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  • 1
    Publication Date: 2022-10-26
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mankin, J. S., Lehner, F., Coats, S., & McKinnon, K. A. The value of initial condition large ensembles to robust adaptation decision-making. Earth's Future, 8(10), (2020): e2012EF001610, doi:10.1029/2020EF001610.
    Description: The origins of uncertainty in climate projections have major consequences for the scientific and policy decisions made in response to climate change. Internal climate variability, for example, is an inherent uncertainty in the climate system that is undersampled by the multimodel ensembles used in most climate impacts research. Because of this, decision makers are left with the question of whether the range of climate projections across models is due to structural model choices, thus requiring more scientific investment to constrain, or instead is a set of equally plausible outcomes consistent with the same warming world. Similarly, many questions faced by scientists require a clear separation of model uncertainty and that arising from internal variability. With this as motivation and the renewed attention to large ensembles given planning for Phase 7 of the Coupled Model Intercomparison Project (CMIP7), we illustrate the scientific and policy value of the attribution and quantification of uncertainty from initial condition large ensembles, particularly when analyzed in conjunction with multimodel ensembles. We focus on how large ensembles can support regional‐scale robust adaptation decision‐making in ways multimodel ensembles alone cannot. We also acknowledge several recently identified problems associated with large ensembles, namely, that they are (1) resource intensive, (2) redundant, and (3) biased. Despite these challenges, we show, using examples from hydroclimate, how large ensembles provide unique information for the scientific and policy communities and can be analyzed appropriately for regional‐scale climate impacts research to help inform risk management in a warming world.
    Description: F. L. has been supported by the Swiss NSF (grant no. PZ00P2_174128), the NSF Division of Atmospheric and Geospace Sciences (grant no. AGS‐0856145, Amendment 87), and the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S.Department of Energy’s Office of Biological & Environmental Research (BER) via NSF IA 1844590. This is SOEST publication no. 11115.
    Keywords: Large ensembles ; Robust decision‐making ; Internal variability ; Initial conditions ; Climate adaptation
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-10-26
    Description: Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 46 (2019): 12909-12918, doi: 10.1029/2019GL084217.
    Description: Seismic signals from ocean‐solid Earth interactions are ubiquitously recorded on our planet. However, these wavefields are typically incoherent in the time domain limiting their utilization for understanding ocean dynamics or solid Earth properties. In contrast, we find that during large storms such as hurricanes and Nor'easters the interaction of long‐period ocean waves with shallow seafloor features located near the edge of continental shelves, known as ocean banks, excites coherent transcontinental Rayleigh wave packets in the 20‐ to 50‐s period band. These “stormquakes” migrate coincident with the storms but are effectively spatiotemporally focused seismic point sources with equivalent earthquake magnitudes that can be greater than 3.5. Stormquakes thus provide new coherent sources to investigate Earth structure in locations that typically lack both seismic instrumentation and earthquakes. Moreover, they provide a new geophysical observable with high spatial and temporal resolution with which to investigate ocean wave dynamics during large storms.
    Description: We would like to thank the Editor Dr. Hayes, Dr. Ekström, Dr. McNamara, Dr. Pollitz, and the other two reviewers for their constructive suggestions, which have led to improvements in our paper. We would also like to thank Dr. Ardhuin and Dr. Gualtieri for helpful discussions, and specifically Dr. Ardhuin for sharing codes to model ocean wave and seafloor topography interference (Ardhuin et al., 2015). The seismic data were provided by Data Management Center (DMC) of the Incorporated Research Institutions for Seismology (IRIS). The facilities of IRIS Data Services, and specifically the IRIS Data Management Center, were used for access to waveforms, related metadata, and/or derived products used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience and EarthScope (SAGE) Proposal of the National Science Foundation under Cooperative Agreement EAR‐1261681. The earthquake catalogs were downloaded from the Global Centroid Moment Tensor GCMT project (Ekström et al., 2012), and the International Seismological Centre (ISC) (International Seismological Centre, 2013). The ocean wave models are obtained from the Environmental Modeling Center at the National Weather Service (NWS) of the National Oceanic and Atmospheric Administration (NOAA; Tolman, 2014). The hurricane tracks are obtained from the National Hurricane Center (NHC) of NOAA (Landsea & Franklin, 2013). The topography is obtained from the ETOPO1 Arc‐Minute Global Relief Model provided by the National Geophysical Data Center (NGDC) of NOAA. Toponymic information, including undersea features, are obtained from the GEONet Names Server (GNS), which is based on the Geographic Names Database, containing official standard names approved by the U.S. Board on Geographic Names and maintained by the National Geospatial‐Intelligence Agency (www.nga.mil, last accessed 21 March 2019). The Bahamas Banks geographic polygons are obtained from the U.S. Geological Survey (USGS) Geographic Names Information System (GNIS) database of names. The AELUMA code can be obtained on request through the IRIS data service product website at https://ds.iris.edu/ds/products/infrasound-aeluma/request(last accessed 21 March 2019). W. F. acknowledges support from the Postdoctoral Scholar Program at the Woods Hole Oceanographic Institution, with funding provided by the Weston Howland Jr. Postdoctoral Scholarship. C. D. G and M. A. H. H acknowledge support from NSF Grant EAR‐1358520. The processed data are available from the authors upon request.
    Description: 2020-04-14
    Keywords: Stormquake ; Surface wave ; USArray ; Hurriance ; Nor'Easter ; Ambient noise
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Limitation Availability
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