Future projection of marine heat waves in a global marine hotspot Case of East/Japan Sea

Future projection of marine heat waves in a global marine hotspot Case of East/Japan Sea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Future projection of marine heat waves in a global marine hotspot Case of East/Japan Sea Danushka Deegala, Eun-Sung Chung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4262751/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The future analysis of the Marine Heat Waves (MHWs) has high uncertainty due to the significant shortcomings of the Global Climate Models (GCMs) in simulating the Sea Surface Temperature (SST) and the MHWs. This study suggests a more systematic approach to do the future projection of MHWs. Our study area is East/Japan Sea which is a large marine ecosystem exposed to rapid warming of the ocean. This study found the models; ACCESS-CM2, BCC-CSM2-MR ACCESS-ESM1, and GFDL-ESM4 from Coupled Model Intercomparison Project sixth phase (CMIP6) are the best performing GCMs in the East Sea by analyzing their grid-wise performance during the historical period (1985–2014). Using the ensemble mean from the selected models, the future MHW metrices of frequency, maximum intensity, and duration during the near future (2041–2070) and far future (2071–2100) was investigated. Following the state-of-art, shifting baseline approach was utilized to identify the MHWs and 30 years were used as the climatology period for each historical and future periods. The time series results from the ensemble mean indicated that high emission scenarios (SSP3-7.0, and SSP5-8.5) would have higher trends than that of low emission scenarios (SSP1-2.6, and SSP2-4.5) as well as that of historical observations. The high emission scenarios would have lower values in the beginning of their respective climatology period when compared to that of low emission scenarios but rather higher values toward the end of the period. The average MHW metrices of near and far futures shows certain shifts compared to that of historical but the numerical values are almost similar to that of historical period. Performance evaluation Model selection Marine Heat Wave (MHW) metrices Future projection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The energy imbalance in Earth’s climate system resulting from the anthropogenic climate change causes high concentrations of heat-trapping gases and about 93% of this imbalanced energy is accumulated in the oceans as increased ocean heat content (Cheng et al., 2019 ). Domingues et al. ( 2008 ) indicated that 91% of the total change of heat content in the upper 0-700 m of the ocean during the period of 1961–2003 is stored in the upper 0-300 m. Episodes of abrupt large-scale warm anomalies of temperature in the ocean have been reported during the recent decades leading to the term, Marine Heat Waves (MHWs). The term was first used by Pearce et al. ( 2011 ) (Oliver et al., 2021 ) and Hobday et al. ( 2016 ) has qualitatively defined MHWs as a discrete prolonged anomalously warm water event occurring in a particular location. MHWs can be driven by a combination of atmospheric and oceanographic processes (Hobday et al., 2016 ). Sen Gupta et al. ( 2020 ) showed that almost all of the most extreme MHWs are almost associated with suppressed wind speeds during their build up phase and some cases are related to the suppressed turbulent heat losses from the ocean. They have showed that the intensity, duration, and extent of extreme MHWs are strongly enhanced during El Niño periods both in the tropical Pacific and beyond. In addition, they have demonstrated that the maximum intensity MHWs tend to occur in the calendar summer of each hemisphere. However, the driving forces of MHWs may differ based on the studied location, i.e., in the Baltic Sea, a mid-latitude marginal sea located on the Northwestern European shelf, summer MHWs are observed to be mainly forced by local meteorological conditions over the open water while winter MHWs are associated with the advection of warm and moist air originating from the North Atlantic (Gröger et al., 2024 ). In the Tasman Sea, MHWs are driven by both ocean advection and by air-sea heat fluxes and the atmospherically driven MHWs are more likely to occur during the positive phase of the asymmetric Southern Annular Mode which is likely to occur during La Niña years (Gregory et al., 2023 ). The impacts of MHWs on the ocean marine life have been widely studied. One of the first documented MHW occurred in the Mediterranean Sea in summer 2003 has caused mass mortality of rocky benthic species (Frölicher et al., 2018 ; Garrabou et al., 2009 ). Wernberg et al. ( 2013 ) showed a significant difference of the biodiversity patterns of temperate seaweeds, sessile invertebrates, and demersal fish along the west coast of Australia in early 2011 after a warming event which was the highest magnitude warming event on record at the time. Gomes et al. ( 2024 ) have compared ecosystem models parameterized before and after the onset of recent MHWs to evaluate the cascading effects on ecosystem structure and function in the Northeast Pacific Ocean and have found that the most functional groups were changed following the heatwaves. Kamenos and Hennige ( 2024 ) showed that MHWs have existed during mass coral bleaching incidents on the Great Barrier Reef while Walker et al. ( 2024 ) indicated that the influence of MHWs on the corals may cause a phase shift beyond which recovery is less likely. Apart from the impact on the marine life caused by the MHWs, researchers have indicated the resulting economic impact. For example, Mills et al. ( 2013 ) showed that the lobster fishery in the northeastern United States was extended after the warm event occurred in the Northwest Atlantic Ocean in 2012 which resulted in low prices and economic losses. Another sea warming event occurred in Dalian city, China in August 2018 caused a mass destruction of sea cucumbers resulting in an economic loss of about 15 billion CNY (~ 2.3 billion USD) (Ma et al., 2021 ; Yao et al., 2020 ). These incidents indicate the necessity of policies that enable the fisheries management to face such abrupt events which would be more frequent with the climate change. Moreover, recent studies have illustrated the influence of MHWs on the atmospheric circumstances such as monsoons, cyclones, and hails. Saranya et al. ( 2022 ) have explored the role of MHWs in the Indian Ocean in modulating the Indian summer monsoon and have found the MHWs in some regions of the Indian ocean has caused reduction of rainfall over some parts of India while some caused an enhancement of rainfall over the other regions of India. Choi et al. (2024) have demonstrated that MHWs play a critical role in intensifying the tropical cyclones by providing more latent heat flux from the ocean and heavier precipitation near the center of the tropical cyclone. Furthermore Martín et al. ( 2024 ) indicated that a record-breaking MHW has enhanced a favorable environment for a severe hailstorm that occurred in Spain during August 2022 which has caused material and human damage including one fatality. The future prediction of MHWs has been explored using Global Climate Models (GCMs) of Sea Surface Temperature (SST) provided by the Coupled Model Intercomparison Project (CMIP). Several such studies predicted more frequent and extreme MHWs in the world in the future climate under global warming (Frölicher et al., 2018 ; Hayashida et al., 2020 ). However, the GCMs present significant shortcomings in reproducing the SST and MHWs (Plecha et al., 2021 ). Holbrook et al. ( 2022 ) have used the latest version of CMIP, i.e., CMIP6 GCMs to study the MHWs across Pacific Island regions and have found that most notable bias appeared in the simulation of moderate category MHW days for the period 2000–2019. Plecha and Soares ( 2019 ) have indicated that the GCMs’ biases obtained from CMIP6 are in line with the previous version of CMIP5 biases suggesting the necessity of the high spatial resolution models to characterize MHWs. However, Darmaraki et al. ( 2019 ) who have used high resolution regional climate system models (RCSMs) from the Med-CORDEX initiative to evaluate the MHWs in the Mediterranean Sea have showed that despite the high-resolution representation of the air-sea interactions, uncertainties still extist due to their individual biases but also due to the availability of small number of the then available six RCSMs. Since individual GCMs vary in their simulations of MHW properties and patterns, Multi Model Ensemble (MME) method is used by many previous studies. Several studies have created the ensemble by selecting models under a specific criterion, i.e., Yao et al. ( 2020 ) have used 12 out of 28 GCMs from CMIP5, selected based on the evaluation of the annual and seasonal SST linear trends and have used equal weight method to analyze the MHWs in China’s marginal seas. Costa and Rodrigues ( 2021 ) have assessed the historical SST from CMIP6 using performance metrices and have assigned weights in creating the ensemble mean. Other studies (Frölicher et al., 2018 ; Hayashida et al., 2020 ; Holbrook et al., 2022 ) without evaluating the SST, have first calculated the MHW metrices from their native grids, interpolated to a common grid and then the ensemble mean has been computed by averaging the results obtained from all the considered models. So, after evaluating the methods utilized by previous studies, MME method was employed by using only a few GCMs selected out of the used 15 GCMs by considering their performance in simulating the MHW metrices (not SST) during the historical period (1985–2014). A grid-wise performance technique and a group decision method were applied to assess the best performing GCMs from CMIP6. Although this method has been applied to evaluate precipitation data from the GCMs (Deepthi and Sivakumar, 2022 ), there have been few studies to apply this technique in evaluating the MHWs. We suggest that grid-wise evaluation is a better approach in selecting the GCMs when evaluating MHWs considering the inadequate spatial simulation of the models. The historical performance of the MME from the selected models versus the MME from all the models were also compared. To apply the proposed technique to the field of MHWs, East Sea was selected as our study area. East Sea is recognized as a marine hotspot, a region where ocean warming is the fastest, by Hobday and Pecl ( 2014 ) after their analysis of the historical and future global SST data. Belkin ( 2009 ) has recognized East Sea as one of the large marine ecosystems which has subjected to recent rapid warming with net SST change > 0.9 o C. They have also observed that the most rapid warming occurs in the semi-enclosed seas including Baltic Sea, North Sea, and East Sea. With the unique location of the East Sea accompanied by its ecological richness and the economic importance from the fishery prevailing in the surrounding countries, East Sea requires an in-depth approach to investigate on the future MHWs. The historical analysis of MHWs in the East Sea (Wang et al., 2022 ), mechanisms and properties of MHWs in the East Asian Marginal Seas (Choi et al., 2022 ; Lee et al., 2023 ) have been studied. Future analysis of MHWs in China’s marginal Seas and Adjacent offshore waters using CMIP5 GCMs (Yao et al., 2020 ) and using CMIP6 GCMs (Xue et al., 2023 ) have also been studied. But a systematic future projection specially focusing on the East Sea using the latest CMIP6 is limited. Therefore, our future projection approach was applied to the East Sea and the nature of MHWs in the near future (2041–2070) and far future (2071–2100) was analyzed using four Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCPs) from CMIP6 i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. 2. Data and Methods 2.1. Study Area The study domain is the East Sea located at 33 o -53 o N and 127 o -143 o E (Fig. 1 ) with area of about 10 6 km 2 . East Sea is a semi-enclosed marginal sea bounded by East Asian continent and Japanese Islands in the northwestern Pacific. It is connected to the western North Pacific through four channels all with less than 140 m deep, i.e., Korean/Tsushima strait, Tsugaru strait, Soya strait, and Tatarsky strait (Yeh et al., 2010 ) (Fig. 1 a). Since the East Sea is topographically isolated from the North Pacific, only a limited amount of Kuroshio currents would enter into the sea (Kim et al., 2016 ). It has phenomena and circulation patterns similar to those found in the open ocean (Ichiye, 1984 ; Kim et al., 2001 ; Park et al., 2005 ) (Ichiye, 1984 ; Kim et al., 2001 ; Park et al., 2005 ) including its own thermohaline circulation (Chu et al., 2001 ) in spite of its small basin size and semi-enclosed topography. The warm and cold currents meet in a deep basin with a maximum depth reaching 3,800 m (Lee et al., 2023 ). Figure 1 a. demonstrates the bathymetry of the East Sea and the deepest regions of the East Sea are found in the Japan Basin (3000–3800 m). Figure 1 b. shows the averaged SST variation during the historical period (1985–2014) and the temperature varies from about 7 o C from northern regions of the East Sea to about 19 o C towards southern regions. Tsushima warm which is a branch of the Kuroshio Current supplies heat and nutrients into the East Sea and is a main factor of the large marine ecosystems in the East Sea (Kim and Kang, 1998 ; Zhang et al., 2019 ). Many fish species including some brackish water species inhabit in the East Sea (Kim and Zhang, 2016 ). Coldwater fish species like chum salmon live in the surface layer in the northern areas of the East Sea and in the subsurface water column throughout the whole East Sea, whereas warm-water pelagic species like anchovies and chub mackerel inhabit in the surface layer towards the south of the East Sea (Kim and Zhang, 2016 ). Due to the current political obstacles between the surrounding nations, the annual fish catch records for the entire East Sea are unavailable, but the historical statistics from the 1980s show that pollock was a main catch species, accounting for over 3 million metric tons of fish throughout the entire East Sea (Kim and Zhang, 2016 ). 2.2. Data To detect the MHWs during the historical period National Oceanic and Atmospheric Administration daily optimum interpolation sea surface temperature version 2 (NOAA OI SST V2) dataset was utilized. The spatial grid resolution of the dataset is 0.25 o with temporal resolution of 1 day and the data is available from 1st of January 1982 up to the present date (2024). The data is provided by the NOAA Physical Sciences Laboratory, Boulder, Colorado, USA, from their website at https://psl.noaa.gov . This product is a blend of both in situ observations from ships, drifting buoys, and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) (Huang et al., 2021 ). For the future analysis of MHWs, GCMs of daily SST data (variable name: tos) available from CMIP6 under four SSP-RCPs, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 under the first variant level (r1i1p1f1) were used. SSP1-2.6 and SSP2-4.5 are under low and medium forcing categories with the radiative forcing of 2.6 Wm − 2 and 4.5 Wm − 2 respectively in year 2100 (O’Neill et al., 2016 ). SSP3-7.0 is under the high forcing category with the 2100 radiative forcing of 7.0 Wm − 2 . SSP5-8.5 is also under high forcing category and it is the only SSP scenario with emissions high enough to produce a radiative forcing of 8.5 Wm − 2 in year 2100 (O’Neill et al., 2016 ). The details of the 15 GCMs used in this study are provided in Table 1 . It was noted that the grid types of the GCMs vary across the models from atmospheric grids, native ocean tri-polar grids to curvilinear grids. In addition, the nominal resolution of the grids changes from 50 km to 250 km (Table 1 ). Table 1 Names, Institution, Country, and nominal resolution of the 15 CMIP6 GCMs used in this study. Model name Institution Country Nominal resolution (km) ACCESS − CM2 Australian Research Council Centre of Excellence for Climate System Science Australia 250 ACCESS − ESM1 Australian Research Council Centre of Excellence for Climate System Science Australia 250 BCC-CSM2-MR Beijing Climate Center China 100 CanESM5 Canadian Centre for Climate Modelling and Analysis Canada 100 CESM2-WACCM National Center for Atmospheric Research USA 100 CMCC-CM2-SR5 Euro-Mediterranean C entre on Climate Change coupled climate model Italy 100 CMCC − ESM2 Euro − Mediterranean Centre on Climate Change Earth System Model Italy 100 EC − Earth3 EC − Earth Consortium Europe 100 EC − Earth3 − Veg EC − Earth Consortium Europe 100 EC − Earth3 − Veg − LR EC − Earth Consortium Europe 100 GFDL − ESM 4 Geophysical Fluid Dynamics Laboratory USA 50 MPI − ESM1 − 2−HR Max Planck Institute for Meteorology Germany 50 MRI − ESM2 − 0 Meteorological Research Institute Japan 100 NorESM2 − LM The Norwegian Earth System Model Norway 100 NorESM2 − MM The Norwegian Earth System Model Norway 100 3. Methods 3.1. Marine heatwave detection The MHWs were detected using the definition proposed by Hobday et al. ( 2016 ). In their definition MHWs are identified relative to a baseline climatology of recommended 30-year period. For our study 30 years were used as the baseline period in historical (1985–2014) and future periods (2041–2070, and 2071–2100). The climatology is established relative to the time of the year considering all the data of an 11-day window centered on that time of the year from which the climatological mean and threshold are computed. The threshold is based on the percentile value (90%) rather than an absolute value above the climatological value. The MHWs need to last at least five days and the events are discrete when the gap is more than two days. The MHW measures of frequency, maximum intensity, and duration were computed in each grid of our study area using the Python package ( http://github.com/ecjoliver/marineHeatWaves ) established from the definition of Hobday et al. ( 2016 ). The definitions, and units of the MHW metrices used in this study are provided in Table 2 . Table 2 MHW metric definitions and units used in Hobday et al. ( 2016 ). MHW metric Definition Unit Frequency Number of MHW events count Maximum intensity Highest temperature anomaly value during the MHW o C Duration Consecutive period of time that temperature exceeds the threshold days The python MHW detecting package provides the facility to calculate the statistics of MHWs averaged over blocks of a specified length of time. For our study one year was considered as the block length. Hence, it should be noted that the terms; frequency, maximum intensity and duration of our study are the annual total (frequency) or annual averaged (maximum intensity, duration) values. So, a grid contains 30 values (for 30-years) for each MHW metric for each historical and future period. When computing the MHW metrices during the historical and future periods using the GCMs from CMIP6, the metrices were first calculated on their native grids and then regridded by linear interpolation to a common grid resolution of 0.25 o which is same resolution as the NOAA OI SST V2. Hayashida et al. ( 2020 ) indicated the interpolation of the gridded data caused smoothening the variability and hence caused an underestimate of eddy kinetic energy in the Southern Ocean. Frölicher et al. ( 2018 ), Hayashida et al. ( 2020 ), and Holbrook et al. ( 2022 ) have also computed the MHW metrices on the native grids of the models and then regridded to the observed data grid to avoid the smoothening of the data. When selecting the baseline to identify the MHWs in the future analysis, previous studies (Costa and Rodrigues, 2021 ; Darmaraki et al., 2019 ; Holbrook et al., 2022 ) have used fixed baseline of historical climatological period. But the fixed baseline approach ignores the fact that oceans are getting warmer over time and using this definition would lead to more frequent and more intense MHWs reaching to a perpetual heatwave state (Amaya et al., 2023 ). Amaya et al. ( 2023 ) have indicated that the public desensitization might happen with the frequent hearing of MHWs causing potential inaction or a lack of preparedness. Some recent studies (Plecha et al., 2021 ; Xue et al., 2023 ) have used both fixed baseline and shifting baseline methods. But considering the state of art, we only considered the shifting baseline approach in our future analysis. Hence for the MHW analysis of near future and far future the baseline of 30 year climatology period of 2041–2070 and 2071–2100 respectively were considered for each SSP-RCP. The summary of the procedure followed in this study is shown in Fig. 2 . 