Li H. Erikson Liv Herdman Chris Flanary Anita C. Engelstad Prasad Pusuluri Patrick L. Barnard Curt D. Storlazzi Michael Beck Borja G. Reguero Kai A. Parker 20241026 netCDF files data release DOI: 10.5066/P9KR0RFM Pacific Coastal and Marine Science Center, Santa Cruz, California U.S. Geological Survey https://doi.org/10.5066/P9KR0RFM Li H. Erikson Liv Herdman Chris Flanary Anita C. Engelstad Prasad Pusuluri Patrick L. Barnard Curt D. Storlazzi Michael Beck Borja G. Reguero Kai A. Parker 2022 2.0 data release 10.5066/P9KR0RFM Pacific Coastal and Marine Science Center, Santa Cruz, CA U.S. Geological Survey https://doi.org/10.5066/P9KR0RFM This dataset presents historical (1979-2014) and projected (2020-2050) hourly time-series of wave heights, wave periods, incident wave directions, and directional spreading at distinct points along the U.S. West Coast and surrounding Hawai’i. The time-series were developed by running the National Oceanic and Atmospheric Administration’s (NOAA’s) WAVEWATCHIII model. Wind and sea-ice fields from seven different Global Climate or General Circulation Models from the CMIP6 High-Resolution Model Intercomparison Project were used to simulate waves across the globe at a 0.5-degree resolution (approximately 50 km, depending on latitude) and further downscaled to 10- (approximately 18 km) and 4-arc-minute (approximately 7 km) model grids. Point model output data extracted from NOAA’s 4 arc-minute grid for the U.S. West Coast and Hawai’i (wc_4m) are provided herein. These wave data were produced as part of a larger investigation into assessing future coastal hazards along the United States open coastlines. Coupled atmosphere-ocean global climate models (or general circulation models, GCMs) are the current standard tool for improving understanding and predictability of climate behavior on seasonal to centennial time-scales. However, GCMs do not currently include ocean wave conditions caused by the exchange of momentum, heat, and mass across the air-sea interface (Hemer and others, 2012). To fulfill this need, projections of wave conditions have been done by independent researchers using statistical and numerical modeling methods driven by atmospheric forcing derived from the 5th generation Coupled Model Intercomparison Project (CMIP5) GCMs (Morim and others, 2019, 2020). This work follows a well-established method of applying GCM-derived wind and sea-ice fields as boundary conditions to the WaveWatchIII model to generate projections of wave climatologies (Hemer and others 2013; Erikson and others, 2015; Mentaschi and others 2017). But in contrast to earlier works, this data set was produced by applying wind and sea-ice fields from the High Resolution Model Intercomparison Project (HighResMIP v1.0), which is part of the CMIP6 framework (Haarsma and others, 2016). With a spatial resolution up to 25-50 km (compared to 150 km for CMIP5), the HighResMIP models are capable of resolving localized climate extremes, such as tropical cyclones (Roberts and others., 2020). Additional information about WAVEWATCHIII and the Production Multigrid model from which these data were derived is available online at https://polar.ncep.noaa.gov/waves/validation/. Computing was performed at the USGS Advanced Research Computing Facility, USGS Denali Supercomputer https://doi.org/10.5066/P9PSW367 and Intel Corporation. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 19790101 20501231 time period for which the data were modeled Complete None planned -160.5300 -116.4000 49.9330 18.1670 USGS Metadata Identifier USGS:8c6e5bbf-5726-49f7-8b18-c7fc30b35ab0 ISO 19115 Topic Category oceans Data Categories for Marine Planning predictions USGS Thesaurus ocean waves Marine Realms Information Bank (MRIB) keywords numerical modeling None U.S. Geological Survey USGS Coastal and Marine Hazards and Resources Program CMHRP Pacific Coastal and Marine Science Center PCMSC Geographic Names Information System (GNIS) Pacific Ocean NGA GEOnet Names Server (GNS) United States No access constraints USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge the U.S. Geological Survey, University of California Santa Cruz (UCSC), and Intel Corporation as the originator(s) of the dataset and in products derived from these data. U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

