unstructured":"Plummer,\u00a0M.: 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\u201322 March 2003, 1\u201310, https:\/\/www.r-project.org\/conferences\/DSC-2003\/Proceedings\/Plummer.pdf(last access: 1 November 2024), 2003."},{"key":"ref59","doi-asserted-by":"crossref","unstructured":"Rasmussen,\u00a0S.\u00a0O., Vinther,\u00a0B.\u00a0M., Clausen,\u00a0H.\u00a0B., and Andersen,\u00a0K.\u00a0K.: Early Holocene climate oscillations recorded in three Greenland ice cores, Quaternary Sci. Rev., 26, 1907\u20131914, https:\/\/doi.org\/10.1016\/j.quascirev.2007.06.015, 2007.","DOI":"10.1016\/j.quascirev.2007.06.015"},{"key":"ref60","doi-asserted-by":"crossref","unstructured":"Smerdon,\u00a0J.\u00a0E. and Pollack,\u00a0H.\u00a0N.: Reconstructing Earth's surface temperature over the past 2000\u00a0years: the science behind the headlines, WIREs Clim. Change, 7, 746\u2013771, https:\/\/doi.org\/10.1002\/wcc.418, 2016.","DOI":"10.1002\/wcc.418"},{"key":"ref61","doi-asserted-by":"crossref","unstructured":"Snyder,\u00a0C.\u00a0W.: The value of paleoclimate research in our changing climate, Clim. Change, 100, 407\u2013418, https:\/\/doi.org\/10.1007\/s10584-010-9842-5, 2010.","DOI":"10.1007\/s10584-010-9842-5"},{"key":"ref62","unstructured":"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."},{"key":"ref63","doi-asserted-by":"crossref","unstructured":"Sweeney,\u00a0J., Salter-Townshend,\u00a0M., Edwards,\u00a0T., Buck,\u00a0C.\u00a0E., and Parnell,\u00a0A.\u00a0C.: Statistical challenges in estimating past climate changes, WIREs Comput. Stat., 10, e1437, https:\/\/doi.org\/10.1002\/wics.1437, 2018.","DOI":"10.1002\/wics.1437"},{"key":"ref64","doi-asserted-by":"crossref","unstructured":"Tardif,\u00a0R., Hakim,\u00a0G.\u00a0J., Perkins,\u00a0W.\u00a0A., Horlick,\u00a0K.\u00a0A., Erb,\u00a0M.\u00a0P., Emile-Geay,\u00a0J., Anderson,\u00a0D.\u00a0M., Steig,\u00a0E.\u00a0J., and Noone,\u00a0D.: Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling, Clim. Past, 15, 1251\u20131273, https:\/\/doi.org\/10.5194\/cp-15-1251-2019, 2019 (data available at: https:\/\/atmos.washington.edu\/~hakim\/lmr\/, last access: 13 June 2025).","DOI":"10.5194\/cp-15-1251-2019"},{"key":"ref65","doi-asserted-by":"crossref","unstructured":"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\u2013502, https:\/\/doi.org\/10.1007\/BF00028046, 1993.","DOI":"10.1007\/978-94-017-3622-0_49"},{"key":"ref66","doi-asserted-by":"crossref","unstructured":"Tierney,\u00a0J.\u00a0E., Malevich,\u00a0S.\u00a0B., Gray,\u00a0W., Vetter,\u00a0L., and Thirumalai,\u00a0K.: Bayesian calibration of the Mg\/Ca paleothermometer in planktic foraminifera, Paleoceanogr. Paleoclimatol., 34, 2005\u20132030, https:\/\/doi.org\/10.1029\/2019PA003744, 2019.","DOI":"10.1029\/2019PA003744"},{"key":"ref67","doi-asserted-by":"crossref","unstructured":"Tingley,\u00a0M.\u00a0P. and Huybers,\u00a0P.: A Bayesian algorithm for reconstructing climate anomalies in space and time. Part I: Development and applications to paleoclimate reconstruction problems,\u00a0J. Climate, 23, 2759\u20132781, https:\/\/doi.org\/10.1175\/2009JCLI3015.1, 2010.","DOI":"10.1175\/2009JCLI3015.1"},{"key":"ref68","doi-asserted-by":"crossref","unstructured":"Tingley,\u00a0M.\u00a0P., Craigmile,\u00a0P.\u00a0F., Haran,\u00a0M., Li,\u00a0B., Mannshardt,\u00a0E., and Rajaratnam,\u00a0B.: Piecing together the past: statistical insights into paleoclimatic reconstructions, Quaternary Sci. Rev., 35, 1\u201322, https:\/\/doi.org\/10.1016\/j.quascirev.2012.01.012, 2012.","DOI":"10.1016\/j.quascirev.2012.01.012"},{"key":"ref69","doi-asserted-by":"crossref","unstructured":"Tjallingii,\u00a0R., R\u00f6hl,\u00a0U., K\u00f6lling,\u00a0M., and Bickert,\u00a0T.: 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.","DOI":"10.1029\/2006GC001393"},{"key":"ref70","doi-asserted-by":"crossref","unstructured":"van de Schoot,\u00a0R., Depaoli,\u00a0S., King,\u00a0R., Kramer,\u00a0B., M\u00e4rtens,\u00a0K., Tadesse,\u00a0M.\u00a0G., Vannucci,\u00a0M., Gelman,\u00a0A., Veen,\u00a0D., Willemsen,\u00a0J., and Yau,\u00a0C.: Bayesian statistics and modelling, Nat. Rev. Methods Primers, 1, 1\u201326, https:\/\/doi.org\/10.1038\/s43586-020-00001-2, 2021.","DOI":"10.1038\/s43586-020-00001-2"},{"key":"ref71","doi-asserted-by":"crossref","unstructured":"Vehtari,\u00a0A., Gelman,\u00a0A., Simpson,\u00a0D., Carpenter,\u00a0B., and B\u00fcrkner,\u00a0P.