Projections of changes in marine environment in coastal China seas over the 21 st 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 fi fth 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 21 st 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, acidifi cation, 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°C, 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/(m 2·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 eff ects 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.

Keyword: Coupled Model Intercomparison Project (CMIP5); sea surface temperature (SST); dissolved oxygen (DO); seawater pH; net primary productivity

1 INTRODUCTION

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) demonstrates with high confi dence that our oceans have undergone fundamental changes (e.g., ocean warming and acidifi cation) 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 aff ect the distribution and abundance of marine organisms and ecosystems. For example, rapid warming has driven many fi sh 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 acidifi cation, which results from increased carbon dioxide (CO2) entering the ocean, has been reported to reduce rates of calcifi cation 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 insuffi cient 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°C 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 CO2emission 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 21stcentury (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 eff ective approach to understand and assess oceanic responses to diff erent 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 fi sheries, 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°C 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 fi sh 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 signifi cant 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.

2 MATERIAL AND METHOD

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 diff erences under diff erent 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 CO2concentration has already risen from 280×10-6(in preindustrial times) to above 400×10-6recently, and will rise to (550–900)×10-6by 2100 depending on the emission scenario (Taylor et al., 2012). In particular, RCP 8.5 represents a high emission scenario (without eff ective climate change mitigation policies) with a continuous increase in radiative forcing to about 8.5 W/m2in 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, inwhich radiative forcing peaks in 2050 before decreasing to an eventual nominal level of 2.6 W/m2in 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.

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

In this study, nine ESMs developed from diff erent 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 diff erent horizontal resolutions ranging from 0.3° to 2°. Some ESMs have several versions with diff erent 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, fi ve 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 refl ects 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 diff erent complexities and biogeochemical processes (Table 1). Some of the models do not cover all fi ve 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.

In the following analysis, all the variables were fi rst 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 identifi ed 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 (www.science.oregonstate.edu/ocean.productivity). We evaluated three metrics (pattern correlation, normalized standard deviation, and root mean square diff erence) 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 diff erence 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 fi elds 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 fi ve 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).

The ensemble mean result of multi-model outputs could skillfully reproduce the spatial patterns of SST, for example, the Indo-Pacifi c 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% confi dence level. Further, each model also represented the SST pattern well, although some specifi c 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 Indo-Pacifi c warm pool (due to large rainfall) and higher salinity in the subtropical ocean (due to intense evaporation). Although theR-value of salinity (0.89) was lower than that of SST, the inter-model diff erences 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 ofR-values (0.7–0.8). However, the models appeared to underestimate the overall DO across the ocean, especially in the western tropical Pacifi c 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 withRvalues ranging from 0.3 to 0.78. There were also obvious diff erences 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 diff erences in specifi c 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 fi ve 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 21stcentury 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.

Fig.1 Comparison of the observations (left panel) and ensemble mean of the CMIP5 models (right panel)

Table 2 Future changes in SST (°C; relative to 1980-2005) in the coastal China seas

3.2.1 Sea surface temperature

Continued warming of the global ocean during the 21stcentury is projected, regardless of the emission scenario used (Kirtman et al., 2013). However, striking regional diff erences 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).

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 21stcentury 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 21stcentury 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°C 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°C during the 2090s, which was about 0.3°C lower than that in the ECS. In addition, the magnitude of warming in the ECS and SCS during diff erent 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.2 The Taylor diagram for displaying normalized pattern statistics of SST, salinity, DO, pH, and NPP between the nine CMIP5 models and observation reference

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

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

Overall, all areas of the coastal China seas were projected to experience robust warming during the 21stcentury, 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 confi rmed 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 signifi cant 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 Pacifi c, and decreased salinity in high precipitation areas, such as the tropical western Pacifi c (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 eff ects of rainfall, evaporation, and runoff . Recent observational research also shows obvious regional diff erences 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 aff ect 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 defi nite regional diff erences 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 suff er 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 quantifi cation, 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 21stcentury (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 O2inventory 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 suff er 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 eff ect 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 stratifi cation and thereby reduce the mixing of oxygen-rich surface layers into the deeper parts of the ocean (Ilyina et al., 2013). These infl uencing mechanisms have recently been verifi ed 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 CO2accumulated in the atmosphere, thereby resulting in a decrease in seawater pH. The IPCC AR5 concluded that the future increases in anthropogenic atmospheric CO2are 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 acidifi cation 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 acidifi cation 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 eff ective to alleviate increasing acidifi cation. 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).