3.2. Performance evaluation The GCMs’ performance in simulating the MHW metrics was evaluated using two indicators, i.e., Kling-Gupta Efficiency (KGE) (Eq. 1) and SPAtial Efficiency (SPAEF) (Eq. 2). These two metrices provide a robust insight to the models’ performance since they consider different aspects at once rather than focusing only on one aspect as of traditional metrics such as bias, mean square error or correlation. Deepthi and Sivakumar ( 2022 ) have analyzed the grid-wise performance of GCMs for monthly precipitation using only these two metrices. Both metrices consider three statistical components, i.e., KGE considers correlation, bias, and variability (Gupta et al., 2009 ) while SPAEF contains correlation, variance, and histogram intersection (Demirel et al., 2018 ). $$KGE=1- \sqrt{{\left(r-1\right)}^{2}+{\left(\gamma -1\right)}^{2}+{(\beta -1)}^{2}} \left(01\right)$$ where, $$r= \frac{\sum _{i=1}^{n}({o}_{i}-{\mu }_{o})({s}_{i}-{\mu }_{s})}{\sqrt{\sum _{i=1}^{n}{({o}_{i}-{\mu }_{o})}^{2}\sum _{i=1}^{n}{({s}_{i}-{\mu }_{s})}^{2}}}$$ $$\gamma = \frac{{\mu }_{s}}{{\mu }_{o}}$$ $$\beta = \frac{{\sigma }_{s}/{\mu }_{s}}{{\sigma }_{o}/{\mu }_{o}}$$ $$SPAEF=1- \sqrt{{\left(r-1\right)}^{2}+{\left(\beta -1\right)}^{2}+{\left(\propto -1\right)}^{2}} \left(02\right)$$ where $$\propto = \frac{\sum _{j=1}^{x}\text{m}\text{i}\text{n}({K}_{j},{L}_{j})}{\sum _{j=1}^{x}{K}_{j}}$$ \(r\) refers to the Pearson correlation coefficient between the model simulation (s) and the observed data (o), \(\gamma\) denotes the bias stabilized by the standard deviation of observed data, \(\beta\) presents the spatial variability, and \(\propto\) refers to the overlap between the histograms of the model simulations and the observed values. \(\mu\) is the mean and \(\sigma\) is the standard deviation. \(L\) is the histogram values of model simulations, \(K\) is the histogram values of observations, and \(x\) is the number of bins in a histogram. Both these indicators range from - \(\infty\) to 1 and closer the values to 1, better the performance. KGE and SPAEF were calculated grid-wise for each GCM from python using the packages from Hallouin ( 2021 ) and Demirel ( 2018 ), respectively. For a given MHW metric, a grid contains KGE and SPAEF results of the 15 individual GCMs. The GCMs were ranked grid-wise in two terms: first using KGE values and then using SPAEF values. In order to find the best alternatives from the 15 GCMs, the rankings provided by each grid need to be analyzed and hence a group-decision making methodology was utilized which will be explained in detail in the next section. 3.3. Group decision making methodology The methodology proposed by Morais and De Almeida ( 2012 ) was adopted for the group decision making. This procedure involves eliminating irrelevant alternatives, counting the strength and weakness of the alternatives, and choosing the alternative based on the net strength of the alternative. The first step of this method is the individual rankings of the alternatives (GCMs) by the decision makers (grids). The GCMs were ranked in two separate methods for each grid using the evaluation metrices of KGE and SPAEF respectively as described in the previous section. The second step is the filter phase in which the irrelevant alternatives are eliminated. This is done by considering the 25% of the alternatives ranked in the upper position (x) (Eq. 03) and the 25% of the alternatives ranked in the lower position (y) (Eq. 04). From Eq. 03 and Eq. 04 the upper positions for this study found to be 1st, 2nd, 3rd, and 4th rankings while the lower positions were 12th, 13th, 14th, and 15th . $$x=\frac{n}{4} \left(\text{r}\text{o}\text{u}\text{n}\text{d} \text{u}\text{p}\right) \left(03\right)$$ $$y=\frac{3n}{4}+1 \left(\text{t}\text{r}\text{u}\text{n}\text{c}\text{a}\text{t}\text{e}\right) \left(04\right)$$ where \(n\) is the total number of ranked alternatives. The third step is calculating the strength \(\left({F}_{i}\right)\) (Eq. 05) and weakness \(\left({f}_{i}\right)\) (Eq. 06) of the alternatives. $${F}_{i}= \sum _{k=1}^{m}\sum _{j=1}^{x}(x-j+1){q}_{ij}^{k} \forall i,k \forall j=1,\dots ..,x \left(05\right)$$ where \({q}_{ij }^{k}= \left\{\begin{array}{c}1, if alternative i is in position j for the decision maker k \\ 0, otherwise \end{array}\right.\) \(i\) corresponds to the alternatives in the upper quartile ( \(i\) = M1, M2, …, Mn); \(j\) is the position in the upper quartile, ranging from the 1st position to the upper quartile limit (x th ) ( \(j\) = 1st, 2nd, …, x th ) and, \(k\) represents the decision maker ( \(k\) = \({\text{g}\text{r}\text{i}\text{d}}_{1}\) , \({\text{g}\text{r}\text{i}\text{d}}_{2}\) , …, \({\text{g}\text{r}\text{i}\text{d}}_{m}\) ). $${f}_{i}= \sum _{k=1}^{m}\sum _{j=y}^{n}(j-y+1){q}_{ij}^{k} \forall i,k \forall j=y,\dots ..,n \left(06\right)$$ where \({q}_{ij }^{k}= \left\{\begin{array}{c}1, if alternative i is in position j for the decision maker k \\ 0, otherwise \end{array}\right.\) \(i\) corresponds to the alternatives in the lower quartile ( \(i\) = M1, M2, …, Mn); \(j\) is the position in the lower quartile, ranging from the lower quartile limit (y th ) to the last position of ranking ( \(n\) th ) ( \(j\) = y th , …, \(n\) th ) and, \(k\) represent the decision maker ( \(k\) = \({\text{g}\text{r}\text{i}\text{d}}_{1}\) , \({\text{g}\text{r}\text{i}\text{d}}_{2}\) , …, \({\text{g}\text{r}\text{i}\text{d}}_{m}\) ). The last phase of this method is calculating the intensity of the strength ( \(\propto\) ) using Eq. 07. The alternative with larger \(\propto\) indicate better performance. This phase gives an insight to a model considering the disagreement of the grids points although that model would be in higher positions for some other grid points. \(\propto\) values were calculated for each GCM for the two criteria of KGE, and SPAEF and for each MHW metric. $${\propto }_{i}={F}_{i}- {f}_{i} \left(07\right)$$ Next, the overall performance of the GCMs need to be evaluated and the GCMs need to be ranked in order to select the best performing GCMs. So, first the GCMs were ranked based on the \(\propto\) values from the two criteria of KGE and SPAEF for the three MHW metrices. Then the ranks obtained for the three MHW metrices under the two criteria (altogether 6 rank values) were averaged, and an overall final ranking was given to the GCMs based on those averaged ranks. The GCMs which ranked in the upper position (1st, 2nd, 3rd, and 4th rankings) were selected for the ensemble. 4. Results 4.1. Performance evaluation of the models KGE and SPAEF were calculated grid-wise for each GCM for the three MHW metrics as demonstrated in Fig. 3 and Fig. 4 respectively. In KGE evaluation, the models showed the lowest performance in simulating the duration when compared to the frequency and maximum intensity (Fig. 3 ). The models: Earth3-Veg-LR, and MPI-ESM1-2-HR showed less skill in most of the grids for both frequency and maximum intensity compared to that of the other GCMs. NorESM-LM indicated less skills in simulating the frequency towards the northwestern regions of the East Sea and also indicated low performance in simulating maximum intensity in most of the central and northern regions of the East Sea (Fig. 3 ). In SPAEF evaluation, the models, Earth3-Veg-LR and MPI-ESM1-2-HR indicated lower performance in simulating the frequency and NorESM2-LM indicated less skills towards the northwestern regions of East Sea (Fig. 4 ) compared to the other GCMs. In the simulation of maximum intensity, Earth3-Veg-LR, MPI-ESM1-2-HR, NorESM2-LM, and CMCC-ESM2 indicated lower performance in most of the areas. Duration estimation was rather low in most of the GCMs and a few GCMs, i.e., MRI-ESM2, and CanESM5 indicated better results relative to the other models (Fig. 4 ). When the spatial patterns of KGE and SPAEF results are considered, it can be noted that some GCMs followed a similar pattern i.e., the models Earth3-Veg-LR, MPI-ESM1-2-HR, and NorESM2-LM showed poor results for frequency and maximum intensity in similar areas in both KGE and SPAEF evaluations. However, the areas where an inadequate performance is evident, vary depending on the model and also the MHW metric. Therefore, deciding which models perform better for the whole study area and also for the overall MHW metrics is comprehensive. To overcome this, a group decision technique was adopted as described in section 3.2. 4.2. Selecting the best GCMs from Group decision making The strength, weakness and strength intensity of each model were calculated from Eq. 05, Eq. 06, and Eq. 07 respectively for the two evaluation criteria of KGE (Table 3 ) and SPAEF (Table 4 ). In KGE, ACCESS-CM2, BCC-CSM2-MR, and GFDL-ESM4 indicated positive strength intensity ( \(\propto\) ) for all the MHW metrics as shown in Table 3 . In SPAEF, ACCESS-CM2, ACCESS-ESM1, and GFDL-ESM4 indicated positive \(\propto\) for all the MHW metrics as shown in Table 4 . It can be highlighted that positive \(\propto\) for both evaluation criteria of KGE and SPAEF was indicated by the two models ACCESS-CM2 and GFDL-ESM4 (Table 3 and Table 4 ). Negative \(\propto\) for all the MHW metrices for both KGE and SPAEF evaluations is shown by Earth3-Veg-LR, and MPI-ESM1-2-HR (Table 3 and Table 4 ). In addition, CMCC-ESM2 indicated negative \(\propto\) for all the MHW metrices in the SPAEF evaluation (Table 4 ). Next the models were ranked separately based on their strength intensities from the two criteria of KGE and SPAEF for each MHW metric as shown in Table 5 . Then the ranks were averaged, and an overall final rank was given to each GCM. Four models ACCESS-CM2, BCC-CSM2-MR, ACCESS-ESM1, and GFDL-ESM4 were ranked in the upper positions, 1st, 2nd, 3rd, and 4th ranks respectively as highlighted in Table 5 and these four models were used in creating the ensemble mean. From the overall ranking of the models, lowest ranks were observed in Earth3-Veg, CMCC-ESM2, MPI-ESM1-2-HR, and Earth3-Veg-LR with rankings of 12th, 13th, 14th, and 15th ranks respectively. Table 3 Model scores for strength (F), weakness (f), and strength intensity () for the MHW metrics of frequency, maximum intensity, and duration, over the East Sea for the KGE criterion. Models Frequency Maximum intensity Duration F f \(\propto\) F f \(\propto\) F f \(\propto\) ACCESS-CM2 1601 156 1445 1298 117 1181 736 204 532 ACCESS-ESM1 500 465 35 1299 252 1047 505 770 -265 Earth3 396 703 -307 905 340 565 241 1346 -1105 Earth3-Veg 238 765 -527 1435 93 1342 231 1754 -1523 Earth3-Veg-LR 363 1954 -1591 22 2442 -2420 55 2078 -2023 MPI-ESM1-2-HR 95 2521 -2426 116 2082 -1966 159 1440 -1281 MRI-ESM2 650 861 -211 242 467 -225 1134 456 678 CanESM5 271 887 -616 1267 268 999 1246 272 974 NorESM2-MM 795 418 377 464 545 -81 1306 180 1126 NorESM2-LM 688 678 10 470 2266 -1796 1568 89 1479 CMCC-CM2-SR5 623 669 -46 683 494 189 576 566 10 CMCC-ESM2 568 797 -229 558 891 -333 634 348 286 BCC-CSM2-MR 1789 34 1755 1142 243 899 926 460 466 CESM2-WACCM 1924 107 1817 722 267 455 662 1045 -383 GFDL-ESM4 889 375 514 767 623 144 1411 382 1029 Table 4 Same as in Table 3 but for the SPAEF criterion. Models Frequency Maximum intensity Duration F f \(\propto\) F f \(\propto\) F f \(\propto\) ACCESS-CM2 1588 154 1434 959 141 818 781 295 486 ACCESS-ESM1 810 383 427 1680 245 1435 1044 637 407 Earth3 721 524 197 463 402 61 448 817 -369 Earth3-Veg 537 792 -255 773 258 515 611 1155 -544 Earth3-Veg-LR 573 1624 -1051 60 2360 -2300 162 1287 -1125 MPI-ESM1-2-HR 127 2301 -2174 298 1736 -1438 440 1453 -1013 MRI-ESM2 445 806 -361 1089 135 954 1783 410 1373 CanESM5 153 1104 -951 1172 310 862 1242 352 890 NorESM2-MM 664 592 72 287 828 -541 732 394 338 NorESM2-LM 672 816 -144 512 2129 -1617 945 351 594 CMCC-CM2-SR5 806 828 -22 1241 308 933 523 734 -211 CMCC-ESM2 460 731 -271 164 1323 -1159 515 763 -248 BCC-CSM2-MR 1541 56 1485 1231 263 968 662 769 -107 CESM2-WACCM 1552 209 1343 815 334 481 543 1342 -799 GFDL-ESM4 741 470 271 646 618 28 959 631 328 Table 5 Model ranks based on KGE, SPAEF for each MHW metrics of frequency (Frq.), Maximum intensity (Int.), duration (Dur.), the averaged ranks and the overall ranks. (The models ranked in the upper position are highlighted in bold with their respective overall ranking.) Models Rank - KGE Rank - SPAEF Average Rank Overall Rank Frq. Int. Dur. Frq. Int. Dur. ACCESS-CM2 3 2 6 2 6 4 3.83 1 ACCESS-ESM1 6 3 10 4 1 5 4.83 3 Earth3 11 6 12 6 9 11 9.17 11 Earth3-Veg 12 1 14 10 7 12 9.33 12 Earth3-Veg-LR 14 15 15 14 15 15 14.67 15 MPI-ESM1-2-HR 15 14 13 15 13 14 14.00 14 MRI-ESM2 9 11 5 12 3 1 6.83 5 CanESM5 13 4 4 13 5 2 6.83 6 NorESM2-MM 5 10 2 7 11 6 6.83 7 NorESM2-LM 7 13 1 9 14 3 7.83 10 CMCC-CM2-SR5 8 8 9 8 4 9 7.67 9 CMCC-ESM2 10 12 8 11 12 10 10.50 13 BCC-CSM2-MR 2 5 7 1 2 8 4.17 2 CESM2-WACCM 1 7 11 3 8 13 7.17 8 GFDL-ESM4 4 9 3 5 10 7 6.33 4 4.3. Performance evaluation of the MME The performance of MME from the selected four models (hereafter referred to as MME-four) was compared with MME from all models (hereafter referred to as MME-all) as well as with the individual models. The grid-wise skill scores for KGE and SPAEF were averaged for the whole area for each model and ensembles as indicated in Fig. 5 . Similar to the observation in section 4.1, the average KGE score of the individual GCMs for the duration is much lower than the other two MHW metrices. Duration was scored lower by most of the GCMs with respect to SPAEF evaluation as well (Fig. 5 ). When the performance of the individual models and the MMEs were considered in terms of KGE, the highest scores were obtained by BCC-CSM2-MR (0.311), MME-four (0.237), and MME-all (0.24) respectively for the MHW metrices of frequency, maximum intensity, and duration. In the aspect of SPAEF, the highest scores were owned by the MME-four for all the MHW metrics; 0.37, 0.283, and 0.234 for frequency, maximum intensity, and duration respectively. The MME-four indicates improvement over the MME-all for all except the duration metric in terms of KGE evaluation, i.e., KGE value for duration from MME-four was 0.146 while MME-all was 0.24, but all the other scores are higher in the MME-four. 4.4. Statistical evaluation of MME The degrees of spread of the time series data in each grid from observed (NOAA), MME-four and MME-all during the historical period were analyzed using boxplot diagrams as shown in Fig. 6 . The boxplot comprises of a box extending from the first quartile (Q1) to the third quartile (Q3), two whiskers of one stretching downwards from Q1 to the smallest non-outlier in the data set and the other stretching upwards from Q3 to largest non-outlier (Acharya et al., 2014 ). For the MHW metric of frequency (Fig. 6 a), the interquartile range (Q1 to Q3) of observed NOAA, MME-four, and MME-all lies in the ranges of 0–3, 0.56–2.2, and 0.72–2.1 respectively. The mean values for the above three data sets are 1.97, 1.5 and 1.44 respectively. This indicates that MME-four is much closer to the observed frequency values than the MME-all in terms of the average values and the spread of data. But it can be also noted that both MMEs underestimates the observed NOAA frequency values. When maximum intensity is considered (Fig. 6 b), the interquartile range lies between 0-2.78, 0.75–1.8, and 0.99–1.85, respectively for observed NOAA, MME-four and MME-all. The observed data is distributed in a larger span compared to the MMEs (Fig. 6 b). The degree of spread of data is more in the MME-four than the MME-all. The mean values are 1.82, 1.3, and 1.41 respectively for historical, MME-four and MME-all and both MMEs underestimates the observed maximum intensity of the MHWs. Duration of the MHWs (Fig. 6 c) lies in the interquartile ranges of 0–12, 3.77–14.5, and 5.59–15.34 for the observed data and the two MME data. The mean values are 7.5, 8.03, and 10.56 respectively. The results indicate that the MMEs overestimated the duration compared to the observations and MME-all rather more overestimate the duration values than the MME-four. In addition, it can be noted that the observed duration values contain more outliers compared to the other two observed MHW metrices. MME-four indicated more outliers for the duration similar to the observed results when compared with the MME-all. From the overall boxplot results it can be noted that MME-four underestimated the frequency and maximum intensity while overestimating the duration. Also, MME-four is much closer to the observed than the MME-all in terms of the degree of spread of the data for all the MHW metrices. Next, year-to-year time series data averaged over the study area was evaluated as shown in Fig. 7 . The observed dataset and both MMEs indicated positive trends for all the MHW metrices during the historical period. MME-four indicated gradient value of 0.09 for the frequency which is closer to the observed slope of 0.115 while MME-all indicated a much lower slope of 0.078 (Fig. 7 a). When the trend of Maximum intensity is considered (Fig. 7 b) the gradients of both MMEs are almost similar (0.055, and 0.051) but the MME-four is much closer to the observed gradient. For duration (Fig. 7 c), the slope of MME-all (0.545) is closer to the observed gradient of (0.419) than that of the MME-four(0.571). This indicates that MME-four is closer to the observed than MME-all in terms of the slope values for frequency and maximum intensity than that of duration. As shown in Fig. 7 , for frequency and maximum intensity, both MMEs showed trends lower than the observed gradient indicating that the MMEs underestimates the observed trends for those metrices. However, for duration both MMEs showed higher trends than the observed, indicating that the MMEs overestimate the observed trends of duration. These results are similar to the boxplots evaluation discussed previously and both results demonstrate that the MMEs underestimated frequency and maximum intensity while overestimating the duration.Plecha and Soares ( 2019 ) have also found reasonable agreement between the modeled and observed MHW property trends of global MHW events but underestimation of events per year and mean intensity while overestimation of the duration. 