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Santa Cruz CA 95060 831-427-4747 pcmsc_data@usgs.gov WavePnts_wc_4m_Fut_Preview.png Water depth at model savepoints (meters) png We thank Dr. Babak Tehranirad (Stantec Inc.), Dr. Sean Vitousek (USGS), and Reyce Bogardus (UAF) for review of the wave data. For the wind and sea-ice fields we thank and acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Data were generated with the use of WCRP CMIP6 data and the spectral wave model WAVEWATCHIII® version 6.07.1 by National Oceanic and Atmospheric Agency National Centers for Environmental Prediction WAVEWATCHIII® Development Group (WW3DG), 2019. Computing was performed at the USGS Advanced Research Computing Facility, USGS Denali Supercomputer https://doi.org/10.5066/P9PSW367 and Intel Corporation. C. Amante B.W. Eakins 2009 Amante, C. and Eakins, B.W., 2009, ETOPO1 Arc-minute global relief model: procedures, data sources and analysis: NOAA Technical Memorandum NESDIS NGDC-24, p. 25, https://www.ngdc.noaa.gov/mgg/global/ https://www.ngdc.noaa.gov/mgg/global/ Li Erikson Christie Hegermiller Patrick L. Barnard Peter Ruggiero Maarten van Ormondt 2015 Erikson, L.H., Hegermiller, C.A., Barnard, P.L., Ruggiero, P., and van Ormondt, M., 2015, Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios: Ocean Modelling, v. 96 no. 1, pp. 171–185, doi: 10.1016/j.ocemod.2015.07.004. https://doi.org/10.1016/j.ocemod.2015.07.004 V. Eyring S. Bony G.A. Meehl C.A. Senior B. Stevens R.J. Stouffer K.E. Taylor 2016 Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., and Taylor, K.E., 2016, Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization: Geoscientific Model Development, v. 9, p. 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016 https://doi.org/10.5194/gmd-9-1937-2016 J.T. Falgout J. Gordon B. Williams M.J. Davis 2024 Falgout, J.T., Gordon J., Williams B., Davis M. J., USGS Advanced Research Computing, USGS Denali Supercomputer: U.S. Geological Survey, https://doi.org/10.5066/P9PSW367 https://doi.org/10.5066/P9PSW367 R.J. Haarsma M.J. Roberts P.L. Vidale C.A. Senior A. Bellucci Q. Bao P. Chang S. Corti N.S. Fučkar V. Guemas J. von Hardenberg W. Hazeleger C. Kodama T. Koenigk L.R. Leung J. Lu J.-J. Luo J. Mao M.S. Mizielinski R. Mizuta P. Nobre M. Satoh E. Scoccimarro T. Semmler J. Small and J.-S. von Storch 2016 Haarsma, R.J., Roberts, M.J., Vidale, P. L., Senior, C.A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L.R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S., 2016, High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6: Geoscientific Model Development, v. 9, p. 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016 https://doi.org/10.5194/gmd-9-4185-2016 Mark Hemer Xiaolan Wang Ralf Weisse Val Swail 2012 Hemer, M., Wang, X., Weisse, R., Swail, V., 2012, Advancing wind-waves climate science: The COWCLIP project: Bulletin of the American Meteorological Society, v. 93, p. 791-796, https://doi.org/10.1175/BAMS-D-11-00184.1 https://doi.org/10.1175/BAMS-D-11-00184.1 Mark A. Hemer Yalin Fan Nobuhito Mori Alvaro Semedo Xiaolan L. Wang 2013 Hemer, M.A., Fan, Y., Mori, N., Semedo A., and Wang, X.L., 2013, Projected changes in wave climate from a multi-model ensemble: Nature Climate Change v. 3, p. 471–476, https://doi.org/10.1038/nclimate1791 https://doi.org/10.1038/nclimate1791 Lorenzo Mentaschi Michalis I. Vousdoukas Evangelos Voukouvalas Alessandro Dosio Luc Feyen 2017 Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Dosio, A., and Feyen, L., 2017, Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns: Geophysical Research Letters, v. 44, p. 2416–2426, doi:10.1002/2016GL072488. https://doi.org/10.1002/2016GL072488 Joao Morim Mark Hemer Xiaolan L. Wang Nick Cartwright Claire Trenham Alvaro Semedo Ian Young Lucy Bricheno Paula Camus Mercè Casas-Prat Li Erikson Lorenzo Mentaschi Nobuhito Mori Tomoya Shimura Ben Timmermans Ole Aarnes Øyvind Breivik Arno Behrens Mikhail Dobrynin Melisa Menendez Joanna Staneva