\u00a0C.: Rank-normalization, folding, and localization: an improved for assessing convergence of MCMC (with discussion), Bayesian Anal., 16, 667\u2013718, https:\/\/doi.org\/10.1214\/20-BA1221, 2021.","DOI":"10.1214\/20-BA1221"},{"key":"ref72","doi-asserted-by":"crossref","unstructured":"Wasteg\u00e5rd,\u00a0S.: The Holocene of Sweden \u2013 a review, GFF, 144, 126\u2013149 https:\/\/doi.org\/10.1080\/11035897.2022.2086290, 2022.","DOI":"10.1080\/11035897.2022.2086290"},{"key":"ref73","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1038\/s43247-023-00872-9"},{"key":"ref74","doi-asserted-by":"crossref","unstructured":"Weltje,\u00a0G.\u00a0J. and Tjallingii,\u00a0R.: Calibration of XRF core scanners for quantitative geochemical logging of sediment cores: theory and application, Earth Planet. Sc. Lett., 274, 423\u2013438, https:\/\/doi.org\/10.1016\/j.epsl.2008.07.054, 2008.","DOI":"10.1016\/j.epsl.2008.07.054"},{"key":"ref75","doi-asserted-by":"crossref","unstructured":"Weltje,\u00a0G.\u00a0J., Bloemsma,\u00a0M.\u00a0R., Tjallingii,\u00a0R., Heslop,\u00a0D., R\u00f6hl,\u00a0U., and Croudace,\u00a0I.\u00a0W.: 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,\u00a0I.\u00a0W. and Rothwell,\u00a0R.\u00a0G., Springer Netherlands, Dordrecht, 507\u2013534, https:\/\/doi.org\/10.1007\/978-94-017-9849-5_21, 2015.","DOI":"10.1007\/978-94-017-9849-5_21"},{"key":"ref76","doi-asserted-by":"crossref","unstructured":"Yu,\u00a0G. and Harrison,\u00a0S.: Holocene changes in atmospheric circulation patterns as shown by lake status changes in northern Europe, Boreas, 24, 260\u2013258, https:\/\/doi.org\/10.1111\/j.1502-3885.1995.tb00778.x, 1995.","DOI":"10.1111\/j.1502-3885.1995.tb00778.x"},{"key":"ref77","doi-asserted-by":"crossref","unstructured":"Zander,\u00a0P.\u00a0D., \u017barczy\u0144ski,\u00a0M., Tylmann,\u00a0W., Vogel,\u00a0H., and Grosjean,\u00a0M.: 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.","DOI":"10.1029\/2024GL110871"},{"key":"ref78","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1029\/2021MS002776"},{"key":"ref79","doi-asserted-by":"crossref","unstructured":"Zolitschka,\u00a0B., Francus,\u00a0P., Ojala,\u00a0A.\u00a0E.\u00a0K., and Schimmelmann,\u00a0A.: Varves in lake sediments\u00a0\u2013 a review, Quaternary Sci. Rev., 117, 1\u201341, https:\/\/doi.org\/10.1016\/j.quascirev.2015.03.019, 2015.","DOI":"10.1016\/j.quascirev.2015.03.019"}],"container-title":["Climate of the Past"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cp.copernicus.org\/articles\/21\/1465\/2025\/cp-21-1465-2025.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T05:42:52Z","timestamp":1756446172000},"score":1,"resource":{"primary":{"URL":"https:\/\/cp.copernicus.org\/articles\/21\/1465\/2025\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":79,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.5194\/cp-21-1465-2025","relation":{"has-preprint":[{"id-type":"doi","id":"10.5194\/cp-2024-82","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.5194\/cp-2024-82-RC1","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/cp-2024-82-AC1","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/cp-2024-82-RC2","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/cp-2024-82-AC2","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/cp-2024-82-RC3","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/cp-2024-82-AC3","asserted-by":"subject"}],"is-part-of":[{"id-type":"doi","id":"10.5281\/zenodo.15168266","asserted-by":"subject"},{"id-type":"doi","id":"10.5281\/zenodo.14645779","asserted-by":"subject"},{"id-type":"doi","id":"10.25921\/vzys-1280","asserted-by":"subject"},{"id-type":"doi","id":"10.5281\/zenodo.6426332","asserted-by":"subject"},{"id-type":"doi","id":"10.25921\/njxd-hg08","asserted-by":"subject"},{"id-type":"doi","id":"10.5194\/cp-15-1251-2019","asserted-by":"subject"},{"id-type":"doi","id":"10.5285\/35fb8318798e437ba5b108e5eca6e92d","asserted-by":"subject"},{"id-type":"doi","id":"10.5281\/zenodo.16883480","asserted-by":"subject"}]},"ISSN":["1814-9332"],"issn-type":[{"value":"1814-9332","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]}}}