Current Issue
2024 Vol. 43, No. 3
Display Method:
2024, 43(3)
Abstract:
2024, 43(3)
Abstract:
2024, 43(3): 1-14.
doi: 10.1007/s13131-023-2281-8
Abstract:
An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport (KGT) and the Kuroshio/Ryukyu Current system. The study found a significant difference in the interannual variability of the upstream and downstream transports of the East China Sea- (ECS-) Kuroshio and the Ryukyu Current. The interannual variability of the KGT was found to be of paramount importance in causing the differences between the upstream and downstream ECS-Kuroshio. Additionally, it contributed approximately 37% to the variability of the Ryukyu Current. The interannual variability of the KGT was well described by a two-layer rotating hydraulic theory. It was dominated by its subsurface-intensified flow core, and the upper layer transport made a weaker negative contribution to the total KGT. The subsurface flow core was found to be mainly driven by the subsurface pressure head across the Kerama Gap, and the pressure head was further dominated by the subsurface density anomalies on the Pacific side. These density anomalies could be traced back to the eastern open ocean, and their propagation speed was estimated to be about 7.4 km/d, which is consistent with the speed of the local first-order baroclinic Rossby wave. When the negative (positive) density anomaly signal reached the southern region of the Kerama Gap, it triggered the increase (decrease) of the KGT towards the Pacific side and the formation of an anticyclonic (cyclonic) vortex by baroclinic adjustment. Meanwhile, there is an increase (decrease) in the upstream transport of the entire Kuroshio/Ryukyu Current system and an offshore flow that decreases (increases) the downstream Ryukyu Current.
An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport (KGT) and the Kuroshio/Ryukyu Current system. The study found a significant difference in the interannual variability of the upstream and downstream transports of the East China Sea- (ECS-) Kuroshio and the Ryukyu Current. The interannual variability of the KGT was found to be of paramount importance in causing the differences between the upstream and downstream ECS-Kuroshio. Additionally, it contributed approximately 37% to the variability of the Ryukyu Current. The interannual variability of the KGT was well described by a two-layer rotating hydraulic theory. It was dominated by its subsurface-intensified flow core, and the upper layer transport made a weaker negative contribution to the total KGT. The subsurface flow core was found to be mainly driven by the subsurface pressure head across the Kerama Gap, and the pressure head was further dominated by the subsurface density anomalies on the Pacific side. These density anomalies could be traced back to the eastern open ocean, and their propagation speed was estimated to be about 7.4 km/d, which is consistent with the speed of the local first-order baroclinic Rossby wave. When the negative (positive) density anomaly signal reached the southern region of the Kerama Gap, it triggered the increase (decrease) of the KGT towards the Pacific side and the formation of an anticyclonic (cyclonic) vortex by baroclinic adjustment. Meanwhile, there is an increase (decrease) in the upstream transport of the entire Kuroshio/Ryukyu Current system and an offshore flow that decreases (increases) the downstream Ryukyu Current.
2024, 43(3): 15-29.
doi: 10.1007/s13131-023-2270-y
Abstract:
The effects of surf zone eddy generated by alongshore currents on the deformation and transport of dye are still poorly understood, and related tracer release experiments are lacking. Therefore, a tracer release laboratory experiment was conducted under monochromatic, unidirectional incident waves with a large incident angle (30°) on a plane beach with a 1:100 slope in a large wave basin. A charge-coupled device suspended above the basin recorded the dye patch image. The evolution of eddy dye patch was observed and the transport and diffusion were analyzed based on the collected images. Subsequently, a linear instability numerical model was adopted to calculate the perturbation velocity field at the initial stage. The observation and image processing results show that surf zone eddy patches occurred and were separated from the original dye patches. Our numerical analysis results demonstrate that the structure of the perturbation velocity field is consistent with the experimental observations, and that the ejection of eddy patches shoreward or offshore may be ascribed to the double vortex.
The effects of surf zone eddy generated by alongshore currents on the deformation and transport of dye are still poorly understood, and related tracer release experiments are lacking. Therefore, a tracer release laboratory experiment was conducted under monochromatic, unidirectional incident waves with a large incident angle (30°) on a plane beach with a 1:100 slope in a large wave basin. A charge-coupled device suspended above the basin recorded the dye patch image. The evolution of eddy dye patch was observed and the transport and diffusion were analyzed based on the collected images. Subsequently, a linear instability numerical model was adopted to calculate the perturbation velocity field at the initial stage. The observation and image processing results show that surf zone eddy patches occurred and were separated from the original dye patches. Our numerical analysis results demonstrate that the structure of the perturbation velocity field is consistent with the experimental observations, and that the ejection of eddy patches shoreward or offshore may be ascribed to the double vortex.
2024, 43(3): 30-39.
doi: 10.1007/s13131-023-2229-z
Abstract:
An anisotropic diffusion filter can be used to model a flow-dependent background error covariance matrix, which can be achieved by solving the advection-diffusion equation. Because of the directionality of the advection term, the discrete method needs to be chosen very carefully. The finite analytic method is an alternative scheme to solve the advection-diffusion equation. As a combination of analytical and numerical methods, it not only has high calculation accuracy but also holds the characteristic of the auto upwind. To demonstrate its ability, the one-dimensional steady and unsteady advection-diffusion equation numerical examples are respectively solved by the finite analytic method. The more widely used upwind difference method is used as a control approach. The result indicates that the finite analytic method has higher accuracy than the upwind difference method. For the two-dimensional case, the finite analytic method still has a better performance. In the three-dimensional variational assimilation experiment, the finite analytic method can effectively improve analysis field accuracy, and its effect is significantly better than the upwind difference and the central difference method. Moreover, it is still a more effective solution method in the strong flow region where the advective-diffusion filter performs most prominently.
An anisotropic diffusion filter can be used to model a flow-dependent background error covariance matrix, which can be achieved by solving the advection-diffusion equation. Because of the directionality of the advection term, the discrete method needs to be chosen very carefully. The finite analytic method is an alternative scheme to solve the advection-diffusion equation. As a combination of analytical and numerical methods, it not only has high calculation accuracy but also holds the characteristic of the auto upwind. To demonstrate its ability, the one-dimensional steady and unsteady advection-diffusion equation numerical examples are respectively solved by the finite analytic method. The more widely used upwind difference method is used as a control approach. The result indicates that the finite analytic method has higher accuracy than the upwind difference method. For the two-dimensional case, the finite analytic method still has a better performance. In the three-dimensional variational assimilation experiment, the finite analytic method can effectively improve analysis field accuracy, and its effect is significantly better than the upwind difference and the central difference method. Moreover, it is still a more effective solution method in the strong flow region where the advective-diffusion filter performs most prominently.
2024, 43(3): 40-47.
doi: 10.1007/s13131-023-2175-9
Abstract:
Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas. Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses. Storm surge numerical models are important for storm surge forecasting. To further improve the performance of the storm surge forecast models, we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation (SCVT) grid. The model is based on shallow water equations in vector-invariant form, and is discretized by Arakawa C grid. The SCVT grid can not only better describe the coastline information but also avoid rigid transitions, and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement. In addition, the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast. It only takes 37 s to simulate a day in the coastal waters of China. The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China. The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges. The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations, and the mean absolute errors are 3.5 cm and 0.6 h respectively, showing high accuracy and application prospects.
Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas. Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses. Storm surge numerical models are important for storm surge forecasting. To further improve the performance of the storm surge forecast models, we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation (SCVT) grid. The model is based on shallow water equations in vector-invariant form, and is discretized by Arakawa C grid. The SCVT grid can not only better describe the coastline information but also avoid rigid transitions, and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement. In addition, the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast. It only takes 37 s to simulate a day in the coastal waters of China. The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China. The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges. The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations, and the mean absolute errors are 3.5 cm and 0.6 h respectively, showing high accuracy and application prospects.
2024, 43(3): 48-58.
doi: 10.1007/s13131-023-2247-x
Abstract:
The importance of the Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO) in influencing zonally asymmetric changes in Antarctic surface air temperature (SAT) has been established. However, previous studies have primarily concentrated on examining the combined impact of the contrasting phases of the AMO and IPO, which have been dominant since the advent of satellite observations in 1979. This study utilizes long-term reanalysis data to investigate the impact of four combinations of +AMO+IPO, –AMO–IPO, +AMO–IPO, and –AMO+IPO on Antarctic SAT over the past 115 years. The +AMO phase is characterized by a spatial mean temperature amplitude of up to 0.5℃ over the North Atlantic Ocean, accompanied by positive sea surface temperature (SST) anomalies in the tropical eastern Pacific and negative SST anomalies in the extratropical-mid-latitude western Pacific, which are indicative of the +IPO phase. The Antarctic SAT exhibits contrasting spatial patterns during the +AMO+IPO and +AMO–IPO periods. However, during the –AMO+IPO period, apart from the Antarctic Peninsula and the vicinity of the Weddell Sea, the entire Antarctic region experiences a warming trend. The most pronounced signal in the SAT anomalies is observed during the austral autumn, whereas the combination of –AMO and –IPO exhibits the smallest magnitude across all the combinations. The wavetrain excited by the SST anomalies associated with the AMO and IPO induces upper-level and surface atmospheric circulation anomalies, which alter the SAT anomalies. Furthermore, downward longwave radiation anomalies related to anomalous cloud cover play a crucial role. In the future, if the phases of AMO and IPO were to reverse (AMO transitioning to a negative phase and IPO transitioning to a positive phase), Antarctica could potentially face more pronounced warming and accelerated melting compared to the current observations.
