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The Coral Reef Temperature Anomaly Database (CoRTAD) - Global, 4 km, Sea Surface Temperature and Related Thermal Stress Metrics for 1985-2005 (NCEI Accession 0044419)


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            title:  The Coral Reef Temperature Anomaly Database (CoRTAD) - Global, 4 km, Sea Surface Temperature and Related Thermal Stress Metrics for 1985-2005 (NCEI Accession 0044419)
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        abstract:  The Coral Reef Temperature Anomaly Database (CoRTAD) is a collection of sea surface temperature (SST) and related thermal stress metrics, developed specifically for coral reef ecosystem applications but relevant to other ecosystems as well. The CoRTAD contains global, approximately 4 km resolution SST data on a weekly time scale from 1985 through 2005. In addition to SST, it contains SST anomaly (SSTA, weekly SST minus weekly climatological SST), thermal stress anomaly (TSA, weekly SST minus the maximum weekly climatological SST), SSTA Degree Heating Week (SSTA_DHW, sum of previous 12 weeks when SSTA >= 1 degree C), SSTA Frequency (number of times over previous 52 weeks that SSTA >= 1 degree C), TSA DHW (TSA_DHW, also known as a Degree Heating Week, sum of previous 12 weeks when TSA >= 1 degree C), and TSA Frequency (number of times over previous 52 weeks that TSA >= 1 degree C). The CoRTAD was created at the NOAA National Oceanographic Data Center in partnership with the University of North Carolina - Chapel Hill, with support from the NOAA Coral Reef Conservation Program. To provide sea surface temperature data and related thermal stress parameters with good temporal consistency, high accuracy, and fine spatial resolution. The CoRTAD is intended primarily for climate and ecosystem related applications and studies and was designed specifically to address questions concerning the relationship between coral disease and bleaching and temperature stress.
        purpose:  To provide sea surface temperature data and related thermal stress parameters with good temporal consistency, high accuracy, and fine spatial resolution. The CoRTAD is intended primarily for climate and ecosystem related applications and studies and was designed specifically to address questions concerning the relationship between coral disease and bleaching and temperature stress.
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            otherConstraints:  Cite as: Casey, Kenneth S.; US National Oceanographic Data Center (2021). The Coral Reef Temperature Anomaly Database (CoRTAD) - Global, 4 km, Sea Surface Temperature and Related Thermal Stress Metrics for 1985-2005 (NCEI Accession 0044419). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0044419. Accessed [date].
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                title:  New insights into global patterns of ocean temperature anomalies: implications for coral reef health and management
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                    individualName:  Elizabeth R. Selig
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                      Anchor:  https://www.ncei.noaa.gov/archive/archive-management-system/OAS/bin/prd/jquery/person/details/2224 Kenneth Casey
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                      Anchor:  https://www.ncei.noaa.gov/archive/archive-management-system/OAS/bin/prd/jquery/institution/details/1730 NOAA National Centers for Environmental Information
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                    name:  Global Ecology and Biogeography
                    issueIdentification:  Volume 19, No. 3
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        topicCategory:  (MD_TopicCategoryCode) environment
        topicCategory:  (MD_TopicCategoryCode) oceans
        topicCategory:  (MD_TopicCategoryCode) economy
        topicCategory:  (MD_TopicCategoryCode) climatologyMeteorologyAtmosphere
        topicCategory:  (MD_TopicCategoryCode) biota
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        supplementalInformation:  [Text below adapted from: Selig, Elizabeth R., Kenneth S. Casey, and John F. Bruno (2008), Global patterns of variability in coral reef temperature anomalies: the importance of fine scale spatial and temporal heterogeneity. Global Change Biology, submitted. Hereafter referred to as "SCB2008".] The CoRTAD was developed using data from the Pathfinder Version 5.0 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(see Figure 1 of SCB2008). Weekly averages of day and night data with a quality flag of 4 or better were used, which is a commonly accepted cutoff for "good" data (Kilpatrick et al., 2001, Casey and Cornillon, 1999). By using a day-night average, the number of missing pixels was reduced by 25% with virtually no loss in accuracy (see Table 1 of SCB2008). The Pathfinder algorithm eliminates any observation with a Sea Surface Temperature (SST) more than 2 degrees C different from a relatively coarse resolution SST value based on the Reynolds Optimum Interpolation Sea Surface Temperature (OISST version 2.0) value, a long-term, in situ-based data set (Kilpatrick et al., 2001, Reynolds et al., 2002). Observations were added back into the analysis if the SST was greater than the OISST-5 degrees C, but less than the OISST+5 degrees C. The 5 degrees C threshold is a reasonable selection that allows diurnal warming events (Kawai and Wada, 2007) or other spatially limited warm spots back into the dataset without including unrealistic and erroneously warm values. Values less than the OISST were not included because they may have been biased by cloud contamination and other satellite errors, which tend to result in cooler SST estimates. These processes resulted in a dataset with only 21.2 percent missing data. 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., 2006) 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 21-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). Sea surface temperatures from AVHRR quantify only the temperature of the 'skin' of the ocean, roughly the first 10 micrometers of the ocean surface (Donlon et al., 2007). Most field surveys of coral cover occur between 1 and 15 m depth. To be useful for coupling with coral reef biological data, these temperature data must be relatively accurate beyond the 'skin' of the ocean. Linear regression was used to examine how data from in situ reef temperature loggers compared with data from the CoRTAD to demonstrate the good accuracy of the CoRTAD temperature data compared to in situ data at a variety of depths and locations around the world (see Table 1 of SCB2008 for details). 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 2 of SCB2008): 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). he 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 SCB2008 for complete list)
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                description:  NCEI Accession 0044419 v1.1 was published.
