|Title||Inception of a global atlas of sea levels since the Last Glacial Maximum|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Horton BP, Khan NS, Engelhart S, Rovere A, Vacchi M, Ashe EL, Törnqvist TE, Dutton A, Hijma M|
|Journal||Quaternary Science Reviews|
Determining the rates, mechanisms, and geographic variability of relative sea-level (RSL) change following the Last Glacial Maximum (LGM) provides insight into the sensitivity of ice sheets to climate change, the response of the solid Earth and gravity field to ice-mass redistribution, and constrains statistical and physical models used to project future sea-level rise. To do so in a scientifically robust way requires standardized datasets that enable broad spatial comparisons that minimize bias. As part of a larger goal to develop a unified, spatially-comprehensive post-LGM global RSL database, in this special issue we provide a standardized global synthesis of regional RSL data that resulted from the first 'Geographic variability of HOLocene relative SEA level (HOLSEA)' meetings in Mt Hood, Oregon (2016) and St Lucia, South Africa (2017). The HOLSEA meetings brought together sea-level researchers to agree upon a consistent protocol to standardize, interpret, and incorporate realistic uncertainties of RSL data. This special issue provides RSL data from ten geographical regions including new databases from Atlantic Europe and the Russian Arctic and revised/expanded databases from Atlantic Canada, the British Isles, the Netherlands, the western Mediterranean, the Adriatic, Israel, Peninsular Malaysia, Southeast Asia, and the Indian Ocean. In total, the database derived from this special issue includes 5634 (5290 validated) index (n = 3202) and limiting points (n = 2088) that span from similar to 20,000 years ago to present. Progress in improving the standardization of sea-level databases has also been accompanied by advancements in statistical and analytical methods used to infer spatial patterns and rates of RSL change from geological data that have a spatially and temporally sparse distribution and geochronological and elevational uncertainties.