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 acidifi cation 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 acidifi cation and closely tracking the changes in atmospheric CO2concentration (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 CO2sink at the global scale, which is likely due to abundant nutrients and high primary productivity (Dai et al., 2013). Strengthened stratifi cation due to enhanced warming in the ECS may exacerbate acidifi cation 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.

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

Table 5 Future changes in the NPP (mg/(m 2·d); relative to 1980-2005) in the China seas

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 stratifi cation 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 diff erences 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 stratifi cation. 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).

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 stratifi cation 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 stratifi cation and photoactivation associated with enhanced warming.

3.3 Cumulative eff ect

Fig.5 Spatial distribution of the cumulative negative eff ect during the 2090s under RCP 8.5

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 acidifi cation 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 eff ects, we focused on the negative environmental changes given their overwhelming global coverage. The term “negative” was used to indicate potential eff ects, and not the actual changes in the direction and magnitude of environmental parameters. The cumulative negative eff ect (I) is defi ned 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 21stcentury (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 eff ects 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 infl uenced by changes in multiple parameters. Geographically, the largest cumulative negative eff ect occurred at the tropical central-eastern Pacifi c and high latitude regions of the northern Pacifi c and northern Atlantic. Large values in the tropical central-eastern Pacifi c were mainly caused by the combined eff ects of robust warming and the decrease in NPP. Otherwise, the high latitude areas of the northern Pacifi c 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 aff ected areas; although none of the changes was the largest, the magnitude of the additive eff ects 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 Pacifi c were less aff ected 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 eff ects was performed in linear composition with equal weight for simplifi cation; however, the biological and ecological responses in the actual ocean do not follow an idealized or linear manner to such changes. The cumulative eff ects provide a synoptic but more comprehensive global assessment of the simultaneous changes in future ocean biogeochemical variables than the consideration of single stressors alone.

In summary, the ECS were projected to be among the regions with the largest cumulative negative eff ect due to the co-occurrence of warming, deoxygenation, acidifi cation, and decrease in NPP. The cascade eff ects of co-occurring changes in these parameters could accelerate and deteriorate the biological and ecological responses either additively or synergistically, thereby aff ecting the social economic system. For instance, warming can facilitate fi sh metabolism and thus increase the demand for oxygen and phytoplankton; however, if this is along with an insuffi cient 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 acidifi cation (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 fi shery yields, and negative impacts on human wellbeing. Therefore, the ECS may be one of the most vulnerable areas to future climate change.

4 DISCUSSION

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 fi rst 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 diff erent 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 confi dence 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 fl ux 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 signifi cant warming regions across the global ocean. The progressively warming and ensuing stratifi cation would result in a reduced oxygen content, in association with compound eff ects of lowered O2solubility 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 stratifi cation, 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 aff ected 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 off set 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.

5 CONCLUSION

Ongoing atmospheric CO2and other greenhouse gas emissions have triggered substantial changes in the ocean’s physical and biogeochemical parameters globally, thereby potentially infl uencing 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 eff ects 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, acidifi cation, and reduction in NPP, the magnitudes of which will increase as the greenhouse gas concentration increases. By the end of the 21stcentury (2090–2099), the upper layer of the ECS could experience an SST increase of 3.24±1.23°C, 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 eff ects 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 aff ected 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.

