Revisiting tectonic corrections applied to Pleistocene sea-level highstands

Tectonic displacement contaminates estimates of peak eustatic sea level (and, equivalently, minimum continental ice volumes) determined from the elevation of Quaternary interglacial highstand markers. For sites at which a stratigraphic or geomorphic marker of peak Marine Isotope Stage (MIS) 5e sea level exists, the standard approach for estimating local tectonic uplift (or subsidence) rates takes the difference between the elevation of the local highstand marker and a reference MIS 5e eustatic value, commonly chosen as +6 m, and divides by the age of the marker. The resulting rate is then applied to correct the elevation of all other local observed sea-level markers for tectonic displacement, including peak highstands of different ages (e.g., MIS 5a, MIS 5c and MIS 11), under the assumption that the tectonic rate remained constant over those periods. This approach introduces two potentially significant errors. First, the peak eustatic value adopted for MIS 5e in most previous studies (i.e., +6 m) is likely incorrect. Second, local peak sea level during MIS 5e is characterized by significant departures from eustasy due to glacial isostatic adjustment in response to both successive glacial–interglacial cycles and excess polar ice-sheet melt relative to present day values. We use numerical models of glacial isostatic adjustment that incorporate both of these effects to quantify the plausible range of the combined error and show that, even at sites far from melting ice sheets, local peak sea level during MIS 5e may depart from eustasy by 2–4 m, or more. We also demonstrate that the associated error in the estimated tectonic rates can significantly alter previous estimates of peak eustatic sea level during Quaternary highstands, notably those associated with earlier interglacials (e.g., MIS 11)

Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux

It has been known for over a century that the melting of individual ice sheets and glaciers drives distinct geographic patterns, or fingerprints, of sea level change, and recent studies have highlighted the implications of this variability for hazard assessment and inferences of meltwater sources. These studies have computed fingerprints using simplified melt geometries; however, a more generalized treatment would be advantageous when assessing or projecting sea level hazards in the face of quickly evolving patterns of ice mass flux. In this paper the usual fingerprint approach is inverted to compute site-specific sensitivity kernels for a global database of coastal localities. These kernels provide a mapping between geographically variable mass flux across each ice sheet and glacier and the associated static sea level change at a given site. Kernels are highlighted for a subset of sites associated with melting from Greenland, Antarctica, and the Alaska-Yukon-British Columbia glacier system. The latter, for example, reveals an underappreciated sensitivity of ongoing and future sea level change along the U.S. West Coast to the geometry of ice mass flux in the region. Finally, the practical utility of these kernels is illustrated by computing sea level predictions at a suite of sites associated with annual variability in Greenland ice mass since 2003 constrained by satellite gravity measurements.Harvard University; NASA [NNX17AE17G, NNX17AE18G, 80NSSC17K0698]; NSF [ICER-1663807]6 month embargo, April 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Evaluation of Paleocene‐Eocene Thermal Maximum Carbon Isotope Record Completeness—An Illustration of the Potential of Dynamic Time Warping in Aligning Paleo‐Proxy Records

Abstract Variations in sedimentation rate, bioturbation, winnowing, and dissolution modify the deep‐sea sedimentary record, complicating the apparent relationship between stratigraphic depth and time of a geochemical proxy record and confounding the extraction of a clear picture of past climates and environments. Dynamic time warping (DTW) is used to align time series with similar patterns. Here we explore the use of DTW to identify gaps in proxy records of the Paleocene‐Eocene thermal maximum (PETM), aligning bulk sediment carbonate isotope records (δ13C) from various deep‐sea sediment core sections spanning the event. Alignment of PETM δ13C records from the Walvis Ridge, South Atlantic transect of ODP Leg 208 (Sites 1262, 1263, and 1265) was similar to previously published manually established alignments and consistent with the expectation that shallower sites have more complete records. The δ13C record from a Southern Ocean site (Maud Rise; ODP Site 690) was then aligned to ODP Site 1263, the most complete Walvis Ridge site. This alignment identifies a gap in Site 690, indicating that peak excursion δ13C values were not recorded. We conclude that DTW provides an objective way to align climate proxy records and rectify data loss associated with unconformities and other types of distortions, leading to a more complete understanding of the geologic record of past episodes of biotic and environmental change

Mapping sea-level change in time, space, and probability

Future sea-level rise generates hazards for coastal populations, economies, infrastructure, and ecosystems around the world. The projection of future sea-level rise relies on an accurate understanding of the mechanisms driving its complex spatio-temporal evolution, which must be founded on an understanding of its history. We review the current methodologies and data sources used to reconstruct the history of sea-level change over geological (Pliocene, Last Interglacial, and Holocene) and instrumental (tide-gauge and satellite alimetry) eras, and the tools used to project the future spatial and temporal evolution of sea level. We summarize the understanding of the future evolution of sea level over the near (through 2050), medium (2100), and long (post-2100) terms. Using case studies from Singapore and New Jersey, we illustrate the ways in which current methodologies and data sources can constrain future projections, and how accurate projections can motivate the development of new sea-level research questions across relevant timescales.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore

Origin of spatial variation in US East Coast sea-level trends during 1900-2017

Author Posting. © The Authors, 2018. This is the author's version of the work. It is posted here by permission of Nature Research for personal use, not for redistribution. The definitive version was published in Piecuch, C. G., Huybers, P., Hay, C. C., Kemp, A. C., Little, C. M., Mitrovica, J. X., Ponte, R. M., & Tingley, M. P. (2018). Origin of spatial variation in US east coast sea-level trends during 1900-2017. Nature, 564(7736), 400-404, doi:10.1038/s41586-018-0787-6.Identifying the causes of historical trends in relative sea level—the height of the sea surface relative to Earth’s crust—is a prerequisite for predicting future changes. Rates of change along the U.S. East Coast during the last century were spatially variable, and relative sea level rose faster along the Mid-Atlantic Bight than the South Atlantic Bight and Gulf of Maine. Past studies suggest that Earth’s ongoing response to the last deglaciation1–5, surface redistribution of ice and water 5–9, and changes in ocean circulation9–13 contributed importantly to this large-scale spatial pattern. Here we analyze instrumental data14, 15 and proxy reconstructions4, 12 using probabilistic methods16–18 to show that vertical motions of Earth’s crust exerted the dominant control on regional spatial differences in relative sea level trends along the U.S. East Coast during 1900–2017, explaining a majority of the large-scale spatial variance. Rates of coastal subsidence caused by ongoing relaxation of the peripheral forebulge associated with the last deglaciation are strongest near North Carolina,Maryland, and Virginia. Such structure indicates that Earth’s elastic lithosphere is thicker than has been assumed in other models19–22. We also find a significant coastal gradient in relative sea level trends over this period that is unrelated to deglaciation, and suggests contributions from twentieth-century redistribution of ice and water. Our results indicate that the majority of large-scale spatial variation in longterm rates of relative sea level rise on the U.S. East Coast was due to geological processes that will persist at similar rates for centuries into the future.Funding came from Woods Hole Oceanographic Institution’s Investment in Science Fund; Harvard University; NSF awards 1558939, 1558966, and 1458921; and NASA awards NNH16CT01C, NNX17AE17G, and 80NSSC17K0698. We acknowledge helpful conversations with S. Adhikari, B.D. Hamlington, F.W. Landerer, S.J. Lentz, and P.R. Thompson. Comments from three anonymous referees and the editor, Michael White, are greatly appreciated.2019-06-1

Statistical modeling of rates and trends in Holocene relative sea level

Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional,and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronological and elevational uncertainties, have advanced considerably over the last decade. Time-series models have adopted more flexible and physically-informed specifications with more rigorous quantification of uncertainties. Spatiotemporal models have evolved from simple regional averaging to frameworks that more richly represent the correlation structure of RSL across space and time. More complex statistical approaches enable rigorous quantification of spatial and temporal variability, the combination of geographically disparate data, and the separation of the RSL field into various components associated with different driving processes. We review the range of statistical modeling and analysis choices used in the literature, reformulating them for ease of comparison in a common hierarchical statistical frame-work. The hierarchical framework separates each model into different levels, clearly partitioning measurement and inferential uncertainty from process variability. Placing models in a hierarchical framework enables us to highlight both the similarities and differences among modeling and analysis choices. We illustrate the implications of some modeling and analysis choices currently used in the literature by comparing the results of their application to common datasets within a hierarchical framework. In light of the complex patterns of spatial and temporal variability exhibited by RSL, were commend non-parametric approaches for modeling temporal and spatio-temporal RSL

Temperature-driven global sea-level variability in the Common Era

We assess the relationship between temperature and global sea-level (GSL) variability over the Common Era through a statistical metaanalysis of proxy relative sea-level reconstructions and tide-gauge data. GSL rose at 0.1 ± 0.1 mm/y (2σ) over 0–700 CE. A GSL fall of 0.2 ± 0.2 mm/y over 1000–1400 CE is associated with ∼0.2 °C global mean cooling. A significant GSL acceleration began in the 19th century and yielded a 20th century rise that is extremely likely (probability P≥0.95) faster than during any of the previous 27 centuries. A semiempirical model calibrated against the GSL reconstruction indicates that, in the absence of anthropogenic climate change, it is extremely likely (P=0.95) that 20th century GSL would have risen by less than 51% of the observed 13.8±1.5 cm. The new semiempirical model largely reconciles previous differences between semiempirical 21st century GSL projections and the process model-based projections summarized in the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.This article is available Open Access at the Link to published version: http://dx.doi.org/10.1016/j.emospa.2016.02.006Also available as related resources: Supporting Information (PDF), Dataset S1 (PDF), Dataset S2 (Excel), Dataset S3 (Excel).Peer reviewe