Global and regional evolution of sea surface temperature under climate change
Introduction
Sea surface temperature (SST) changes are one of the most important sources of uncertainty in future climate changes predictions and analysis (Good et al., 2008). Variations in global and regional SST patterns influence zonal and meridional circulations, which in turn affect the precipitation and air temperature patterns across the globe (Bjerknes, 1969; Hastenrath, 1978; Folland et al., 1986; Rodwell et al., 1999; Chang et al., 2000). Changes in ocean circulation patterns modify the warm and cold water transport patterns around the globe, affecting which species are represented in marine ecosystems, altering migration and breeding patterns, threatening corals, and changing the frequency and intensity of harmful algal blooms (Ostrander et al., 2000). Over the long term, increases in SST could decline the circulation patterns that bring nutrients from the deep sea to surface waters, contributing to declining fish populations, which will affect people who depend on fishing for food or jobs (Pratchett et al., 2004).
Data from the U.K. Meteorological Office Hadley Centre climatology from 1957 to 2006 published in Belkin (2009), establish that most of the worldwide coasts present an SST increasing, especially in the Subarctic Gyre, in the European Seas and in the East Asian Seas. Studies with daily SST Analysis provided by Optimum Interpolation Sea Surface Temperature (OISST) 1/4 database were also performed. These studies found that more than 71% of the world's coastlines have warmed over the period 1982–2010 (Lima and Wethey, 2012; Rodríguez, 2017). However, this warming magnitude depends on different globe locations, which has led to numerous studies at the regional scale (Gómez-Gesteira et al., 2008; Yoon et al., 2012; Bao and Ren, 2014; Xu et al., 2015; Bouali et al., 2017).
In particular, Gómez-Gesteira et al. (2008) analysed coastal warming by means of satellite-derived SST along the continental Atlantic Arc from 1985 to 2005. These authors detected an inhomogeneous warming trend, ranging from 3.5 °C century−1 at latitudes close to 48°N to 1.2 °C century−1 at latitudes close to 37°N. Yoon et al. (2012) and Bao and Ren (2014) used monthly SST data from the Met Office Hadley Center's (HadISST) for the periods 1950–2007 and 1870–2011, respectively. Yoon et al. (2012) examined western North Pacific SST under the influence of two different types of El Niño (NINO3 and NINO4). Their results suggest that the physical processes, responsible for the western North Pacific SST are similar, regardless of different types of El Niño. Bao and Ren (2014) shows that the warming trends of the marginal seas of China during the period 1870–2011 are generally larger than the global and hemispheric averages. Using a Global Circulation Model (GCM), Caesar et al. (2018) identified the weakening of the Atlantic meridional overturning circulation (AMOC) through SST fingerprint from 1870 to 2016. The fingerprint consists of a pattern of cooling in the subpolar Atlantic Ocean and warming in the Gulf Stream region.
As reported by previously studies, SST changes cause different impacts depending on globe location. Thus, several studies were performed in order to assess future SST changes by means of GCMs projections. Firstly, historical SST observations were compared with historical SST data produced by GCMs from the third (Gillett et al., 2008; Ting et al., 2013) and fifth (Wang et al., 2014; Chan and Wu, 2015) phase of the Coupled Model Intercomparison Project. These studies concluded that GCMs present a good reproducibility of SST data. Global SST projections also reported general warming, although with different magnitudes (Xie et al., 2010; He and Soden, 2016).
In this sense, Shimura et al. (2015) used 18 models of CMIP3 with four SST conditions. All the SST patterns show that SST in the future climate increases up to about 3 °C. The North Pacific, especially, shows a greater increase in temperature than any other region. Moreover, some authors compared CMIP3 and CMIP5 models results. Pushpadas (2015) compared CMIP3 and CMIP5 implementations by multi-model ensembles in Baltic and North Sea. They concluded that the averaged SST in the CMIP3 simulations (~ 2.3 °C in the North Sea and ~ 3.3 °C in the Baltic Sea) is larger than the projected increase for the CMIP5 scenarios (~ 1.7 °C in the North Sea and ~ 2.3 °C in the Baltic Sea).
