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
20220707
Ocean wave time-series data surrounding Hawai’i and U.S. territories in the Pacific Ocean simulated with a global-scale numerical wave model under the influence of CMIP6 wind and sea ice fields
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
Ocean wave time-series data simulated with a global-scale numerical wave model under the influence of projected CMIP6 wind and sea ice fields
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 projected hourly time-series of wave heights, wave periods, incident wave directions, and directional spreading at distinct points surrounding Hawai’i and U.S. territories in the Pacific Ocean, for the years 2020 through 2050. The projections 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 kms, depending on latitude) and further downscaled to 10- (approximately 18 kilometer) and 4-arc-minute (approximately 7 kilometer) model grids. Point model output data extracted from NOAA’s 10-arc-minute grid for Hawai’i and U.S. territories in the Pacific Ocean (ep_10m) 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.
20200101
20501231
time period for which the data were modeled
None planned
-171.5000
171.6600
24.3500
-15.1670
USGS Metadata Identifier
USGS:88b12dc0-eaff-4a0e-a869-6313dcd19373
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
None
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
2885 Mission Street
Santa Cruz
CA
95060
831-427-4747
pcmsc_data@usgs.gov
WavePnts_ep_10m_Fut_Preview.png
model output points (savepoints) at which time-series data are provided
png
We thank Drs. Babak Tehranirad and Sean Vitousek 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
ETOPO1 Arc-minute global relief model: procedures, data sources and analysis
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
Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios
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
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization.
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
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
High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6
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
Advancing wind-waves climate science: The COWCLIP project
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
Projected changes in wave climate from a multi-model ensemble
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
Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns
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
Robustness and uncertainties in global multivariate wind-wave climate projections
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
A global ensemble of ocean wave climate projections from CMIP5-driven models
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
Projected future changes in tropical cyclones using the CMIP6 HighResMIP multimodel ensemble
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
User manual and system documentation of WAVEWATCHIII R version 6.07.
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/).
The data provided matches the wave source information and falls within the expected ranges for wave parameters.
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
CMCC CMCC-CM2-VHR4 model output prepared for CMIP6 HighResMIP
netCDF files
online
Earth System Grid Federation
http://doi.org/10.22033/ESGF/CMIP6.1367
online database
20200101
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
CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp585
netCDF files
online
Earth System Grid Federation
http://doi.org/10.22033/ESGF/CMIP6.4225
online database
20200101
20501231
2021
CNRM
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)
2019
EC-Earth-Consortium EC-Earth3P-HR model output prepared for CMIP6 HighResMIP highres-future
netCDF files
online
Earth System Grid Federation
http://doi.org/10.22033/ESGF/CMIP6.4912
online database
20200101
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
NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP ssp585
netCDF files
online
Earth System Grid Federation
http://doi.org/10.22033/ESGF/CMIP6.9268
online database
20200101
20501231
2021
GFDL
East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model.
Malcolm Roberts
2019
MOHC HadGEM3-GC31-HH model output prepared for CMIP6 HighResMIP highres-future
netCDF files
online
Earth System Grid Federation
http://doi.org/10.22033/ESGF/CMIP6.5982
online database
20200101
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
2019
MOHC HadGEM3-GC31-HM model output prepared for CMIP6 HighResMIP highres-future
netCDF files
online
Earth System Grid Federation
http://doi.org/10.22033/ESGF/CMIP6.5984
online database
20200101
20501231
2021
HadgemHM
East-west and north-south wind components and sea-ice concentrations were used as boundary conditions for the WAVEWATCHIII® model.
Malcolm Roberts
2017
MOHC HadGEM3-GC31-HM model output prepared for CMIP6 HighResMIP highresSST-future
netCDF files
online
Earth System Grid Federation
http://doi.org/10.22033/ESGF/CMIP6.6024
online database
20200101
20501231
2021
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: CMCCwinds: CMCC-CM2-VHR4-r1i1p1f1_gn, 6-hourly, 25 km resolution, CMCC sea-ice: CMCC-CM2-VHR4-r1i1p1f1_gn, daily, 25 km resolution, CNRM sea-ice: CNRM-CM6-1-HR-r1i1p1f2, daily, 25 km resolution, ECEARTH winds: EC-Earth3P-HR-r1i1p1f1_gr, 3-hourly, 50 km resolution, ECEARTH sea-ice: EC-Earth3P-HR-r1i1p2f1_gr, daily, 25 km resolution, GFDL winds: GFDL-CM4C192-highresSST-r1i1p1f1_gr3, 3-hourly, 50 km resolution, GFDL sea-ice: GFDL-CM4_ssp585_r1i1p1f1_gr2, daily, 25 km resolution, HadgemHH winds: HadGEM3-GC31-HH_highres-future_r1i1p1f1, 3-hourly, 50 km resolution. HadgemHH sea-ice: HadGEM3-GC31-HM_highres-future_r1i1p1f1, daily, 25 km resolution, HadgemHM winds: HadGEM3-GC31-HM_highres-future_r1i1p1f1, 3-hourly, 50 km resolution, HadgemHM sea-ice: HadGEM3-GC31-HM_highres-future_r1i1p1f1, daily, 25 km resolution, HadgemSST winds: HadGEM3-GC31-HM_highresSST-future_r1i1p1f1_gn, forced atmosphere experiment using SST/sea ice derived from CMIP5 RCP8.5, 3-hourly, 50 km resolution. HadgemSST sea-ice: HadGEM3-GC31-HM_highresSST-future_r1i1p1f1_gn, 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, CNRM, ECEARTH, GFDL, 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.
