Journal of Oceanology and Limnology   2020, Vol. 38 issue(6): 1676-1691     PDF
Institute of Oceanology, Chinese Academy of Sciences

Article Information

TAN Hongjian, CAI Rongshuo, HUO Yunlong, GUO Haixia
Projections of changes in marine environment in coastal China seas over the 21st century based on CMIP5 models
Journal of Oceanology and Limnology, 38(6): 1676-1691

Article History

Received May. 27, 2019
accepted in principle Aug. 29, 2019
accepted for publication Oct. 11, 2019
Projections of changes in marine environment in coastal China seas over the 21st century based on CMIP5 models
TAN Hongjian, CAI Rongshuo, HUO Yunlong, GUO Haixia     
Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
Abstract: The increases of atmospheric carbon dioxide and other greenhouse gases have caused fundamental changes to the physical and biogeochemical properties of the oceans, and it will continue to occur in the foreseeable future. Based on the outputs of nine Earth System Models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), in this study, we provided a synoptic assessment of future changes in the sea surface temperature (SST), salinity, dissolved oxygen (DO), seawater pH, and marine net primary productivity (NPP) in the coastal China seas over the 21st century. The results show that the mid-high latitude areas of the coastal China seas (East China Seas (ECS), including the Bohai Sea, Yellow Sea, and East China Sea) will be simultaneously exposed to enhanced warming, deoxygenation, acidification, and decreasing NPP as a consequence of increasing greenhouse gas emissions. The magnitudes of the changes will increase as the greenhouse gas concentrations increase. Under the high emission scenario (Representative Concentration Pathway 8.5), the ECS will experience an SST increase of 3.24±1.23℃, a DO concentration decrease of 10.90±3.92 μmol/L (decrease of 6.3%), a pH decline of 0.36±0.02, and a NPP reduction of -17.7±6.2 mg/(m2·d) (decrease of 12.9%) relative to the current levels (1980-2005) by the end of this century. The co-occurrence of these changes and their cascade effects are expected to induce considerable biological and ecological responses, thereby making the ECS among the most vulnerable ocean areas to future climate change. Despite high uncertainties, our results have important implications for regional marine assessments.
Keywords: Coupled Model Intercomparison Project (CMIP5)    sea surface temperature (SST)    dissolved oxygen (DO)    seawater pH    net primary productivity    

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) demonstrates with high confidence that our oceans have undergone fundamental changes (e.g., ocean warming and acidification) over the past decades according to various in situ and remote sensing observations (Intergovernmental Panel on Climate Change, 2014). These physical and chemical changes within the ocean, which to a great extent are caused by anthropogenic emissions of greenhouse gases, have a large potential to affect the distribution and abundance of marine organisms and ecosystems. For example, rapid warming has driven many fish and plankton to relocate to higher latitudes, while other ecosystems, such as coral reefs that are less able to move, have experienced high rates of mortality and loss (Hoegh-Guldberg and Bruno, 2010). Ocean acidification, which results from increased carbon dioxide (CO2) entering the ocean, has been reported to reduce rates of calcification and growth for organisms such as corals and pteropods (Zeebe et al., 2008). In the coming decades, with the continued increase in greenhouse gases, the global marine environment will deteriorate further, thereby posing serious risks to marine organisms and ecosystems as well as to human society.

However, owing to limited observations and intertwined system process, our current knowledge of ocean systems, especially for biogeochemical processes, is still insufficient for deriving precise information for climate change prediction or projection. The IPCC AR5 and previous studies provide a large account of evaluations of historical and future changes in the physical, chemical, and biological properties (e.g., temperature, pH, and primary productivity) of the open ocean (Bopp et al., 2013; Mora et al., 2013) based on the observations and outputs of the Coupled Model Intercomparison Project phase 5 (CMIP5) (Taylor et al., 2012). For example, the upper ocean has warmed rapidly over the past four decades (i.e., approximately 0.1℃ per decade during 1971–2010), and the warming will continue as greenhouse gas concentrations increase. The sea surface pH has decreased by about 0.1 units since the preindustrial period owing to oceanic absorption of atmospheric CO2 (Intergovernmental Panel on Climate Change, 2014). Under the high CO2 emission scenario, Bopp et al. (2013) projected substantial changes in the global mean pH, dissolved oxygen (DO), and net primary productivity (NPP) of -0.33, -3.45%, and -8.6%, respectively, in the late 21st century (the 2090s relative to the 1990s). Despite the high uncertainties in the projections of some biogeochemistry variables, the current state-of-the-art CMIP5 models provide an effective approach to understand and assess oceanic responses to different greenhouse gas emission scenarios.