4.5. Future changes The trends of time series of the MHW metrices of frequency (Fig. 8 ), maximum intensity (Fig. 9 ), and duration (Fig. 10 ) in the near future (2041–2070) and far future (2071–2100) were analyzed under four SSP-RCPs, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 using MME-four. From Fig. 8 , it can be observed that two scenarios, SSP1-2.6, and SSP2-4.5 have similar patterns while the high emission scenarios of SSP3-7.0, and SSP5-8.5 followed a closer pattern. The slopes of the time series are increasing from SSP1-2.6 to SSP5-8.5 in both near and far futures. But the slopes are relatively higher for the high emission scenarios when compared to that of the low emission scenarios. It can also be noted that the frequency of the MHWs of two high emission scenarios (SSP3-7.0, and SSP5-8.5) are beneath the low emission scenarios (SSP1-2.6, and SSP2-4.5) during the first half of both future periods. But since the high emission scenarios have higher trends, the values exceed that of the low emission scenarios towards the end of the considered climatology period. Figure 9 indicated time series of maximum intensity. Here also the two low emission scenarios SSP1-2.6, and SSP2-4.5 and the two high emission scenarios SSP3-7.0, and SSP5-8.5 behave similarly. The values of the high emission scenarios are beneath that of the low emission scenarios during the beginning of the climatology period but becomes higher towards the end due to the larger slopes. The trend is increasing from SSP1-2.6 to SSP5-8.5 during the near future and the trend increases in the order of SSP1-2.6, SSP2-4.5, SSP5-8.5, and SSP3-7.0 during the far future. For duration (Fig. 10 ), during the near future, the trend of SSP2-4.5 is relatively higher than that of SSP1-2.6 when compared to the trend of far future but the slopes of SSP3-7.0 and SSP5-8.5 are closer to each other during both future periods. In near future, during the early to mid-periods, SSP3-7.0 and SSP5-8.5 values are beneath the low emission scenarios of SSP1-2.6 and SSP2-4.5. But towards the end of the period, SSP1-2.6 values are the lowest while the remaining three scenario values are higher. In the far future, SSP3-7.0 and SSP5-8.5 values are lower than SSP1-2.6 and SSP2-4.5 similar to the near future. But towards the last decade, the values of all four scenarios become closer during the far future period. When the overall time series graphs of all the MHW metrices of frequency, maximum intensity and duration are considered, one common feature is that the values of high emission scenarios (SSP3-7.0, and SSP5-8.5) are beneath the low emission scenarios (SSP1-2.6, and SSP2-4.5) at the early period of the considered climate period but becomes higher towards the end of the considered climate period. Except for the duration in the near future period, for all the other considered events, the time series of SSP1-2.6 and SSP2-4.5 follows closely to each other. Similarly, SSP3-7.0 and SSP5-8.5 follow closer pattern for each MHW metric in both future periods. It can also be highlighted that the trends of the two high emission scenarios (SSP3-7.0, and SSP5-8.5) are higher than that of the low emission scenarios (SSP1-2.6, and SSP2-4.5) for all the MHW metrices during both future periods. The results indicated that if high emission scenarios are going to prevail in the future periods, there would be higher trends of frequency, maximum intensity, and duration of MHWs based on the expected climate of those future periods in the East Sea. When the historical trends from the MME-four were considered (Fig. 7 ), the slope values of the historical period are higher than that of low emission scenarios (SSP1-2.6, and SSP2-4.5) but lower than that of high emission scenarios (SSP3-7.0, and SSP5-8.5) in all the MHW metrices except for duration in the far future. Next, the average values of the MHWS metrices in the future periods and the historical periods were compared. The time series values of each grid point for the historical and future periods were averaged and plotted in boxplots as shown in Fig. 11 . For frequency (Fig. 11 a), the average future values were higher than the historical values for all scenarios except for SSP3-7.0 in the near future. For maximum intensity (Fig. 11 b) all the future values were higher than the historical values under all scenarios, but it was noted that the values of high emission scenarios (SSP3-7.0, and SSP5-8.5) were lower than the low emission scenarios (SSP1-2.6, and SSP2-4.5) values in both future periods. For duration (Fig. 11 c) it can be highlighted that the values of the low emission scenarios are higher than the historical values while the values of the high emission scenarios are lower than the historical values. But when the numerical values of the averages of each MHW metric were considered, there was no significant difference between the historical and the future average values. Plecha et al. ( 2021 ) indicated that when a non-stationary threshold is considered, the characteristics of the MHW properties are similar across all periods and RCPs. From the overall results of the trend analysis and the average value analysis for the future periods it can be noted that although the high emission scenarios indicated higher trends, when the average values were considered, those values are lower than the low emission scenarios. This is mainly because the high emission scenarios have comparatively low values in the beginning of the climatology period and high values only toward the end of the climate period. 5. Conclusions This study proposed a novel approach to implement the future projection of the MHWs using CMIP6 GCMs. The performance of GCMs were analyzed by applying more robust performance evaluation metrices i.e., KGE and SPAEF to each grid point. The individual models showed the lowest performance in simulating the duration of the MHWs than frequency or maximum intensity. A group decision making technique was employed to select the best GCMs which would perform well in all the considered MHW metrices. The selected best models for East Sea were ACCESS-CM2, BCC-CSM2-MR ACCESS-ESM1, and GFDL-ESM4. In the performance evaluation during the historical period using KGE and SPAEF, MME-four performed better than MME-all for all the MHW properties except KGE value for duration. When the spread of data and the trends were analyzed for the historical period, MMEs underestimate the frequency and maximum intensity while duration is overestimated. The future trends were analyzed by employing the state-of-art shifting baseline approach. High emission scenarios (SSP3-7.0, and SSP5-8.5) have higher slopes than the low emission scenarios (SSP1-2.6 and SSP2-4.5). In addition, the results showed similar patterns in the two low emission scenarios and similar patterns in the two high emission scenarios. The high emission scenarios indicated lower values in the beginning of the climatology period but higher values toward the end of the period. The average values in the future periods are almost similar to that of the historical period. Frequency averages indicated higher values than that of historical for all except SSP3-7.0 in the near future. Average values for maximum intensity indicated higher results than the historical for all scenarios during both near and far futures. The average values of duration of the low emission scenarios indicated higher values while the high emission scenario values indicated lower results. Although MME-four indicated better performance than considering individual models or considering MME-all, still the results should be interpretated with caution due to the uncertainty. Future studies may attempt to apply suitable bias correction of the data for the study domain. Declarations Author contributions ESC led this research. DD performed all of the analysis, draw the figures, and led the draft of this manuscript. ESC provided constructive suggestions for improving the research and writing. All authors read and approved the final manuscript. Data availability The data used in this study are freely available. Acknowledgement This study was supported by National Research Foundation of Korea (NRF) (2021R1A2C2005699). Competing interests The authors have not disclosed any competing interests References Acharya, N., Shrivastava, N. A., Panigrahi, B. K., & Mohanty, U. C. (2014). Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: An application of extreme learning machine. Climate Dynamics , 43 (5–6), 1303–1310. https://doi.org/10.1007/s00382-013-1942-2 Amaya, D. J., Jacox, M. G., Fewings, M. R., Saba, V. S., Stuecker, M. F., Rykaczewski, R. R., Ross, A. C., Stock, C. A., Capotondi, A., Petrik, C. M., Bograd, S. J., Alexander, M. A., Cheng, W., Hermann, A. J., Kearney, K. A., & Powell, B. S. (2023). Marine heatwaves need clear definitions so coastal communities can adapt. Nature , 616 (7955), 29–32. Belkin, I. M. (2009). Rapid warming of Large Marine Ecosystems. Progress in Oceanography , 81 (1–4), 207–213. https://doi.org/10.1016/j.pocean.2009.04.011 Cheng, L., Abraham, J., Hausfather, Z., & Trenberth, K. E. (2019). How fast are the oceans warming? Science , 363 (6423), 128–129. https://doi.org/10.1126/science.aav7619 Choi, W., Bang, M., Joh, Y., Ham, Y. G., Kang, N., & Jang, C. J. (2022). Characteristics and Mechanisms of Marine Heatwaves in the East Asian Marginal Seas: Regional and Seasonal Differences. Remote Sensing , 14 (15). https://doi.org/10.3390/rs14153522 Chu, P. C., Lan, J., & Fan, C. (2001). Japan Sea Thermohaline Structure and Circulation. Part I: Climatology. Journal of Physical Oceanography , 31 (1), 244–271. https://doi.org/10.1175/1520-0485(2001)0312.0.CO;2 Costa, N. V., & Rodrigues, R. R. (2021). Future Summer Marine Heatwaves in the Western South Atlantic. Geophysical Research Letters , 48 (22). https://doi.org/10.1029/2021GL094509 Darmaraki, S., Somot, S., Sevault, F., Nabat, P., Cabos Narvaez, W. D., Cavicchia, L., Djurdjevic, V., Li, L., Sannino, G., & Sein, D. V. (2019). Future evolution of Marine Heatwaves in the Mediterranean Sea. Climate Dynamics , 53 (3–4), 1371–1392. https://doi.org/10.1007/s00382-019-04661-z Deepthi, B., & Sivakumar, B. (2022). Performance assessment of general circulation models: application of compromise programming method and global performance indicator technique. Stochastic Environmental Research and Risk Assessment , 36 (6), 1761–1778. https://doi.org/10.1007/s00477-021-02124-8 Demirel, M. C. (2018). SPAEF version 1.0 with histogram match . Zenodo. https://doi.org/10.5281/zenodo.1158890 Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., & Stisen, S. (2018). Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. Hydrology and Earth System Sciences , 22 (2), 1299–1315. https://doi.org/10.5194/hess-22-1299-2018 Domingues, C. M., Church, J. A., White, N. J., Gleckler, P. J., Wijffels, S. E., Barker, P. M., & Dunn, J. R. (2008). Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature , 453 (7198), 1090–1093. https://doi.org/10.1038/nature07080 Frölicher, T. L., Fischer, E. M., & Gruber, N. (2018). Marine heatwaves under global warming. Nature , 560 (7718), 360–364. https://doi.org/10.1038/s41586-018-0383-9 Garrabou, J., Coma, R., Bensoussan, N., Bally, M., Chevaldonné, P., Cigliano, M., Díaz, D., Harmelin, J.-G., Gambi, M. C., & Kersting, D. K. (2009). Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave. Global Change Biology , 15 (5), 1090–1103. Gomes, D. G. E., Ruzicka, J. J., Crozier, L. G., Huff, D. D., Brodeur, R. D., & Stewart, J. D. (2024). Marine heatwaves disrupt ecosystem structure and function via altered food webs and energy flux. Nature Communications , 15 (1). https://doi.org/10.1038/s41467-024-46263-2 Gregory, C. H., Holbrook, N. J., Marshall, A. G., & Spillman, C. M. (2023). Atmospheric Drivers of Tasman Sea Marine Heatwaves. Journal of Climate , 36 (15), 5197–5214. https://doi.org/https://doi.org/10.1175/JCLI-D-22-0538.1 Gröger, M., Dutheil, C., Börgel, F., & Meier, M. H. E. (2024). Drivers of marine heatwaves in a stratified marginal sea. Climate Dynamics . https://doi.org/10.1007/s00382-023-07062-5 Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology , 377 (1–2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003 Hallouin, T. (2021). hydroeval: an evaluator for streamflow time series in Python . Zenodo. https://doi.org/10.5281/zenodo.4709652 Hayashida, H., Matear, R. J., Strutton, P. G., & Zhang, X. (2020). Insights into projected changes in marine heatwaves from a high-resolution ocean circulation model. Nature Communications , 11 (1). https://doi.org/10.1038/s41467-020-18241-x Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Moore, P. J., Scannell, H. A., Sen Gupta, A., & Wernberg, T. (2016). A hierarchical approach to defining marine heatwaves. Progress in Oceanography , 141 , 227–238. https://doi.org/10.1016/j.pocean.2015.12.014 Hobday, A. J., & Pecl, G. T. (2014). Identification of global marine hotspots: Sentinels for change and vanguards for adaptation action. Reviews in Fish Biology and Fisheries , 24 (2), 415–425. https://doi.org/10.1007/s11160-013-9326-6 Holbrook, N. J., Hernaman, V., Koshiba, S., Lako, J., Kajtar, J. B., Amosa, P., & Singh, A. (2022). Impacts of marine heatwaves on tropical western and central Pacific Island nations and their communities. Global and Planetary Change , 208 . https://doi.org/10.1016/j.gloplacha.2021.103680 Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., & Zhang, H.-M. (2021). Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1. Journal of Climate , 34 (8), 2923–2939. https://doi.org/10.1175/JCLI-D-20-0166.1 Ichiye, T. (1984). Some Problems of Circulation and Hydrography of the Japan Sea and the Tsushima Current. In T. Ichiye (Ed.), Ocean Hydrodynamics of the Japan and East China Seas (Vol. 39, pp. 15–54). Elsevier. https://doi.org/https://doi.org/10.1016/S0422-9894(08)70289-7 Kamenos, N. A., & Hennige, S. J. (2024). A Historical Perspective on Thermal-and Heatwave-Induced Bleaching on the Great Barrier Reef. In Oceanographic Processes of Coral Reefs (pp. 282–289). CRC Press. Kim, K., Kim, K.-R., Min, D.-H., Volkov, Y., Yoon, J.-H., & Takematsu, M. (2001). Warming and structural changes in the east (Japan) Sea: A clue to future changes in global oceans? Geophysical Research Letters , 28 (17), 3293–3296. https://doi.org/https://doi.org/10.1029/2001GL013078 Kim, K.-R., Lee, S. H., Park, K.-A., Park, J. J., Suh, Y.-S., Lee, D.-K., Kang, D.-J., & Chang, K.-I. (2016). General Introduction. In K.-I. Chang, C.-I. Zhang, C. Park, D.-J. Kang, S.-J. Ju, S.-H. Lee, & M. Wimbush (Eds.), Oceanography of the East Sea (Japan Sea) (pp. 1–31). Springer International Publishing. https://doi.org/10.1007/978-3-319-22720-7_1 Kim, S., & Kang, S. (1998). The status and research direction for fishery resources in the East Sea/Sea of Japan. J Korean Soc Fish Res , 1 , 44–58. Kim, S., & Zhang, C.-I. (2016). Fish and fisheries. Oceanography of the East Sea (Japan Sea) , 327–345. Lee, S., Park, M. S., Kwon, M., Park, Y. G., Kim, Y. H., & Choi, N. (2023). Rapidly Changing East Asian Marine Heatwaves Under a Warming Climate. Journal of Geophysical Research: Oceans , 128 (6). https://doi.org/10.1029/2023jc019761 Ma, C., Zhu, X., Liao, M., Dong, S., & Dong, Y. (2021). Heat sensitivity of mariculture species in China. ICES Journal of Marine Science , 78 (8), 2922–2930. https://doi.org/10.1093/icesjms/fsab168 Martín, M. L., Calvo‐Sancho, C., Taszarek, M., González‐Alemán, J. J., Montoro‐Mendoza, A., Díaz‐Fernández, J., Bolgiani, P., Sastre, M., & Martín, Y. (2024). Major role of marine heatwave and anthropogenic climate change on a Giant hail Event in Spain. Geophysical Research Letters , 51 (6), e2023GL107632. Mills, K. E., Pershing, A. J., Brown, C. J., Chen, Y., Chiang, F.