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                code:  AVHRR-2
            type:  AVHRR-2
            description:  Advanced Very High Resolution Radiometer-2 The AVHRR-2 is a multi purpose imaging instrument used for global measurement of cloud cover, sea surface temperature, ice, snow and vegetation cover and characteristics. This instrument was initially carried on NOAA-7 (launched June 1981) and was last flown on NOAA-14 which was decommissioned in 2005. This 5-channel radiometer covering 2 visible and 3 thermal infrared between 0.63 and 12.0 micrometers, has an instantaneous footprint of 1.1 km at the sub satellite point. Additional Information: https://nwpsaf.eu/deliverables/aapp/avhrr_2.html
        instrument:  (MI_Instrument)
            identifier:  (MD_Identifier)
                code:  AVHRR-3
            type:  AVHRR-3
            description:  Advanced Very High Resolution Radiometer-3 The AVHRR-3 is a six channel scanning radiometer providing 3 solar channels in the visible-near infrared region and 3 thermal infrared channels. The AVHRR-3 has two one-micrometer wide channels between 10.3 and 12.5 micrometers. This latest version of AVHRR was first carried on NOAA-15 launched in May 1998 and subsequently in all current NOAA and MetOp platforms. Measuring the same view, this array of diverse wavelengths, after processing, permits multi spectral analysis for more precisely defining hydrologic, oceanographic, and meteorological parameters. Comparison of data from two channels is often used to observe features or measure various environmental parameters. The three channels operating entirely within the infrared band are used to detect the heat radiation from and hence, the temperature of land, water, sea surfaces, and the clouds above them. Additional Information: http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/klm/html/c3/sec3-1.htm
        platform:  (MI_Platform)
            identifier:  (MD_Identifier)
                code:  NOAA-11
            description: https://www.wmo-sat.info/oscar/satellites/view/333 Instruments: AVHRR/2 et al
            instrument:  #inst_be0e
        platform:  (MI_Platform)
            identifier:  (MD_Identifier)
                code:  NOAA-14
            description: https://www.wmo-sat.info/oscar/satellites/view/336 Instruments: AVHRR/2 et al
            instrument:  #inst_be0e
        platform:  (MI_Platform)
            identifier:  (MD_Identifier)
                code:  NOAA-16
            description:  Updated information from ICES for the platform NOAA-16 from United States. Name NOAA-16 Country(s) US: UNITED STATES Platform Class 65: Orbiting satellite Native Name Synonyms Call Sign MMSI IMO Pennant Title Commissioned Date 2000 Decommissioned Date 2014 Notes Polar-orbiting environmental satellite launched September 2000. The NODC has archived AVHRR SST data collected from this platform as part of the Pathfinder project. Instruments: AVHRR/3 et al Information Source (URL) https://www.wmo-sat.info/oscar/satellites/view/338 Confirmed by NOAA on 2018-07-17.
            instrument:  #inst_be0f
        platform:  (MI_Platform)
            identifier:  (MD_Identifier)
                code:  NOAA-17
            description:  Updated information from ICES for the platform NOAA-17 from United States. Name NOAA-17 Country(s) US: UNITED STATES Platform Class 65: Orbiting satellite Native Name Synonyms Call Sign MMSI IMO Pennant Title Commissioned Date 2002 Decommissioned Date 2013 Notes Polar-orbiting environmental satellite launched June 2002. The NODC has archived AVHRR SST data collected from this platform as part of the Pathfinder project. Instruments: AVHRR/3 et al Information source (URL) https://www.wmo-sat.info/oscar/satellites/view/339 Confirmed by NOAA on 2018-07-17.
            instrument:  #inst_be0f