Previous reports and literature generally operate on the global scale with less concern for regional seas (Cannaby et al., 2015). In particular, the coastal China seas, which comprise many estuaries, bays, coral reefs, and fisheries, are highly productive areas and hot spots of global marine biodiversity. Meanwhile, the coastal China seas are sensitive to anthropogenic and natural climate change. For instance, enhanced warming has been observed in the coastal China seas, with the regional mean sea surface temperature (SST) rising by 2℃ during 1958–2014, which far exceeded the globally averaged rate of ocean surface warming (Cai et al., 2016). The warming, in combination with increased nutrient loading and hypoxia, has been shown to be related to the increased occurrences of harmful algae blooms, shifts in the distribution of marine species, and reduction in fish capture rates, thereby posing serious threats to the health of coastal marine ecosystems (Tian et al., 2006; Gobler et al., 2017). Further, recent research suggests that the coastal China seas will be among the most significant areas of warming for oceans worldwide in the next few decades (Tan et al., 2016). Comparatively, existing research on assessments of the marine environment mainly focus on the changes in physical factors, such as sea temperature and sea level, but less on marine biogeochemistry.

Built on the IPCC AR5 and previous studies (e.g., Bopp et al., 2013), this research attempted to assess the future changes (to 2099) in marine environmental drivers, i.e., SST, salinity, pH, DO, and NPP, based on an ensemble mean of the CMIP5 outputs. We concentrated our analysis on the region of the coastal China seas and compared this with the global mean. Our research may be useful for regional marine climate change assessments.


Within the framework of the CMIP5, many atmosphere-ocean coupled models have been improved and extended into Earth System Models (ESMs) by including the representation of an interactive carbon cycle and biogeochemical cycle, thereby making it possible to evaluate the oceanic biogeochemical response to future greenhouse gas forcing (Taylor et al., 2012). The outputs from the CMIP5 include historical simulations (1850–2005) and future projections of climate change scenarios (2006–2100). The former is used for the evaluation of the model performance in reproducing the modern climate, while the latter is for projecting and comparing the differences under different greenhouse gas emission scenarios, namely representative concentration pathways (RCPs). There are four future scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) designed depending on an estimate of the radiative forcing by the end of this century. The atmospheric CO2 concentration has already risen from 280×10-6 (in preindustrial times) to above 400×10-6 recently, and will rise to (550–900)×10-6 by 2100 depending on the emission scenario (Taylor et al., 2012). In particular, RCP 8.5 represents a high emission scenario (without effective climate change mitigation policies) with a continuous increase in radiative forcing to about 8.5 W/m2 in the year 2100 (relative to preindustrial conditions). In addition, there are two intermediate emission scenarios, namely RCP 4.5 and RCP 6.0, and a low emission scenario, namely RCP 2.6. The latter denotes a low greenhouse gas emission, a high mitigation future scenario, in which radiative forcing peaks in 2050 before decreasing to an eventual nominal level of 2.6 W/m2 in 2100. Achieving the RCP2.6 pathway would require implementation of robust emission reduction technologies to remove greenhouse gases from the air, in addition to existing mitigation strategies.

In this study, nine ESMs developed from different research institutes were used based on the availability of necessary variables in marine biogeochemistry. Table 1 presents a brief description of the models used in this study. Each ocean model has different horizontal resolutions ranging from 0.3° to 2°. Some ESMs have several versions with different resolutions, and we arbitrarily selected the higher resolutions (e.g., MPI-ESM-MR vs. MPI-ESM-LR), even though that did not guarantee better performance. In addition, five marine environmental parameters were employed, including two physical variables (SST and salinity), two chemical variables (pH and DO), and one biological variable (NPP). Marine NPP is the product of phytoplankton growth rate and standing stock, and it often reflects cumulative changes in phytoplankton biomass and the short-term modulation of phytoplankton performance by ambient factors like light, seawater temperature, and micronutrient concentrations. In CMIP5 ESMs, all marine biogeochemical components within the ESMs are typical nutrient-phytoplankton-zooplankton-detritus models, but with different complexities and biogeochemical processes (Table 1). Some of the models do not cover all five variables; for example, CanESM2 and MIROC-ESM lack DO. Outputs under three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) were selected to assess future change, as some models did not perform all RCP simulations.

Table 1 Descriptions of the models used in this study indicating the oceanic horizontal resolution, marine biogeochemical component, selected variables, and different representative concentration pathway (RCP) scenarios

In the following analysis, all the variables were first interpolated onto a common 1°×1° regular grid considering the latitudinal weight. We used the ensemble mean of the outputs from the nine models (multi-model average), with each model contributing the same. Before this, the robustness of each model was identified by comparing the historical simulation with observational climatology. The referenced observations were the HadISST for SST (Rayner et al., 2003), World Ocean Atlas 2013 for salinity and DO, Global Ocean Data Analysis Project (Lauvset et al., 2016) for surface pH, and satellites-based estimates of NPP with adequate global coverage of phytoplankton populations. We conducted a continuous record of 20 years (1999-2018) of NPP in terms of a standard Vertically Generalized Production Model (VGPM) proposed by Behrenfeld and Falkowski (1997) from the Ocean Productivity website ( We evaluated three metrics (pattern correlation, normalized standard deviation, and root mean square difference) between the annual mean simulations for 1980-2005 and the observations, and illustrated them in Taylor diagrams. The uncertainty of the projections was estimated using the inter-model difference or model spread (ranging from the minimum to maximum among all the models). Unless otherwise stated, all the results about temperature, salinity and pH were based on the ocean surface data. Average of subsurface DO from 5-100 m was used, as most of shelf sea area in the coastal China seas is less than 100-m deep (Xie et al., 2002). For NPP, we employed annual mean fields of vertically integrated total primary production by all types of phytoplankton (namely "intpp" in CMIP5 model lexicon), because individual components may be unavailable in some models.

3 RESULT AND DISCUSSION 3.1 Model robustness

The performance of most models employed in this study has been evaluated previously (see references in Table 1). The spatial distributions of present-day (1980–2005) SST, salinity, DO, pH, and NPP of each individual model are shown in the supplementary materials (Supplementary Figs.S1–S5). We discussed the results from the ensemble mean of all the models (Fig. 1). For the quantitative evaluation, the skill of each model and their ensemble mean in representing the spatial variations of the five variables are summarized in Fig. 2 by calculating the individual pattern correlation, normalized standard deviation, and root mean square error relative to the observations. Given the fact that the available observations were restricted to recent periods, it was assumed that the models that could simulate the present climate well would produce better projections of future climates. A perfect match of observations to the outputs of any individual model was unlikely for all places and times. However, the average of multi-model outputs has been found to partially ameliorate these errors between a given global model and observations (e.g., Mora et al., 2013).

Fig.1 Comparison of the observations (left panel) and ensemble mean of the CMIP5 models (right panel) SST: sea surface temperature (℃); DO: dissolved oxygen (μmol/L); NPP: net primary productivity (mg/(m2∙d)). The white area denotes a lack of sufficient observations.
Fig.2 The Taylor diagram for displaying normalized pattern statistics of SST, salinity, DO, pH, and NPP between the nine CMIP5 models and observation reference The numbers 1-9 represent the Model ID in Table 1 and number 10 represents their ensemble mean. The standard deviation and centered root mean square difference are normalized by the reference standard deviation. The radial distance from the origin is the normalized standard deviation of the model. The pattern of correlation between the model and the reference is given by the azimuthal position of the model, and the centered root mean square difference between the model and the reference is their distance apart. In brief, the nearer the distance between the number and REF, the better the performance of the corresponding model.

The ensemble mean result of multi-model outputs could skillfully reproduce the spatial patterns of SST, for example, the Indo-Pacific warm pool in comparison with the HadISST dataset during 1980–2005 (Fig. 1). The pattern correlation (R) reached up to 0.99, which exceeded the 99.9% confidence level. Further, each model also represented the SST pattern well, although some specific models (e.g., IPSL-CM5A-MR) had a large systematic bias (Fig. 2). However, the ensemble mean for SST (No. 10 in Fig. 2) was very close to the reference value. For salinity, the ensemble mean result could reproduce the lower salinity in the IndoPacific warm pool (due to large rainfall) and higher salinity in the subtropical ocean (due to intense evaporation). Although the R-value of salinity (0.89) was lower than that of SST, the inter-model differences were small and the ensemble mean nearly represented the optimal value. Additionally, the models could capture essential features of DO at a large scale, of which DO increases with latitude, with an acceptable range of R-values (0.7–0.8). However, the models appeared to underestimate the overall DO across the ocean, especially in the western tropical Pacific and polar ocean regions (Fig. 1). The score and rank of pH and NPP were less adequate. For pH, large model spreads existed in the pattern correlations with R-values ranging from 0.3 to 0.78. There were also obvious differences in the standard deviations and root mean square errors across the models (e.g., CMCC-CESM vs. IPSL-CM5A-MR). For NPP, although it roughly captured the patterns of high and low values between upwelling in high latitude, equatorial, and nutrient-limited subtropical regions, the simulated high NPP in the coastal seas and polar oceans was too low relative to the observations derived from satellite data (Fig. 1). This underestimation may be due to the lack of consideration of the impact of human activity, for example, the input of fresh water high in nutrients from the Changjiang (Yangtze) River along the coastal China seas. As a whole, the models scored poorly for NPP compared to other environmental parameters in terms of the three metrics.

Overall, the models performed well in reproducing the present-day spatial patterns and magnitudes of most environmental drivers compared to observations despite the substantial differences in specific variables (e.g., pH and NPP) across models. The ensemble mean results could improve, at least in part, the skill of simulations, as their multi-model average was often closer to the actual observations (Fig. 2). Thus, the ensemble mean of multiple models was used for the future scenario projections.

3.2 Future scenario projections

In this section, we discussed the projections of future changes in the five marine environmental drivers in the coastal China seas based on the ensemble mean outputs of nine ESMs under three typical scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). Both the spatial distributions and area-averaged time series through the 21st century were presented. To reduce systematic bias, all the projections were evaluated against the present-day results by subtracting the mean of 1980–2005 for each model.

3.2.1 Sea surface temperature

Continued warming of the global ocean during the 21st century is projected, regardless of the emission scenario used (Kirtman et al., 2013). However, striking regional differences exist, with the strongest ocean warming being estimated for the surface in tropical regions and Northern Hemisphere subtropical regions (Intergovernmental Panel on Climate Change, 2014). In the present study, remarkable warming was examined across all coastal China seas under all three scenarios. It was noted that the magnitude of warming in mid-high latitude areas within the coastal China seas, e.g., the Bohai Sea, Yellow Sea, and East China Sea (hereinafter referred to as East China Seas; ECS), was larger than that of low latitude areas (South China Sea; SCS). The mean SST was projected to increase as greenhouse gas emissions increase (Fig. 3).

Fig.3 Future changes in SST, salinity, DO, pH, and NPP in the coastal China seas under representative concentration pathway (RCP) 2.6 (left panel), RCP 4.5 (middle panel), and RCP 8.5 (right panel) during 2090–2099 relative to 1980–2005

For the convenience of evaluation and comparison, we divided the coastal China seas into two areas, namely the ECS (23°N–40°N, 120°E–130°E) and SCS (2°N–20°N, 110°E–120°E), and then calculated the area-averaged time series of SST for 2006–2099 (Fig. 4). Both the ECS and SCS showed a distinct warming trend through the 21st century with a nearly linear trend of increase, which was coincident with the increases in atmospheric greenhouse gas concentration under RCP 4.5 and RCP 8.5. Meanwhile, for RCP 2.6, SST was projected to increase until the middle of the 21st century and then be maintained at a stable level. By the end of this century (2090–2099), the largest warming in the ECS will reach up to 0.74±0.49, 1.75±0.65, and 3.24±1.23℃ for RCP 2.6, RCP 4.5, and RCP 8.5, respectively, relative to 1980– 2005 levels (Table 2). Furthermore, the ECS were projected to warm faster than the SCS under the same scenario; for example, under RCP 8.5, SST in the SCS was projected to increase by 2.92±0.77℃ during the 2090s, which was about 0.3℃ lower than that in the ECS. In addition, the magnitude of warming in the ECS and SCS during different periods in the future (2020–2029, 2050–2059, and 2090–2099) are summarized in Table 2. The global ocean mean (70°S–70°N, omitting the polar ocean) warming values are also shown as a comparison. It should be noted that the magnitudes of warming of both the ECS and SCS were larger than the global mean.

Fig.4 Time series of SST, salinity, DO, pH, and NPP in the East China Seas (left panel) and South China Sea (right panel) under RCP 2.6, RCP 4.5, and RCP 8.5 for 2006–2099 The shaded areas denote the ranges from minimum to maximum among all the model results.
Table 2 Future changes in SST (℃; relative to 1980–2005) in the coastal China seas

Overall, all areas of the coastal China seas were projected to experience robust warming during the 21st century, the magnitude of which was stronger than that of the global mean level. Further, the midhigh latitude sea areas (ECS) were projected to warm faster than the low latitude areas (SCS). This was confirmed by every model additional to the ensemble mean results. Thus, the mid-high latitude sea areas within the coastal China seas may be among the most significant warming regions across the global ocean in the future.

3.2.2 Salinity

Changes in ocean salinity are controlled by the balance between evaporation and rainfall, which suggests elevated salinity in evaporation-dominated regions, such as the subtropical North Pacific, and decreased salinity in high precipitation areas, such as the tropical western Pacific (Durack et al., 2012). In this study, the models projected decreased surface salinity in the low latitude areas of the coastal China seas, except for the northern area of the SCS that is close to the southern coastline of China (Fig. 3). The magnitude of simulated salinity variation was mostly dictated by the RCP scenario, i.e., by the amount of greenhouse gas emissions. Compared to the presentday conditions, salinity in the southern SCS will decrease by about 0.4, 0.6, and 0.9 during the 2090s under RCP 2.6, RCP 4.5, and RCP 8.5, respectively. There also appeared to be a decrease in salinity in the ECS according to the ensemble mean outputs, especially in the northern part of the ECS. However, the projected results were not consistent across models. Two models (IPSL-CM5A-MR and NorESM1-ME) simulated an increase in most areas of the ECS. Further, there were large uncertainties in the changes in surface salinity in the mid-high latitude areas of the coastal China seas. The errors in accuracy and precision of the modeled salinity within the coastal China seas seemed to be larger than their projected changes. Thus, we did not conduct a quantitative comparison between the changes in salinity in the ECS and SCS with the global mean because of the large uncertainties and model spreads.

