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 0054501), but contains one additional year of data (2009). Version 2 was
created in 2009 with a few important updates to the CoRTAD Version 1 (NODC Accession
Number 0044419). Whereas Version 1 is in HDF4 Scientific Data Set format, Version 2
is in HDF5. 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
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.
[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 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 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:
Aronson R.B. and W.F. Precht (2001). White-band disease and the changing face of
Caribbean coral reefs. Hydrobiologia, 460, 25-38. Berkelmans R. (2002).
Time-integrated thermal bleaching thresholds of reefs and their variation on the
Great Barrier Reef. Marine Ecology Progress Series, 229, 73-82. Berkelmans R., G.
De'ath, S. Kininmonth and W.J. Skirving (2004). A comparison of the 1998 and 2002
coral bleaching events on the Great Barrier Reef: spatial correlation, patterns, and
predictions. Coral Reefs, 23, 74-83. Berkelmans R. and B.L. Willis (1999). Seasonal
and local spatial patterns in the upper thermal limits of corals on the inshore
central Great Barrier Reef. Coral Reefs, 18, 219-228. Bruno J.F. and E.R. Selig
(2007). Regional decline of coral cover in the Indo-Pacific: timing, extent, and
subregional comparisons. Public Library of Science One, 2, e711. Bruno, J.F., E.R.
Selig, K.S. Casey, C.A. Page, B.L. Willis, C.D. Harvell, H. Sweatman, and A. Melendy
(2007). Thermal stress and coral cover as drivers of coral disease outbreaks, Public
Library of Science Biology, Vol. 5, No. 6, e124.(DOI:10.1371/journal.pbio.0050124)
Casey K.S. and P. Cornillon (1999). A comparison of satellite and in situ-based sea
surface temperature climatologies. Journal of Climate, 12, 1848-1863. Donlon C.,
Robinson I., Casey K.S., Vazquez-Cuervo J., Armstrong E., Arino O., Gentemann C.,
May D., LeBorgne P., Piolle J., Barton I., Beggs H., Poulter D.J.S., Merchant C.J.,
Bingham A., Heinz S., Harris A., Wick G., Emery B., Minnett P., Evans R.,
Llewellyn-Jones D., Mutlow C., Reynolds R.W., Kawamura H. and Rayner N. (2007). The
global ocean data assimilation experiment high-resolution sea surface temperature
pilot project. Bulletin of the American Meteorological Society, 88, 1197-1213.Glynn
P.W. (1993). Coral reef bleaching - ecological perspectives. Coral Reefs, 12, 1-17.
Glynn P.W. (1996). Coral reef bleaching: facts, hypotheses and implications. Global
Change Biology, 2, 495-509. Hoegh-Guldberg O. and Fine M.(2004). Low temperatures
cause coral bleaching. Coral Reefs, 23, 444-444. Kawai Y. and Wada A. (2007).
Diurnal sea surface temperature variation and its impact on the atmosphere and
ocean: A review. Journal of Oceanography, 63, 721-744. Kilpatrick K.A., Podesta G.P.
and Evans R.(2001). Overview of the NOAA/NASA advanced very high resolution
radiometer Pathfinder algorithm for sea surface temperature and associated matchup
database. Journal of Geophysical Research-Oceans, 106, 9179-9197. Liu G., Skirving
W. and Strong A.E. (2003). Remote sensing of sea surface temperatures during 2002
Barrier Reef coral bleaching. EOS, 84, 137-144. Mesias J.M., Bisagni J.J. and
Brunner A. (2007). A high-resolution satellite-derived sea surface temperature
climatology for the western North Atlantic Ocean. Continental Shelf Research, 27,
191-207. Newman M.J.H., Paredes G.A., Sala E. and Jackson J.B.C. (2006). Structure
of Caribbean coral reef communities across a large gradient of fish biomass. Ecology
Letters, 9, 1216-1227. Podesta G.P., Brown O.B. and Evans R.H. (1991). The annual
cycle of satellite-derived sea-surface temperature in the southwestern Atlantic
Ocean. Journal of Climate, 4, 457-467. Reynolds R.W., Rayner N.A., Smith T.M.,
Stokes D.C. and Wang W.Q. (2002). An improved in situ and satellite SST analysis for
climate. Journal of Climate, 15, 1609-1625. Roberts C.M., McClean C.J., Veron
J.E.N., Hawkins J.P., Allen G.R., McAllister D.E., Mittermeier C.G., Schueler F.W.,
Spalding M., Wells F., Vynne C. and Werner T.B. (2002). Marine biodiversity hotspots
and conservation priorities for tropical reefs. Science, 295, 1280-1284. Selig,
E.R., C.D. Harvell, J.F. Bruno, B.L. Willis, C.A. Page, K.S. Casey and H. Sweatman
(2006). Analyzing the relationship between ocean temperature anomalies and coral
disease outbreaks at broad spatial scales. In; J.T. Phinney, O. Hoegh-Guldberg, J.
