Articles | Volume 8, issue 1
https://doi.org/10.5194/ascmo-8-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-8-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level
Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, NJ, USA
Rutgers Institute of Earth, Ocean, and Atmospheric Sciences, Rutgers University, New Brunswick, NJ, USA
Nicole S. Khan
Department of Earth Sciences, the University of Hong Kong, Hong Kong SAR, China
Swire Institute of Marine Sciences, the University of Hong Kong, Hong Kong SAR, China
Lauren T. Toth
U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, USA
Andrea Dutton
Department of Geoscience, University of Wisconsin–Madison, Madison, WI, USA
Robert E. Kopp
Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, NJ, USA
Rutgers Institute of Earth, Ocean, and Atmospheric Sciences, Rutgers University, New Brunswick, NJ, USA
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Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
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PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
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This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Alessio Rovere, Deirdre D. Ryan, Matteo Vacchi, Andrea Dutton, Alexander R. Simms, and Colin V. Murray-Wallace
Earth Syst. Sci. Data, 15, 1–23, https://doi.org/10.5194/essd-15-1-2023, https://doi.org/10.5194/essd-15-1-2023, 2023
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In this work, we describe WALIS, the World Atlas of Last Interglacial Shorelines. WALIS is a sea-level database that includes sea-level proxies and samples dated to marine isotope stage 5 (~ 80 to 130 ka). The database was built through topical data compilations included in a special issue in this journal.
Andrea Dutton, Alexandra Villa, and Peter M. Chutcharavan
Earth Syst. Sci. Data, 14, 2385–2399, https://doi.org/10.5194/essd-14-2385-2022, https://doi.org/10.5194/essd-14-2385-2022, 2022
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This paper includes data that have been compiled to identify the position of sea level during a warm period about 125 000 years ago that is known as the Last Interglacial. Here, we have focused on compiling data for the region of the Bahamas, Turks and Caicos, and the east coast of Florida. These data were compiled and placed within a standardized format prescribed by a new database known as WALIS, which stands for World Atlas of Last Interglacial Shorelines Database.
Peter M. Chutcharavan and Andrea Dutton
Earth Syst. Sci. Data, 13, 3155–3178, https://doi.org/10.5194/essd-13-3155-2021, https://doi.org/10.5194/essd-13-3155-2021, 2021
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This paper summarizes a global database of fossil coral U-series ages for the Last Interglacial period and was compiled as a contribution to the World Atlas of Last Interglacial Shorelines. Each entry contains relevant age, elevation and sample metadata, and all ages and isotope activity ratios have been normalized and recalculated using the same decay constant values. We also provide two example geochemical screening criteria to help users assess sample age quality.
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Short summary
We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
We develop a new technique to integrate realistic uncertainties in probabilistic models of past...