In summary, it was projected that there will be an overall decrease in surface salinity in the coastal China seas over the coming decades, but uncertainty exists in some regions. All the models projected a consistent decrease in salinity in the low latitude areas of the coastal China seas, whereas the results were not consistent along the southern coastline of China and in the ECS. The decrease in salinity may be caused by increased precipitation in the tropical ocean owing to global warming (Durack et al., 2012). The changes in salinity along the coastline would be more complex, with combined effects of rainfall, evaporation, and runoff. Recent observational research also shows obvious regional differences in salinity changes in the coastal China seas. An overall increased sea surface salinity was observed from 1970s in the coastline of the northern ECS, e.g., Bohai Sea, which may be due to the continuous declined discharge of the Yellow River. While other shallow areas (e.g., north of the East China Sea and most of the SCS) display a decreased trend, which may be associated with the local rainfall changes and intrusion of warm and heavy salt water from low latitude (Li et al., 2015; Wang and Lin, 2018). Changes in salinity could form a threshold for marine and freshwater species distributions, and affect the survival and growth of marine species in coastal seas. This is especially true for estuaries and lagoons, that is of great importance for marine aquaculture. For example, the declined and unstable salinity in estuarine areas has threatened the survival and growth of sea cucumbers (Li and Li, 2010).

3.2.3 Dissolved oxygen

Previous studies have presented an overall decrease in global ocean mean DO, with definite regional differences in the magnitude of the change (Bopp et al., 2013; Intergovernmental Panel on Climate Change, 2014). In the coastal China seas, it was projected that there will be a substantial decrease in DO, but the distribution was not spatially uniform. The mid-high latitude sea areas were projected to suffer from more severe deoxygenation than low latitude areas, especially for the high emissions scenario (RCP 8.5). The robustness of these regional projections was high, even for the low emissions scenario (RCP 2.6). For quantification, the relative changes in DO (upper ocean averaged from 5–100 m) under RCP 8.5 compared to those during 1980–2005 in the ECS amounted to -3.13±0.78, -6.88±3.32, and -10.90±3.92 μmol/L for the near-term (2020–2029), mid-term (2050–2059), and long-term (2090–2099) periods, respectively (Table 3). These values were equivalent to 2.3%, 3.6%, and 6.3% decreases in the current level (1980–2005). Meanwhile, the SCS and global mean DO were projected to decline by 5.5% and 4.6%, respectively, in the 2090s. The estimated values for global mean DO under RCP 8.5 (Table 3) were similar to those reported in previous model intercomparison studies (Bopp et al., 2013). The magnitude of change in DO in the ECS was larger than those contemporaneous ones of the SCS and global mean. Other scenarios (RCP 2.6 and RCP 4.5) also revealed enhanced deoxygenation in the ECS compared to the SCS and global mean during the 21st century (Table 3).

Table 3 Future changes in dissolved oxygen (μmol/L; relative to 1980–2005) in the coastal China seas

Overall, it was projected that there will be a continued decrease in the O2 inventory within the coastal China seas in response to climate change under every RCP scenario. This was especially true for the mid-high latitude sea areas, which will suffer from more severe deoxygenation than low latitude areas. Furthermore, the estimated rates of deoxygenation were similar to the simulated sea surface warming. The nearly linear relationship between SST and DO that was projected in the ECS for 2006–2099 implied that ocean warming would have an effect on the future reduction in DO. Previous studies have suggested that the higher sea temperature will reduce the solubility of oxygen, which is not conducive to the absorption and dissolution of oxygen in seawater (Dufresne et al., 2013). On the other hand, the enhanced warming will strengthen water stratification and thereby reduce the mixing of oxygen-rich surface layers into the deeper parts of the ocean (Ilyina et al., 2013). These influencing mechanisms have recently been verified by observations in the ECS where frequent hypoxia occurs off the Changjiang River estuary in association with enhanced warming (Wei et al., 2017).