Kleypas, W. Skirving, and A. Strong (eds.). Coral reefs and climate change: science
and management. American Geophysical Union, Washington, DC, Pages 111-128. Selig,
E.R., K.S. Casey, and J. Bruno (2008). Global patterns of variability in coral reef
temperature anomalies: the importance of fine scale spatial and temporal
heterogeneity. Global Change Biology, submitted. Strong A.E., Liu G., Meyer J.,
Hendee J.C. and Sasko D. (2004). Coral Reef Watch 2002. Bulletin of Marine Science,
75, 259-268.The Mathworks Inc. (2006) Matlab. In. The Mathworks Inc., Natick, MA.
Resource Description: NODC Accession Number 0054501
Please acknowledge the use of these data with "The Coral Reef Temperature Anomaly
Database (CoRTAD) was developed by the NOAA National Oceanographic Data Center and the
University of North Carolina - Chapel Hill (Selig, E.R., K.S. Casey, and J. Bruno
(2009), New insights into global patterns of ocean temperature anomalies: implications
for coral reef health and management, Global Ecology and Biogeography, in press). It was
provided by the NOAA National Oceanographic Data Center at
NOAA National Oceanographic Data Center, SSMC3, 4th Floor, Room 4853, E/OC1, 1315 East-West Highway
Elizabeth R. Selig (UNC-Chapel Hill, currently with Conservation International),
Kenneth S. Casey (NODC), and John F. Bruno (UNC-Chapel Hill)
Details on the accuracy of the CoRTAD are provided in: 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.
Files are run against the program (algorithm) MD5 to verify data integrity which generates a code, called an MD5 checksum. After files are transferred from one place to another, the program can be run on the file again and a new code generated. The old MD5 checksum code should be identical to the new MD5 checksum code. If not, the file was somehow corrupted during transfer (see original MD5 documentation athttp://www.isi.edu/in-notes/rfc1321.txt)
The CoRTAD uses global, 4km sea surface temperature data from the Pathfinder Version 5 collection. Each version of the CoRTAD was developed using the most current data available from that collection at the time of development. Version 1 of the CoRTAD uses final data for 1985-2001 and 2003, and interim data for 2002 and 2004-2005. Version 2 of the CoRTAD uses final data for 1982-2006 and interim data for 2007-2008. Version 3 of the CoRTAD uses final data for 1982-2006 and interim data for 2007-2009. Each Pathfinder pixel over the global ocean is processed to create a gap-free weekly time series of sea surface temperature. A few pixels over the ocean never have a valid Pathfinder pixel. These locations are not gap filled and are identified in each CoRTAD file by an array called AllBad. Note: Two problems with the processing of the original Pathfinder data from 1982-1984 have led to some anomalous pixels in inland and immediate coastal areas of the gap-filled SST fields for those years. 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 in Version 3. Thus, the CoRTAD Version 3 climatology was calculated using only 1985-2009 data. All CoRTAD Version 3 fields have been calculated for the entire time series (1982-2009) based on this climatology.
