2024 Vol. 43, No. 7
Display Method:
2024, 43(7): 1-2.
Abstract:
2024, 43(7): 1-14.
doi: 10.1007/s13131-023-2228-0
Abstract:
Based on the Ocean Reanalysis System version 5 (ORAS5) and the fifth-generation reanalysis datasets derived from European Centre for Medium-Range Weather Forecasts (ERA5), we investigate the different impacts of the central Pacific (CP) El Niño and the eastern Pacific (EP) El Niño on the Southern Ocean (SO) mixed layer depth (MLD) during austral winter. The MLD response to the EP El Niño shows a dipole pattern in the South Pacific, namely the MLD dipole, which is the leading El Niño-induced MLD variability in the SO. The tropical Pacific warm sea surface temperature anomaly (SSTA) signal associated with the EP El Niño excites a Rossby wave train propagating southeastward and then enhances the Amundsen Sea low (ASL). This results in an anomalous cyclone over the Amundsen Sea. As a result, the anomalous southerly wind to the west of this anomalous cyclone advects colder and drier air into the southeast of New Zealand, leading to surface cooling through less total surface heat flux, especially surface sensible heat (SH) flux and latent heat (LH) flux, and thus contributing to the mix layer (ML) deepening. The east of the anomalous cyclone brings warmer and wetter air to the southwest of Chile, but the total heat flux anomaly shows no significant change. The warm air promotes the sea ice melting and maintains fresh water, which strengthens stratification. This results in a shallower MLD. During the CP El Niño, the response of MLD shows a separate negative MLD anomaly center in the central South Pacific. The Rossby wave train triggered by the warm SSTA in the central Pacific Ocean spreads to the Amundsen Sea, which weakens the ASL. Therefore, the anomalous anticyclone dominates the Amundsen Sea. Consequently, the anomalous northerly wind to the west of anomalous anticyclone advects warmer and wetter air into the central and southern Pacific, causing surface warming through increased SH, LH, and longwave radiation flux, and thus contributing to the ML shoaling. However, to the east of the anomalous anticyclone, there is no statistically significant impact on the MLD.
Based on the Ocean Reanalysis System version 5 (ORAS5) and the fifth-generation reanalysis datasets derived from European Centre for Medium-Range Weather Forecasts (ERA5), we investigate the different impacts of the central Pacific (CP) El Niño and the eastern Pacific (EP) El Niño on the Southern Ocean (SO) mixed layer depth (MLD) during austral winter. The MLD response to the EP El Niño shows a dipole pattern in the South Pacific, namely the MLD dipole, which is the leading El Niño-induced MLD variability in the SO. The tropical Pacific warm sea surface temperature anomaly (SSTA) signal associated with the EP El Niño excites a Rossby wave train propagating southeastward and then enhances the Amundsen Sea low (ASL). This results in an anomalous cyclone over the Amundsen Sea. As a result, the anomalous southerly wind to the west of this anomalous cyclone advects colder and drier air into the southeast of New Zealand, leading to surface cooling through less total surface heat flux, especially surface sensible heat (SH) flux and latent heat (LH) flux, and thus contributing to the mix layer (ML) deepening. The east of the anomalous cyclone brings warmer and wetter air to the southwest of Chile, but the total heat flux anomaly shows no significant change. The warm air promotes the sea ice melting and maintains fresh water, which strengthens stratification. This results in a shallower MLD. During the CP El Niño, the response of MLD shows a separate negative MLD anomaly center in the central South Pacific. The Rossby wave train triggered by the warm SSTA in the central Pacific Ocean spreads to the Amundsen Sea, which weakens the ASL. Therefore, the anomalous anticyclone dominates the Amundsen Sea. Consequently, the anomalous northerly wind to the west of anomalous anticyclone advects warmer and wetter air into the central and southern Pacific, causing surface warming through increased SH, LH, and longwave radiation flux, and thus contributing to the ML shoaling. However, to the east of the anomalous anticyclone, there is no statistically significant impact on the MLD.
2024, 43(7): 15-25.
doi: 10.1007/s13131-023-2279-2
Abstract:
This study evaluates the performance of 16 models sourced from the coupled model intercomparison project phase 6 (CMIP6) in simulating marine heatwaves (MHWs) in the South China Sea (SCS) during the historical period (1982−2014), and also investigates future changes in SCS MHWs based on simulations from three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245, and SSP585) using CMIP6 models. Results demonstrate that the CMIP6 models perform well in simulating the spatial-temporal distribution and intensity of SCS MHWs, with their multi-model ensemble (MME) results showing the best performance. The reasonable agreement between the observations and CMIP6 MME reveals that the increasing trends of SCS MHWs are attributed to the warming sea surface temperature trend. Under various SSP scenarios, the year 2040 emerges as pivotal juncture for future shifts in SCS MHWs, marked by distinct variations in changing rate and amplitudes. This is characterized by an accelerated decrease in MHWs frequency and a notably heightened increase in mean intensity, duration, and total days after 2040. Furthermore, the projection results for SCS MHWs suggest that the spatial pattern of MHWs remains consistent across future periods. However, the intensity shows higher consistency only during the near-term period (2021−2050), while notable inconsistencies are observed during the medium-term (2041−2070) and long-term (2071−2100) periods under the three SSP scenarios. During the near-term period, the SCS MHWs are characterized by moderate and strong events with high frequencies and relatively shorter durations. In contrast, during the medium-term period, MHWs are also characterized by moderate and strong events, but with longer-lasting and more intense events under the SSP245 and SSP585 scenarios. However, in the long-term period, extreme MHWs become the dominant feature under the SSP585 scenario, indicating a substantial intensification of SCS MHWs, effectively establishing a near-permanent state.
This study evaluates the performance of 16 models sourced from the coupled model intercomparison project phase 6 (CMIP6) in simulating marine heatwaves (MHWs) in the South China Sea (SCS) during the historical period (1982−2014), and also investigates future changes in SCS MHWs based on simulations from three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245, and SSP585) using CMIP6 models. Results demonstrate that the CMIP6 models perform well in simulating the spatial-temporal distribution and intensity of SCS MHWs, with their multi-model ensemble (MME) results showing the best performance. The reasonable agreement between the observations and CMIP6 MME reveals that the increasing trends of SCS MHWs are attributed to the warming sea surface temperature trend. Under various SSP scenarios, the year 2040 emerges as pivotal juncture for future shifts in SCS MHWs, marked by distinct variations in changing rate and amplitudes. This is characterized by an accelerated decrease in MHWs frequency and a notably heightened increase in mean intensity, duration, and total days after 2040. Furthermore, the projection results for SCS MHWs suggest that the spatial pattern of MHWs remains consistent across future periods. However, the intensity shows higher consistency only during the near-term period (2021−2050), while notable inconsistencies are observed during the medium-term (2041−2070) and long-term (2071−2100) periods under the three SSP scenarios. During the near-term period, the SCS MHWs are characterized by moderate and strong events with high frequencies and relatively shorter durations. In contrast, during the medium-term period, MHWs are also characterized by moderate and strong events, but with longer-lasting and more intense events under the SSP245 and SSP585 scenarios. However, in the long-term period, extreme MHWs become the dominant feature under the SSP585 scenario, indicating a substantial intensification of SCS MHWs, effectively establishing a near-permanent state.
2024, 43(7): 26-39.
doi: 10.1007/s13131-023-2252-0
Abstract:
Ocean temperature is an important physical variable in marine ecosystems, and ocean temperature prediction is an important research objective in ocean-related fields. Currently, one of the commonly used methods for ocean temperature prediction is based on data-driven, but research on this method is mostly limited to the sea surface, with few studies on the prediction of internal ocean temperature. Existing graph neural network-based methods usually use predefined graphs or learned static graphs, which cannot capture the dynamic associations among data. In this study, we propose a novel dynamic spatiotemporal graph neural network (DSTGN) to predict three-dimensional ocean temperature (3D-OT), which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge. Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions. We also integrated dynamic graph learning, static graph learning, graph convolution, and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data. In this study, we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis, with data covering the vertical variation of temperature from the sea surface to 1 000 m below the sea surface. We compared five mainstream models that are commonly used for ocean temperature prediction, and the results showed that the method achieved the best prediction results at all prediction scales.
Ocean temperature is an important physical variable in marine ecosystems, and ocean temperature prediction is an important research objective in ocean-related fields. Currently, one of the commonly used methods for ocean temperature prediction is based on data-driven, but research on this method is mostly limited to the sea surface, with few studies on the prediction of internal ocean temperature. Existing graph neural network-based methods usually use predefined graphs or learned static graphs, which cannot capture the dynamic associations among data. In this study, we propose a novel dynamic spatiotemporal graph neural network (DSTGN) to predict three-dimensional ocean temperature (3D-OT), which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge. Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions. We also integrated dynamic graph learning, static graph learning, graph convolution, and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data. In this study, we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis, with data covering the vertical variation of temperature from the sea surface to 1 000 m below the sea surface. We compared five mainstream models that are commonly used for ocean temperature prediction, and the results showed that the method achieved the best prediction results at all prediction scales.
