![]() We named this dataset GDPoSSTA ( Global Dataset of Process- Oriented SSTA). Unfortunately, until now, there have been no datasets concerned with the evolution of SSTAs.īased on these considerations, we designed a process-oriented algorithm to develop a global dataset that describes the evolution of SSTAs based on commonly used satellite-derived SST products. Thus, knowing where, when and how SSTAs vary and evolve is important to understanding regional and global climate change. ( 2019) analyzed the relative contributions of North and South Pacific SSTAs to ENSO events Xue, Wu, Liu, and Su ( 2019a) analyzed the merging and splitting of SSTAs and found a close relationship between the evolution of SSTAs and the strength or weakness of the ENSO. Song, Dong, and Xue ( 2016) used variations in SSTAs to define a new ENSO (El Niño Southern Oscillation) index and identify ENSO events Ding et al. And their movement of SSTAs’ spatial coverage was also used to derive more meaningful findings: e.g. ![]() These studies found that the variation of SSTA has a spatial coverage. Xue, Dong, and Qin ( 2015b) proposed a cluster-based method for identifying sensitively spatial regions and temporal duration of SSTAs. ( 2013) took a time as an additional dimension and designed an SRNN method for finding the dipole modes of SSTAs in ocean. ( 2006) proposed a cluster-based method for finding the time-averaged spatial distribution of SSTAs and tried to construct patterns showing the spatial relationship between SSTAs. However, methods for studying the evolution of SSTAs in space and time are lacking.īased on these satellite-derived SST datasets (Reynolds et al., 2002 Saha et al., 2018 Wentz et al., 2014), many studies have focused on new approaches to identifying the dynamic characteristics of SSTs. 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. From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates.
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