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|Title:||The Coral Reef Temperature Anomaly Database (CoRTAD) Version 3 - Global, 4 km Sea Surface Temperature and Related Thermal Stress Metrics for 1982-2009 (NODC Accession 0068999)|
|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 Version 2 contains global, approximately 4 km resolution SST data on a weekly time scale from 1982 through 2009. It is related to the CoRTAD Version 2 (NODC Accession Number 0054501), but contains one additional year of data (2009). Version 2was created in 2009 with a few important updates to the CoRTAD Version 1 (NODC Accession Number 0044419). Whereas Version 1 covers the time period from 1985-2005, Version 2 contains 6 additional years of data, extending that period to 1982-2008. Also, whereas Version 1 is in HDF4 Scientific Data Set format, Version 2 is in HDF5 format|
In addition to SST, the CoRTAD 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 the previous 52 weeks that SSTA >= 1 degree C), TSA DHW (TSA_DHW, also known as 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.
The purpose of the CoRTAD is 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.
|Observation types:||satellite data|
|Instrument types:||AVHRR > Advanced Very High Resolution Radiometer, AVHRR-GAC|
|Datatypes:||SEA SURFACE TEMPERATURE, WATER TEMPERATURE|
|Submitter:||Casey, Dr. Kenneth|
|Collecting institutions:||NODC, UNC|
|Contributing projects:||COASTAL, CORAL REEF STUDIES, CoRIS|
|Platforms:||NOAA-11 SATELLITE, NOAA-14 SATELLITE, NOAA-16 SATELLITE, NOAA-17 SATELLITE, NOAA-18 SATELLITE, NOAA-7 SATELLITE, NOAA-9 SATELLITE|
|Number of observations:|
|Supplementary information:||[Text below adapted from: Selig, Elizabeth R., Kenneth S. Casey, and John F. Bruno (2009), New insights into global patterns of ocean temperature anomalies: implications for coral reef health and management, Global Ecology and Biogeography, in press. Hereafter referred to as "SCB2009".]|
The CoRTAD was developed using data from the Pathfinder Version 5 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 SCB2009). 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 2 of SCB2009).
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.
[*NOTE: The higher resolution Reynolds 25km Daily Optimum Interpolation Sea Surface Temperature (DOISST version 2.0) dataset was used in place of the OISST version 2.0 for the Pathfinder data from 1982-1984. The primary effect of this change is to retain more data in the high gradient regions and in regions where meandering or feature advection is present; effect on the retrieved SST is minimal.
Two problems with the original Pathfinder data from 1982-1984 have been identified. An error was discovered in the processing of the reference SST fields, which created a "halo" of cold pixels around coastlines in the reference field. As a result, several anomalously cool Pathfinder SST pixels have passed the reference test during processing and been assigned quality flag values that are too high. The second problem arises from the fact that the reference SST field used for 1982-1984 data lacks inland SST observations. As a result, the gap-filling routine employed by the CoRTAD fails for inland pixels for the entire 1982-1984 period. In order to avoid contamination of climatology-based thermal metrics and statistics calculated in the CoRTAD, all data from 1982-1984 were omitted from the climatology. Thus, the CoRTAD Version 3 climatology was calculated using only 1985-2009 data. All CoRTAD fields have been calculated for the entire time series (1982-2009) based on this climatology. For an image demonstrating these problems in Pathfinder processing, please see the "Known Problems" section of the Pathfinder Version 5 User Guide at http://pathfinder.nodc.noaa.gov/userguide.html.]
Using these gap-filled data, we then created site-specific climatologies for each reef grid cell to describe long-term temperature patterns over the 25-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 2 of SCB2009 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 1 of SCB2009): 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 SCB2009 for complete list)
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