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Sea Surface Temperature Fronts

submitted by Jason Roberts

Figure 1: Mean frequency of sea surface temperature (SST) fronts off the Pacific coast of Central America, 1985-2005, detected by applying Cayula and Cornillon’s SIED algorithm to 15,340 twice-daily SST images from the NOAA NODC 4 km AVHRR Pathfinder

Dynamic physical ocean processes such as upwellings, currents, and eddies promote biological productivity and structure marine ecosystems by aggregating and dispersing nutrients and organisms. In this illustration, we identify potential EBSAs in two zones of high dynamic activity, detected by measuring how frequently sea surface temperature fronts occur.

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Scientific Background

Biological productivity does not occur evenly throughout the ocean. At the base of many marine food webs are phytoplankton, microscopic floating plants that require sunlight and dissolved nutrients to grow and reproduce. While sunlight availability is related mainly to geographic distance from the equator, nutrient availability is governed by complex, dynamic circulatory processes such as upwellings, currents, and eddies. These processes disperse nutrients unevenly and create patches of high and low phytoplankton productivity.

Unable to resist the flow of ocean currents, phytoplankton drift passively and are subject to the same circulatory processes that control the flow of nutrients. Marine animals such as copepods, krill, and jellyfish also drift passively, as well as the larvae of fish and other higher order organisms. Currents and eddies can entrain drifting organisms and carry them far from their points of origin. As distinct water masses flow past each other, they aggregate drifting organisms along their boundaries, called fronts. These frontal aggregations of drifters attract mobile predators such as fish, turtles, birds, and marine mammals.

Phytoplankton can be detected at the ocean surface by satellites that measure specific wavelengths of reflected sunlight. But current satellite technology cannot detect animals. Until this is possible, scientists must infer the presence of animals by looking for patterns in satellite images that are correlated with the presence of animals, such as fronts visible in images of the sea surface temperature (SST). In this illustration we apply an algorithm to estimate the frequency of SST fronts in the eastern tropical Pacific Ocean near Central America, and identify EBSAs in two zones of high frontal frequency: one south of the Gulf of Tehuantepec and one east of the Gulf of Papagayo.


How the area of high primary productivity was identified

To create a map showing the long-term mean frequency of SST fronts in our region of interest, we downloaded 21 years of SST images from the NOAA NODC 4 km AVHRR Pathfinder 5.0 database. This data comprised 15,340 images between 1985 and 2005 (two per day). For each image, we executed Cayula and Cornillon’s single-image edge detection (SIED) algorithm, using the implementation of this algorithm available in Marine Geospatial Ecology Tools (MGET), an open-source collection of geoprocessing tools for marine research. Figure 2 shows example output from this algorithm. Finally, we estimated the mean frequency of fronts for each cell by dividing the number of times that it contained a front in the 15,340 images by the number of times that the algorithm could be executed. Because clouds frequently blocked the satellites’ view of the ocean, the algorithm could be executed, on average, for 3654 images (23.2%) for a given cell. To identify the area representing the zones of highest frontal activity, we configured a GIS to select the cells falling within 0.025 frequency contours, as shown in Figure 1.


 Figure 2 

Figure 2: Surface temperature fronts (black lines) identified by Cayula and Cornillon’s SIED algorithm in the NOAA NESDIS GOES L3 6 km Near Real-Time SST image for 5 January 2009.

Although there are alternative algorithms for identifying SST fronts, the SIED algorithm provides several advantages: It was shown to be as good at finding large fronts such as the Gulf Stream northern boundary as the simplest alternative, manual classification, in which a trained GIS operator draws the fronts on the image by hand. It was shown to be better than or comparable to several other simple automated methods. It has been validated against fronts identified at sea with oceanographic instruments7.  Finally, although alternatives and improvements have been suggested, SIED is the only algorithm that has been implemented as a freely-available, GIS-integrated tool.

Dynamic regions of the ocean can also be identified by looking for other types of physical features in other types of satellite data. Using data from satellites that measure the height and roughness of the ocean surface with radar, oceanographers are able to estimate the velocity and direction of surface waters and the winds immediately above the surface. From this, major currents and features such as eddies can be identified. An upcoming version of MGET will include a tool for identifying eddies using the Okubo-Weiss algorithm, which was used in a recent global census of eddies.