Based on 19 GCMs CMIP5 under representative concentration pathway 8.5 (RCP 8.5) climatic scenario, Brown et al. (2014) explored the projected SST warming along the equator relative to the edge of the Western Pacific Warm Pool. The authors compared the second halves of the 20th (1950–2000) and 21st (2050–2100) centuries and found SST anomalies of 2–3 °C for the 3 best models and SST anomalies of 2–5 °C for 11 worst models. In the tropical region, Huang (2015) studied the seasonal SST changes under global warming using 31 models based on the RCP 8.5 and historical runs. The magnitude of the seasonal warming is comparable to the tropical-mean and the annual-mean warming, implying great impacts on global climate changes. Khalil et al. (2016) concluded that forecasts suggest a future warming of 0.004 °C yr−1 in the Indo Pacific region until 2100 by means of 22 GCMs from the CMIP5 project under the RCP 2.6 scenario.
More recently, Alexander et al. (2018) used 26 models of CMIP5 and 30 simulations from National Center for Atmospheric research Large Ensemble Community Project (CESM – LENS) for the RCP 8.5 climatic scenario to study how climate change affects the mean, variability, and SST extremes in northern oceans areas. The SST trend over the period 1976–2099 is positive over most of the domain, which includes the eastern North Pacific, the North Atlantic and the Artic Oceans. This trend varies from approximately 0.25° to 0.5 °C decade−1 with the strongest warming in the Bering Sea, in the western North Atlantic and in the Norwegian and Barents Seas. The region between Labrador Sea and Southeast of Greenland presents a cooling trend. Considering the SST results of CMES – LENS, SST trend presents a similar pattern and magnitude, including an absence of warming in the southeast of Greenland, but with stronger warming in the Bering and Greenland Seas.
All these studies provide new insights about the evolution of the SST at different places in the world for the 21st century. In order to give a step forward on this subject, the aim of the present study is to perform the worldwide regionalization of the evolution of the SST for the 21st century under the climate change context.
Section snippets
SST and sea ice data
Monthly ocean SST data from 1850 to 2100 were obtained from 27 simulations (Table 1in the supporting information), carried out with GCM developed for the CMIP5 project (cmip.llnl.gov/cmip5/data_portal.html).
Historical SST data was extracted from the historical run that covers the period 1850–2005. The historical run was forced by the observed ocean and atmospheric changes, both from anthropogenic and natural sources (Taylor et al., 2012).
Future SST data was taken for two RCP future climate
Worldwide SST data division
The worldwide regions with consistent SST changes (magnitude and variability) were identified through K-means results of Era Interim reanalysis SST data (Fig. 1a).
Gap value measures the difference within intra-cluster variation between the observed and a random uniform distribution data. A large gap statistics means the clustering structure is very far away from the random uniform distribution. The number of clusters (k) is the smallest value of k such that the gap statistic is within one
Conclusions
Different GCMs from the CMIP5 project were used to analyse SST variability worldwide for the 21st century. An assessment of the skill of these GCMs on predicting SST was performed by a comparative analysis with Era Interim SST data. According to the statistical analysis of Taylor Diagrams and Kernel Distribution Estimator, it was possible to conclude that most of GCMs reproduce real SST variations for the historical period. However, some GCMs (CSIRO-Mk3–6-0, MPI-ESM-MR and MPI-ESM-LR) presented
Acknowledgements
The second author is funded by national funds (OE), through FCT, I.P., in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. Thanks are due to FCT/MCTES for the financial support to (UIDP/50017/2020+ UIDB/50017/2020), through national funds. This study was partially funded under the project AquiMap (MAR-02.01.01-FEAMP-0022) cofinanced by MAR2020 Program, Portugal 2020 and European
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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