3.2.4 pH

The ocean has absorbed a large fraction of anthropogenic CO2 accumulated in the atmosphere, thereby resulting in a decrease in seawater pH. The IPCC AR5 concluded that the future increases in anthropogenic atmospheric CO2 are very likely to further acidify the ocean, especially at high latitudes (Intergovernmental Panel on Climate Change, 2014). The coastal China seas will expect a sharp reduction in the ocean pH, the magnitude of which will increases as greenhouse gas concentration increases. Enhanced ocean acidification was found in the mid-high latitude sea areas (e.g., ECS), which is consistent with the SST and DO (Fig. 3). The model spread under each scenario was very low, thereby indicating robust agreement across all models for surface pH projections (Fig. 4). Furthermore, there is a nearly linear decline in seawater pH after the 2050s under RCP 4.5 and RCP 8.5, suggesting that the level of acidification is directly proportional to atmospheric CO2. Moreover, under RCP 2.6, the pH level seemed to be maintained at a stable level around the 2050s, when robust mitigation measures achieved nearly zero net greenhouse gas emissions under this scenario. This implied that extreme mitigation strategies might be effective to alleviate increasing acidification. The projected change in pH in the ECS during 2090–2099 ranged from -0.08±0.01 under RCP 2.6 to -0.36±0.02 under RCP 8.5. The magnitude of change in the ECS was larger than that of the global ocean and SCS. On the contrary, the magnitude of change in pH in the SCS was smaller than that of the global ocean (Table 4).

Table 4 Future changes in pH (relative to 1980–2005) in the China seas

Overall, there was a strong agreement across all models that the pH in the coastal China seas will decline in the future, and it was inversely proportional to the concentration of anthropogenic atmospheric CO2. Further, the ECS will experience more severe acidification than the SCS and the global ocean. By the 2090s, the pH in the ECS was projected to decrease by 0.36±0.02 under RCP 8.5. In contrast to the projections of other variables, the model spread for pH projection was very low among all three scenarios (less than 0.02), thereby indicating the robust performance of the current ESMs in modeling ocean acidification and closely tracking the changes in atmospheric CO2 concentration (Ilyina et al., 2013). The IPCC AR5 and previous studies (Bopp et al., 2013; Intergovernmental Panel on Climate Change, 2014) suggested that ocean regions at high latitudes (e.g., the Arctic Ocean) are likely to acidify faster than those at low latitudes because the latter is characterized by high temperature and thus a higher partial pressure of CO2. Meanwhile, most of the coastal ocean is generally seen as a prominent CO2 sink at the global scale, which is likely due to abundant nutrients and high primary productivity (Dai et al., 2013). Strengthened stratification due to enhanced warming in the ECS may exacerbate acidification through inhibiting the upward supply of underlying nutrients and thereby leading to a reduction in primary productivity (Cao and Zhang, 2017), as shown in Section 3.2.5.

3.2.5 Marine primary production

Ocean primary productivity is a key process in the marine carbon cycle and pelagic ocean ecosystems. Previous studies have suggested that continued ocean warming as well as strengthened vertical stratification will potentially result in a broad-scale decrease in NPP across the open ocean (Intergovernmental Panel on Climate Change, 2014). Here, we discuss a synoptic assessment of the change in NPP in the coastal China seas. As a whole, it was projected that there will be a decline in NPP in most areas of the coastal China seas, but with obvious regional differences in magnitude based on the multi-model average (Fig. 3). There was a greater decrease under the higher emissions scenario, which may be associated with enhanced warming and ensuing stratification. By the 2090s, NPP in the ECS will experience changes of -5.9±2.6, -8.5±4.3, and -17.7±6.2 mg/(m2∙d) under RCP 2.6, RCP 4.5, and RCP 8.5, respectively, which amount to decreases of 2.1%, 9.3%, and 12.9% relative to current levels, respectively. Comparatively, the magnitude of the decrease in NPP in the SCS was slightly lower, with decreases of -1.6±1.1, -10.6±5.0, and -16.9±6.0 mg/(m2∙d) under the three scenarios, respectively (Table 5).

Table 5 Future changes in the NPP (mg/(m2∙d); relative to 1980–2005) in the China seas

Overall, most models suggested a substantial decrease in the NPP in the coastal China seas as the warming continues in the future. The distribution and amount of NPP are generally controlled by multiple environmental factors (e.g., light, temperature, and nutrients) that regulate the activities of phytoplankton (Lozier et al., 2011). It is assumed that higher temperatures are favorable for the photosynthesis of phytoplankton and result in elevation of NPP. However, on the other hand, the strengthened stratification due to enhanced warming suppresses nutrient exchange through vertical mixing. Phytoplankton in the upper ocean rely on vertical nutrient transport to sustain production. Thus, the projected substantial decrease in the NPP in the ECS may be caused by the strengthened stratification and photoactivation associated with enhanced warming.

3.3 Cumulative effect

As indicated in Section 3.2, all areas of the coastal China seas will be simultaneously exposed to changes in physical and biogeochemical parameters with enhanced warming, deoxygenation, and acidification and a decrease in NPP co-occurring in the mid-high latitude regions. These changes have been highlighted as potentially exerting negative consequences. To identify patterns of co-occurrence of physical and biogeochemical changes and their cumulative effects, we focused on the negative environmental changes given their overwhelming global coverage. The term "negative" was used to indicate potential effects, and not the actual changes in the direction and magnitude of environmental parameters. The cumulative negative effect (I) is defined as the sum of scaled absolute change in each parameter, i.e., increase in SST and decreases in DO, pH, and NPP. We did not take the salinity into consideration mainly for the following reasons: 1) changes in salinity exhibit pronounced spatial heterogeneity globally, with elevated salinity in the subtropical gyres and decreased salinity in tropical ocean; 2) large uncertainties exist in projection of salinity (Supplementary Fig.S6).


For each parameter (Ii), the absolute change (|Vi|) by the end of the 21st century (relative to current levels) was scaled from 0 to 1 by dividing the local values by the maximum global value (i.e., 0 meaning no change and 1 meaning the largest absolute change). Thus, the resulting scores of cumulative negative effects were the composite of the four variables with a range from 0 to 4. Note that 4 is a theoretical full score because the maximums of the four parameters were not in the same place.

Figure 5 shows the co-occurring physical and biogeochemical changes for 2090–2099 under RCP 8.5 within the global ocean (70°S–70°N, 180°E–180°W). The Polar regions were omitted because a reduction in sea ice may lead to greater productivity as more sunlight reaches the ocean surface. Most of the world's oceans will be influenced by changes in multiple parameters. Geographically, the largest cumulative negative effect occurred at the tropical central-eastern Pacific and high latitude regions of the northern Pacific and northern Atlantic. Large values in the tropical central-eastern Pacific were mainly caused by the combined effects of robust warming and the decrease in NPP. Otherwise, the high latitude areas of the northern Pacific were projected to simultaneously experience changes in all four parameters. In particular, mid-high latitude areas of the coastal China seas (e.g., ECS) were among the most affected areas; although none of the changes was the largest, the magnitude of the additive effects of all parameters was equivalent to that in the higher latitude areas. Comparatively, the low latitude areas of the coastal China seas (e.g., SCS) as well as the tropical western Pacific were less affected by the biogeochemical changes. In addition, the results under RCP 2.6 and RCP 4.5 were similar to those obtained under RCP 8.5. The present assessment of cumulative effects was performed in linear composition with equal weight for simplification; however, the biological and ecological responses in the actual ocean do not follow an idealized or linear manner to such changes. The cumulative effects provide a synoptic but more comprehensive global assessment of the simultaneous changes in future ocean biogeochemical variables than the consideration of single stressors alone.

Fig.5 Spatial distribution of the cumulative negative effect during the 2090s under RCP 8.5 The index of the cumulative impact is the sum of the equally weighted changes in sea surface temperature, dissolved oxygen, pH, and NPP from 0 (no change) to 4 (maximum change).

In summary, the ECS were projected to be among the regions with the largest cumulative negative effect due to the co-occurrence of warming, deoxygenation, acidification, and decrease in NPP. The cascade effects of co-occurring changes in these parameters could accelerate and deteriorate the biological and ecological responses either additively or synergistically, thereby affecting the social economic system. For instance, warming can facilitate fish metabolism and thus increase the demand for oxygen and phytoplankton; however, if this is along with an insufficient supply of DO and NPP, then it could lead to negative consequences. Evidence has been presented that there is a considerable decrease in body size, abundance, and survival of some species (e.g., Larimichthys polyactis) in the ECS owing to climate change (Tian et al., 2006). Studies have also revealed that embryos of invertebrates that could have survived the exposure to warming may die soon if exposed to acidification (Allison et al., 2009). It could be inferred that the future increase in ocean temperature and reductions in DO, pH, and NPP in the ECS are expected to cause degradation of diversity and ecosystems, a decrease in fishery yields, and negative impacts on human wellbeing. Therefore, the ECS may be one of the most vulnerable areas to future climate change.


Regional seas are potentially highly vulnerable to climate variability and change, yet are the most directly societally important regions of the marine environment (Holt et al., 2016). The present study provided a regional assessment for the future trends and magnitude of environmental drivers in the coastal China seas. Global outputs of CMIP5 ESMs were directly employed presuming the coastal China seas as 'driven' systems and local factors (e.g., river discharge) are not of first order importance. That means future changes of marine environment in these areas are primarily caused by global change due to the increase in greenhouse gas concentrations and transferred by large scale atmospheric and oceanic circulation to regional physics and biogeochemistry. So, how well these coarse-resolution models resolve the circulation of the marginal seas is critical. To this end, we estimated upper ocean circulation in the coastal China seas derived from CMIP5 models with different resolutions. Two ESMs were considered as an example, with the relatively high (~0.4°×0.4° for MPI-ESM-MR) and low (1°×(1°–3°) for HadGEM2- ES) resolutions in the Table 1. The models could reasonably reproduce basic spatial structure and seasonal variation characteristics of upper circulation in the coastal China seas (Supplementary Fig.S7). Both models, regardless of resolutions, present the robust spreading of the western boundary currents, i.e., Kuroshio Current in the ECS, and seasonal reversal of the basin-scale circulation, which is similar to the observed results from Simple Ocean Data Assimilation reanalysis.