-S., Holland, D. S., Lehuta, S., Nye, J. A., Sun, J. C., & Thomas, A. C. (2013). Fisheries management in a changing climate: lessons from the 2012 ocean heat wave in the Northwest Atlantic. Oceanography , 26 (2), 191–195. Morais, D. C., & De Almeida, A. T. (2012). Group decision making on water resources based on analysis of individual rankings. Omega , 40 (1), 42–52. https://doi.org/10.1016/j.omega.2011.03.005 Oliver, E. C. J., Benthuysen, J. A., Darmaraki, S., Donat, M. G., Hobday, A. J., Holbrook, N. J., Schlegel, R. W., & Sen Gupta, A. (2021). Marine heatwaves. Annual Review of Marine Science , 13 , 313–342. O’Neill, B. C., Tebaldi, C., Van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J. F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development , 9 (9), 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016 Park, K. A., Chung, J. Y., Kim, K., & Cornillon, P. C. (2005). Wind and bathymetric forcing of the annual sea surface temperature signal in the East (Japan) Sea. Geophysical Research Letters , 32 (5), 1–4. https://doi.org/10.1029/2004GL022197 Pearce, A. F., Lenanton, R., Jackson, G., Moore, J., Feng, M., & Gaughan, D. (2011). The" marine heat wave" off Western Australia during the summer of 2010/11 . Western Australian Fisheries and Marine Research Laboratories Hillarys, WA …. Plecha, S. M., & Soares, P. M. M. (2019). Global marine heatwave events using the new CMIP6 multi-model ensemble: From shortcomings in present climate to future projections. Environmental Research Letters , 15 (12). https://doi.org/10.1088/1748-9326/abc847 Plecha, S. M., Soares, P. M. M., Silva-Fernandes, S. M., & Cabos, W. (2021). On the uncertainty of future projections of Marine Heatwave events in the North Atlantic Ocean. Climate Dynamics , 56 (7–8), 2027–2056. https://doi.org/10.1007/s00382-020-05529-3 Saranya, J. S., Roxy, M. K., Dasgupta, P., & Anand, A. (2022). Genesis and Trends in Marine Heatwaves Over the Tropical Indian Ocean and Their Interaction With the Indian Summer Monsoon. Journal of Geophysical Research: Oceans , 127 (2). https://doi.org/10.1029/2021JC017427 Sen Gupta, A., Thomsen, M., Benthuysen, J. A., Hobday, A. J., Oliver, E., Alexander, L. V., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Perkins-Kirkpatrick, S., Moore, P. J., Rodrigues, R. R., Scannell, H. A., Taschetto, A. S., Ummenhofer, C. C., Wernberg, T., & Smale, D. A. (2020). Drivers and impacts of the most extreme marine heatwaves events. Scientific Reports , 10 (1). https://doi.org/10.1038/s41598-020-75445-3 Walker, A. S., Kratochwill, C. A., & van Woesik, R. (2024). Past disturbances and local conditions influence the recovery rates of coral reefs. Global Change Biology , 30 (1), e17112. Wang, D., Xu, T., Fang, G., Jiang, S., Wang, G., Wei, Z., & Wang, Y. (2022). Characteristics of Marine Heatwaves in the Japan/East Sea. Remote Sensing , 14 (4). https://doi.org/10.3390/rs14040936 Wernberg, T., Smale, D. A., Tuya, F., Thomsen, M. S., Langlois, T. J., de Bettignies, T., Bennett, S., & Rousseaux, C. S. (2013). An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nature Climate Change , 3 (1), 78–82. https://doi.org/10.1038/nclimate1627 Xue, J., Shan, H., Liang, J. H., & Dong, C. (2023). Assessment and Projections of Marine Heatwaves in the Northwest Pacific Based on CMIP6 Models. Remote Sensing , 15 (12). https://doi.org/10.3390/rs15122957 Yao, Y., Wang, J., Yin, J., & Zou, X. (2020). Marine Heatwaves in China’s Marginal Seas and Adjacent Offshore Waters: Past, Present, and Future. Journal of Geophysical Research: Oceans , 125 (3). https://doi.org/10.1029/2019JC015801 Yeh, S. W., Park, Y. G., Min, H. S., Kim, C. H., & Lee, J. H. (2010). Analysis of characteristics in the sea surface temperature variability in the East/Japan Sea. Progress in Oceanography , 85 (3–4), 213–223. https://doi.org/10.1016/j.pocean.2010.03.001 Zhang, C. I., Seo, Y. Il, Kang, H. J., & Lim, J. H. (2019). Exploitable carrying capacity and potential biomass yield of sectors in the East China Sea, Yellow Sea, and East Sea/Sea of Japan large marine ecosystems. Deep-Sea Research Part II: Topical Studies in Oceanography , 163 , 16–28. https://doi.org/10.1016/j.dsr2.2018.11.016 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4262751","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294282223,"identity":"8fae0ea6-10c9-4f86-9bfb-4ec45be4cb0b","order_by":0,"name":"Danushka Deegala","email":"","orcid":"","institution":"Seoul National University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Danushka","middleName":"","lastName":"Deegala","suffix":""},{"id":294282224,"identity":"8b3b3b58-99a0-4503-8f4d-746027d27c87","order_by":1,"name":"Eun-Sung Chung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACNhAhB2Y+AGIDMOsAYS3GYGZCAglaEhuI1sLPf/jZY94dtenz25uPSST+sJE3Z2B++IHhzD2cWiRnpJkb8545nrvhzLE0iYSENMOdDWzGEgw3inFqMbjBwybN23Ysd4NEjhlQy+EEgwMMZgwMHxJwarE/fwasJV1+/huQlv9ALezf8GoxYMgBaalJYLjBA9JyAKiFB2jLDdxaJG6kmUnObTtguOFMWrJFQlqy4YbDPMUSCWdwa+HvP/xM4m1bnbx8++GDNz7Y2MkbHG/f+OHDMdxaoOAwEpsZiAlqYGCoI6xkFIyCUTAKRi4AAPP8U7iYtD/MAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4329-1800","institution":"Seoul National University of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Eun-Sung","middleName":"","lastName":"Chung","suffix":""}],"badges":[],"createdAt":"2024-04-13 18:01:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4262751/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4262751/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55552117,"identity":"dfba8117-31a1-45b4-8bee-40094fdab22d","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71500,"visible":true,"origin":"","legend":"\u003cp\u003ea) Bathymetry of the East Sea (z) from ETOPO1 (1 arc minute Global Relief Model) from National Oceanic and Atmospheric Administration (NOAA), and b) daily sea surface temperature (SST) from NOAA daily optimum interpolation sea surface temperature version 2 (NOAA OI SST V2) dataset, averaged during the historical period (1985-2014).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/4c6a78b38ae18d423cee5959.png"},{"id":55552114,"identity":"43ac0943-7fda-425b-93be-1956c9c19709","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39047,"visible":true,"origin":"","legend":"\u003cp\u003eProcedure used in this study\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/7827d441a188921ab6919bfb.png"},{"id":55552116,"identity":"7bc91311-a614-4287-a801-56fb7a79508b","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":809576,"visible":true,"origin":"","legend":"\u003cp\u003eKGE performance of individual CMIP6 GCMs during the historical period (1985-2014) for the MHW metrics; (a) Frequency, (b) Maximum intensity, and (c) Duration, over the East Sea.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/6ec4470348ae4773b9384dab.jpeg"},{"id":55552121,"identity":"7e744e42-a698-4b2a-ac13-ffe35638235f","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":825715,"visible":true,"origin":"","legend":"\u003cp\u003eSPAEF performance of individual CMIP6 GCMs during the historical period (1985-2014) for the MHW metrics; (a) Frequency, (b) Maximum intensity, and (c) Duration, over the East Sea.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/0636eba596d69cc88f7d9000.jpeg"},{"id":55552115,"identity":"95601475-3410-4e7c-be94-81f2dbdd50dd","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78074,"visible":true,"origin":"","legend":"\u003cp\u003eAveraged KGE and SPAEF values over the study area for the individual GCMs, MME-all and MME-four for the MHW metrics of frequency (Frq.), Maximum intensity (Int.), and duration (Dur.).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/18cbbbcf0505b95fc3e714e2.png"},{"id":55552118,"identity":"fd6f2fa6-cc51-4d77-8be9-c41629855d49","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14915,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of time series data from the grids from NOAA (observed), MME-four, and MME-all during the historical period (1985-2014) with respect to the MHW metrics; (a) Frequency, (b) Maximum intensity, and (c) Duration. The mean values are shown in triangles and median values are represented by the horizotal line in the box.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/c8de52cc0ada0116fa1393b9.png"},{"id":55552123,"identity":"46de9bca-4c80-41b1-ba52-b4b63a8c7139","added_by":"auto","created_at":"2024-04-29 22:12:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":210607,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the MHW properties of (a) Frequency, (b) Maximum intensity, (c) Duration during the historical period (1985-2014) averaged over East sea for observed-NOAA (black), MME-four (blue), MME-all (red) and individual GCMs (light blue).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/f1842cf537b4e0ba131e00f9.png"},{"id":55552119,"identity":"e266515f-7e71-4f76-b593-b29e4127b924","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":81158,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the MME-four over East Sea during (a) near future (2041-2070) and (b) far future (2071-2100) under four SSP-RCPs (SSP1-2.6: green, SSP2-4.5: blue, SSP3-7.0: orange, and SSP5-8.5: red) for the MHW metric of frequency.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/d093b56c6c4df44d0b24843d.png"},{"id":55552120,"identity":"61209d30-b7ee-4d2a-aa19-6c57b3543cb5","added_by":"auto","created_at":"2024-04-29 22:12:45","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":80578,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 8 but for the maximum intensity.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/d9590677cc8ba9eaeff5628c.png"},{"id":55553185,"identity":"7292c651-bcaf-4ef5-8b44-8ddc51e05e88","added_by":"auto","created_at":"2024-04-29 22:20:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":83016,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 8 but for the duration.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/7071b85e055fe8b2808873ce.png"},{"id":55552124,"identity":"5541c103-fe60-4bc6-a431-269459bb7339","added_by":"auto","created_at":"2024-04-29 22:12:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":37383,"visible":true,"origin":"","legend":"\u003cp\u003eAverage values of the MHW metrices; (a) Frequency, (b) Maximum intensity, and (c) Duration, during the historical (1985-2014), near future (2041-2070) and far future (2071-2100) periods under four SSP-RCPs (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) using MME-four over the East Sea.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/0f2b0ef6513f4ed92e399404.png"},{"id":70430327,"identity":"4785b0cb-a4e7-4646-b656-45571cb6b843","added_by":"auto","created_at":"2024-12-03 06:07:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3187481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4262751/v1/08bc56a8-5fc5-4763-863d-cbcff54cb1c9.pdf"}],"financialInterests":"","formattedTitle":"Future projection of marine heat waves in a global marine hotspot Case of East/Japan Sea","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe energy imbalance in Earth\u0026rsquo;s climate system resulting from the anthropogenic climate change causes high concentrations of heat-trapping gases and about 93% of this imbalanced energy is accumulated in the oceans as increased ocean heat content (Cheng et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Domingues et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) indicated that 91% of the total change of heat content in the upper 0-700 m of the ocean during the period of 1961\u0026ndash;2003 is stored in the upper 0-300 m. Episodes of abrupt large-scale warm anomalies of temperature in the ocean have been reported during the recent decades leading to the term, Marine Heat Waves (MHWs). The term was first used by Pearce et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) (Oliver et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Hobday et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) has qualitatively defined MHWs as a discrete prolonged anomalously warm water event occurring in a particular location.\u003c/p\u003e \u003cp\u003eMHWs can be driven by a combination of atmospheric and oceanographic processes (Hobday et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sen Gupta et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that almost all of the most extreme MHWs are almost associated with suppressed wind speeds during their build up phase and some cases are related to the suppressed turbulent heat losses from the ocean. They have showed that the intensity, duration, and extent of extreme MHWs are strongly enhanced during El Ni\u0026ntilde;o periods both in the tropical Pacific and beyond. In addition, they have demonstrated that the maximum intensity MHWs tend to occur in the calendar summer of each hemisphere. However, the driving forces of MHWs may differ based on the studied location, i.e., in the Baltic Sea, a mid-latitude marginal sea located on the Northwestern European shelf, summer MHWs are observed to be mainly forced by local meteorological conditions over the open water while winter MHWs are associated with the advection of warm and moist air originating from the North Atlantic (Gr\u0026ouml;ger et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the Tasman Sea, MHWs are driven by both ocean advection and by air-sea heat fluxes and the atmospherically driven MHWs are more likely to occur during the positive phase of the asymmetric Southern Annular Mode which is likely to occur during La Ni\u0026ntilde;a years (Gregory et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe impacts of MHWs on the ocean marine life have been widely studied. One of the first documented MHW occurred in the Mediterranean Sea in summer 2003 has caused mass mortality of rocky benthic species (Fr\u0026ouml;licher et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Garrabou et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Wernberg et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) showed a significant difference of the biodiversity patterns of temperate seaweeds, sessile invertebrates, and demersal fish along the west coast of Australia in early 2011 after a warming event which was the highest magnitude warming event on record at the time. Gomes et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have compared ecosystem models parameterized before and after the onset of recent MHWs to evaluate the cascading effects on ecosystem structure and function in the Northeast Pacific Ocean and have found that the most functional groups were changed following the heatwaves. Kamenos and Hennige (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that MHWs have existed during mass coral bleaching incidents on the Great Barrier Reef while Walker et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicated that the influence of MHWs on the corals may cause a phase shift beyond which recovery is less likely.\u003c/p\u003e \u003cp\u003eApart from the impact on the marine life caused by the MHWs, researchers have indicated the resulting economic impact. For example, Mills et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) showed that the lobster fishery in the northeastern United States was extended after the warm event occurred in the Northwest Atlantic Ocean in 2012 which resulted in low prices and economic losses. Another sea warming event occurred in Dalian city, China in August 2018 caused a mass destruction of sea cucumbers resulting in an economic loss of about 15\u0026nbsp;billion CNY (~\u0026thinsp;2.3\u0026nbsp;billion USD) (Ma et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These incidents indicate the necessity of policies that enable the fisheries management to face such abrupt events which would be more frequent with the climate change.\u003c/p\u003e \u003cp\u003eMoreover, recent studies have illustrated the influence of MHWs on the atmospheric circumstances such as monsoons, cyclones, and hails. Saranya et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have explored the role of MHWs in the Indian Ocean in modulating the Indian summer monsoon and have found the MHWs in some regions of the Indian ocean has caused reduction of rainfall over some parts of India while some caused an enhancement of rainfall over the other regions of India. Choi et al. (2024) have demonstrated that MHWs play a critical role in intensifying the tropical cyclones by providing more latent heat flux from the ocean and heavier precipitation near the center of the tropical cyclone. Furthermore Mart\u0026iacute;n et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicated that a record-breaking MHW has enhanced a favorable environment for a severe hailstorm that occurred in Spain during August 2022 which has caused material and human damage including one fatality.\u003c/p\u003e \u003cp\u003eThe future prediction of MHWs has been explored using Global Climate Models (GCMs) of Sea Surface Temperature (SST) provided by the Coupled Model Intercomparison Project (CMIP). Several such studies predicted more frequent and extreme MHWs in the world in the future climate under global warming (Fr\u0026ouml;licher et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hayashida et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the GCMs present significant shortcomings in reproducing the SST and MHWs (Plecha et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Holbrook et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have used the latest version of CMIP, i.e., CMIP6 GCMs to study the MHWs across Pacific Island regions and have found that most notable bias appeared in the simulation of moderate category MHW days for the period 2000\u0026ndash;2019. Plecha and Soares (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) have indicated that the GCMs\u0026rsquo; biases obtained from CMIP6 are in line with the previous version of CMIP5 biases suggesting the necessity of the high spatial resolution models to characterize MHWs. However, Darmaraki et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) who have used high resolution regional climate system models (RCSMs) from the Med-CORDEX initiative to evaluate the MHWs in the Mediterranean Sea have showed that despite the high-resolution representation of the air-sea interactions, uncertainties still extist due to their individual biases but also due to the availability of small number of the then available six RCSMs.\u003c/p\u003e \u003cp\u003eSince individual GCMs vary in their simulations of MHW properties and patterns, Multi Model Ensemble (MME) method is used by many previous studies. Several studies have created the ensemble by selecting models under a specific criterion, i.e., Yao et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have used 12 out of 28 GCMs from CMIP5, selected based on the evaluation of the annual and seasonal SST linear trends and have used equal weight method to analyze the MHWs in China\u0026rsquo;s marginal seas. Costa and Rodrigues (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have assessed the historical SST from CMIP6 using performance metrices and have assigned weights in creating the ensemble mean. Other studies (Fr\u0026ouml;licher et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hayashida et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Holbrook et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) without evaluating the SST, have first calculated the MHW metrices from their native grids, interpolated to a common grid and then the ensemble mean has been computed by averaging the results obtained from all the considered models. So, after evaluating the methods utilized by previous studies, MME method was employed by using only a few GCMs selected out of the used 15 GCMs by considering their performance in simulating the MHW metrices (not SST) during the historical period (1985\u0026ndash;2014). A grid-wise performance technique and a group decision method were applied to assess the best performing GCMs from CMIP6. Although this method has been applied to evaluate precipitation data from the GCMs (Deepthi and Sivakumar, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there have been few studies to apply this technique in evaluating the MHWs. We suggest that grid-wise evaluation is a better approach in selecting the GCMs when evaluating MHWs considering the inadequate spatial simulation of the models. The historical performance of the MME from the selected models versus the MME from all the models were also compared.\u003c/p\u003e \u003cp\u003eTo apply the proposed technique to the field of MHWs, East Sea was selected as our study area. East Sea is recognized as a marine hotspot, a region where ocean warming is the fastest, by Hobday and Pecl (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) after their analysis of the historical and future global SST data. Belkin (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) has recognized East Sea as one of the large marine ecosystems which has subjected to recent rapid warming with net SST change\u0026thinsp;\u0026gt;\u0026thinsp;0.9\u003csup\u003eo\u003c/sup\u003eC. They have also observed that the most rapid warming occurs in the semi-enclosed seas including Baltic Sea, North Sea, and East Sea. With the unique location of the East Sea accompanied by its ecological richness and the economic importance from the fishery prevailing in the surrounding countries, East Sea requires an in-depth approach to investigate on the future MHWs. The historical analysis of MHWs in the East Sea (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), mechanisms and properties of MHWs in the East Asian Marginal Seas (Choi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have been studied. Future analysis of MHWs in China\u0026rsquo;s marginal Seas and Adjacent offshore waters using CMIP5 GCMs (Yao et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and using CMIP6 GCMs (Xue et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have also been studied. But a systematic future projection specially focusing on the East Sea using the latest CMIP6 is limited. Therefore, our future projection approach was applied to the East Sea and the nature of MHWs in the near future (2041\u0026ndash;2070) and far future (2071\u0026ndash;2100) was analyzed using four Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCPs) from CMIP6 i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eThe study domain is the East Sea located at 33\u003csup\u003eo\u003c/sup\u003e-53\u003csup\u003eo\u003c/sup\u003eN and 127\u003csup\u003eo\u003c/sup\u003e-143\u003csup\u003eo\u003c/sup\u003eE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with area of about 10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e. East Sea is a semi-enclosed marginal sea bounded by East Asian continent and Japanese Islands in the northwestern Pacific. It is connected to the western North Pacific through four channels all with less than 140 m deep, i.e., Korean/Tsushima strait, Tsugaru strait, Soya strait, and Tatarsky strait (Yeh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Since the East Sea is topographically isolated from the North Pacific, only a limited amount of Kuroshio currents would enter into the sea (Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It has phenomena and circulation patterns similar to those found in the open ocean (Ichiye, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) (Ichiye, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) including its own thermohaline circulation (Chu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) in spite of its small basin size and semi-enclosed topography. The warm and cold currents meet in a deep basin with a maximum depth reaching 3,800 m (Lee et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. demonstrates the bathymetry of the East Sea and the deepest regions of the East Sea are found in the Japan Basin (3000\u0026ndash;3800 m). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. shows the averaged SST variation during the historical period (1985\u0026ndash;2014) and the temperature varies from about 7\u003csup\u003eo\u003c/sup\u003eC from northern regions of the East Sea to about 19\u003csup\u003eo\u003c/sup\u003eC towards southern regions.\u003c/p\u003e\u003cp\u003eTsushima warm which is a branch of the Kuroshio Current supplies heat and nutrients into the East Sea and is a main factor of the large marine ecosystems in the East Sea (Kim and Kang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Many fish species including some brackish water species inhabit in the East Sea (Kim and Zhang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Coldwater fish species like chum salmon live in the surface layer in the northern areas of the East Sea and in the subsurface water column throughout the whole East Sea, whereas warm-water pelagic species like anchovies and chub mackerel inhabit in the surface layer towards the south of the East Sea (Kim and Zhang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to the current political obstacles between the surrounding nations, the annual fish catch records for the entire East Sea are unavailable, but the historical statistics from the 1980s show that pollock was a main catch species, accounting for over 3\u0026nbsp;million metric tons of fish throughout the entire East Sea (Kim and Zhang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data\u003c/h2\u003e \u003cp\u003eTo detect the MHWs during the historical period National Oceanic and Atmospheric Administration daily optimum interpolation sea surface temperature version 2 (NOAA OI SST V2) dataset was utilized. The spatial grid resolution of the dataset is 0.25\u003csup\u003eo\u003c/sup\u003e with temporal resolution of 1 day and the data is available from 1st of January 1982 up to the present date (2024). The data is provided by the NOAA Physical Sciences Laboratory, Boulder, Colorado, USA, from their website at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psl.noaa.gov\u003c/span\u003e\u003cspan address=\"https://psl.noaa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This product is a blend of both in situ observations from ships, drifting buoys, and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the future analysis of MHWs, GCMs of daily SST data (variable name: tos) available from CMIP6 under four SSP-RCPs, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 under the first variant level (r1i1p1f1) were used. SSP1-2.6 and SSP2-4.5 are under low and medium forcing categories with the radiative forcing of 2.6 Wm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 4.5 Wm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e respectively in year 2100 (O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). SSP3-7.0 is under the high forcing category with the 2100 radiative forcing of 7.0 Wm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. SSP5-8.5 is also under high forcing category and it is the only SSP scenario with emissions high enough to produce a radiative forcing of 8.5 Wm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in year 2100 (O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The details of the 15 GCMs used in this study are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It was noted that the grid types of the GCMs vary across the models from atmospheric grids, native ocean tri-polar grids to curvilinear grids. In addition, the nominal resolution of the grids changes from 50 km to 250 km (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNames, Institution, Country, and nominal resolution of the 15 CMIP6 GCMs used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNominal resolution (km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCESS\u0026thinsp;\u0026minus;\u0026thinsp;CM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralian Research Council Centre of Excellence for Climate System Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCESS\u0026thinsp;\u0026minus;\u0026thinsp;ESM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralian Research Council Centre of Excellence for Climate System Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeijing Climate Center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanadian Centre for Climate Modelling and Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2-WACCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Center for Atmospheric Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuro-Mediterranean C\u003c/p\u003e \u003cp\u003eentre on Climate Change coupled climate model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMCC\u0026thinsp;\u0026minus;\u0026thinsp;ESM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuro\u0026thinsp;\u0026minus;\u0026thinsp;Mediterranean Centre on Climate Change Earth System Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u0026thinsp;\u0026minus;\u0026thinsp;Earth3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC\u0026thinsp;\u0026minus;\u0026thinsp;Earth Consortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u0026thinsp;\u0026minus;\u0026thinsp;Earth3\u0026thinsp;\u0026minus;\u0026thinsp;Veg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC\u0026thinsp;\u0026minus;\u0026thinsp;Earth Consortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u0026thinsp;\u0026minus;\u0026thinsp;Earth3\u0026thinsp;\u0026minus;\u0026thinsp;Veg\u0026thinsp;\u0026minus;\u0026thinsp;LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC\u0026thinsp;\u0026minus;\u0026thinsp;Earth Consortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFDL\u0026thinsp;\u0026minus;\u0026thinsp;ESM 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeophysical Fluid Dynamics Laboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI\u0026thinsp;\u0026minus;\u0026thinsp;ESM1\u0026thinsp;\u0026minus;\u0026thinsp;2\u0026minus;HR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax Planck Institute for Meteorology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI\u0026thinsp;\u0026minus;\u0026thinsp;ESM2\u0026thinsp;\u0026minus;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeteorological Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorESM2\u0026thinsp;\u0026minus;\u0026thinsp;LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Norwegian Earth System Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorESM2\u0026thinsp;\u0026minus;\u0026thinsp;MM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Norwegian Earth System Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Marine heatwave detection\u003c/h2\u003e \u003cp\u003eThe MHWs were detected using the definition proposed by Hobday et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In their definition MHWs are identified relative to a baseline climatology of recommended 30-year period. For our study 30 years were used as the baseline period in historical (1985\u0026ndash;2014) and future periods (2041\u0026ndash;2070, and 2071\u0026ndash;2100). The climatology is established relative to the time of the year considering all the data of an 11-day window centered on that time of the year from which the climatological mean and threshold are computed. The threshold is based on the percentile value (90%) rather than an absolute value above the climatological value. The MHWs need to last at least five days and the events are discrete when the gap is more than two days. The MHW measures of frequency, maximum intensity, and duration were computed in each grid of our study area using the Python package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://github.com/ecjoliver/marineHeatWaves\u003c/span\u003e\u003cspan address=\"http://github.com/ecjoliver/marineHeatWaves\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) established from the definition of Hobday et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The definitions, and units of the MHW metrices used in this study are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMHW metric definitions and units used in Hobday et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHW metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of MHW events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecount\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighest temperature anomaly value during the MHW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsecutive period of time that temperature exceeds the threshold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe python MHW detecting package provides the facility to calculate the statistics of MHWs averaged over blocks of a specified length of time. For our study one year was considered as the block length. Hence, it should be noted that the terms; frequency, maximum intensity and duration of our study are the annual total (frequency) or annual averaged (maximum intensity, duration) values. So, a grid contains 30 values (for 30-years) for each MHW metric for each historical and future period.\u003c/p\u003e \u003cp\u003eWhen computing the MHW metrices during the historical and future periods using the GCMs from CMIP6, the metrices were first calculated on their native grids and then regridded by linear interpolation to a common grid resolution of 0.25\u003csup\u003eo\u003c/sup\u003e which is same resolution as the NOAA OI SST V2. Hayashida et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicated the interpolation of the gridded data caused smoothening the variability and hence caused an underestimate of eddy kinetic energy in the Southern Ocean. Fr\u0026ouml;licher et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Hayashida et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Holbrook et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have also computed the MHW metrices on the native grids of the models and then regridded to the observed data grid to avoid the smoothening of the data.\u003c/p\u003e \u003cp\u003eWhen selecting the baseline to identify the MHWs in the future analysis, previous studies (Costa and Rodrigues, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Darmaraki et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Holbrook et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have used fixed baseline of historical climatological period. But the fixed baseline approach ignores the fact that oceans are getting warmer over time and using this definition would lead to more frequent and more intense MHWs reaching to a perpetual heatwave state (Amaya et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Amaya et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have indicated that the public desensitization might happen with the frequent hearing of MHWs causing potential inaction or a lack of preparedness. Some recent studies (Plecha et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have used both fixed baseline and shifting baseline methods. But considering the state of art, we only considered the shifting baseline approach in our future analysis. Hence for the MHW analysis of near future and far future the baseline of 30 year climatology period of 2041\u0026ndash;2070 and 2071\u0026ndash;2100 respectively were considered for each SSP-RCP. The summary of the procedure followed in this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Performance evaluation\u003c/h2\u003e \u003cp\u003eThe GCMs\u0026rsquo; performance in simulating the MHW metrics was evaluated using two indicators, i.e., Kling-Gupta Efficiency (KGE) (Eq.\u0026nbsp;1) and SPAtial Efficiency (SPAEF) (Eq.\u0026nbsp;2). These two metrices provide a robust insight to the models\u0026rsquo; performance since they consider different aspects at once rather than focusing only on one aspect as of traditional metrics such as bias, mean square error or correlation. Deepthi and Sivakumar (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have analyzed the grid-wise performance of GCMs for monthly precipitation using only these two metrices. Both metrices consider three statistical components, i.e., KGE considers correlation, bias, and variability (Gupta et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) while SPAEF contains correlation, variance, and histogram intersection (Demirel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$KGE=1- \\sqrt{{\\left(r-1\\right)}^{2}+{\\left(\\gamma -1\\right)}^{2}+{(\\beta -1)}^{2}} \\left(01\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere,\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$r= \\frac{\\sum _{i=1}^{n}({o}_{i}-{\\mu }_{o})({s}_{i}-{\\mu }_{s})}{\\sqrt{\\sum _{i=1}^{n}{({o}_{i}-{\\mu }_{o})}^{2}\\sum _{i=1}^{n}{({s}_{i}-{\\mu }_{s})}^{2}}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\gamma = \\frac{{\\mu }_{s}}{{\\mu }_{o}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\beta = \\frac{{\\sigma }_{s}/{\\mu }_{s}}{{\\sigma }_{o}/{\\mu }_{o}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$SPAEF=1- \\sqrt{{\\left(r-1\\right)}^{2}+{\\left(\\beta -1\\right)}^{2}+{\\left(\\propto -1\\right)}^{2}} \\left(02\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\propto = \\frac{\\sum _{j=1}^{x}\\text{m}\\text{i}\\text{n}({K}_{j},{L}_{j})}{\\sum _{j=1}^{x}{K}_{j}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(r\\)\u003c/span\u003e \u003c/span\u003e refers to the Pearson correlation coefficient between the model simulation (s) and the observed data (o), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003e denotes the bias stabilized by the standard deviation of observed data, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e presents the spatial variability, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e refers to the overlap between the histograms of the model simulations and the observed values. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e is the mean and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma\\)\u003c/span\u003e\u003c/span\u003e is the standard deviation. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(L\\)\u003c/span\u003e\u003c/span\u003e is the histogram values of model simulations, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(K\\)\u003c/span\u003e\u003c/span\u003e is the histogram values of observations, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e is the number of bins in a histogram.\u003c/p\u003e \u003cp\u003eBoth these indicators range from -\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\infty\\)\u003c/span\u003e\u003c/span\u003e to 1 and closer the values to 1, better the performance. KGE and SPAEF were calculated grid-wise for each GCM from python using the packages from Hallouin (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Demirel (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), respectively.\u003c/p\u003e \u003cp\u003eFor a given MHW metric, a grid contains KGE and SPAEF results of the 15 individual GCMs. The GCMs were ranked grid-wise in two terms: first using KGE values and then using SPAEF values. In order to find the best alternatives from the 15 GCMs, the rankings provided by each grid need to be analyzed and hence a group-decision making methodology was utilized which will be explained in detail in the next section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Group decision making methodology\u003c/h2\u003e \u003cp\u003eThe methodology proposed by Morais and De Almeida (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) was adopted for the group decision making. This procedure involves eliminating irrelevant alternatives, counting the strength and weakness of the alternatives, and choosing the alternative based on the net strength of the alternative. The first step of this method is the individual rankings of the alternatives (GCMs) by the decision makers (grids). The GCMs were ranked in two separate methods for each grid using the evaluation metrices of KGE and SPAEF respectively as described in the previous section. The second step is the filter phase in which the irrelevant alternatives are eliminated. This is done by considering the 25% of the alternatives ranked in the upper position (x) (Eq.\u0026nbsp;03) and the 25% of the alternatives ranked in the lower position (y) (Eq.\u0026nbsp;04). From Eq.\u0026nbsp;03 and Eq.\u0026nbsp;04 the upper positions for this study found to be 1st, 2nd, 3rd, and 4th rankings while the lower positions were 12th, 13th, 14th, and 15th .\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$x=\\frac{n}{4} \\left(\\text{r}\\text{o}\\text{u}\\text{n}\\text{d} \\text{u}\\text{p}\\right) \\left(03\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$y=\\frac{3n}{4}+1 \\left(\\text{t}\\text{r}\\text{u}\\text{n}\\text{c}\\text{a}\\text{t}\\text{e}\\right) \\left(04\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e is the total number of ranked alternatives.\u003c/p\u003e \u003cp\u003eThe third step is calculating the strength \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({F}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e (Eq.\u0026nbsp;05) and weakness \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({f}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e (Eq.\u0026nbsp;06) of the alternatives.\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$${F}_{i}= \\sum _{k=1}^{m}\\sum _{j=1}^{x}(x-j+1){q}_{ij}^{k} \\forall i,k \\forall j=1,\\dots ..,x \\left(05\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({q}_{ij }^{k}= \\left\\{\\begin{array}{c}1, if alternative i is in position j for the decision maker k \\\\ 0, otherwise \\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e \u003c/span\u003e corresponds to the alternatives in the upper quartile (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e = M1, M2, \u0026hellip;, Mn); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e is the position in the upper quartile, ranging from the 1st position to the upper quartile limit (x\u003csup\u003eth\u003c/sup\u003e) ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e= 1st, 2nd, \u0026hellip;, x\u003csup\u003eth\u003c/sup\u003e) and, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e represents the decision maker (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{g}\\text{r}\\text{i}\\text{d}}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{g}\\text{r}\\text{i}\\text{d}}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{g}\\text{r}\\text{i}\\text{d}}_{m}\\)\u003c/span\u003e\u003c/span\u003e).\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$${f}_{i}= \\sum _{k=1}^{m}\\sum _{j=y}^{n}(j-y+1){q}_{ij}^{k} \\forall i,k \\forall j=y,\\dots ..,n \\left(06\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({q}_{ij }^{k}= \\left\\{\\begin{array}{c}1, if alternative i is in position j for the decision maker k \\\\ 0, otherwise \\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e \u003c/span\u003e corresponds to the alternatives in the lower quartile (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e = M1, M2, \u0026hellip;, Mn); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e is the position in the lower quartile, ranging from the lower quartile limit (y\u003csup\u003eth\u003c/sup\u003e) to the last position of ranking (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eth\u003c/sup\u003e) (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e = y\u003csup\u003eth\u003c/sup\u003e, \u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eth\u003c/sup\u003e) and, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e represent the decision maker (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{g}\\text{r}\\text{i}\\text{d}}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{g}\\text{r}\\text{i}\\text{d}}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{g}\\text{r}\\text{i}\\text{d}}_{m}\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe last phase of this method is calculating the intensity of the strength (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e) using Eq.\u0026nbsp;07. The alternative with larger \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e indicate better performance. This phase gives an insight to a model considering the disagreement of the grids points although that model would be in higher positions for some other grid points. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e values were calculated for each GCM for the two criteria of KGE, and SPAEF and for each MHW metric.\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$${\\propto }_{i}={F}_{i}- {f}_{i} \\left(07\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNext, the overall performance of the GCMs need to be evaluated and the GCMs need to be ranked in order to select the best performing GCMs. So, first the GCMs were ranked based on the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e values from the two criteria of KGE and SPAEF for the three MHW metrices. Then the ranks obtained for the three MHW metrices under the two criteria (altogether 6 rank values) were averaged, and an overall final ranking was given to the GCMs based on those averaged ranks. The GCMs which ranked in the upper position (1st, 2nd, 3rd, and 4th rankings) were selected for the ensemble.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1. Performance evaluation of the models\u003c/h2\u003e\n\u003cp\u003eKGE and SPAEF were calculated grid-wise for each GCM for the three MHW metrics as demonstrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e respectively. In KGE evaluation, the models showed the lowest performance in simulating the duration when compared to the frequency and maximum intensity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The models: Earth3-Veg-LR, and MPI-ESM1-2-HR showed less skill in most of the grids for both frequency and maximum intensity compared to that of the other GCMs. NorESM-LM indicated less skills in simulating the frequency towards the northwestern regions of the East Sea and also indicated low performance in simulating maximum intensity in most of the central and northern regions of the East Sea (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn SPAEF evaluation, the models, Earth3-Veg-LR and MPI-ESM1-2-HR indicated lower performance in simulating the frequency and NorESM2-LM indicated less skills towards the northwestern regions of East Sea (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) compared to the other GCMs. In the simulation of maximum intensity, Earth3-Veg-LR, MPI-ESM1-2-HR, NorESM2-LM, and CMCC-ESM2 indicated lower performance in most of the areas. Duration estimation was rather low in most of the GCMs and a few GCMs, i.e., MRI-ESM2, and CanESM5 indicated better results relative to the other models (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWhen the spatial patterns of KGE and SPAEF results are considered, it can be noted that some GCMs followed a similar pattern i.e., the models Earth3-Veg-LR, MPI-ESM1-2-HR, and NorESM2-LM showed poor results for frequency and maximum intensity in similar areas in both KGE and SPAEF evaluations. However, the areas where an inadequate performance is evident, vary depending on the model and also the MHW metric. Therefore, deciding which models perform better for the whole study area and also for the overall MHW metrics is comprehensive. To overcome this, a group decision technique was adopted as described in section 3.2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2. Selecting the best GCMs from Group decision making\u003c/h2\u003e\n\u003cp\u003eThe strength, weakness and strength intensity of each model were calculated from Eq.\u0026nbsp;05, Eq.\u0026nbsp;06, and Eq.\u0026nbsp;07 respectively for the two evaluation criteria of KGE (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and SPAEF (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In KGE, ACCESS-CM2, BCC-CSM2-MR, and GFDL-ESM4 indicated positive strength intensity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e) for all the MHW metrics as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In SPAEF, ACCESS-CM2, ACCESS-ESM1, and GFDL-ESM4 indicated positive \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e for all the MHW metrics as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. It can be highlighted that positive \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e for both evaluation criteria of KGE and SPAEF was indicated by the two models ACCESS-CM2 and GFDL-ESM4 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Negative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e for all the MHW metrices for both KGE and SPAEF evaluations is shown by Earth3-Veg-LR, and MPI-ESM1-2-HR (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition, CMCC-ESM2 indicated negative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e for all the MHW metrices in the SPAEF evaluation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNext the models were ranked separately based on their strength intensities from the two criteria of KGE and SPAEF for each MHW metric as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Then the ranks were averaged, and an overall final rank was given to each GCM. Four models ACCESS-CM2, BCC-CSM2-MR, ACCESS-ESM1, and GFDL-ESM4 were ranked in the upper positions, 1st, 2nd, 3rd, and 4th ranks respectively as highlighted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and these four models were used in creating the ensemble mean. From the overall ranking of the models, lowest ranks were observed in Earth3-Veg, CMCC-ESM2, MPI-ESM1-2-HR, and Earth3-Veg-LR with rankings of 12th, 13th, 14th, and 15th ranks respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eModel scores for strength (F), weakness (f), and strength intensity () for the MHW metrics of frequency, maximum intensity, and duration, over the East Sea for the KGE criterion.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModels\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eFrequency\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMaximum intensity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDuration\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACCESS-CM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1601\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e532\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACCESS-ESM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1299\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e505\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e770\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-265\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e340\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e565\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1105\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3-Veg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-527\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1754\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1523\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3-Veg-LR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1591\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2078\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2521\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1281\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMRI-ESM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e861\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e467\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e678\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanESM5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e974\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorESM2-MM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e795\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e377\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1126\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorESM2-LM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e688\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e623\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e669\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e494\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMCC-ESM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e634\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e286\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCC-CSM2-MR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e243\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e899\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e460\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e466\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCESM2-WACCM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1817\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-383\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGFDL-ESM4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e767\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e623\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1411\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSame as in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e but for the SPAEF criterion.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModels\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eFrequency\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMaximum intensity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDuration\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\propto\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACCESS-CM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e959\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e818\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e486\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACCESS-ESM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1680\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e407\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e463\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e817\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-369\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3-Veg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e792\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-255\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e773\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3-Veg-LR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e573\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1125\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2301\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1453\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMRI-ESM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1373\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanESM5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-951\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e352\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e890\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorESM2-MM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e338\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorESM2-LM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1617\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e351\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e594\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e734\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-211\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMCC-ESM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e460\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e731\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-248\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCC-CSM2-MR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e263\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e769\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-107\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCESM2-WACCM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1552\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1343\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-799\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGFDL-ESM4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e959\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eModel ranks based on KGE, SPAEF for each MHW metrics of frequency (Frq.), Maximum intensity (Int.), duration (Dur.), the averaged ranks and the overall ranks. (The models ranked in the upper position are highlighted in bold with their respective overall ranking.)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModels\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRank - KGE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRank - SPAEF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAverage Rank\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOverall Rank\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrq.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInt.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDur.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrq.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInt.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDur.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eACCESS-CM2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eACCESS-ESM1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3-Veg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEarth3-Veg-LR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMRI-ESM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanESM5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorESM2-MM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorESM2-LM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMCC-ESM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCC-CSM2-MR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCESM2-WACCM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGFDL-ESM4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3. Performance evaluation of the MME\u003c/h2\u003e\n\u003cp\u003eThe performance of MME from the selected four models (hereafter referred to as MME-four) was compared with MME from all models (hereafter referred to as MME-all) as well as with the individual models. The grid-wise skill scores for KGE and SPAEF were averaged for the whole area for each model and ensembles as indicated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Similar to the observation in section 4.1, the average KGE score of the individual GCMs for the duration is much lower than the other two MHW metrices. Duration was scored lower by most of the GCMs with respect to SPAEF evaluation as well (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWhen the performance of the individual models and the MMEs were considered in terms of KGE, the highest scores were obtained by BCC-CSM2-MR (0.311), MME-four (0.237), and MME-all (0.24) respectively for the MHW metrices of frequency, maximum intensity, and duration. In the aspect of SPAEF, the highest scores were owned by the MME-four for all the MHW metrics; 0.37, 0.283, and 0.234 for frequency, maximum intensity, and duration respectively. The MME-four indicates improvement over the MME-all for all except the duration metric in terms of KGE evaluation, i.e., KGE value for duration from MME-four was 0.146 while MME-all was 0.24, but all the other scores are higher in the MME-four.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e4.4. Statistical evaluation of MME\u003c/h2\u003e\n\u003cp\u003eThe degrees of spread of the time series data in each grid from observed (NOAA), MME-four and MME-all during the historical period were analyzed using boxplot diagrams as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The boxplot comprises of a box extending from the first quartile (Q1) to the third quartile (Q3), two whiskers of one stretching downwards from Q1 to the smallest non-outlier in the data set and the other stretching upwards from Q3 to largest non-outlier (Acharya et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor the MHW metric of frequency (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea), the interquartile range (Q1 to Q3) of observed NOAA, MME-four, and MME-all lies in the ranges of 0\u0026ndash;3, 0.56\u0026ndash;2.2, and 0.72\u0026ndash;2.1 respectively. The mean values for the above three data sets are 1.97, 1.5 and 1.44 respectively. This indicates that MME-four is much closer to the observed frequency values than the MME-all in terms of the average values and the spread of data. But it can be also noted that both MMEs underestimates the observed NOAA frequency values.\u003c/p\u003e\n\u003cp\u003eWhen maximum intensity is considered (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb), the interquartile range lies between 0-2.78, 0.75\u0026ndash;1.8, and 0.99\u0026ndash;1.85, respectively for observed NOAA, MME-four and MME-all. The observed data is distributed in a larger span compared to the MMEs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). The degree of spread of data is more in the MME-four than the MME-all. The mean values are 1.82, 1.3, and 1.41 respectively for historical, MME-four and MME-all and both MMEs underestimates the observed maximum intensity of the MHWs.\u003c/p\u003e\n\u003cp\u003eDuration of the MHWs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec) lies in the interquartile ranges of 0\u0026ndash;12, 3.77\u0026ndash;14.5, and 5.59\u0026ndash;15.34 for the observed data and the two MME data. The mean values are 7.5, 8.03, and 10.56 respectively. The results indicate that the MMEs overestimated the duration compared to the observations and MME-all rather more overestimate the duration values than the MME-four. In addition, it can be noted that the observed duration values contain more outliers compared to the other two observed MHW metrices. MME-four indicated more outliers for the duration similar to the observed results when compared with the MME-all.\u003c/p\u003e\n\u003cp\u003eFrom the overall boxplot results it can be noted that MME-four underestimated the frequency and maximum intensity while overestimating the duration. Also, MME-four is much closer to the observed than the MME-all in terms of the degree of spread of the data for all the MHW metrices.\u003c/p\u003e\n\u003cp\u003eNext, year-to-year time series data averaged over the study area was evaluated as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The observed dataset and both MMEs indicated positive trends for all the MHW metrices during the historical period. MME-four indicated gradient value of 0.09 for the frequency which is closer to the observed slope of 0.115 while MME-all indicated a much lower slope of 0.078 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). When the trend of Maximum intensity is considered (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb) the gradients of both MMEs are almost similar (0.055, and 0.051) but the MME-four is much closer to the observed gradient. For duration (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec), the slope of MME-all (0.545) is closer to the observed gradient of (0.419) than that of the MME-four(0.571). This indicates that MME-four is closer to the observed than MME-all in terms of the slope values for frequency and maximum intensity than that of duration.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, for frequency and maximum intensity, both MMEs showed trends lower than the observed gradient indicating that the MMEs underestimates the observed trends for those metrices. However, for duration both MMEs showed higher trends than the observed, indicating that the MMEs overestimate the observed trends of duration. These results are similar to the boxplots evaluation discussed previously and both results demonstrate that the MMEs underestimated frequency and maximum intensity while overestimating the duration.Plecha and Soares (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) have also found reasonable agreement between the modeled and observed MHW property trends of global MHW events but underestimation of events per year and mean intensity while overestimation of the duration.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e4.5. Future changes\u003c/h2\u003e\n\u003cp\u003eThe trends of time series of the MHW metrices of frequency (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), maximum intensity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e), and duration (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e) in the near future (2041\u0026ndash;2070) and far future (2071\u0026ndash;2100) were analyzed under four SSP-RCPs, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 using MME-four.\u003c/p\u003e\n\u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, it can be observed that two scenarios, SSP1-2.6, and SSP2-4.5 have similar patterns while the high emission scenarios of SSP3-7.0, and SSP5-8.5 followed a closer pattern. The slopes of the time series are increasing from SSP1-2.6 to SSP5-8.5 in both near and far futures. But the slopes are relatively higher for the high emission scenarios when compared to that of the low emission scenarios. It can also be noted that the frequency of the MHWs of two high emission scenarios (SSP3-7.0, and SSP5-8.5) are beneath the low emission scenarios (SSP1-2.6, and SSP2-4.5) during the first half of both future periods. But since the high emission scenarios have higher trends, the values exceed that of the low emission scenarios towards the end of the considered climatology period.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e indicated time series of maximum intensity. Here also the two low emission scenarios SSP1-2.6, and SSP2-4.5 and the two high emission scenarios SSP3-7.0, and SSP5-8.5 behave similarly. The values of the high emission scenarios are beneath that of the low emission scenarios during the beginning of the climatology period but becomes higher towards the end due to the larger slopes. The trend is increasing from SSP1-2.6 to SSP5-8.5 during the near future and the trend increases in the order of SSP1-2.6, SSP2-4.5, SSP5-8.5, and SSP3-7.0 during the far future.\u003c/p\u003e\n\u003cp\u003eFor duration (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e), during the near future, the trend of SSP2-4.5 is relatively higher than that of SSP1-2.6 when compared to the trend of far future but the slopes of SSP3-7.0 and SSP5-8.5 are closer to each other during both future periods. In near future, during the early to mid-periods, SSP3-7.0 and SSP5-8.5 values are beneath the low emission scenarios of SSP1-2.6 and SSP2-4.5. But towards the end of the period, SSP1-2.6 values are the lowest while the remaining three scenario values are higher. In the far future, SSP3-7.0 and SSP5-8.5 values are lower than SSP1-2.6 and SSP2-4.5 similar to the near future. But towards the last decade, the values of all four scenarios become closer during the far future period.\u003c/p\u003e\n\u003cp\u003eWhen the overall time series graphs of all the MHW metrices of frequency, maximum intensity and duration are considered, one common feature is that the values of high emission scenarios (SSP3-7.0, and SSP5-8.5) are beneath the low emission scenarios (SSP1-2.6, and SSP2-4.5) at the early period of the considered climate period but becomes higher towards the end of the considered climate period. Except for the duration in the near future period, for all the other considered events, the time series of SSP1-2.6 and SSP2-4.5 follows closely to each other. Similarly, SSP3-7.0 and SSP5-8.5 follow closer pattern for each MHW metric in both future periods. It can also be highlighted that the trends of the two high emission scenarios (SSP3-7.0, and SSP5-8.5) are higher than that of the low emission scenarios (SSP1-2.6, and SSP2-4.5) for all the MHW metrices during both future periods. The results indicated that if high emission scenarios are going to prevail in the future periods, there would be higher trends of frequency, maximum intensity, and duration of MHWs based on the expected climate of those future periods in the East Sea.\u003c/p\u003e\n\u003cp\u003eWhen the historical trends from the MME-four were considered (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e), the slope values of the historical period are higher than that of low emission scenarios (SSP1-2.6, and SSP2-4.5) but lower than that of high emission scenarios (SSP3-7.0, and SSP5-8.5) in all the MHW metrices except for duration in the far future.\u003c/p\u003e\n\u003cp\u003eNext, the average values of the MHWS metrices in the future periods and the historical periods were compared. The time series values of each grid point for the historical and future periods were averaged and plotted in boxplots as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eFor frequency (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ea), the average future values were higher than the historical values for all scenarios except for SSP3-7.0 in the near future. For maximum intensity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eb) all the future values were higher than the historical values under all scenarios, but it was noted that the values of high emission scenarios (SSP3-7.0, and SSP5-8.5) were lower than the low emission scenarios (SSP1-2.6, and SSP2-4.5) values in both future periods. For duration (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ec) it can be highlighted that the values of the low emission scenarios are higher than the historical values while the values of the high emission scenarios are lower than the historical values. But when the numerical values of the averages of each MHW metric were considered, there was no significant difference between the historical and the future average values. Plecha et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) indicated that when a non-stationary threshold is considered, the characteristics of the MHW properties are similar across all periods and RCPs.\u003c/p\u003e\n\u003cp\u003eFrom the overall results of the trend analysis and the average value analysis for the future periods it can be noted that although the high emission scenarios indicated higher trends, when the average values were considered, those values are lower than the low emission scenarios. This is mainly because the high emission scenarios have comparatively low values in the beginning of the climatology period and high values only toward the end of the climate period.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study proposed a novel approach to implement the future projection of the MHWs using CMIP6 GCMs. The performance of GCMs were analyzed by applying more robust performance evaluation metrices i.e., KGE and SPAEF to each grid point. The individual models showed the lowest performance in simulating the duration of the MHWs than frequency or maximum intensity. A group decision making technique was employed to select the best GCMs which would perform well in all the considered MHW metrices. The selected best models for East Sea were ACCESS-CM2, BCC-CSM2-MR ACCESS-ESM1, and GFDL-ESM4.\u003c/p\u003e \u003cp\u003eIn the performance evaluation during the historical period using KGE and SPAEF, MME-four performed better than MME-all for all the MHW properties except KGE value for duration. When the spread of data and the trends were analyzed for the historical period, MMEs underestimate the frequency and maximum intensity while duration is overestimated.\u003c/p\u003e \u003cp\u003eThe future trends were analyzed by employing the state-of-art shifting baseline approach. High emission scenarios (SSP3-7.0, and SSP5-8.5) have higher slopes than the low emission scenarios (SSP1-2.6 and SSP2-4.5). In addition, the results showed similar patterns in the two low emission scenarios and similar patterns in the two high emission scenarios. The high emission scenarios indicated lower values in the beginning of the climatology period but higher values toward the end of the period.\u003c/p\u003e \u003cp\u003eThe average values in the future periods are almost similar to that of the historical period. Frequency averages indicated higher values than that of historical for all except SSP3-7.0 in the near future. Average values for maximum intensity indicated higher results than the historical for all scenarios during both near and far futures. The average values of duration of the low emission scenarios indicated higher values while the high emission scenario values indicated lower results.\u003c/p\u003e \u003cp\u003eAlthough MME-four indicated better performance than considering individual models or considering MME-all, still the results should be interpretated with caution due to the uncertainty. Future studies may attempt to apply suitable bias correction of the data for the study domain.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eESC led this research. DD performed all of the analysis, draw the figures, and led the draft of this manuscript. ESC provided constructive suggestions for improving the research and writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are freely available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Research Foundation of Korea (NRF) (2021R1A2C2005699).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have not disclosed any competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcharya, N., Shrivastava, N. A., Panigrahi, B. K., \u0026amp; Mohanty, U. C. (2014). Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: An application of extreme learning machine. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(5\u0026ndash;6), 1303\u0026ndash;1310. https://doi.org/10.1007/s00382-013-1942-2\u003c/li\u003e\n\u003cli\u003eAmaya, D. J., Jacox, M. G., Fewings, M. R., Saba, V. S., Stuecker, M. F., Rykaczewski, R. R., Ross, A. C., Stock, C. A., Capotondi, A., Petrik, C. M., Bograd, S. J., Alexander, M. A., Cheng, W., Hermann, A. J., Kearney, K. A., \u0026amp; Powell, B. S. (2023). Marine heatwaves need clear definitions so coastal communities can adapt. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e616\u003c/em\u003e(7955), 29\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eBelkin, I. M. (2009). Rapid warming of Large Marine Ecosystems. \u003cem\u003eProgress in Oceanography\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(1\u0026ndash;4), 207\u0026ndash;213. https://doi.org/10.1016/j.pocean.2009.04.011\u003c/li\u003e\n\u003cli\u003eCheng, L., Abraham, J., Hausfather, Z., \u0026amp; Trenberth, K. E. (2019). How fast are the oceans warming? \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e363\u003c/em\u003e(6423), 128\u0026ndash;129. https://doi.org/10.1126/science.aav7619\u003c/li\u003e\n\u003cli\u003eChoi, W., Bang, M., Joh, Y., Ham, Y. G., Kang, N., \u0026amp; Jang, C. J. (2022). Characteristics and Mechanisms of Marine Heatwaves in the East Asian Marginal Seas: Regional and Seasonal Differences. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(15). https://doi.org/10.3390/rs14153522\u003c/li\u003e\n\u003cli\u003eChu, P. C., Lan, J., \u0026amp; Fan, C. (2001). Japan Sea Thermohaline Structure and Circulation. Part I: Climatology. \u003cem\u003eJournal of Physical Oceanography\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(1), 244\u0026ndash;271. https://doi.org/10.1175/1520-0485(2001)031\u0026lt;0244:JSTSAC\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003cli\u003eCosta, N. V., \u0026amp; Rodrigues, R. R. (2021). Future Summer Marine Heatwaves in the Western South Atlantic. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(22). https://doi.org/10.1029/2021GL094509\u003c/li\u003e\n\u003cli\u003eDarmaraki, S., Somot, S., Sevault, F., Nabat, P., Cabos Narvaez, W. D., Cavicchia, L., Djurdjevic, V., Li, L., Sannino, G., \u0026amp; Sein, D. V. (2019). Future evolution of Marine Heatwaves in the Mediterranean Sea. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3\u0026ndash;4), 1371\u0026ndash;1392. https://doi.org/10.1007/s00382-019-04661-z\u003c/li\u003e\n\u003cli\u003eDeepthi, B., \u0026amp; Sivakumar, B. (2022). Performance assessment of general circulation models: application of compromise programming method and global performance indicator technique. \u003cem\u003eStochastic Environmental Research and Risk Assessment\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(6), 1761\u0026ndash;1778. https://doi.org/10.1007/s00477-021-02124-8\u003c/li\u003e\n\u003cli\u003eDemirel, M. C. (2018). \u003cem\u003eSPAEF version 1.0 with histogram match\u003c/em\u003e. Zenodo. https://doi.org/10.5281/zenodo.1158890\u003c/li\u003e\n\u003cli\u003eDemirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., \u0026amp; Stisen, S. (2018). Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 1299\u0026ndash;1315. https://doi.org/10.5194/hess-22-1299-2018\u003c/li\u003e\n\u003cli\u003eDomingues, C. M., Church, J. A., White, N. J., Gleckler, P. J., Wijffels, S. E., Barker, P. M., \u0026amp; Dunn, J. R. (2008). Improved estimates of upper-ocean warming and multi-decadal sea-level rise. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e453\u003c/em\u003e(7198), 1090\u0026ndash;1093. https://doi.org/10.1038/nature07080\u003c/li\u003e\n\u003cli\u003eFr\u0026ouml;licher, T. L., Fischer, E. M., \u0026amp; Gruber, N. (2018). Marine heatwaves under global warming. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e560\u003c/em\u003e(7718), 360\u0026ndash;364. https://doi.org/10.1038/s41586-018-0383-9\u003c/li\u003e\n\u003cli\u003eGarrabou, J., Coma, R., Bensoussan, N., Bally, M., Chevaldonn\u0026eacute;, P., Cigliano, M., D\u0026iacute;az, D., Harmelin, J.-G., Gambi, M. C., \u0026amp; Kersting, D. K. (2009). Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(5), 1090\u0026ndash;1103.\u003c/li\u003e\n\u003cli\u003eGomes, D. G. E., Ruzicka, J. J., Crozier, L. G., Huff, D. D., Brodeur, R. D., \u0026amp; Stewart, J. D. (2024). Marine heatwaves disrupt ecosystem structure and function via altered food webs and energy flux. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1). https://doi.org/10.1038/s41467-024-46263-2\u003c/li\u003e\n\u003cli\u003eGregory, C. H., Holbrook, N. J., Marshall, A. G., \u0026amp; Spillman, C. M. (2023). Atmospheric Drivers of Tasman Sea Marine Heatwaves. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(15), 5197\u0026ndash;5214. https://doi.org/https://doi.org/10.1175/JCLI-D-22-0538.1\u003c/li\u003e\n\u003cli\u003eGr\u0026ouml;ger, M., Dutheil, C., B\u0026ouml;rgel, F., \u0026amp; Meier, M. H. E. (2024). Drivers of marine heatwaves in a stratified marginal sea. \u003cem\u003eClimate Dynamics\u003c/em\u003e. https://doi.org/10.1007/s00382-023-07062-5\u003c/li\u003e\n\u003cli\u003eGupta, H. V., Kling, H., Yilmaz, K. K., \u0026amp; Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e377\u003c/em\u003e(1\u0026ndash;2), 80\u0026ndash;91. https://doi.org/10.1016/j.jhydrol.2009.08.003\u003c/li\u003e\n\u003cli\u003eHallouin, T. (2021). \u003cem\u003ehydroeval: an evaluator for streamflow time series in Python\u003c/em\u003e. Zenodo. https://doi.org/10.5281/zenodo.4709652\u003c/li\u003e\n\u003cli\u003eHayashida, H., Matear, R. J., Strutton, P. G., \u0026amp; Zhang, X. (2020). Insights into projected changes in marine heatwaves from a high-resolution ocean circulation model. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1). https://doi.org/10.1038/s41467-020-18241-x\u003c/li\u003e\n\u003cli\u003eHobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Moore, P. J., Scannell, H. A., Sen Gupta, A., \u0026amp; Wernberg, T. (2016). A hierarchical approach to defining marine heatwaves. \u003cem\u003eProgress in Oceanography\u003c/em\u003e, \u003cem\u003e141\u003c/em\u003e, 227\u0026ndash;238. https://doi.org/10.1016/j.pocean.2015.12.014\u003c/li\u003e\n\u003cli\u003eHobday, A. J., \u0026amp; Pecl, G. T. (2014). Identification of global marine hotspots: Sentinels for change and vanguards for adaptation action. \u003cem\u003eReviews in Fish Biology and Fisheries\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(2), 415\u0026ndash;425. https://doi.org/10.1007/s11160-013-9326-6\u003c/li\u003e\n\u003cli\u003eHolbrook, N. J., Hernaman, V., Koshiba, S., Lako, J., Kajtar, J. B., Amosa, P., \u0026amp; Singh, A. (2022). Impacts of marine heatwaves on tropical western and central Pacific Island nations and their communities. \u003cem\u003eGlobal and Planetary Change\u003c/em\u003e, \u003cem\u003e208\u003c/em\u003e. https://doi.org/10.1016/j.gloplacha.2021.103680\u003c/li\u003e\n\u003cli\u003eHuang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., \u0026amp; Zhang, H.-M. (2021). Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(8), 2923\u0026ndash;2939. https://doi.org/10.1175/JCLI-D-20-0166.1\u003c/li\u003e\n\u003cli\u003eIchiye, T. (1984). Some Problems of Circulation and Hydrography of the Japan Sea and the Tsushima Current. In T. Ichiye (Ed.), \u003cem\u003eOcean Hydrodynamics of the Japan and East China Seas\u003c/em\u003e (Vol. 39, pp. 15\u0026ndash;54). Elsevier. https://doi.org/https://doi.org/10.1016/S0422-9894(08)70289-7\u003c/li\u003e\n\u003cli\u003eKamenos, N. A., \u0026amp; Hennige, S. J. (2024). A Historical Perspective on Thermal-and Heatwave-Induced Bleaching on the Great Barrier Reef. In \u003cem\u003eOceanographic Processes of Coral Reefs\u003c/em\u003e (pp. 282\u0026ndash;289). CRC Press.\u003c/li\u003e\n\u003cli\u003eKim, K., Kim, K.-R., Min, D.-H., Volkov, Y., Yoon, J.-H., \u0026amp; Takematsu, M. (2001). Warming and structural changes in the east (Japan) Sea: A clue to future changes in global oceans? \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(17), 3293\u0026ndash;3296. https://doi.org/https://doi.org/10.1029/2001GL013078\u003c/li\u003e\n\u003cli\u003eKim, K.-R., Lee, S. H., Park, K.-A., Park, J. J., Suh, Y.-S., Lee, D.-K., Kang, D.-J., \u0026amp; Chang, K.-I. (2016). General Introduction. In K.-I. Chang, C.-I. Zhang, C. Park, D.-J. Kang, S.-J. Ju, S.-H. Lee, \u0026amp; M. Wimbush (Eds.), \u003cem\u003eOceanography of the East Sea (Japan Sea)\u003c/em\u003e (pp. 1\u0026ndash;31). Springer International Publishing. https://doi.org/10.1007/978-3-319-22720-7_1\u003c/li\u003e\n\u003cli\u003eKim, S., \u0026amp; Kang, S. (1998). The status and research direction for fishery resources in the East Sea/Sea of Japan. \u003cem\u003eJ Korean Soc Fish Res\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e, 44\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eKim, S., \u0026amp; Zhang, C.-I. (2016). Fish and fisheries. \u003cem\u003eOceanography of the East Sea (Japan Sea)\u003c/em\u003e, 327\u0026ndash;345.\u003c/li\u003e\n\u003cli\u003eLee, S., Park, M. S., Kwon, M., Park, Y. G., Kim, Y. H., \u0026amp; Choi, N. (2023). Rapidly Changing East Asian Marine Heatwaves Under a Warming Climate. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e, \u003cem\u003e128\u003c/em\u003e(6). https://doi.org/10.1029/2023jc019761\u003c/li\u003e\n\u003cli\u003eMa, C., Zhu, X., Liao, M., Dong, S., \u0026amp; Dong, Y. (2021). Heat sensitivity of mariculture species in China. \u003cem\u003eICES Journal of Marine Science\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(8), 2922\u0026ndash;2930. https://doi.org/10.1093/icesjms/fsab168\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;n, M. L., Calvo‐Sancho, C., Taszarek, M., Gonz\u0026aacute;lez‐Alem\u0026aacute;n, J. J., Montoro‐Mendoza, A., D\u0026iacute;az‐Fern\u0026aacute;ndez, J., Bolgiani, P., Sastre, M., \u0026amp; Mart\u0026iacute;n, Y. (2024). Major role of marine heatwave and anthropogenic climate change on a Giant hail Event in Spain. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(6), e2023GL107632.\u003c/li\u003e\n\u003cli\u003eMills, K. E., Pershing, A. J., Brown, C. J., Chen, Y., Chiang, F.-S., Holland, D. S., Lehuta, S., Nye, J. A., Sun, J. C., \u0026amp; Thomas, A. C. (2013). Fisheries management in a changing climate: lessons from the 2012 ocean heat wave in the Northwest Atlantic. \u003cem\u003eOceanography\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 191\u0026ndash;195.\u003c/li\u003e\n\u003cli\u003eMorais, D. C., \u0026amp; De Almeida, A. T. (2012). Group decision making on water resources based on analysis of individual rankings. \u003cem\u003eOmega\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(1), 42\u0026ndash;52. https://doi.org/10.1016/j.omega.2011.03.005\u003c/li\u003e\n\u003cli\u003eOliver, E. C. J., Benthuysen, J. A., Darmaraki, S., Donat, M. G., Hobday, A. J., Holbrook, N. J., Schlegel, R. W., \u0026amp; Sen Gupta, A. (2021). Marine heatwaves. \u003cem\u003eAnnual Review of Marine Science\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 313\u0026ndash;342.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Neill, B. C., Tebaldi, C., Van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J. F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., \u0026amp; Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. \u003cem\u003eGeoscientific Model Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(9), 3461\u0026ndash;3482. https://doi.org/10.5194/gmd-9-3461-2016\u003c/li\u003e\n\u003cli\u003ePark, K. A., Chung, J. Y., Kim, K., \u0026amp; Cornillon, P. C. (2005). Wind and bathymetric forcing of the annual sea surface temperature signal in the East (Japan) Sea. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(5), 1\u0026ndash;4. https://doi.org/10.1029/2004GL022197\u003c/li\u003e\n\u003cli\u003ePearce, A. F., Lenanton, R., Jackson, G., Moore, J., Feng, M., \u0026amp; Gaughan, D. (2011). \u003cem\u003eThe\u0026quot; marine heat wave\u0026quot; off Western Australia during the summer of 2010/11\u003c/em\u003e. Western Australian Fisheries and Marine Research Laboratories Hillarys, WA \u0026hellip;.\u003c/li\u003e\n\u003cli\u003ePlecha, S. M., \u0026amp; Soares, P. M. M. (2019). Global marine heatwave events using the new CMIP6 multi-model ensemble: From shortcomings in present climate to future projections. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(12). https://doi.org/10.1088/1748-9326/abc847\u003c/li\u003e\n\u003cli\u003ePlecha, S. M., Soares, P. M. M., Silva-Fernandes, S. M., \u0026amp; Cabos, W. (2021). On the uncertainty of future projections of Marine Heatwave events in the North Atlantic Ocean. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(7\u0026ndash;8), 2027\u0026ndash;2056. https://doi.org/10.1007/s00382-020-05529-3\u003c/li\u003e\n\u003cli\u003eSaranya, J. S., Roxy, M. K., Dasgupta, P., \u0026amp; Anand, A. (2022). Genesis and Trends in Marine Heatwaves Over the Tropical Indian Ocean and Their Interaction With the Indian Summer Monsoon. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e(2). https://doi.org/10.1029/2021JC017427\u003c/li\u003e\n\u003cli\u003eSen Gupta, A., Thomsen, M., Benthuysen, J. A., Hobday, A. J., Oliver, E., Alexander, L. V., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Perkins-Kirkpatrick, S., Moore, P. J., Rodrigues, R. R., Scannell, H. A., Taschetto, A. S., Ummenhofer, C. C., Wernberg, T., \u0026amp; Smale, D. A. (2020). Drivers and impacts of the most extreme marine heatwaves events. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1). https://doi.org/10.1038/s41598-020-75445-3\u003c/li\u003e\n\u003cli\u003eWalker, A. S., Kratochwill, C. A., \u0026amp; van Woesik, R. (2024). Past disturbances and local conditions influence the recovery rates of coral reefs. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), e17112.\u003c/li\u003e\n\u003cli\u003eWang, D., Xu, T., Fang, G., Jiang, S., Wang, G., Wei, Z., \u0026amp; Wang, Y. (2022). Characteristics of Marine Heatwaves in the Japan/East Sea. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4). https://doi.org/10.3390/rs14040936\u003c/li\u003e\n\u003cli\u003eWernberg, T., Smale, D. A., Tuya, F., Thomsen, M. S., Langlois, T. J., de Bettignies, T., Bennett, S., \u0026amp; Rousseaux, C. S. (2013). An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 78\u0026ndash;82. https://doi.org/10.1038/nclimate1627\u003c/li\u003e\n\u003cli\u003eXue, J., Shan, H., Liang, J. H., \u0026amp; Dong, C. (2023). Assessment and Projections of Marine Heatwaves in the Northwest Pacific Based on CMIP6 Models. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(12). https://doi.org/10.3390/rs15122957\u003c/li\u003e\n\u003cli\u003eYao, Y., Wang, J., Yin, J., \u0026amp; Zou, X. (2020). Marine Heatwaves in China\u0026rsquo;s Marginal Seas and Adjacent Offshore Waters: Past, Present, and Future. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e, \u003cem\u003e125\u003c/em\u003e(3). https://doi.org/10.1029/2019JC015801\u003c/li\u003e\n\u003cli\u003eYeh, S. W., Park, Y. G., Min, H. S., Kim, C. H., \u0026amp; Lee, J. H. (2010). Analysis of characteristics in the sea surface temperature variability in the East/Japan Sea. \u003cem\u003eProgress in Oceanography\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(3\u0026ndash;4), 213\u0026ndash;223. https://doi.org/10.1016/j.pocean.2010.03.001\u003c/li\u003e\n\u003cli\u003eZhang, C. I., Seo, Y. Il, Kang, H. J., \u0026amp; Lim, J. H. (2019). Exploitable carrying capacity and potential biomass yield of sectors in the East China Sea, Yellow Sea, and East Sea/Sea of Japan large marine ecosystems. \u003cem\u003eDeep-Sea Research Part II: Topical Studies in Oceanography\u003c/em\u003e, \u003cem\u003e163\u003c/em\u003e, 16\u0026ndash;28. https://doi.org/10.1016/j.dsr2.2018.11.016\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Performance evaluation, Model selection, Marine Heat Wave (MHW) metrices, Future projection","lastPublishedDoi":"10.21203/rs.3.rs-4262751/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4262751/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe future analysis of the Marine Heat Waves (MHWs) has high uncertainty due to the significant shortcomings of the Global Climate Models (GCMs) in simulating the Sea Surface Temperature (SST) and the MHWs. This study suggests a more systematic approach to do the future projection of MHWs. Our study area is East/Japan Sea which is a large marine ecosystem exposed to rapid warming of the ocean. This study found the models; ACCESS-CM2, BCC-CSM2-MR ACCESS-ESM1, and GFDL-ESM4 from Coupled Model Intercomparison Project sixth phase (CMIP6) are the best performing GCMs in the East Sea by analyzing their grid-wise performance during the historical period (1985\u0026ndash;2014). Using the ensemble mean from the selected models, the future MHW metrices of frequency, maximum intensity, and duration during the near future (2041\u0026ndash;2070) and far future (2071\u0026ndash;2100) was investigated. Following the state-of-art, shifting baseline approach was utilized to identify the MHWs and 30 years were used as the climatology period for each historical and future periods. The time series results from the ensemble mean indicated that high emission scenarios (SSP3-7.0, and SSP5-8.5) would have higher trends than that of low emission scenarios (SSP1-2.6, and SSP2-4.5) as well as that of historical observations. The high emission scenarios would have lower values in the beginning of their respective climatology period when compared to that of low emission scenarios but rather higher values toward the end of the period. The average MHW metrices of near and far futures shows certain shifts compared to that of historical but the numerical values are almost similar to that of historical period.\u003c/p\u003e","manuscriptTitle":"Future projection of marine heat waves in a global marine hotspot Case of East/Japan Sea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 22:12:40","doi":"10.21203/rs.3.rs-4262751/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3822c141-12f5-4079-9bac-6abb708eef15","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-03T05:43:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-29 22:12:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4262751","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4262751","identity":"rs-4262751","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}