The Coral Reef Temperature Anomaly Database (CoRTAD) Version 4 - Global, 4 km Sea Surface Temperature and Related Thermal Stress Metrics for 1981-10-31 to 2010-12-31 (NODC Accession 0087989)

[Text below adapted from: Selig, E.R., K.S. Casey, and J.F. Bruno (2010). New insights into global patterns of ocean temperature anomalies: implications for coral reef health and management, Global Ecology and Biogeography, DOI: 10.1111/j.1466-8238.2009.00522.x. Hereafter referred to as "SCB2010".]

The CoRTAD was developed using data from the Pathfinder Version 5.2 collection produced by the National Oceanic and Atmospheric
Administration's (NOAA) National Oceanographic Data Center (NODC) and the University of Miami's Rosenstiel School of Marine and Atmospheric Science (http://pathfinder.nodc.noaa.gov). These sea surface temperature data are derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor and are processed to a resolution of approximately 4.6 km at the equator. These data have the highest resolution covering the longest time period of any satellite-based ocean temperature dataset. Daytime and nighttime data were averaged weekly using data with a quality flag of 4 or better. This level is a commonly accepted cutoff for "good" data (Kilpatrick et al., 2001, Casey and Cornillon, 1999). Previous versions of CoRTAD also incorporated pixels up to 5 degrees warmer than a course resolution reference SST based on the Reynolds Optimum Interpolation Sea Surface Temperature (OISST version 2.0). Since Pathfinder Version 5.2 already uses a finer resolution reference field (25 km Daily OISST) this additional step was not incorporated. These processes resulted in a weekly SST dataset that is roughly 80% gap free.

To create a gap-free dataset for analysis, 3 x 3 pixel median spatial fill was used. A temporal fill was performed using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function in Matlab (The Mathworks Inc.) to fill the remaining gaps. This conservative approach was chosen because it provided interpolated SSTs that are bounded by the nearest available values in time. It also used data from only a very limited spatial domain, which is an important consideration given the variability of coral reef environments.

Using these gap-filled data, we then created site-specific climatologies for each reef grid cell to describe long-term temperature patterns over the 29-year dataset (Eqn. 1). The climatology was generated using a harmonic analysis procedure that fits annual and semi-annual signals to the time series of weekly SSTs at each grid cell:

climSST(t) = A*cos(2pi*t + B) + C*cos(4pi*t + D) + E (1)

where t is time, A and B are coefficients representing the annual phase and amplitude, C and D are the semi-annual phase and amplitude, and E is the long-term temperature mean. Similar approaches have been used for generating climatologies because they are more robust than simple averaging techniques, which can be more susceptible to data gaps from periods of cloudiness (Podesta et al., 1991, Mesias et al., 2007).

Earlier CoRTAD versions were derived from Pathfinder 5.0/5.1 which attempted to represent the SST's at depths of about 1 meter, similar to the SST as measured by drifting buoys. Following the recommendations of the Group for High Resolution SST (GHRSST), Pathfinder 5.2 represents the "skin" SST that is actually measured by the AVHRR instruments. Thus, Pathfinder Version 5.2 SST's are on average 0.17K lower than those of earlier versions (Donlon et al., 2007). This difference is only in the temperature parameters of CoRTAD, since it is subtracted out in the calculations of the anomalies and all the derived thermal stress metrics.

As of the release of CoRTAD version 4 a gap exists in the Pathfinder 5.2 dataset from 1994 day 275 to 1995 day 17 since Level 1B GAC data from NOAA-9 is not available from the NOAA archives. For these 3.5 months SST has been interpolated from Pathfinder 5.0 and 0.17K is subtracted to get the skin temperature.


Temperature anomaly metrics:

Several metrics could be used to link coral reef ecosystem health with temperature including trophic structure, diversity or percent coral cover (Newman et al., 2006, Roberts et al., 2002, Bruno and Selig, 2007). However, this analysis focused on coral bleaching and disease because they are key drivers of coral decline and their relationships with temperature patterns are better understood (Aronson and Precht, 2001, Bruno et al., 2007, Glynn, 1993). Analyses were performed on two metrics (see Table 1 of SCB2010): one that is commonly known to lead to bleaching (Liu et al., 2003, Strong et al., 2004, Glynn, 1993), and one that is correlated with increased disease severity (Selig et al., 2006, Bruno et al., 2007).

Coral bleaching results when corals lose their symbiotic zooxanthellae (Glynn, 1993, Glynn, 1996). Bleaching is a natural stress response not only to warm temperatures, but also to cool temperatures (Hoegh-Guldberg and Fine, 2004) as well as light and salinity values different from the normal range (Glynn, 1993). Corals can recover from bleaching, but their ability to do so is dependent on the magnitude and duration of the anomaly event (Glynn, 1993). The temperature thresholds that result in coral bleaching vary by location and species (Berkelmans and Willis, 1999).

Bleaching is often connected to Thermal Stress Anomalies (TSAs), which are defined as areas where temperatures exceed by 1 degree C or more the climatologically warmest week of the year (Table 2, Glynn, 1993). The temperature anomaly thresholds relevant to disease have been studied in only one pathogen-host system (Selig et al., 2006, Bruno et al., 2007). In that system, changes in disease cases were correlated with Weekly Sea Surface Temperature Anomalies (WSSTAs), temperatures that were 1 degree C greater than the weekly average for that location. The best metric for predicting bleaching or disease may vary according to location, species, and pathogen (Selig et al., 2006, Bruno et al., 2007, Berkelmans, 2002). For example, bleaching on the Great Barrier Reef was best predicted by the maximum anomaly over a 3 day period (Berkelmans et al., 2004), rather than an anomaly metric like the TSA. Although the 7-day averaging approach in the CoRTAD may be too temporally coarse to capture all bleaching events, it is necessary to maintain consistency and minimize gaps in the dataset across broad spatial scales. In addition, the data are less likely to yield false positives for TSAs and will likely capture most WSSTA events, which have a lower temperature threshold.

References: (see SCB2010 for complete list)

In this accession, NODC has archived multiple versions of these data. The latest (and best) version of these data has the largest version number.