The major sources of error in geo-locating AVHRR data are (a) drift in the spacecraft clock (which causes errors in the estimated along-track position), and (b) uncertainty errors in spacecraft and sensor attitude.(a) Clock Correction to minimize error in the along track position estimated by the orbital model, a satellite a clock correction factor is applied to the time code embedded in each piece. The method used to determine these clock correction factors is presented below. The clock aboard a given satellite drifts continually at a relatively constant rate (e.g., for NOAA-14,~9msday-1) compared to the reference clock on Earth. Because of this drift, the NOAA/NESDIS Satellite Operation Control Center periodically sends a command to the satellite to reset the on-board clock to a new baseline thereby eliminating the accumulation of a large time offset error between the Earth and satellite clocks. To correct for clock drift between these resets, correction factors were determined from a database of satellite clock time and Earth time offsets collected at the RSMAS High Resolution Picture Transmission (HRPT) receiving station. During HRPT transmission, both the satellite clock (used to create the embedded time code in each piece) and the Earth clock are simultaneously available. The clock correction bias was determined by (1)visual examination of the Earth/satellite clock differences collected in the database to locate the precise magnitude and timing of clock resets performed by the Satellite Operation Control Center and (2) recorded time differences between the identified reset periods were then filtered to remove spurious noise, and regressed against the corresponding satellite time to determine the clock drift correction. These drift corrections were then applied to all data time-stamped during a given reset period. Refer to Sea Surface Temperature Global Area Coverage (GAC) Processing Appendix A: Calibration and Navigation Correction Factors for a list of clock offsets for each NOAA spacecraft(http://www.rsmas.miami.edu/groups/rrsl/pathfinder/Processing/proc_app_a.html).(b) Attitude Corrections After clock correction, a nominal attitude correction is then applied to minimize the uncertainty in regard to the direction in which the spacecraft is pointing. The nominal attitude correction applied was determined by averaging the absolute attitude of the spacecraft over many geographic locations and times along the orbital track. The method used to determine the absolute attitude of the spacecraft involves matching a digital coastal outline to a given image and recording the amount of pitch, yaw, and roll required to make the outline and land coincide. This method has the advantage that it can be performed over small geographical distances and is similar to other techniques which rely on widely separated geographical control points to anchor the navigation. The resultant navigation information, output by the SECTOR procedure for each piece, provides the mapping parameters needed to convert between the satellite perspective of pixel and scan line, and Earth-based latitude and longitude coordinates. Refer to Sea Surface Temperature Global Area Coverage (GAC) Processing Appendix A:Calibration and Navigation Correction Factors for attitude correction factors for each NOAA spacecraft (http://www.rsmas.miami.edu/groups/rrsl/pathfinder/Processing/proc_app_a.html).
Refer to the Horizontal Positional Accuracy Report for a discussion of sources of error in geo-locating AVHRR data.
The CoRTAD uses sea surface temperature data from the Pathfinder Version 5 collection produced by NOAA's National Oceanographic Data Center and the University of Miami's School of Marine and Atmospheric Science. Each version of the CoRTAD was developed using the most current data available in the Pathfinder Version 5 collection at the time of development. Version 1 of the CoRTAD uses final data for 1985-2001 and 2003 and interim data for 2002 and 2004-2005, with a climatology based on data from 1985-2001. Version 2 of the CoRTAD uses final data for 1982-2006 and interim data for 2007-2008, with a climatology based on data from 1985-2008. Version 3 of the CoRTAD uses final data for 1982-2006 and interim data for 2007-2009, with a climatology based on data from 1985-2009. For more information about Version 5 Pathfinder, see the user guide at http://pathfinder.nodc.noaa.gov/userguide.html. Details on the processing of the CoRTAD are provided in: 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.
The CoRTAD contains global, approximately 4 km resolution SST data on a weekly time scale from 1982 through 2009. 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).
See the CoRTAD web site at http://www.nodc.noaa.gov/SatelliteData/Cortad for more information.
SSMC3, 4th Floor, E/OC11315 East-West Highway
Phone/FAX/E-mail/letter during business hours
NOAA makes no warranty regarding these data, expressed or implied, nor does the fact of distribution constitute such a warranty. NOAA and NODC cannot assume liability for any damages caused by any errors or omissions in these data, nor as a result of the failure of these data to function on a particular system.
NCSA Hierarchical Data Format (HDF 5)
Sea surface temperature (SST) and derived thermal stress metrics.
These data are available from multiple online sources; see the "DIGITAL FORM" section of this metadata record and follow the instructions for "Online Options".
Contact the NODC User Services Group via phone/FAX/E-mail: email@example.com
NOAA National Oceanographic Data Center, SSMC3, 4th Floor, Room 4853, E/OC, 1315 East-West Highway