Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the annual average of the maximum anomaly of wave power (kW/m) from 2000-2013. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Time series of anomalies were calculated by quantifying the number and magnitude of events from the maximum daily data set that exceeded the maximum climatological monthly mean. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The average annual maximum wave power anomaly was calculated by taking the average of the annual maximum wave power values in exceedance of the maximum monthly climatological wave power from 2000-2013 for each 500-m grid cell.

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the mean of maximum daily wave power (kW/m) from 2000-2013. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The long-term mean wave power was calculated by taking the average of the maximum daily time series of wave power data from 2000-2013 for each 500-m grid cell.

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the maximum of the monthly mean climatology of SST (degrees Celsius) from 1985-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. An SST climatology was first calculated by taking the average of the 5-km weekly SST data for each month, and then averaging for all same-months (e.g., January) over the 1985-2013 time period.

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the annual average frequency of anomalies of wave power (kW/m) from 2000-2013, with values presented as fraction of a year. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Time series of anomalies were calculated by quantifying the number and magnitude of events from the maximum daily data set that exceeded the maximum climatological monthly mean during 2000-2013. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The average annual frequency of wave power anomalies was calculated by taking the average number of days that exceeded the maximum monthly climatological wave power from 2000-2013 for each 500-m grid cell. Values are represented as a fraction of a year.

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the standard deviation of maximum daily wave power (kW/m) from 2000-2013. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The standard deviation of the long-term mean wave power was calculated by taking the standard deviation of the maximum daily time series of wave power data from 2000-2013 for each 500-m grid cell.

Wave power is a major environmental forcing mechanism that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing can be highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the mean of maximum daily wave power (kW/m) from 2002-2012. In the absence of numerical wave model and wave forcing observational site-level data at the desired spatial resolution, a wave exposure proxy, developed by S. Jeanette Clark, is used to examine wave exposure. Wave energy estimates were derived at 1-km resolution utilizing NOAA WaveWatch III (WW3) global 0.5-deg wave model data and coastline analysis of wave exposure. This is achieved by: 1) Determining the incident wave swath for a specific site at an island using a 360-degree radial plot and degree-bin elimination based on a swath's intersection with land or relevant bathymetric contour. 2) Selecting the closest WW3 pixel and extracting the time series for significant wave height, peak period, and peak direction. 3) Calculating wave power (kW/m) with significant wave height and peak period using the following equation: Ef = pg / 64pi * Hs^2 * Tp / 1000 where p is the density of sea water (1024 kg m-3), g is the acceleration of gravity (9.8 m s-2), Hs is the offshore significant wave height, and Tp is the dominant wave period (1/wavelength). 4) Lastly, annual wave power data are filtered and organized into respective degree bins based on peak direction and summed to give a wave power estimate at each site. The wave power metric calculated here is based on offshore wave height and does not account for variation with depth.

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the maximum of the monthly mean climatology of SST (degrees Celsius) from 1985-2018. These SST dataset are derived from CoralTemp 5-km gap-free analyzed blended sea surface temperature over the global ocean. CoralTemp is derived from three different but related 5-km daily gap-free SST data sets and provides an internally consistent SST product that stretches from 1985 to present. 1) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature Reanalysis (1985-2002). 2) Geo-Polar Blended Night-Only Sea Surface Temperature Reanalysis (2002-2016). 3) Geo-Polar Blended Night-Only Sea Surface Temperature Near Real-Time (2017 to present). The 8-day composites are generated from daily Coral Reef Watch (CRW) files by OceanWatch Central Pacific. An SST climatology was first calculated by taking the average of the 5-km weekly SST data for each month, and then averaging for all same-months (e.g., January) over the 1985-2018 period. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_8day.graph

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the annual average of the maximum anomaly of SST (degrees Celsius) from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The SST average annual maximum anomaly was calculated by taking the average of the annual maximum SST values in exceedance of the maximum monthly climatological SST from 2000-2013 for each pixel.

Chlorophyll-a, is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the maximum monthly climatological mean of chlorophyll-a (mg/m3) from 1998-2018. Data products generated by the Ocean Colour component of the European Space Agency (ESA) Climate Change Initiative (CCI) project. These files are 8-day 4-km composites of merged sensor products: Global Area Coverage (GAC), Local Area Coverage (LAC), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Ocean and Land Colour Instrument (OLCI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS). A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Monthly climatologies were calculated from monthly time series averaging for all same-months (e.g., January). Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-8d-v5-0.graph

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the mean SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The SST long-term mean was calculated by taking the average of all weekly data from 2000-2013 for each pixel.