6 DATA AVAILABILITY STATEMENT

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 http://cmip-pcmdi.llnl.gov/cmip5/data_portal. html.

References

Allison E H, Perry A L, Badjeck M C, Adger W N, Brown K, Conway D, Halls A S, Pilling G, Reynolds J D, Andrew N L, Dulvy N. 2009. Vulnerability of national economies to the impacts of climate change on fi sheries.Fish and Fisheries, 10(2): 173-196.

Behrenfeld M J, Falkowski P G. 1997. A consumer’s guide to phytoplankton primary productivity models.Limnology andOceanography, 42(7): 1 479-1 491.

Bopp L, Resplandy L, Orr J C, Doney S, Dunne J, Gehlen M, Halloran P, Heinze C, Ilyina T, Seferian R, Tjiputra J, Vichi M. 2013. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models.Biogeosciences, 10: 6 225-6 245.

Cai R S, Tan H J, Qi Q H. 2016. Impacts of and adaptation to inter-decadal marine climate change in coastal China seas.International Journal of Climatology, 36(11): 3 770-3 780.

Cai R S, Tan H J, Kontoyiannis H. 2017. Robust surface warming in off shore China seas and its relationship to the East Asian Monsoon wind fi eld and ocean forcing on interdecadal time scales.Journal of Climate, 30(22): 8 987-9 005, https://doi.org/10.1175/JCLI-D-16-0016.1.

Cannaby H, Fach B A, Arkin S S, Salihoglu B. 2015. Climatic controls on biophysical interactions in the Black Sea under present day conditions and a potential future (A1B) climate scenario.Journal of Marine Systems, 141: 149-166.

Cao L, Zhang H. 2017. The role of biological rates in the simulated warming eff ect on oceanic CO2uptake.JournalofGeophysicalResearch:Biogeosciences, 122(5): 1 098-1 106.

Christian J R, Arora V K, Boer G J, Curry C L, Zahariev K, Denman K L, Flato G M, Lee W G, Merryfi eld W J, Roulet N T, Scinocca J F. 2010. The global carbon cycle in the Canadian Earth System Model (CanESM1): preindustrial control simulation.JournalofGeophysicalResearch:Biogeosciences, 115(G3): G03014, https://doi.org/10.1029/2008JG000920.

Collins W J, Bellouin N, Doutriaux-Boucher M, Gedney N, Halloran P, Hinton T, Hughes J, Jones C D, Joshi M, Liddicoat S, Martin G, O'Connor F, Rae J, Senior C, Sitch S, Totterdell I, Wiltshire A, Woodward S. 2011. Development and evaluation of an Earth-System model-HadGEM2.Geoscientifi c Model Development, 4(4): 1 051-1 075.

Dai M H, Cao Z M, Guo X H, Zhai W D, Liu Z Y, Yin Z Q, Xu Y P, Gan J P, Hu J H, Du C J. 2013. Why are some marginal seas sources of atmospheric CO2?Geophysical Research Letters, 40(10): 2 154-2 158.

Dufresne J L, Foujols M A, Denvil S, Caubel A, Marti O, Aumont O, Balkanski Y, Bekki S, Bellenger H, Benshila R, Bony S, Bopp L, Braconnot P, Brockmann P, Cadule P, Cheruy F, Codron F, Cozic A, Cugnet D, De Noblet N, Duvel J P, Ethé C, Fairhead L, Fichefet T, Flavoni S, Friedlingstein P, Grandpeix J Y, Guez L, Guilyardi E, Hauglustaine D, Hourdin F, Idelkadi A, Ghattas J, Joussaume S, Kageyama M, Krinner G, Labetoulle S, Lahellec A, Lefebvre M P, Lefevre F, Levy C, Li Z X, Lloyd J, Lott F, Madec G, Mancip M, Marchand M, Masson S, Meurdesoif Y, Mignot J, Musat I, Parouty S, Polcher J, Rio C, Schulz M, Swingedouw D, Szopa S, Talandier C, Terray P, Viovy N, Vuichard N. 2013. Climate change projections using the IPSL-CM5 earth system model: from CMIP3 to CMIP5.Climate Dynamics, 40(9-10): 2 123-2 165.

