Temperature anomaly4/6/2023 ![]() This spatiotemporal evolution of SSTAs may be able to provide information that is more important than information about the SST itself for studying global climate change (McPhaden, Zebiak, & Glantz, 2006 Saulquin et al., 2014 Wu et al., 2008). Changes in SST anomalies (SSTAs) in space and time can be a driver of extreme regional climate events such as extreme rainfall (Guo et al., 2021 Yu, Fan, Zhang, Zheng, & Li, 2021). A large number of widely used global SST datasets are produced both in China and abroad some of these are listed in Table 1. Advanced Earth-observing technologies make it possible to acquire lengthy time series of SSTs from multiple remote-sensing images (Yang et al., 2013), and many algorithms have been developed to produce SST products from satellite imagery in recent decades (Cao et al., 2021 Banzon, Reynolds, Stokes, & Xue, 2014 Legeckis & Zhu, 1997 Liao, Dong, Xue, Bi, & Wan, 2017 McClain, Pichel, & Walton, 1985 Merchant, Borgne, Borgne, Marsouin, & Roquet, 2008 Ping, Su, & Meng, 2015 Reynolds, Rayner, Smith, Stokes, & Wang, 2002 Walton, Pichel, Sapper, & May, 1998). The sea surface temperature (SST) is one of the most important marine climate variables (GCOS, 2011 Hollmann et al., 2013) and plays an essential role in climate change monitoring, weather forecasting, and marine fishery monitoring (Dai, 2016 Murtugudde et al., 2004). The GDPoSSTA dataset is available on ScienceDB platform ( ). Finally, geographic spatiotemporal statistics are derived for the DSPOSSTA and a comparison of applying TITAN to DSVOSSTA and DSPOSSTA is carried out which demonstrates the feasibility and applicability of GDPoSSTA. The two relationship files, which are in CSV format, store the evolving behavior of the SSTA sequence object and SSTA variation objects. The three datasets are in SHP format and consist of a dataset of processed object-oriented SSTAs named DSPOSSTA, a dataset of sequenced object-oriented SSTA series named DSSOSSTA, and a dataset of variation object-oriented SSTA named DSVOSSTA. GDPoSSTA is comprised of three datasets and two relationship files and covers the period from January 1982 to December 2009. To address some of these problems, in this study, we developed a global SSTA dataset that included details of the spatial structure of SSTAs and their temporal evolution. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. 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.From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. To provide SST 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."ĬoRTAD: Coral Reef Temperature Anomaly Database (SST) 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 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.
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