WW3
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. 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 year 2020 is a cold-start run 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 year 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 Hawai’i and U.S. territories in the Pacific Ocean.
WW3
20211013
Data were generated within a numerical model scheme. Refer to self-contained netCDF files for location information.
0.1667
0.1667
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_ep_10m_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 = 'GMex and ep 10min 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_Dir = '/caldera/projects/usgs/water/nywsc/lherdman/WW4/WW3- 6.07.1'
> Extracted_Files = '*ep_10m*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 = 74
>Variables:
> hs
> Size: 74x271752
> 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 = 0
> valid_max = 16.1497
> content = 'TX'
> associates = 'time station'
> fp
> Size: 74x271752
> 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: 74x271752
> 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 = 19.8727
> content = 'TX'
> associates = 'time station'
> th1m
> Size: 74x271752
> 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: 74x271752
> 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: 74x271752
> 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 = 81.0214
> content = 'TX'
> associates = 'time station'
> sth1p
> Size: 74x271752
> 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 = 81.0202
> content = 'TX'
> associates = 'time station'
> time
> Size: 271752x1
> Dimensions: time
> Datatype: int64
> 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: 74x1
> Dimensions: station
> Datatype: int32
> Attributes:
> _FillValue = -9999
> long_name = 'station id'
> axis = 'X'
> longitude
> Size: 74x1
> Dimensions: station
> Datatype: single
> Attributes:
> _FillValue = -9999
> long_name = 'longitude'
> standard_name = 'longitude'
> globwave_name = 'longitude'
> units = 'degree_east'
> valid_min = -171.5
> valid_max = -171.66
> content = 'TX'
> associates = 'time station'
> latitude
> Size: 74x1
> Dimensions: station
> Datatype: single
> Attributes:
> _FillValue = -9999
> long_name = 'latitude'
> standard_name = 'latitude'
> globwave_name = 'latitude'
> units = 'degree_north'
> valid_min = -15.167
> valid_max = 24.35
> content = 'TX'
> associates = 'time station'
> Station_Depth
> Size: 74x1
> Dimensions: station
> Datatype: single
> Attributes:
>_FillValue = -9999
long_name = 'Station Depth Below MSL'
standard_name = 'station_depth'
units = 'm'
Convention = 'Positive Downward'
valid_min = 130
valid_max = 5840.8999
none
U.S. Geological Survey - CMGDS
mailing and physical
2885 Mission Street
Santa Cruz
CA
95060
1-831-427-4747
pcmsc_data@usgs.gov
The dataset consists of seven netCDF files, each containing hourly wave projections for the years 2020 through 2050 at 74 output points extracted from NOAA’s 10-minute nested “East Pacific 10 min” grid (ep_10m; 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>_Fut.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). The common field ‘Fut’ represents the time-period of provided data: 2020 through 2050.
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 in standard netCDF version 4 format.
No compression applied.
616.7
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 in standard netCDF version 4 format.
No compression applied.
533.7
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 in standard netCDF version 4 format.
No compression applied.
533.8
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) wave parameters in standard netCDF version 4 format.
No compression applied.
533.7
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 in standard netCDF version 4 format.
No compression applied.
533.7
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 in standard netCDF version 4 format.
No compression applied.
531.8
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 in standard netCDF version 4 format.
No compression applied.
533.7
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).
20220707
U.S. Geological Survey, Pacific Coastal and Marine Science Center
PCMSC Science Data Coordinator
mailing and physical
2885 Mission Street
Santa Cruz
CA
95060
831-427-4747
pcmsc_data@usgs.gov
Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998