Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
BlackRock, 601 Union Street, Seattle, WA 98101 USA
Climate Impact Lab, 1714 Franklin St. 100–405, Oakland, CA 94612, USA
Rhodium Group, 5 Columbus Circle, New York, NY 10019 USA
Steven B. Malevich
Climate Impact Lab, 1714 Franklin St. 100–405, Oakland, CA 94612, USA
Rhodium Group, 5 Columbus Circle, New York, NY 10019 USA
Kelly E. McCusker
Climate Impact Lab, 1714 Franklin St. 100–405, Oakland, CA 94612, USA
Rhodium Group, 5 Columbus Circle, New York, NY 10019 USA
Emile Tenezakis
Rhodium Group, 5 Columbus Circle, New York, NY 10019 USA
Michael T. Delgado
Rhodium Group, 5 Columbus Circle, New York, NY 10019 USA
Meredith A. Fish
Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, NJ, USA
Robert E. Kopp
Climate Impact Lab, 1714 Franklin St. 100–405, Oakland, CA 94612, USA
Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, NJ, USA
Rutgers Climate and Energy Institute, Rutgers University, New Brunswick, NJ, USA
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Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
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.
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.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
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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.
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Short summary
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.
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset...