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Modulation of marine heatwaves by salinity effect in the Northeast Pacific Ocean in 2013–2014
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Xiaokun Wang1, 2, Hai Zhi1, *, Ronghua Zhang3, 4, Jiaxiang Gao3, 5, Pengfei Lin6
Acta Oceanologica Sinica | 2025, 44(1) : 17 - 27
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Acta Oceanologica Sinica | 2025, 44(1): 17-27
• Physical Oceanography, Marine Meteorology and Marine Physics •
Modulation of marine heatwaves by salinity effect in the Northeast Pacific Ocean in 2013–2014
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Xiaokun Wang1, 2, Hai Zhi1, *, Ronghua Zhang3, 4, Jiaxiang Gao3, 5, Pengfei Lin6
Affiliations
  • 1 School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2 Henan Meteorological Disaster Prevention Technology Center, Zhengzhou 450003, China
  • 3 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 4 Laoshan Laboratory, Qingdao 266237, China
  • 5 Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 6 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Published: 2025-01-25 doi: 10.1007/s13131-024-2440-6
Outline
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Marine heatwaves (MHWs) are extreme ocean events characterized by anomalously warm upper-ocean temperatures, posing significant threats to marine ecosystems. While various factors driving MHWs have been extensively studied, the role of ocean salinity remains poorly understood. This study investigates the influence of salinity on the major 2013–2014 MHW event in the Northeast Pacific using reanalysis data and climate model outputs. Our results show that salinity variabilities are crucial for the development of the MHW event. Notably, a significant negative correlation exists between sea surface temperature anomalies (SSTAs) and sea surface salinity anomalies (SSSAs) during the MHW, with the SSSAs emerging simultaneously with SSTAs in the same area. Negative salinity anomalies (SAs) result in a shallower mixed layer, which suppresses vertical mixing and thus sustains the upper-ocean warming. Moreover, salinity has a greater impact on mixed layer depth anomalies than temperature. Model sensitivity experiments further demonstrate that negative SAs during MHWs amplify positive SSTAs by enhancing upper-ocean stratification, intensifying the MHW. Additionally, our analysis indicates that the SAs are predominantly driven by local freshwater flux anomalies, which are mainly induced by positive precipitation anomalies during the MHW event.

marine heatwave  /  salinity effect  /  ocean stratification and mixing  /  sea surface temperature  /  Northeast Pacific Ocean
Xiaokun Wang, Hai Zhi, Ronghua Zhang, Jiaxiang Gao, Pengfei Lin. Modulation of marine heatwaves by salinity effect in the Northeast Pacific Ocean in 2013–2014[J]. Acta Oceanologica Sinica, 2025 , 44 (1) : 17 -27 . DOI: 10.1007/s13131-024-2440-6
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1 Introduction
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Marine heatwaves (MHWs), defined as prolonged periods of anomalously high sea surface temperature (SST), were first defined following an unusual ocean warming event off the western coast of Australia in 2010/2011 by Pearce et al. (Pearce et al., 2011). MHWs exert considerable ecological and socio-economic impacts, including shifts in marine species distribution, local extinctions, and significant damage to seafood industries (Kintisch, 2015; Cavole et al., 2016; Hobday et al., 2016; Viglione, 2021). Throughout the 21st century, the frequency, duration, and intensity of MHWs worldwide are projected to increase significantly under global warming (Oliver et al., 2017, 2018; Cheng et al., 2023), leading to greater harm to marine ecosystems and more substantial socio-economic impacts. Hence, understanding the physical mechanisms driving MHW occurrence and development is essential for improving predictions of these events and mitigating their impacts (Hu et al., 2017; Holbrook et al., 2019; Gupta et al., 2020; Zhang et al., 2020; Wang et al., 2021).
In recent years, the Northeast Pacific has experienced severe and widespread MHWs. Specifically, significant MHWs occurred during the winter of 2013–2014 and the summer of 2019 (Bond et al., 2015; Amaya et al., 2020; Song et al., 2023). The 2013–2014 MHW event had devastating effects on phytoplankton growth due to the persistent presence of warm, nutrient-poor waters (Bond et al., 2015; Kintisch, 2015). This event triggered cascading ecological impacts, including a sharp decline in the Chinook salmon population and the distressing loss of up to one million seabirds near the Gulf of Alaska (Smale et al., 2019). Meanwhile, the prolonged heat, combined with unusually weak coastal winds, disrupted upwelling along much of the Pacific coast, leading to atmospheric rivers being blocked by a persistent ridge of high pressure over the North Pacific from 2012 to 2015 (Chen et al., 2023). These catastrophic consequences highlight the urgency of investigating the causes of MHWs in the Northeast Pacific.
The driving mechanisms of MHWs are very complex. Holbrook et al. (2019) examined the formation mechanisms of MHWs across various ocean regions and found substantial variations in the processes involved. In general, MHWs are driven by the combined effects of internal climate variability and external forcing from human activities (Hu and Li, 2022). Previous studies have identified three primary mechanisms contributing to MHW formation. (1) Atmospheric forcings, such as increased solar radiation, sensible and latent heat fluxes, and wind stress can drive MHWs (Wang et al., 2021; Holbrook et al., 2020). (2) Oceanic dynamic processes play an important role in the formation of MHWs, especially in the development of subsurface MHWs. Key processes include the strengthening of warm advection, shoaling of the mixed layer, enhanced oceanic stratification, weakened vertical mixing, reduced upwelling, and suppressed Ekman suction (Oliver et al., 2021; Holbrook et al., 2019; Miao et al., 2021). (3) Large-scale climate models such as El Niño-Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) can significantly influence MHW formation through atmospheric forcing or teleconnection (Scannell et al., 2016).
Salinity is a fundamental ocean state variable that plays a vital role in oceanic physical processes. Previous studies have shown that salinity significantly influences various processes by directly affecting seawater density, which in turn impacts upper-ocean stratification and mixed layer depth (MLD) (Miller, 1976; Lukas and Lindstrom, 1991; Manabe and Stouffer, 1995; Oka and Hasumi, 2004; Delcroix et al., 2007; Bosc et al., 2009; Zhang and Busalacchi, 2009). For instance, Zhu et al. (2014) performed two forecast experiments to explore the impact of salinity interannual variability on ocean initial state, which highlights the indispensable role of salinity variability in accurately identifying and understanding extreme climatic events in the ocean. Zheng and Zhang (2015) further emphasized that interannual variations in salinity contribute more significantly to density and mixed layer-related stratification than temperature, underlining its importance. Furthermore, ocean stratification associated with salinity changes contributes significantly to maintaining the heat balance of the upper ocean at both seasonal and interannual variabilities (Zhang et al., 2010, 2012; Jouanno et al., 2011; Vinogradova and Ponte, 2013). Low-salinity water promotes strong stratification, inhibiting vertical mixing processes (Sprintall and Tomczak, 1992). Positive anomalies in barrier layer thickness prevent cooler, saltier water from mixing into the upper layer, leading to increased SST (Maes et al., 2005; Da-Allada et al., 2014; Hu and Sprintall, 2016). Consequently, decreases (increases) in salinity result in a shallower (deeper) mixed layer, causing SSTs to warm (cool) (Zheng et al., 2014; Zhi et al., 2015, 2019b; Shi et al., 2022).
The relationship between salinity and MLD suggests that salinity variations can significantly influence the development of MHWs. A previous study found that decreased salinity in the mixed layer causes more stable stratification, reducing the entrainment of subsurface cold water into the mixed layer (Li and Zheng, 2022). This process allows anomalous heat to accumulate in the mixed layer, thereby increasing the likelihood of MHWs. Scannell et al. (2020) compared two recent MHWs in the Northeast Pacific using observational data and found that salinity plays an essential role in their development by modifying ocean stability. While these studies have highlighted the importance of salinity in MHW development, several key questions remain unanswered. For example, what are the driving factors behind salinity anomalies (SAs) during the formation of MHWs? How do SAs contribute to upper-ocean stratification changes in association with MHWs?
In this study, we aim to explore the relationship between sea surface salinity anomalies (SSSAs) and MHWs, quantify the contribution of salinity changes to MHW development, and identify the sources of salinity changes. We focus on the 2013–2014 MHW event in the Northeast Pacific using both reanalysis data and climate model outputs. The remainder of this paper is organized as follows. Data and methods are introduced in Section 2. Section 3 presents our major findings about the role of SSSAs in upper-ocean stratification during the MHW event. Finally, conclusions and discussion are given in Section 4.
2 Data, methods, and experiment
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2.1 Data
We use the Optimal Interpolation Sea Surface Temperature (OISST) data from the National Oceanic and Atmospheric Administration to identify observational features of MHWs, including their onset and end dates and cumulative intensity. The OISST data incorporate observations from various instruments (satellites, ships, and buoys) into a regular global grid (Reynolds et al., 2007; Huang et al., 2021). We use daily OISST data from 1992 to 2021 with a horizontal resolution of 0.25° × 0.25° in this study.
To examine the subsurface evolution of MHWs, we adopt 3-dimensional ocean temperature and salinity data from the high-resolution version of the Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-H) (Bao et al., 2020). This model is developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, and the model experiments are part of the Ocean Model Intercomparison Project (OMIP), an endorsed project by the Coupled Model Intercomparison Project Phase 6 (CMIP6). OMIP conducts global ocean-sea ice coupled simulations with unified external atmospheric forcings and flux calculation schemes (Yu et al., 2019). The FGOALS-f3-H model outputs span the period from 1958 to 2018, with a horizontal resolution of 0.25° × 0.25°, comparable to the OISST data, and 55 vertical layers ranging from 2.5 m to 5372.0 m. In this study, we analyze outputs from 2009 to 2018, with 28 vertical layers within the 2.5–195.4 m range.
Because the FGOALS-f3-H output does not include precipitation and evaporation data, we incorporate hourly precipitation and evaporation data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) (Hersbach et al., 2018) to calculate freshwater flux (FWF) anomalies during MHW events. Although these two reanalysis variables may have biases, they provide spatially and temporally consistent fields for our FWF calculation, which is important for diagnosing the impact of FWF anomalies. The ERA5 data have a horizontal resolution of 0.25° × 0.25° and cover the period from 1992 to 2021. The original hourly data are further processed into daily data to maintain consistency with other datasets used in this study. FWF is calculated as precipitation (P) minus evaporation (E), with positive values indicating downward fluxes into the ocean.
Before analysis, we calculate daily anomalies for all variables by subtracting daily climatology from the original data. Specifically, the daily climatology is computed over the periods 1992–2021 for OISST, 2009–2018 for ERA5, and 2009–2018 for model outputs, based on the availability of each dataset. The daily anomalies are then detrended to remove the global warming signal. These detrended anomaly time series are subsequently used for MHW identification and further analyses.
2.2 Methods
In this study, we follow the definition of MHWs proposed by Hobday et al. (2016), that is, SST anomalies (SSTAs) exceeding the 90th percentile of daily climatology for at least five days in a given location. For each day of the year, the daily climatology is derived using data within an 11-d window centered on that day, so a total of 330 d (11 d/a $ \times $ 30 a) are used to calculate the 90th percentile. MHW events separated by two days or less are treated as a single event.
To analyze the evolution of the 2013–2014 MHW event, three-dimensional salinity and temperature fields from the FGOALS-f3-H are used to determine the MLD. The MLD is defined as the depth at which seawater density deviates by a threshold value of Δρ from the density at 10 m depth, where Δρ is the density difference corresponding to a temperature drop of 0.2℃ at 10 m (Kara et al., 2000; de Boyer Montégut et al., 2004). The resultant MLD anomaly time series from 2009 to 2018 are also detrended.
To better quantify the contribution of the salinity effect to the 2013–2014 MHW event, we conduct an analysis to separate the contributions of temperature and salinity to MLD. Specifically, The MLD field, expressed as F (T, S), is a function of temperature (T) and salinity (S), and its interannual variations can be attributed to changes in either or both variables. Following Zheng and Zhang (2012), we calculate MLD anomalies using interannually varying and climatological temperature and salinity fields from 2009 to 2018. The differences between these MLD anomalies quantify the individual contribution of temperature and salinity changes. We then separately assess the relative impacts of temperature and salinity anomalies on MLD variability.
In addition, we conduct a heat budget analysis to investigate the ocean processes influencing mixed layer temperature variability. This approach has been extensively employed in previous studies (Huang and Mehta, 2004; Huang et al., 2005; Guan et al., 2023; Ye et al., 2023). The governing equation for the mixed layer temperature budget is as follows (Zhang et al., 2005).
$ \begin{split}\frac{{\partial T}}{{\partial t}} =& - \left(u\frac{{\partial T}}{{\partial x}} + v\frac{{\partial T}}{{\partial y}}\right) - w\frac{{\partial T}}{{\partial z}} + \frac{{{\kappa _h}}}{{{H_m}}}{\nabla _h} \cdot ({H_m}{\nabla _h}T)\, +\\&\frac{{2{\kappa _v}}}{{{H_m}{H_2}}}({T_e} - T) + \frac{{{Q_{{\mathrm{net}}}}}}{{\rho {C_p}{H_m}}} {\mathrm{,}}\end{split} $
where $ \dfrac{{\partial T}}{{\partial t}} $ is the tendency of the mixed layer temperature, $ - \left(u\dfrac{{\partial T}}{{\partial x}} + v\dfrac{{\partial T}}{{\partial y}}\right) $ is the horizontal advection term, $ - w\dfrac{{\partial T}}{{\partial z}} $ is the vertical advection term, $ \dfrac{{{\kappa _h}}}{{{H_m}}}{\nabla _h} \cdot ({H_m}{\nabla _h}T) $ is the horizontal diffusion term, $ \dfrac{{2{\kappa _v}}}{{{H_m}{H_2}}}({T_e} - T) $ is vertical mixing and sub-surface entrainment terms, $ \dfrac{{{Q_{{\mathrm{net}}}}}}{{\rho {C_p}{H_m}}} $ is the net heat flux term. In the following analysis, the horizontal diffusion, vertical mixing and subsurface entrainment terms are omitted due to their negligible magnitude compared to other terms.
2.3 Model and experiment
To validate the potential salinity effect on MHWs, we use the LICOM3 model (Liu et al., 2012) coupled with the Community Ice Code, version 4 (CICE4) (Lin et al., 2020) to perform sensitivity experiments. The model has a horizontal grid resolution of 360 × 218 in the zonal and meridional directions, respectively, with 30 vertical levels. Spherical coordinates are used in the horizontal direction, while η coordinates are used in the vertical direction.
A series of sensitivity experiments are conducted using the LICOM3 model. Initial and boundary conditions are derived from JRA55-do data. The model is first spun up to a climate state using temperature and salinity data from PHC3.0 over a 200-year integration. Subsequently, daily JRA55-do data with interannual variability are used to force the model from 1958 to 2013, yielding the initial state on January 1, 2013. To investigate the impact of FWF on MHWs, we conduct sensitivity experiments by adjusting precipitation levels within the analysis region to 0%, 50%, 100%, and 200% of the observed values, respectively. The experiments are hereafter referred to as fwf_0, fwf_0.5, fwf_ctl, and fwf_2.0.
3 Results
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3.1 SSTAs and SSSAs associated with the 2013–2014 MHW
We apply the MHW identification procedure to the OISST data in the Northeast Pacific and successfully identify major MHW events from 1992 to 2021 (Fig. 1a). It can be observed from Fig. 1a that the 2013–2014 MHW event stands out as the most significant event in terms of both duration and intensity. Specifically, the 2013–2014 MHW event spans from November 21, 2013, to March 30, 2014, with a cumulative intensity of 226.76℃, a maximum intensity of 2.55℃, and a duration of 130 d. These characteristics are consistent with previous studies (Zhi et al., 2019a; Scannell et al., 2020; Shi et al., 2022).
Figures 1b−d show the average SSTAs during the 2013–2014 MHW event using both the OISST data and FGOALS-f3-H model outputs. The spatial pattern and temporal variations of SSTAs in the FGOALS-f3-H results are highly consistent with the OISST data. The maximum SSTA during this event reaches 2.75℃ in the OISST data and 2.70℃ in the FGOALS-f3-H outputs. These results indicate that the FGOALS-f3-H effectively reproduces the 2013–2014 MHW event in the Northeast Pacific. In the following analyses, we use the FGOALS-f3-H outputs to examine the subsurface evolution of this MHW event and assess the role of salinity.
To gain a detailed understanding of the physical processes associated with the MHW event, results of the mixed-layer heat budget analysis are presented in Fig. 2. It can be seen from Fig. 2a that the temperature tendency on the right-hand side of Eq. (1) is consistent with the tendency calculated from mixed layer temperature. In Fig. 2b, we present the cumulative heat anomalies by integrating the heat budget terms over time to better illustrate the contribution and evolution of each term during the MHW event. The result shows that the anomalous warming is predominantly driven by the surface heat flux term, as its magnitude is roughly comparable with the total change. In contrast, the horizontal and vertical advection terms contribute significantly less. The residual term remains close to zero during the analysis period, further indicating that the selected terms can well represent the major processes responsible for the anomalous warming. Overall, these results highlight that surface heat flux is the dominant factor driving the upper-ocean warming during this MHW, with limited influence from advection and vertical mixing. This conclusion agrees with previous studies like Chen et al. (2023), indicating the central role of atmospheric anomalies.
The spatial distributions and temporal variations of the SSTAs and SSSAs during the event are displayed in Fig. 3. The positive SSTAs off the west coast of North America coincide well with the negative SSSAs in the same region. Figure 3c shows a strong negative correlation between the time series of SSTAs and SSSAs averaged over 40°–50°N and 135°–155°W, with a correlation coefficient of −0.78. Furthermore, we present the temporal evolutions of SSTAs and SSSAs averaged over the latitudinal range of 40°–50°N in Fig. 4. A prominent positive SSTA center emerges since December 2013, gradually extending eastward as the MHW event progresses. Similarly, a strong negative SSSA center appears in December 2013 and exhibits an eastward extension alongside the SSTA center. These results confirm that the SSTAs and SSSAs develop concurrently in the same region during the MHW event.
3.2 Salinity-induced stratification anomalies during the MHW
To better understand the influence of SSSA on the MHW event, the temporal variation of MLD anomalies averaged over 40°–50°N and 135°–155°W is presented in Fig. 5a. Notable MLD anomalies emerge as the MHW event begins to develop. During the early stage of this event (from November 2013 to January 2014), the MLD anomalies remained negative. A shallower mixed layer means that anomalous heat is more likely to accumulate in the upper ocean, leading to stronger MHW intensity (Scannell et al., 2020). After January 2014, the MLD anomalies show alternating positive and negative anomalies, with a gradually increasing amplitude for positive anomalies. This pattern likely contributes to a decrease in positive SSTA, causing the dissipation of the MHW event.
The above analysis qualitatively demonstrates the relationships among SSTA, SSSA, and MLD, emphasizing the crucial role of salinity in influencing SST through its impact on the MLD. However, since the MLD is influenced by both temperature and salinity, it is necessary to separate their individual contributions to the MLD anomalies. To achieve this, we employ the method proposed by Zheng and Zhang (2012) for a quantitative assessment.
We quantify the contributions of SSTA and SSSA to the MLD anomalies during the 2013–2014 MHW event in Figs 5, 6b and 6c. It can be observed that during the early stage of the event, both the SSTA and SSSA contribute to negative MLD anomalies, and the contribution of SSSA is generally greater than that of SSTA. This highlights the important role of SSSA in the onset of the MHW event through its modulation of the MLD. After January 2014, both the SSTA and SSSA contribute to positive MLD anomalies. The timing corresponds to the start of the dissipation stage of the MHW event, so the positive MLD anomalies may facilitate the decline in MHW intensity. It is thus suggested that the modulation effect of salinity is not consistent throughout the event. However, it is not clear what processes cause the shift in the role of SSSA. In this study, we mainly focus on the negative MLD anomalies induced by SSSA during the early stage of the 2013–2014 MHW.
SSSA is typically influenced by surface fluxes, oceanic advection, and subsurface processes, with surface FWF playing a dominant role (Zhang et al., 2006; Hasson et al., 2013). Figure 7a shows the spatial distribution of the average FWF anomalies during the 2013–2014 MHW event. Remarkable positive FWF anomalies can be observed in the same region as the negative SSSA, indicating that an influx of freshwater into the ocean reduces salinity. In Fig. 7b, the peaks in FWF anomalies from November 2013 to January 2014 generally correspond to the dips in both SSSA and MLD anomalies (Figs 3c and 5a). These results show that the negative SSSA observed during the MHW event is likely induced by the positive FWF anomalies.
3.3 Results from sensitivity experiments
In order to verify the processes by which FWF anomalies affect the MHW intensity, we use LICOM3 to examine the SSTAs during the 2013–2014 MHW under varying FWF conditions. Figure 8a presents the time series of mean sea surface salinity (SSS) for each experiment, which exhibit considerable differences under varying FWF forcings, highlighting the significant influence of FWF on SSS. In the fwf_ctl experiment, SSS stabilizes at an equilibrium value of 32.2. As the FWF increases, regional SSS decreases since December 2013. Specifically, the fwf_2.0 (a doubled FWF) experiment shows a freshening of salinity 0.5 in comparison to fwf_ctl. The SSS variations significantly impact ocean stratification, as the MLD increases (decreases) in response to higher (lower) SSS (Fig. 8b), indicating a positive correlation between SSSA and MLD anomalies. Notably, the MLD differences between fwf_0 (no FWF) and fwf_2.0 can reach up to 15 m. Corresponding to the MLD changes, the SST demonstrates varying evolutionary features across different experiments, with stronger (weaker) warming observed under increased (decreased) FWF.
It is worth noting that the MLD and SST responses in the experiments are more pronounced after January 2014, which is not quite consistent with the observations presented above. This delayed response in the model is likely due to the time required for the MLD and SST to adjust to FWF forcing in LICOM3, leading to a more substantial effect during the later stages of the event.
In summary, the model sensitivity experiments confirm the close connection among FWF anomalies (i.e., precipitation changes), SSS changes, and the MHW intensity. Specifically, positive FWF anomalies reduce SSS, leading to mixed layer shoaling and enhanced upper-ocean warming. Although the timing of warming responses in the model is delayed compared to the observations, the experiments effectively demonstrate how SSSAs influence MLD and SST, highlighting the significant role of SSSA in modulating MHW intensity.
4 Conclusions and discussion
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In this study, we have examined the role of salinity in the 2013–2014 MHW event in the Northeast Pacific using reanalysis data and climate model outputs. Our findings reveal a significant negative correlation between SSSAs and SSTAs during the MHW event. The SSSAs reduce the MLD in the region of the MHW, which facilitates heat accumulation in the upper ocean, thereby enhancing the MHW intensity. Notably, the salinity effect accounts for a larger portion of the negative MLD anomalies during the early stage of the MHW event compared to those induced by SSTAs. Further analysis suggests that the FWF anomalies during the MHW event significantly impact the SSSA, which is supported by model sensitivity experiments conducted using the LICOM3 model.
Previously, Zhi et al. (2019a) also examined the salinity effect on the 2013–2014 MHW in the Northeast Pacific using monthly data. In this study, we use daily data to investigate the influence of ocean salinity on shorter time scales, along with a more accurate temporal range of the 2013–2014 MHW event (November 21, 2013 to March 30, 2014). Moreover, we specifically examine the impact of FWF anomalies on the SSSA, while Zhi et al. (2019a) mainly focus on how the SSSA is driven by physical processes within the ocean interior. Our results agree with those of Zheng and Zhang (2015), who found that ENSO-related interannual salinity variability has a more pronounced impact on ocean stratification than temperature in the tropical Pacific. It is thus suggested that salinity also plays a crucial role in modulating stratification and associated processes at higher latitudes.
To validate the conclusions of this study, two additional MHW events in the Northeast Pacific have been analyzed, referred to as Event 2 (February 28–April 3, 2015) and Event 3 (May 23–July 11, 2015). Event 2 lasts 35 d with a cumulative intensity of 45.03℃ and a maximum intensity of 1.51℃. The correlation coefficient between SSTAs and SSSAs is –0.95 during Event 2, with salinity having a greater impact on MLD anomalies than temperature. Co-occurrences of positive SSTAs, negative SSSAs, and negative MLD anomalies during Event 2 are also observed in ARGO data. These results are consistent with our findings for the 2013–2014 MHW.
On the other hand, Event 3 exhibits a different relationship between SSTAs and SSSAs. This event persists for 50 d with a cumulative intensity of 73.42℃ and reached a maximum intensity of 2.08℃. Unlike Event 2, Event 3 exhibited a positive correlation between SSTA and SSSA, with temperature playing a more dominant role in driving MLD anomalies than salinity. These contrasting results highlight the complex mechanisms behind MHWs and underscore the need for further studies to better understand the salinity effect and its relation to MHWs.
While our study reveals a close connection between the SSSAs and the MHW event, the present results do not indicate a direct cause-effect relationship between SSSAs and MHWs. As shown in Fig. 3b, the SSSAs and SSTAs emerge and evolve concurrently, making it challenging to establish a clear causal relationship between these two variables. Nevertheless, it is important to note the modulation effect of salinity on MLD can be greater than temperature, which is not extensively explored in many previous studies. It is worth noting that the influence of salinity on negative MLD anomalies is more pronounced during the early stage of the MHW event rather than exerting a sustained, net influence over the entire event period, which requires further investigation in future research.
This study reveals the close relationship between MHWs and ocean salinity. Our findings demonstrate that SAs significantly affect the intensity of the 2013–2014 MHW event. Therefore, it is imperative to consider the salinity effect on future MHW studies. Given the complexity of MHW dynamics, it is essential to examine the role of salinity under global warming (Schlegel et al., 2017; Zhang and Zheng, 2022). Specifically, a more comprehensive study is desired to further quantify the contributions of multiple processes, including the long-term warming trend in the Northeast Pacific (Hu et al., 2024), to the unprecedented intensity of the 2013–2014 MHW. In addition, further study is needed to clarify the sources of precipitation that cause FWF anomalies. We believe incorporating salinity as a metric for MHWs could improve forecasting efforts and deepen our understanding of their development.
Acknowledgements
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We thank the anonymous reviewers for their constructive feedback that helps greatly improve the manuscript. We thank Nanjing Hurricane Translation for reviewing the English language quality of this paper.
Funding
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  • The Laoshan Laboratory under contract(LSKJ202202403)
  • The Laoshan Laboratory under contract(LSKJ202202402)
  • National Natural Science Foundation of China under contract(42030410)
  • National Natural Science Foundation of China under contract(42406202)
  • Natural Science Foundation of Jiangsu Province under contract(BK20240718)
  • Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology
  • Jiangsu Innovation Research Group under contract(JSSCTD202346)
  • Jiangsu Funding Program for Excellent Postdoctoral Talent under contract(2023ZB690)
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Year 2025 volume 44 Issue 1
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Article Info
doi: 10.1007/s13131-024-2440-6
  • Receive Date：2024-08-19
  • Online Date：2025-10-27
  • Published：2025-01-25
Article Data
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History
  • Received：2024-08-19
  • Accepted：2024-12-07
Funding
The Laoshan Laboratory under contract(LSKJ202202403)
The Laoshan Laboratory under contract(LSKJ202202402)
National Natural Science Foundation of China under contract(42030410)
National Natural Science Foundation of China under contract(42406202)
Natural Science Foundation of Jiangsu Province under contract(BK20240718)
Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology
Jiangsu Innovation Research Group under contract(JSSCTD202346)
Jiangsu Funding Program for Excellent Postdoctoral Talent under contract(2023ZB690)
Affiliations
    1 School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
    2 Henan Meteorological Disaster Prevention Technology Center, Zhengzhou 450003, China
    3 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
    4 Laoshan Laboratory, Qingdao 266237, China
    5 Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
    6 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

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* Zhi Hai, E-mail:
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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