Michael Wehner Judith Wolf Bahareh Kamranzad Adrean Webb Justin Stopa Fernando Andutta 2019 Morim, J., Hemer, M., Wang, X.L., Cartwright, N., Trenham, C., Semedo, A., Young, I., Bricheno, L., Camus, P., Casas-Prat, M., Erikson, L., Mentaschi, L., Mori, M. Shimura, T., Timmermans, B., Aarnes, O., Breivik, O., Behrens, A., Dobrynin, M., Menendez, M., Staneva, J., Wehner, M., Wolf, J., Kamranzad, B., Webb, A., Stopa, J., and Andutta, F., 2019, Robustness and uncertainties in global multivariate wind-wave climate projections: Nature Climate Change, v. 9,p. 711–718, https://doi.org/10.1038/s41558-019-0542-5 https://doi.org/10.1038/s41558-019-0542-5 Joao Morim Claire Trenham Mark Hemer Xiaolan L. Wang Nobuhito Mori Mercè Casas-Prat Alvaro Semedo Tomoya Shimura Ben Timmermans Paula Camus Lucy Bricheno Lorenzo Mentaschi Mikhail Dobrynin Yang Feng Li Erikson 2020 Morim, J., Trenham, C., Hemer, M. et al. A global ensemble of ocean wave climate projections from CMIP5-driven models: Scientific Data, v. 7, no. 105, https://doi.org/10.1038/s41597-020-0446-2 https://doi.org/10.1038/s41597-020-0446-2 Malcolm J. Roberts Joanne Camp Jon Seddon Pier L. Vidale Kevin Hodges Benoît Vannière Jenny Mecking Rein Haarsma Alessio Bellucci Enrico Scoccimarro Louis-Philippe Caron Fabrice Chauvin Laurent Terray Sophie Valcke Marie-Pierre Moine Dian Putrasahan Christopher D.Roberts Retish Senan Colin Zarzycki Paul Ullrich Yohei Yamada Ryo Mizuta Chihiro Kodama Dan Fu Qiuying Zhang Gokhan Danabasoglu Nan Rosenbloom Hong Wang Lixin Wu 2020 Roberts, M.J., Camp, J., Seddon, J., Vidale, P.L., Hodges, K., Vannière, B., Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L.-P., Chauvin, F., Terray, L., Valcke, S., Moine, M.-P., Putrasahan, D., Roberts, C.D., Senan, R., Zarzycki, C., Ullrich, P., Yamada, Y., Mizuta, R., Kodama, C., Fu, D., Zhang, Q., Danabasoglu, G., Rosenbloom, N., Wang, H. and Wu, L., 2020, Projected future changes in tropical cyclones using the CMIP6 HighResMIP multimodel ensemble: Geophysical Research Letters, v. 47, https://doi.org/10.1029/2020GL088662 https://doi.org/10.1029/2020GL088662 WAVEWATCHIII R Development Group (WW3DG) 2019 WAVEWATCHIII R Development Group (WW3DG), 2019, Tech. Note 333, NOAA/NWS/NCEP/MMAB, College Park, MD, USA, p. 465 + Appendices, https://github.com/NOAA-EMC/WW3/wiki/files/manual.pdf https://github.com/NOAA-EMC/WW3/wiki/files/manual.pdf This study does not include model hindcast runs and comparisons to buoy or altimeter observations, but comparisons to various measurements have been done by previous studies (for example, https://polar.ncep.noaa.gov/waves/validation/prod/). All data provided match the wave source information and fall within expected ranges for wave parameters. The GFDL wave model runs do not include the influence of ice in parts of the Arctic Ocean. The effect of this on modeled waves within this wc_4m grid dataset is expected to be small, but no formal checks have been done. The affected data files are noted by a modification to the filename: ‘LimitedUse’. Spatial and attribute properties are believed to be complete. The geospatial data were checked for integrity. Possible data duplicates have been checked and eliminated. The horizontal accuracy is inherited from the source model grid (NOAA’s WAVEWATCHIII model). Because the overall horizontal accuracy depends on the underlying bathymetry, forcing values used, and the accuracy of the model, the spatial accuracy of this data layer cannot be meaningfully quantified. Enrico Scoccimarro Alessinio Bellucci Daniele Peano 2017 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.1367 online database 19790101 20501231 2021 CMCC East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. Aurore Voldoire 2019 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.4225 online database 20200101 20501231 2021 CNRM projected East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. Aurore Voldoire 2019 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.4067 online database 19790101 20141231 2021 CNRM historical East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. EC-Earth Consortium (EC-Earth) 2018 netCDF files online Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.2323 online database 19790101 20501231 2021 EC-Earth East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. Huan Guo Jasmin G. John Chris Blanton Colleen McHugh Serguei Nikonov Aparna Radhakrishnan Kristopher Rand Niki T. Zadeh V. Balaji Jeff Durachta Christopher Dupuis Raymond Menzel Thomas Robinson Seth Underwood Hans Vahlenkamp Krista A. Dunne Paul P.G. Gauthier Paul Ginoux Stephen M. Griffies Robert Hallberg Matthew Harrison William Hurlin Pu Lin Sergey Malyshev Vaishali Naik Fabien Paulot David J. Paynter Jeffrey Ploshay Daniel M. Schwarzkopf Charles J. Seman Andrew Shao Levi Silvers Bruce Wyman Xiaoqin Yan Yujin Zeng Alistair Adcroft John P. Dunne Isaac M. Held John P. Krasting Larry W. Horowitz Chris Milly Elena Shevliakova Michael Winton Ming Zhao Rong Zhang 2018 netCDF files online Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.9242 online database 19790101 20501231 2021 GFDL wind and projected sea-ice concentrations East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. John Kennedy Holly Titchner Nick Rayer Malcom Roberts 2017 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/input4MIPs.1221 online database 19790101 20141231 2021 GFDL historical sea-ice concentrations sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. Malcolm Roberts 2018 netCDF files online Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.445 online database 19790101 20501231 2021 HadgemHH East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. Malcolm Roberts 2018 netCDF files online Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.446 online database 19790101 20501231 2021 HadgemHM, HadgemSST East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model. The data are presented by geographic areas bounded by model grids which were nested within a common global grid. All process steps apply to all data and geographic areas. Download and rewriting of General Circulation Model (GCM)wind (near-surface 10-m height) and sea-ice fields. Specific GCM variants used were as follows: CMCC winds: CMCC-CM2-VHR4_hist-1950_r1i1p1f1_gn (historical time-period) and CMCC-CM2-VHR4_highres-future_r1i1p1f1_gn (projected time-period), 6-hourly, 25 km resolution, CMCC sea-ice: CMCC-CM2-VHR4_hist-1950_r1i1p1f1_gn (historical time-period) and CMCC-CM2-VHR4_highres-future_r1i1p1f1_gn (projected time-period), daily, 25 km resolution, CNRM winds: CNRM-CM6-1-HR_historical_r1i1p1f2_gr (historical time-period)and CNRM-CM6-1-HR_ssp585_r1i1p1f2_gr (projected time-period), 3-hourly, 25 km resolution, CNRM sea-ice: CNRM-CM6-1-HR_historical_r1i1p1f2_gn (historical time-period) and CNRM-CM6-1-HR_ssp585_r1i1p1f2_gn (projected time-period), daily, 25 km resolution, ECEARTH winds: EC-Earth3P-HR_hist1950_r1i1p2f1_gr (historical time-period) and EC-Earth3P-HR_highres-future_r1i1p2f1_gr (projected time-period) 3-hourly, 50 km resolution, ECEARTH sea-ice: EC-Earth3P-HR_hist1950_r1i1p2f1_gn (historical time-period) and EC-Earth3P-HR_highres-future_r1i1p2f1_gn (projected time-period), daily, 25 km resolution, GFDL winds: GFDL-CM4C192_highresSST-present_r1i1p1f1_gr3 (historical time-period) and GFDL-CM4C192_highresSST-future_r1i1p1f1_gr3 (projected time-period), 3-hourly, 50 km resolution, GFDL sea-ice: input4MIPs_SSTsAndSeaIce_HighResMIP_MOHC-HadISST-2-2-0-0-0_gn (historical time-period), GFDL-CM4_ssp585_r1i1p1f1_gr2 (projected time-period), daily, 25 km resolution; HadgemHH winds: HadGEM3-GC31-HH_hist-1950_r1i1p1f1_gn (historical time-period) and HadGEM3-GC31-HH_highres-future_r1i1p1f1_gn (projected time-period), 3-hourly, 50 km resolution. HadgemHH sea-ice: HadGEM3-GC31-HM_hist-1950_r1i1p1f1_gn (historical time-period) and HadGEM3-GC31-HM_highres-future_r1i1p1f1_gn (projected time-period), daily, 25 km resolution, HadgemHM winds: HadGEM3-GC31-HM_hist-1950_r1i1p1f1_gn (historical time-period) and HadGEM3-GC31-HM_highres-future_r1i1p1f1_gn (projected time-period), 3-hourly, 50 km resolution, HadgemHM sea-ice: HadGEM3-GC31-HM_hist-1950-r1i1p1f1_gn (historical time-period) and HadGEM3-GC31-HM_highres-future_r1i1p1f1_gn(projected time-period), daily, 25 km