Abstract
The quantification and description of sea surface temperature (SST) is critically important because it can influence the distribution, migration, and invasion of marine species; furthermore, SSTs are expected to be affected by climate change. To better understand present temperature regimes, we assembled a 29-year nearshore time series of mean monthly SSTs along the North Pacific coastline using remotely-sensed satellite data collected with the Advanced Very High Resolution Radiometer (AVHRR) instrument. We then used the dataset to describe nearshore (<20 km offshore) SST patterns of 16 North Pacific ecoregions delineated by the Marine Ecoregions of the World (MEOW) hierarchical schema. Annual mean temperature varied from 3.8°C along the Kamchatka ecoregion to 24.8°C in the Cortezian ecoregion. There are smaller annual ranges and less variability in SST in the Northeast Pacific relative to the Northwest Pacific. Within the 16 ecoregions, 31–94% of the variance in SST is explained by the annual cycle, with the annual cycle explaining the least variation in the Northern California ecoregion and the most variation in the Yellow Sea ecoregion. Clustering on mean monthly SSTs of each ecoregion showed a clear break between the ecoregions within the Warm and Cold Temperate provinces of the MEOW schema, though several of the ecoregions contained within the provinces did not show a significant difference in mean seasonal temperature patterns. Comparison of these temperature patterns shared some similarities and differences with previous biogeographic classifications and the Large Marine Ecosystems (LMEs). Finally, we provide a web link to the processed data for use by other researchers.
Highlights
Considerable progress has been made in understanding ocean dynamics through the analysis of remotely-sensed sea surface temperature data [1,2,3,4]
We focused the present analysis on the ecoregions within the Temperate North Pacific realm, which is comprised of four Provinces containing 17 individual ecoregions (Figure 1)
The percent of variance in sea surface temperature (SST) explained by the annual cycle varies from 31% for the Northern California ecoregion to 94% in the Yellow Sea ecoregion (Table S1)
Summary
Considerable progress has been made in understanding ocean dynamics through the analysis of remotely-sensed sea surface temperature (hereafter SST) data [1,2,3,4]. Potential contamination by land signal and coastal weather often hampers efforts to compile a comprehensive nearshore SST dataset This issue was encountered in the study by Blanchette et al [5], which examined the relationship between temperature and species assemblages in rocky shores from southeast Alaska to Baja California, and in a study by Broitman et al [6] of a smaller region along the coasts of Oregon and California. Both inquiries experienced an issue with missing pixels, forcing them to spatially average the SST data at their coastal sites. These types of climate studies are critical because nearshore environments and the distributions of organisms within them are expected to be highly influenced by climate change [6,14,15]
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