Dunne J P, John J G, Shevliakova E, Stouff er R J, Krasting J P, Malyshev S L, Milly P C D, Sentman L T, Adcroft A J, Cooke W, Dunne K A, Griffi es S M, Hallberg R W, Harrison M J, Levy H, Wittenberg A T, Phillips P J, Zadeh N. 2013. GFDL’s ESM2 global coupled climate-carbon Earth System Models. Part II: Carbon system formulation and baseline simulation characteristics.Journal of Climate, 26(7): 2 247-2 267.

Durack P J, Wijff els S E, Matear R J. 2012. Ocean salinities reveal strong global water cycle intensifi cation during 1950 to 2000.Science, 336(6080): 455-458.

Gobler C J, Doherty O M, Hattenrath-Lehmann T K, Griffi th A W, Kang Y, Litaker R W. 2017. Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacifi c oceans.Proceedings of the National Academy of Sciences of the United States of America, 114(19): 4 975-4 980, https://doi.org/10.1073/pnas.1619575114.

Hoegh-Guldberg O, Bruno J F. 2010. The impact of climate change on the world’s marine ecosystems.Science, 328(5985): 1 523-1 528.

Holt J, Schrum C, Cannaby H, Daewel U, Allen I, Artioli Y, Artioli L, Bopp L, Butenschon M, Fach B A, Harle J, Pushpadas D, Salihoglu B, Wakelin S. 2016. Potential impacts of climate change on the primary production of regional seas: a comparative analysis of fi ve European seas.ProgressinOceanography, 140: 91-115.

Ilyina T, Six K D, Segschneider J, Maier-Reimer E, LI H M, Núñez-Riboni I. 2013. Global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI-Earth System Model in diff erent CMIP5 experimental realizations.Journal of Advances in Modeling Earth Systems, 5(2): 287-315.

Intergovernmental Panel on Climate Change. 2014. Climate Change 2013-the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

Kirtman B, Power S B, Adedoyin J A, Boer J G, Bojariu R, Camilloni I, Doblas-Reyes F J, Fiore A M, Kimoto M, Meehl G A, Prather M, Sarr A, Schär C, Sutton R, Van Oldenborgh J G, Vecchi G, Wang H J. 2013. Near-term climate change: projections and predictability.In: Stocker T F, Qin D H, Plattner G K, Tignor M M B, Allen S K, Boschung J, Nauels A, Xia Y, Bex V, Midgley P M eds. Climate Change 2013-The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Kitoh A. 2006. Asian monsoons in the future.In: Wang B ed. The Asian Monsoon. Springer, Berlin, Heidelberg.

Lauvset S K, Key R M, Olsen A, van Heuven S, Velo A, Lin X H, Schirnick C, Kozyr A, Tanhua T, Hoppema M, Jutterström S, Steinfeldt R, Jeansson E, Ishii M, Perez F F, Suzuki T, Watelet S. 2016. A new global interior ocean mapped climatology: The 1°×1°GLODAP version 2.Earth System Science Data, 8: 325-340.

Li A, Yu F, Diao X Y. 2015. Interannual salinity variability of the Northern Yellow Sea cold water mass.Chinese Journal of Oceanology and Limnology, 33(3): 779-789.

Li L, Li Q. 2010. Eff ects of stocking density, temperature, and salinity on larval survival and growth of the red race of the sea cucumberApostichopusjaponicus(Selenka).Aquaculture International, 18(3): 447-460.

Lozier M S, Dave A C, Palter J B, Gerber L M, Barber R T. 2011. On the relationship between stratifi cation and primary productivity in the North Atlantic.Geophysical Research Letters, 38(18): L18609.

Mora C, Wei C L, Rollo A, Amaro T, Baco A R, Billett D, Bopp L, Chen Q, Collier M, Danovaro R, Gooday A J, Grupe B M, Halloran P R, Ingels J, Jones D O B, Levin L A, Nakano H, Norling K, Ramirez-Llodra E, Rex M, Ruhl H A, Smith C R, Sweetman A K, Thurber A R, Tjiputra J F, Usseglio P, Watling L, Wu T W, Yasuhara M. 2013. Biotic and human vulnerability to projected changes in ocean biogeochemistry over the 21st century.PLoSBiology, 11(10): e1001682, https://doi.org/10.1371/journal.pbio.100168.

Rayner N A, Parker D E, Horton E B, Folland C K, Alexander L V, Rowell D P, Kent E C, Kaplan A. 2003. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century.Journal of Geophysical Research, 108(D14): 4 407, https://doi.org/10.1029/2002JD002670.

Tan H J, Cai R S, Yan X H. 2016. Projected 21st century sea surface temperature over off shore China based on IPCCCMIP5 models.Journal of Applied Oceanography, 35(4): 451-458. (in Chinese with English abstract)

Taylor K E, Stouff er R J, Meehl G A. 2012. An overview of CMIP5 and the experiment design.Bulletin of the American Meteorological Society, 93(4): 485-498.

Tian Y J, Kidokoro H, Watanabe T. 2006. Long-term changes in the fi sh community structure from the Tsushima warm current region of the Japan/East Sea with an emphasis on the impacts of fi shing and climate regime shift over the last four decades.Progress in Oceanography, 68(2-4): 217-237.

Tjiputra J F, Roelandt C, Bentsen M, Lawrence D M, Lorentzen T, Schwinger J, Seland Ø, Heinze C. 2013. Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM).Geoscientifi cModel Development, 6(2): 301-325.

Vichi M, Manzini E, Fogli P G, Alessandri A, Patara L, Scoccimarro E, Masina S, Navarra A. 2011. Global and regional ocean carbon uptake and climate change: Sensitivity to a substantial mitigation scenario.Climate Dynamics, 37(9-10): 1 929-1 947.

Voldoire A, Sanchez-Gomez E, y Mélia D S, Decharme B, Cassou C, Sénési S, Valcke S, Beau I, Alias A, Chevallier M, Déqué M, Deshayes J, Deshayes H, Fernandez E, Madec G, Maisonnave E, Moine M P, Planton S, Saint-Martin D, Szopa S, Tyteca S, Alkama R, Belamari S, Braun A, Coquart L, Coquart F. 2013. The CNRM-CM5.1 global climate model: Description and basic evaluation.Climate Dynamics, 40(9-10): 2 091-2 121.

Wang Y Q, Lin X P. 2018. A preliminary study on the trends and mechanism of the upper ocean salinity in the East China Seas during 1976-1996.Periodical of Ocean University of China, 48(11): 11-18. (in Chinese with English abstract)

Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Nozawa T, Kawase H, Abe M, Yokohata T, Ise T, Sato H, Kato E, Takata K, Emori S, Kawamiya M. 2011. MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments.Geoscientifi c Model Development, 4(4): 845-872.

Wei Q S, Wang B D, Yu Z G, Chen J F, Xue L. 2017. Mechanisms leading to the frequent occurrences of hypoxia and a preliminary analysis of the associated acidifi cation off the Changjiang Estuary in summer.Science China Earth Sciences, 60(2): 158-179.

Xie S P, Hafner J, Tanimoto Y, Liu W T, Tokinaga H, Xu H M. 2002. Bathymetric eff ect on the winter sea surface temperature and climate of the Yellow and East China Seas.Geophysical Research Letters, 29(24): 2 228, https://doi.org/10.1029/2002GL015884.

Zeebe R E, Zachos J C, Caldeira K, Tyrrell T. 2008. Carbon emissions and acidifi cation.Science, 321(5885): 51-52.