resolution, HadgemSST winds: HadGEM3-GC31-HM_highresSST-present_r1i1p1f1_gn (historical time-period) and HadGEM3-GC31-HM_highresSST-future_r1i1p1f1_gn (projected time-period), forced atmosphere experiment using SST/sea ice derived from CMIP5 RCP8.5, 3-hourly, 50 km resolution. HadgemSST sea-ice: HadGEM3-GC31-HM_highresSST-present_r1i1p1f1_gn (historical time-period) and HadGEM3-GC31-HM_highresSST-future_r1i1p1f1_gn (projected time-period), daily, 25 km resolution native grid. The near-surface wind fields and sea-ice cover were downloaded from ESGF CMIP6 Data Holdings (pcmdi.llnl.gov/CMIP6) in March 2021 (June 2021 for CMCC), and they were interpolated to common 3-hourly time-points and grid resolutions of 0.5 degrees for GFDL and CNRM, 0.35 degrees for Hadgem and ECEARTH, and 0.3125 degrees for CMCC. All data were written to netCDF format files ingestible by the wave model. The GCM wind and ice fields are part of the High-Resolution Model Intercomparison Project (HighResMIP, Haarsma and others, 2016). The primary goal of HighResMIP is to assess the robustness of improvements in the representation of important climate processes with weather-resolving global model resolutions, using the physical climate system only with constrained aerosol forcing (Eyring and others, 2016). The higher resolution and inclusion of more forcings and detailed physics is expected to reduce bias compared to the standard CMIP6 and CMIP5 predecessor. CMCC, CNRM projected, CNRM historical, EC-Earth, GFDL wind and projected sea-ice concentrations, GFDL historical sea-ice concentrations, HadgemHH, HadgemHM, HadgemSST 20210331 Wave model setup. The third-generation, spectral wave model WAVEWATCHIII (WW3; version 6.07.1; WAVEWATCHIII® Development Group (WW3DG), 2019) was downloaded from GitHub at https://github.com/NOAA-EMC/WW3/releases). WW3 is a ‘phase-averaged model’ that solves the random phase spectral action density balance equation for wavenumber-direction spectra based on the assumption that water depths, currents, and wave fields vary on time and spatial scales much larger than that of a single wave. This version of the model includes transitional- and shallow-water equations. Source terms for physical processes include parameterizations for wind-driven wave growth, parametrized forms for nonlinear resonant wave-wave interactions, scattering due to wave-bottom interactions, triad interactions, and dissipation due to whitecapping, bottom friction, surf-breaking, and interactions with ice. Model switches used in this study were as follows (see WW3DG, 2019 for further explanations): F90 DIST MPI OMPG OMPH PR3 UQ FLX0 LN1 ST4 STAB0 NL1 BT1 IC4 IS0 REF0 DB0 TR1 MLIM BS0 XX0 WNT1 WNX1 CRT1 CRX1 NOGRB O0 O1 O2 O3 O4 O5 O6 O7 O11 NC4. Model grids consist of one 0.5 x 0.5 degree global grid, 4 nested 10 arc-minute resolution (approximately 18 km) ‘child’ grids, and 3 nested 4 arc-minute resolution (approximately 7 km) ‘grand-child’ grids (grid, geographic coverage, resolution in arc-minutes and approximate km). The finer resolution nested grids each take inputs along their open boundaries from the increasingly coarse grids. The finest resolution wave grids (approximately 7 km) align the outer coast of Alaska, including the Aleutian Islands, and the U.S. East and West coasts, including the Gulf of Mexico, Hawai’i, and Puerto Rico. U.S. territories in the Pacific Ocean are represented by a 10 arc-minute (approximately 18 km) grid. Please see the overview image provided as part of this data release for a visual representation of the spatial grid coverage. Bathymetry and landmasks for all grids were obtained from the 1-arc-minute ETOPO1 global relief model (Amante and Eakins, 2009). In an effort to optimize model output data storage needs, more than 5000 model output points (‘savepoints’) were placed along the approximate 20m, 50m, and 100m isobaths (as derived from the ETOPO1 bathymetry), spaced approximately 10 km in the alongshore direction of all U.S. coastlines or co-located with buoys or other points of interest. Output point locations were snapped to grid points and thus are not necessarily precisely coincident with long-term buoy observation locations. 