Reasonability in reproducing the observed circulation and other environmental drivers contributes to our confidence in the models' suitability for both qualitative and quantitative future projections and in explanation of enhanced response in the ECS (e.g., warming). The projected remarkable warming in the ECS is reminiscent of observed evidence. The past decades have seen a pronounced warming in the ECS, with the warming rate above 3 times larger than the global mean (Cai et al., 2017). The rapid warming has been shown to be associated with oceanic and atmospheric forcing. The decadal weakened East Asian monsoon has been proven to favor the enhanced ECS warming through impeding the release of latent heat flux from the ocean. On the other hand, the remarkable warming could also be linked to the increased spreading of heat from the Kuroshio Current, which carries warm tropical water to the mid-latitudes (Cai et al., 2017). In the future, global warming will potentially reduce the sea surface wind speed and weaken cold surge over the East Asia (Kitoh, 2006). The combined exposure to atmospheric forcing and open-ocean will make ECS among the most significant warming regions across the global ocean. The progressively warming and ensuing stratification would result in a reduced oxygen content, in association with compound effects of lowered O2 solubility and a stronger respiration of organic matter due to enhanced physical isolation of subsurface waters (Dufresne et al., 2013). Although warming tends to enhance growth of phytoplankton, the reduced nutrient supply due to increased stratification, in combination with hypoxia, will likely inhibit phytoplankton growth, and in turn induce a decreased NPP (Holt et al., 2016). Comparatively, the SCS was less affected by changes in marine environment (i.e., SST, DO, and NPP) than the ECS. In addition, the ECS seems to acidify faster than the SCS, because the latter is characterized by high mean temperature and thus a higher partial pressure of CO2 (Dai et al., 2013).

It should be noted that there were large uncertainties in our projections. The uncertainty was partially derived from the internal systematic bias from individual models. For example, the simulated historical values of salinity and DO from IPSLCM5A-MR were clearly lower than the contemporary observations. This bias could be in part offset by subtracting the respective reference level (climatic mean of 1980–2005) within each model. The current ESMs were far from comprehensive when dealing with biogeochemical processes, and most of them still rely on relatively simple representations of ocean biogeochemical cycling and the linkages to ocean ecosystem structure and function. Assessing and addressing this uncertainty remains an ongoing challenge, calling for continued comparisons and improvement in future models. Additionally, gaps remain in projecting regional marine environment in coastal areas. Local factors, e.g., river discharge, upwelling system and anthropogenic coastal eutrophication, are also critical for shaping future regional biogeochemistry under future scenarios. Dealing with these local-scale changes requires a dynamical downscaling approach, like a highresolution regional ocean model forced by the largescale boundary conditions from ESMs and prescribed loadings, such as runoff and nutrients (Cannaby et al., 2015). The ambitious work deserves a further investigation in the future.


Ongoing atmospheric CO2 and other greenhouse gas emissions have triggered substantial changes in the ocean's physical and biogeochemical parameters globally, thereby potentially influencing marine ecosystems and human society. Focusing on the areas of the coastal China seas, this study provided an assessment of the future changes in several marine environmental drivers (i.e., SST, salinity, DO, pH, and NPP) and their cumulative effects based on the outputs of nine CMIP5 ESMs that involve biogeochemical components. The results show that mid-high latitude areas of the coastal China seas (i.e., ECS) would be simultaneously exposed to enhanced warming, oxygen depletion, acidification, and reduction in NPP, the magnitudes of which will increase as the greenhouse gas concentration increases. By the end of the 21st century (2090–2099), the upper layer of the ECS could experience an SST increase of 3.24±1.23℃, a DO concentration decrease of -10.90±3.92 μmol/L (decrease of 6.3%), a pH decline of 0.36±0.02, and a NPP reduction of -17.7±6.2 mg/(m2∙d) (decrease of 12.9%) relative to current levels (1980–2005) under the RCP 8.5 scenario. The co-occurrence of these changes and their cascade effects are expected to induce considerable biological and ecological responses, thereby making the ECS among the most vulnerable ocean areas to future climate change. Comparatively, the SCS was projected to be less affected by these biogeochemical changes. It should be noted that there are large uncertainties in above projections, and addressing these uncertainties calls for continued comparisons and improvement in future models.


All data analyzed in this study are publicly available. Outputs from the nine Earth System Models from the CMIP5 were downloaded from the archive at

Electronic supplementary material

Supplementary material (Supplementary Figs.S1–S7) is available in the online version of this article at

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