Francesco Muschitiello, Marco Antonio Aquino-Lopez
{"title":"Continuous synchronization of the Greenland ice-core and U–Th timescales using probabilistic inversion","authors":"Francesco Muschitiello, Marco Antonio Aquino-Lopez","doi":"10.5194/cp-20-1415-2024","DOIUrl":null,"url":null,"abstract":"Abstract. This study presents the first continuously measured transfer functions that quantify the age difference between the Greenland ice-core chronology 2005 (GICC05) and the U–Th timescale during the last glacial period. The transfer functions were estimated using an automated algorithm for Bayesian inversion that allows inferring a continuous and objective synchronization between Greenland ice-core and East Asian summer monsoon speleothem data, and a total of three transfer functions were inferred using independent ice-core records. The algorithm is based on an alignment model that considers prior knowledge of the GICC05 counting error but also samples synchronization scenarios that exceed the differential dating uncertainty of the annual-layer count in ice cores, which are currently hard to detect using conventional alignment techniques. The transfer functions are on average 48 % more precise than previous estimates and significantly reduce the absolute dating uncertainty of the GICC05 back to 48 kyr ago. The results reveal that GICCC05 is, on average, systematically younger than the U–Th timescale by 0.86 %. However, they also highlight that the annual-layer counting error is not strictly correlated over extended periods of time and that within the coldest Greenland Stadials the differential dating uncertainty is likely underestimated by up to ∼13 %. Importantly, the analysis implies for the first time that during the Last Glacial Maximum GICC05 may overcount ice layers by ∼10 % – a bias possibly attributable to a higher frequency of sub-annual layers due to changes in the seasonal cycle of precipitation and mode of dust deposition to the Greenland Ice Sheet. The new timescale transfer functions provide important constraints on the uncertainty surrounding the stratigraphic dating of the Greenland age scale and enable an improved chronological integration of ice cores as well as U–Th-dated and radiocarbon-dated paleoclimate records on a common timeline. The transfer functions are available as a Supplement to this study.","PeriodicalId":10332,"journal":{"name":"Climate of The Past","volume":"17 1","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Climate of The Past","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/cp-20-1415-2024","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. This study presents the first continuously measured transfer functions that quantify the age difference between the Greenland ice-core chronology 2005 (GICC05) and the U–Th timescale during the last glacial period. The transfer functions were estimated using an automated algorithm for Bayesian inversion that allows inferring a continuous and objective synchronization between Greenland ice-core and East Asian summer monsoon speleothem data, and a total of three transfer functions were inferred using independent ice-core records. The algorithm is based on an alignment model that considers prior knowledge of the GICC05 counting error but also samples synchronization scenarios that exceed the differential dating uncertainty of the annual-layer count in ice cores, which are currently hard to detect using conventional alignment techniques. The transfer functions are on average 48 % more precise than previous estimates and significantly reduce the absolute dating uncertainty of the GICC05 back to 48 kyr ago. The results reveal that GICCC05 is, on average, systematically younger than the U–Th timescale by 0.86 %. However, they also highlight that the annual-layer counting error is not strictly correlated over extended periods of time and that within the coldest Greenland Stadials the differential dating uncertainty is likely underestimated by up to ∼13 %. Importantly, the analysis implies for the first time that during the Last Glacial Maximum GICC05 may overcount ice layers by ∼10 % – a bias possibly attributable to a higher frequency of sub-annual layers due to changes in the seasonal cycle of precipitation and mode of dust deposition to the Greenland Ice Sheet. The new timescale transfer functions provide important constraints on the uncertainty surrounding the stratigraphic dating of the Greenland age scale and enable an improved chronological integration of ice cores as well as U–Th-dated and radiocarbon-dated paleoclimate records on a common timeline. The transfer functions are available as a Supplement to this study.
期刊介绍:
Climate of the Past (CP) is a not-for-profit international scientific journal dedicated to the publication and discussion of research articles, short communications, and review papers on the climate history of the Earth. CP covers all temporal scales of climate change and variability, from geological time through to multidecadal studies of the last century. Studies focusing mainly on present and future climate are not within scope.
The main subject areas are the following:
reconstructions of past climate based on instrumental and historical data as well as proxy data from marine and terrestrial (including ice) archives;
development and validation of new proxies, improvements of the precision and accuracy of proxy data;
theoretical and empirical studies of processes in and feedback mechanisms between all climate system components in relation to past climate change on all space scales and timescales;
simulation of past climate and model-based interpretation of palaeoclimate data for a better understanding of present and future climate variability and climate change.