20210125 Wave model implementation and post-processing. The WW3 model was compiled and run on two separate high-performance computing systems: the USGS Advanced Research Computing Facility, USGS Denali Supercomputer https://doi.org/10.5066/P9PSW367 and Intel Corporation for part of the projection GCMs. Computing efficiency was optimized resulting in approximately 80 hours of computation (wall-clock) time per 10-year simulation. The WW3 model was run with the spatiotemporally varying wind and ice fields, described in the first process step, applied across all model domains. Each year was run individually, with restart files from the previous year. Data from the years 1979 and 2020 are cold-start runs and hence model ‘spin-up’ is included in the approximate first week of data outputs for that year; users are cautioned on using the first weeks of data in years 1979 and 2020. Hourly time-series of bulk wave statistics were saved at each global model grid point (0.5-degree resolution) and coastal ‘savepoint’ from the nested grids (see second process_step) in the form of binary files. A post-processor script included with the WAVEWATCHIII® 6.07.01 release was used to write out select bulk parameters from the binary data files to netCDF format. The annual data files were subsequently re-written to produce netCDF files with continuous time-series spanning all years from 2020 through 2050; model depths at each of the savepoint locations were extracted from the grids and added to the final netCDF files. The data are presented for U.S. coastlines within nested model domains (grids) which correspond to the U.S. West Coast and Hawai’i (wc_4m), East Coast and Gulf of Mexico and Puerto Rico (at_4m), Alaska (ak_4m), and U.S. territories in the Pacific Ocean (ep_10m). This dataset is for the coast of Alaska. 20231130 Wave time-series for the hindcast period were added to the existing data release of wave time-series for the projected period. Corrections were made to the metadata to fix the erroneous version description of EC-Earth projections (EC-Earth3P-HR_highres-future_r1i1p1f1_gr for wind was changed to EC-Earth3P-HR_highres-future_r1i1p2f1_gr, and EC-Earth3P-HR_highres-future_r1i1p2f1_gr was changed to and EC-Earth3P-HR_highres-future_r1i1p2f1_gn). In the previously released data files for the projection period, the global attribute description was also corrected to EC-Earth3P-HR_highres-future_r1i1p2f1_gn. Additionally, erroneous non-zero wave data in the data files for the projection period in the Arctic regions, at times when the Arctic Ocean is usually ice-covered during these times and waves in this region are not expected, were fixed. Erroneous variable datatypes were corrected. 20240820 Data were generated within a numerical model scheme. Refer to self-contained netCDF files for location information. 0.0167 0.0167 Decimal degrees WGS_1984 WGS_1984 6378137.0 298.257223563 netCDF files are self-contained and attribute information may be found in the header of the file itself. The netCDF attributes for the file WavePnts_CMCC_wc_4m_Fut.nc are provided below as a sample. All attributes are the same for each file with the exception of reference to the filename, forcing file names, and max/min values. >Format: > netcdf4 >Global Attributes: > product_name = 'CMIP6 WW3 Extracted Station Wave Parameters' > area = 'West Coast 4 min wave grid' > data_type = 'OCO spectra 2D' > format_version = '1.1' > CMIP6_Mod = 'CMCC' > start_date = '2020-01-01' > stop_date = '2050-12-31' > Temporal_Res = 'hourly' > Extracted_Files = 'cmcc.pnts.wc_4m*_tab.nc' > author = 'USGS, lerikson@usgs.gov' > CMIP6_Winds = 'CMCC-CM2-VHR4-r1i1p1f1_gn, 6-hourly, 25 km resolution' > CMIP6_SeaIce = ' CMCC-CM2-VHR4-r1i1p1f1_gn, daily, 25 km resolution ' >Dimensions: > time = 271752 > station = 516 >Variables: > time > Size: 271752x1 > Dimensions: time > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'julian day (UT)' > standard_name = 'time' > conventions = 'Relative julian days with decimal part (as parts of the day)' > axis = 'T' > units = 'hours since 2020-01-01 00:00:00' > calendar = 