2024, 43(7): 40-51.
doi: 10.1007/s13131-024-2343-6
Abstract:
The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers. Long short-term memory (LSTM) networks excel in processing and predicting crucial events with extended intervals and time delays in time series data. Additionally, the sequence-to-sequence (Seq2Seq) model, known for handling temporal relationships, adapting to variable-length sequences, effectively capturing historical information, and accommodating various influencing factors, emerges as a robust and flexible tool in discharge forecasting. In this study, we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River (Yangtze River) Estuary. This study focuses on discharge forecasting using three key input characteristics: flow velocity, water level, and discharge, which means the structure of multiple input and single output is adopted. The experiment used the discharge data of the whole year of 2020, of which the first 80% is used as the training set, and the last 20% is used as the test set. This means that the data covers different tidal cycles, which helps to test the forecasting effect of different models in different tidal cycles and different runoff. The experimental results indicate that the proposed models demonstrate advantages in long-term, mid-term, and short-term discharge forecasting. The Seq2Seq models improved by 6%–60% and 5%–20% of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction, respectively. In addition, the relative accuracy of the Seq2Seq model is 1% to 3% higher than that of the LSTM model. Analytical assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle well. This indicates the significance of the Seq2Seq models.
The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers. Long short-term memory (LSTM) networks excel in processing and predicting crucial events with extended intervals and time delays in time series data. Additionally, the sequence-to-sequence (Seq2Seq) model, known for handling temporal relationships, adapting to variable-length sequences, effectively capturing historical information, and accommodating various influencing factors, emerges as a robust and flexible tool in discharge forecasting. In this study, we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River (Yangtze River) Estuary. This study focuses on discharge forecasting using three key input characteristics: flow velocity, water level, and discharge, which means the structure of multiple input and single output is adopted. The experiment used the discharge data of the whole year of 2020, of which the first 80% is used as the training set, and the last 20% is used as the test set. This means that the data covers different tidal cycles, which helps to test the forecasting effect of different models in different tidal cycles and different runoff. The experimental results indicate that the proposed models demonstrate advantages in long-term, mid-term, and short-term discharge forecasting. The Seq2Seq models improved by 6%–60% and 5%–20% of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction, respectively. In addition, the relative accuracy of the Seq2Seq model is 1% to 3% higher than that of the LSTM model. Analytical assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle well. This indicates the significance of the Seq2Seq models.
2024, 43(7): 52-69.
doi: 10.1007/s13131-024-2379-7
Abstract:
Nitrification, a central process in the marine nitrogen cycle, produces regenerated nitrate in the euphotic zone and emits N2O, a potent greenhouse gas as a by-product. The regulatory mechanisms of nitrification in the Southern Ocean, which is a critical region for CO2 sequestration and radiative benefits, remain poorly understood. Here, we investigated the in situ and dark nitrification rates in the upper 500 m and conducted substrate kinetics experiments across the Indian Sector in the Cosmonaut and Cooperation seas in the late austral summer. Our findings indicate that light inhibition of nitrification decreases exponentially with depth, exhibiting a light threshold of 0.53% photosynthetically active radiation. A positive relationship between dark nitrification and apparent oxygen utilization suggests a dependence on substrate availability from primary production. Importantly, an increased\begin{document}${\mathrm{NH}}_4^+ $\end{document} ![]()
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Nitrification, a central process in the marine nitrogen cycle, produces regenerated nitrate in the euphotic zone and emits N2O, a potent greenhouse gas as a by-product. The regulatory mechanisms of nitrification in the Southern Ocean, which is a critical region for CO2 sequestration and radiative benefits, remain poorly understood. Here, we investigated the in situ and dark nitrification rates in the upper 500 m and conducted substrate kinetics experiments across the Indian Sector in the Cosmonaut and Cooperation seas in the late austral summer. Our findings indicate that light inhibition of nitrification decreases exponentially with depth, exhibiting a light threshold of 0.53% photosynthetically active radiation. A positive relationship between dark nitrification and apparent oxygen utilization suggests a dependence on substrate availability from primary production. Importantly, an increased
2024, 43(7): 70-77.
doi: 10.1007/s13131-023-2275-6
Abstract:
Marine heatwaves (MHWs) caused by anthropogenic climate change are becoming a key driver of change at the ecosystem level. Thermal conditions experienced by marine organisms across their distribution, particularly towards the equator, are likely to approach their physiological limits, resulting in extensive mortality and subsequent changes at the population level. Populations at the margins of their species’ distribution are thought to be more sensitive to climate-induced environmental pressures than central populations, but our understanding of variability in fitness-related physiological traits in trailing versus leading-edge populations is limited. In a laboratory simulation study, we tested whether two leading (Iceland) and two trailing (Spain) peripheral populations of the intertidal macroalga Corallina officinalis display different levels of maximum potential quantum efficiency (Fv/Fm) resilience to current and future winter MHWs scenarios. Our study revealed that ongoing and future local winter MHWs will not negatively affect leading-edge populations of C. officinalis, which exhibited stable photosynthetic efficiency throughout the study. Trailing edge populations showed a positive though non-significant trend in photosynthetic efficiency throughout winter MHWs exposure. Poleward and equatorward populations did not produce significantly different results, with winter MHWs having no negative affect on Fv/Fm of either population. Additionally, we found no long-term regional or population-level influence of a winter MHWs on this species’ photosynthetic efficiency. Thus, we found no statistically significant difference in thermal stress responses between leading and trailing populations. Nonetheless, C. officinalis showed a trend towards higher stress responses in southern than northern populations. Because responses rest on a variety of local population traits, they are difficult to predict based solely on thermal pressures.
Marine heatwaves (MHWs) caused by anthropogenic climate change are becoming a key driver of change at the ecosystem level. Thermal conditions experienced by marine organisms across their distribution, particularly towards the equator, are likely to approach their physiological limits, resulting in extensive mortality and subsequent changes at the population level. Populations at the margins of their species’ distribution are thought to be more sensitive to climate-induced environmental pressures than central populations, but our understanding of variability in fitness-related physiological traits in trailing versus leading-edge populations is limited. In a laboratory simulation study, we tested whether two leading (Iceland) and two trailing (Spain) peripheral populations of the intertidal macroalga Corallina officinalis display different levels of maximum potential quantum efficiency (Fv/Fm) resilience to current and future winter MHWs scenarios. Our study revealed that ongoing and future local winter MHWs will not negatively affect leading-edge populations of C. officinalis, which exhibited stable photosynthetic efficiency throughout the study. Trailing edge populations showed a positive though non-significant trend in photosynthetic efficiency throughout winter MHWs exposure. Poleward and equatorward populations did not produce significantly different results, with winter MHWs having no negative affect on Fv/Fm of either population. Additionally, we found no long-term regional or population-level influence of a winter MHWs on this species’ photosynthetic efficiency. Thus, we found no statistically significant difference in thermal stress responses between leading and trailing populations. Nonetheless, C. officinalis showed a trend towards higher stress responses in southern than northern populations. Because responses rest on a variety of local population traits, they are difficult to predict based solely on thermal pressures.
2024, 43(7): 78-92.
doi: 10.1007/s13131-023-2184-8
Abstract:
Conch Island is a typical artificial island at the Tanghe Estuary in Bohai Sea, China. To improve natural environment and boost local tourism, beach nourishment will be applied to its north-western shore. The projected beach is landward and opposite to the Jinmeng Bay Beach. Nowadays, with climate changes, frequent heavy rainfalls in Hebei Province rise flood hazards at the Tanghe Estuary. Under this circumstance, potential influences on the projected beach of a flood are investigated for sustainable managements. A multi-coupled model is established and based on the data from field observations, where wave model, flow model and multi-fraction sediment transport model are included. In addition, the impacts on the projected beach of different components in extreme events are discussed, including the spring tides, storm winds, storm waves, and sediment inputs. The numerical results indicate the following result. (1) Artificial islands protect the coasts from erosion by obstructing landward waves, but rise the deposition risks along the target shore. (2) Flood brings massive sediment inputs and leads to scours at the estuary, but the currents with high sediment concentration contribute to the accretions along the target shore. (3) The projected beach mitigates flood actions and reduces the maximum mean sediment concentration along the target shore by 20%. (4) The storm winds restrict the flood and decrease the maximum mean sediment concentration by 21%. With the combined actions of storm winds and waves, the maximum value further declines by 38%. (5) A quadratic polynomial relationship between the deposition depths and the maximum sediment inputs with flood is established for estimations on the potential morphological changes after the flood process in extreme events. For the uncertainty of estuarine floods, continuous monitoring on local hydrodynamic variations and sediment characteristics at Tanghe Estuary is necessary.
Conch Island is a typical artificial island at the Tanghe Estuary in Bohai Sea, China. To improve natural environment and boost local tourism, beach nourishment will be applied to its north-western shore. The projected beach is landward and opposite to the Jinmeng Bay Beach. Nowadays, with climate changes, frequent heavy rainfalls in Hebei Province rise flood hazards at the Tanghe Estuary. Under this circumstance, potential influences on the projected beach of a flood are investigated for sustainable managements. A multi-coupled model is established and based on the data from field observations, where wave model, flow model and multi-fraction sediment transport model are included. In addition, the impacts on the projected beach of different components in extreme events are discussed, including the spring tides, storm winds, storm waves, and sediment inputs. The numerical results indicate the following result. (1) Artificial islands protect the coasts from erosion by obstructing landward waves, but rise the deposition risks along the target shore. (2) Flood brings massive sediment inputs and leads to scours at the estuary, but the currents with high sediment concentration contribute to the accretions along the target shore. (3) The projected beach mitigates flood actions and reduces the maximum mean sediment concentration along the target shore by 20%. (4) The storm winds restrict the flood and decrease the maximum mean sediment concentration by 21%. With the combined actions of storm winds and waves, the maximum value further declines by 38%. (5) A quadratic polynomial relationship between the deposition depths and the maximum sediment inputs with flood is established for estimations on the potential morphological changes after the flood process in extreme events. For the uncertainty of estuarine floods, continuous monitoring on local hydrodynamic variations and sediment characteristics at Tanghe Estuary is necessary.
2024, 43(7): 93-111.
doi: 10.1007/s13131-023-2210-x
Abstract:
Retrieval of Thin-Ice Thickness (TIT) using thermodynamic modeling is sensitive to the parameterization of the independent variables (coded in the model) and the uncertainty of the measured input variables. This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness. Moreover, it estimates the uncertainty of the output in response to the uncertainties of the input variables. The parameterized independent variables include atmospheric longwave emissivity, air density, specific heat of air, latent heat of ice, conductivity of ice, snow depth, and snow conductivity. Measured input parameters include air temperature, ice surface temperature, and wind speed. Among the independent variables, the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth, followed ice conductivity. The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity, atmospheric emissivity, and snow conductivity and depth. The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data. From in situ measurements, the uncertainties of the measured air temperature and surface temperature are found to be high. The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error. The results show that the overall uncertainty of TIT to air temperature, surface temperature, and wind speed uncertainty is around 0.09 m, 0.049 m, and −0.005 m, respectively.
Retrieval of Thin-Ice Thickness (TIT) using thermodynamic modeling is sensitive to the parameterization of the independent variables (coded in the model) and the uncertainty of the measured input variables. This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness. Moreover, it estimates the uncertainty of the output in response to the uncertainties of the input variables. The parameterized independent variables include atmospheric longwave emissivity, air density, specific heat of air, latent heat of ice, conductivity of ice, snow depth, and snow conductivity. Measured input parameters include air temperature, ice surface temperature, and wind speed. Among the independent variables, the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth, followed ice conductivity. The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity, atmospheric emissivity, and snow conductivity and depth. The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data. From in situ measurements, the uncertainties of the measured air temperature and surface temperature are found to be high. The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error. The results show that the overall uncertainty of TIT to air temperature, surface temperature, and wind speed uncertainty is around 0.09 m, 0.049 m, and −0.005 m, respectively.
2024, 43(7): 112-124.
doi: 10.1007/s13131-023-2209-3
Abstract:
The spaceborne platform has unprecedently provided the global eddy-permitting (typically about 0.25°) products of sea surface salinity (SSS), however the existing SSS products can hardly resolve mesoscale motions due to the heavy noises therein and the over-smoothing in denoising processes. By means of the multi-fractal fusion (MFF), the high-resolution SSS product is synthesized with the template of sea surface temperature (SST). Two low-resolution SSS products and four SST products are considered as the source data and the templates respectively to determine the best combination. The fused products are validated by the in situ observations and intercompared via SSS maps, Singularity Exponent maps and wavenumber spectra. The results demonstrate that the MFF can perform a good work in mitigating the noises and improving the resolution. The combination of the climate change initiative SSS and the remote sensing system SST can produce the 0.1° denoised product whose global mean standard derivation of salinity against Argo is 0.21 and the feature resolution can reach 30−40 km.