Upwellings occur when winds or currents draw cold, nutrient-rich water from the depths to the ocean surface. These influxes of nutrients into the sun-lit surface layer produce large phytoplankton blooms, which often lead to high productivity of fish and other animals. Scientists have developed methods for identifying upwellings in satellite images of SST and chlorophyll concentration.


Sources of data

While there have been many scientific publications about front detection algorithms and studies of fronts in specific regions, there are few sources of freely accessible fronts data. One source is the OceanWatch North Pacific Demonstration Project ( operated by the NOAA Pacific Fisheries Environmental Laboratory (PFEL), but the data here are limited to the North Pacific region.

To obtain fronts data for other regions, your best option may be to contact an oceanographer who has published a study of the fronts in your region of interest. Alternatively, if you have a scientist or GIS specialist on staff, you can produce your own fronts data using the SEID algorithm available in Marine Geospatial Ecology Tools. This tool requires satellite images of SST or chlorophyll concentration as input. There are many freely accessible sources of global satellite imagery, including the NOAA NODC SST data mentioned above, SST and chlorophyll data from the MODIS satellites offered by the NASA GSFC OceanColor Group ( and several other SST databases offered by the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC,, including the NOAA NESDIS GOES L3 6 km Near Real-Time SST data which was used in Figure 2.

Aviso ( provides freely-accessible ocean currents and sea surface height data. Although we know of no present source for eddy data, eddies are an active area of oceanographic research and at least one oceanographer plans to bring an eddy database online soon. Alternatively, once the Okubo-Weiss algorithm has been implemented in MGET, you can use it to identify eddies in sea surface height data provided by Aviso.

Laboratorio de Sensores Remotos, Centro de Biodiversidad Marina at the Instituto de Tecnología y Ciencias Marinas, Universidad Simón Bolívar (INTECMAR-USB) provides an online, freely-accessible tool for identifying upwellings in the southern Caribbean region10. A scientific publication describing the algorithm used by this tool is currently in preparation.


Important considerations

Evidence of dynamic ocean activity, such as a high frequency of SST fronts, does not necessarily indicate high biological productivity. In the illustration presented here, both EBSAs exhibit high dynamic ocean activity (Figure 1) and high production of phytoplankton (Figure 3a), but the Papagayo EBSA exhibits higher productivity of zooplankton (Figure 3b,c) and sightings of marine mammals (Figure 3d,e,f). The reasons for these differences are complex. Both EBSAs experience strong seasonal upwellings and large, powerful eddies generated winds blowing through gaps the mountains of Central America. The Papagayo EBSA also encompasses the Costa Rica Dome, a phenomenon similar to an upwelling, in which physical forces thin the layer of warm, nutrient-poor water at the ocean surface, producing an underwater “dome” of cold, nutrient-rich water close to the surface. The Costa Rica Dome has long been known by oceanographers to be a region of high productivity and important animal habitat. It may be that the Costa Rica Dome is more important to biological productivity than the other dynamic processes discussed here, and while SST front frequency is an indicator for both the Costa Rica Dome and those other phenomena, this indicator does not allow us to distinguish between the two.


Figure 3

Figure 3: Estimates of productivity and locations of marine mammal sightings for our region of interest. (a) Mean production of phytoplankton estimated by the Vertically Generalized Productivity Model (VGPM) from SeaWiFS chlorophyll concentration and AVHRR SST, 1997-2007. (b, c) Mean production of microzooplankton and mesozooplankton estimated by the Pacific ROMS-CoSINE model, 1991-2007. (d, e, f) Sighting locations of common dolphins (Delphinus delphis), spotted dolphins (Stenella attenuata), and blue whales (Balaenoptera musculus) from research and tuna vessels in the NOAA/NMFS/SWFSC sightings database, 1971-1999.

In light of this complexity, we offer several recommendations for achieving best results. First, if biological productivity data are available, consider using them rather than estimates of dynamic processes, which only suggest the possibility of biological productivity. Second, if several kinds of productivity or dynamic process data are available, look at all of them before forming an opinion of the overall biological productivity of an area. It may be that a given area is highly productive for some species but not others. Finally, obtain the assistance of oceanographers and biologists familiar with your field of interest.


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