'proleptic_gregorian' > station > Size: 516x1 > Dimensions: station > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'station id' > axis = 'X' > longitude > Size: 516x1 > Dimensions: station > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'longitude' > standard_name = 'longitude' > globwave_name = 'longitude' > units = 'degree_east' > valid_min = -160.53 > valid_max = -116.4 > content = 'TX' > associates = 'time station' > latitude > Size: 516x1 > Dimensions: station > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'latitude' > standard_name = 'latitude' > globwave_name = 'latitude' > units = 'degree_north' > valid_min = 18.167 > valid_max = 49.933 > content = 'TX' > associates = 'time station' > depth > Size: 516x1 > Dimensions: station > Datatype: single > Attributes: >_FillValue = -9999 long_name = 'Station Depth Below MSL' standard_name = 'station_depth' units = 'm' Convention = 'Positive Downward' valid_min = 10.8 valid_max = 5524.2 associates = 'station' > hs > Size: 516x271752 > Dimensions: station,time > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'spectral estimate of significant wave height' > standard_name = 'sea_surface_wave_significant_height' > globwave_name = 'significant_wave_height' > units = 'm' > valid_min = 2.6626e-05 > valid_max = 18.3988 > content = 'TX' > associates = 'time station' > fp > Size: 516x271752 > Dimensions: station,time > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'peak frequency (Fp=1/Tp)' > standard_name = 'dominant_wave_frequency' > globwave_name = 'dominant_wave_frequency' > units = 's-1' > valid_min = 0 > valid_max = 0.52997 > content = 'TX' > associates = 'time station' > tr > Size: 516x271752 > Dimensions: station,time > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'mean period normalised by the relative frequency' > standard_name = 'mean_period_normalised_by_the_relative_frequency' > globwave_name = 'mean period normalised by the relative frequency' > units = 's' > valid_min = 0 > valid_max = 21.8097 > content = 'TX' > associates = 'time station' > th1m > Size: 516x271752 > Dimensions: station,time > Datatype: single > Attributes: > _FillValue = -9999 > standard_name = 'mean_wave_direction' > globwave_name = 'mean_wave_direction' > units = 'degree' > valid_min = 0 > valid_max = 359.9999 > content = 'TX' > associates = 'time station' > th1p > Size: 516x271752 > Dimensions: station,time > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'mean wave direction from spectral moments at spectral peak' > standard_name = 'dominant_wave_direction' > globwave_name = 'dominant_wave_direction' > units = 'degree' > valid_min = 0 > valid_max = 359.9999 > content = 'TX' > associates = 'time station' > sth1m > Size: 516x271752 > Dimensions: station,time > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'directional spread from spectral moments' > standard_name = 'mean_wave_spreading' > globwave_name = 'mean_wave_spreading' > units = 'degree' > valid_min = 0 > valid_max = 81.0162 > content = 'TX' > associates = 'time station' > sth1p > Size: 516x271752 > Dimensions: station,time > Datatype: single > Attributes: > _FillValue = -9999 > long_name = 'directional spread at spectral peak' > standard_name = 'dominant_wave_spreading' > globwave_name = 'dominant_wave_spreading' > units = 'degree' > valid_min = 0 > valid_max = 80.9974 > content = 'TX' > associates = 'time station' none U.S. Geological Survey - CMGDS mailing and physical

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Santa Cruz CA 95060 1-831-427-4747 pcmsc_data@usgs.gov The dataset consists of 14 netCDF files, seven of which contain hourly wave parameters for the years 1979 through 2014 and seven of which contain hourly wave projections for the years 2020 through 2050 at 516 output points extracted from the 4-minute nested “U.S. West Coast 4 min” grid (wc_4m; see https://polar.ncep.noaa.gov/waves/validation/). Wave parameters provided are as follows: significant wave heights (swell and seas combined), mean and peak wave periods, and mean and peak wave directions and associated spread. The motivation for providing high temporal resolution values of these variables is