The spaceborne platform has unprecedently provided the global eddy-permitting (typically about 0.25°) products of sea surface salinity (SSS), however the existing SSS products can hardly resolve mesoscale motions due to the heavy noises therein and the over-smoothing in denoising processes. By means of the multi-fractal fusion (MFF), the high-resolution SSS product is synthesized with the template of sea surface temperature (SST). Two low-resolution SSS products and four SST products are considered as the source data and the templates respectively to determine the best combination. The fused products are validated by the in situ observations and intercompared via SSS maps, Singularity Exponent maps and wavenumber spectra. The results demonstrate that the MFF can perform a good work in mitigating the noises and improving the resolution. The combination of the climate change initiative SSS and the remote sensing system SST can produce the 0.1° denoised product whose global mean standard derivation of salinity against Argo is 0.21 and the feature resolution can reach 30−40 km.
2024, 43(7): 125-137.
doi: 10.1007/s13131-024-2384-x
Abstract:
The oceanic trace metals iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), and cadmium (Cd) are crucial to marine phytoplankton growth and global carbon cycle, and the analysis of their stable isotopes can provide valuable insights into their biogeochemical cycles within the ocean. However, the simultaneous isotopic analysis of multiple elements present in seawater is challenging because of their low concentrations, limited volumes of the test samples, and high salt matrix. In this study, we present the novel method developed for the simultaneous analysis of five isotope systems by 1 L seawater sample. In the developed method, the NOBIAS Chelate-PA1 resin was used to extract metals from seawater, the AG MP-1M anion-exchange resin to purify Cu, Fe, Zn, Cd, and the NOBIAS Chelate-PA1 resin to further extract Ni from the matrix elements. Finally, a multi-collector inductively coupled plasma mass spectroscope (MC-ICPMS) was employed for the isotopic measurements using a double-spike technique or sample-standard bracketing combined with internal normalization. This method exhibited low total procedural blanks (0.04 pg, 0.04 pg, 0.21 pg, 0.15 pg, and 3 pg for Ni, Cu, Fe, Zn, and Cd, respectively) and high extraction efficiencies (100.5% ± 0.3%, 100.2% ± 0.5%, 97.8% ± 1.4%, 99.9% ± 0.8%, and 100.1% ± 0.2% for Ni, Cu, Fe, Zn, and Cd, respectively). The external errors and external precisions of this method could be considered negligible. The proposed method was further tested on the seawater samples obtained from the whole vertical profile of a water column during the Chinese GEOTRACES GP09 cruise in the Northwest Pacific, and the results showed good agreement with previous related data. This innovative method will contribute to the advancement of isotope research and enhance our understanding of the marine biogeochemical cycling of Fe, Ni, Cu, Zn, and Cd.
The oceanic trace metals iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), and cadmium (Cd) are crucial to marine phytoplankton growth and global carbon cycle, and the analysis of their stable isotopes can provide valuable insights into their biogeochemical cycles within the ocean. However, the simultaneous isotopic analysis of multiple elements present in seawater is challenging because of their low concentrations, limited volumes of the test samples, and high salt matrix. In this study, we present the novel method developed for the simultaneous analysis of five isotope systems by 1 L seawater sample. In the developed method, the NOBIAS Chelate-PA1 resin was used to extract metals from seawater, the AG MP-1M anion-exchange resin to purify Cu, Fe, Zn, Cd, and the NOBIAS Chelate-PA1 resin to further extract Ni from the matrix elements. Finally, a multi-collector inductively coupled plasma mass spectroscope (MC-ICPMS) was employed for the isotopic measurements using a double-spike technique or sample-standard bracketing combined with internal normalization. This method exhibited low total procedural blanks (0.04 pg, 0.04 pg, 0.21 pg, 0.15 pg, and 3 pg for Ni, Cu, Fe, Zn, and Cd, respectively) and high extraction efficiencies (100.5% ± 0.3%, 100.2% ± 0.5%, 97.8% ± 1.4%, 99.9% ± 0.8%, and 100.1% ± 0.2% for Ni, Cu, Fe, Zn, and Cd, respectively). The external errors and external precisions of this method could be considered negligible. The proposed method was further tested on the seawater samples obtained from the whole vertical profile of a water column during the Chinese GEOTRACES GP09 cruise in the Northwest Pacific, and the results showed good agreement with previous related data. This innovative method will contribute to the advancement of isotope research and enhance our understanding of the marine biogeochemical cycling of Fe, Ni, Cu, Zn, and Cd.