driven by the notion that storm impacts on coastal processes, including wave runup and erosion, are often investigated with these parameters. Each netCDF file represents results from model runs using wind forcing and sea ice boundary conditions from one Global Climate Model / General Circulation Model (GCM). Filenames follow the format: WavePnts_<GCM>_<grid>_<period>.nc where <GCM> is the abbreviated name of one of the GCMs listed above, <grid> refers to the NOAA-named model grid from which data were extracted. The common field ‘WavePnts’ is meant to indicate wave time-series at select model output points (as opposed to gridded data). <period> refers to the time-period for which data is provided where ‘His’ represents the historical time-period (1979 through 2014) and ‘Fut’ represents the projected time-period (2020 through 2050). The filename for the GFDL data includes the addition of ‘LimitedUse’ (full filename: WavePnts_GFDL_wc_4m_Fut_LimitedUse.nc) for the historical time-period to emphasize the limitations of the GFDL model results outlined in the Data Quality Information Section. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. netCDF Python 3.7 CF4 files contain time-series of modeled (CMCC) wave parameters for the historical time-period in standard netCDF version 4 format. No compression applied. 4200 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (CMCC) wave parameters for the projected time-period in standard netCDF version 4 format. No compression applied. 3600 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (CNRM) wave parameters for the historical time-period in standard netCDF version 4 format. No compression applied. 4200 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (CNRM) wave parameters for the projected time-period in standard netCDF version 4 format. No compression applied. 3600 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (ECEARTH) wave parameters for the historical time-period in standard netCDF version 4 format. No compression applied. 4200 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (ECEARTH) wave parameters for the projected time-period in standard netCDF version 4 format. No compression applied. 3600 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (GFDL) for the historical time-period wave parameters in standard netCDF version 4 format. No compression applied. 4200 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (GFDL) for the projected time-period wave parameters in standard netCDF version 4 format. No compression applied. 3600 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (HadgemHH) wave parameters for the historical time-period in standard netCDF version 4 format. No compression applied. 4200 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (HadgemHH) wave parameters for the projected time-period in standard netCDF version 4 format. No compression applied. 3600 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (HadgemHM) wave parameters for the historical time-period in standard netCDF version 4 format. No compression applied. 4200 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (HadgemHM) wave parameters for the projected time-period in standard netCDF version 4 format. No compression applied. 3600 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (HadgemSST) wave parameters for the historical time-period in standard netCDF version 4 format. No compression applied. 4200 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. netCDF Python 3.7 CF4 files contain time-series of modeled (HadgemSST) wave parameters for the projected time-period in standard netCDF version 4 format. No compression applied. 3600 https://doi.org/10.5066/P9KR0RFM Data can be downloaded using the Network_Resource_Name links and then scrolling down to the appropriate Wave Data section. None. These data can be viewed with any software that reads netCDF files (such as Mathworks MATLAB™, Python, Panoply). 20241026 U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

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Santa Cruz CA 95060 831-427-4747 pcmsc_data@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998