The importance of the Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO) in influencing zonally asymmetric changes in Antarctic surface air temperature (SAT) has been established. However, previous studies have primarily concentrated on examining the combined impact of the contrasting phases of the AMO and IPO, which have been dominant since the advent of satellite observations in 1979. This study utilizes long-term reanalysis data to investigate the impact of four combinations of +AMO+IPO, –AMO–IPO, +AMO–IPO, and –AMO+IPO on Antarctic SAT over the past 115 years. The +AMO phase is characterized by a spatial mean temperature amplitude of up to 0.5℃ over the North Atlantic Ocean, accompanied by positive sea surface temperature (SST) anomalies in the tropical eastern Pacific and negative SST anomalies in the extratropical-mid-latitude western Pacific, which are indicative of the +IPO phase. The Antarctic SAT exhibits contrasting spatial patterns during the +AMO+IPO and +AMO–IPO periods. However, during the –AMO+IPO period, apart from the Antarctic Peninsula and the vicinity of the Weddell Sea, the entire Antarctic region experiences a warming trend. The most pronounced signal in the SAT anomalies is observed during the austral autumn, whereas the combination of –AMO and –IPO exhibits the smallest magnitude across all the combinations. The wavetrain excited by the SST anomalies associated with the AMO and IPO induces upper-level and surface atmospheric circulation anomalies, which alter the SAT anomalies. Furthermore, downward longwave radiation anomalies related to anomalous cloud cover play a crucial role. In the future, if the phases of AMO and IPO were to reverse (AMO transitioning to a negative phase and IPO transitioning to a positive phase), Antarctica could potentially face more pronounced warming and accelerated melting compared to the current observations.
2024, 43(3): 59-65.
doi: 10.1007/s13131-023-2294-y
Abstract:
Negative Indian Ocean Dipole (nIOD) can exert great impacts on global climate and can also strongly influence the climate in China. Early nIOD is a major type of nIOD, which can induce more pronounced climate anomalies in summer than La Niña-related nIOD. However, the characteristics and triggering mechanisms of early nIOD are unclear. Our results based on reanalysis datasets indicate that the early nIOD and La Niña-related nIOD are the two major types of nIOD, and the former accounts for over one third of all the nIOD events in the past six decades. These two types of nIODs are similar in their intensities, but are different in their spatial patterns and seasonal cycles. The early nIOD, which develops in spring and peaks in summer, is one season earlier than the La Niña-related nIOD. The spatial pattern of the wind anomaly associated with early nIOD exhibits a winter monsoon-like pattern, with strong westerly anomalies in the equatorial Indian Ocean and eastly anomalies in the northern Indian Ocean. Opposite to the triggering mechanism of early positve IOD, the early nIOD is induced by delayed Indian summer monsoon onset. The results of this study are helpful for improving the prediction skill of IOD and its climate impacts.
Negative Indian Ocean Dipole (nIOD) can exert great impacts on global climate and can also strongly influence the climate in China. Early nIOD is a major type of nIOD, which can induce more pronounced climate anomalies in summer than La Niña-related nIOD. However, the characteristics and triggering mechanisms of early nIOD are unclear. Our results based on reanalysis datasets indicate that the early nIOD and La Niña-related nIOD are the two major types of nIOD, and the former accounts for over one third of all the nIOD events in the past six decades. These two types of nIODs are similar in their intensities, but are different in their spatial patterns and seasonal cycles. The early nIOD, which develops in spring and peaks in summer, is one season earlier than the La Niña-related nIOD. The spatial pattern of the wind anomaly associated with early nIOD exhibits a winter monsoon-like pattern, with strong westerly anomalies in the equatorial Indian Ocean and eastly anomalies in the northern Indian Ocean. Opposite to the triggering mechanism of early positve IOD, the early nIOD is induced by delayed Indian summer monsoon onset. The results of this study are helpful for improving the prediction skill of IOD and its climate impacts.
2024, 43(3): 66-86.
doi: 10.1007/s13131-023-2284-5
Abstract:
Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys. A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas. This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors. The fishery ranching index (FRI1, FRI2) was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan, using Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellite data with a special resolution of 2 m. In the second step, the environmental factors such as sea surface temperature (SST), chlorophyll a (Chl-a) concentration, current and tide, suspended sediment concentration (SSC) in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail. The results show the following three points. (1) For the extraction of the mussel aquaculture area, FRI1 and FRI2 are complementary, and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area. As for the large yellow croaker aquaculture area extraction, FRI2 is suitable. (2) Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters. The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined. (3) This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future. the fishery ranching index (FRI1, FRI2) in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.
Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys. A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas. This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors. The fishery ranching index (FRI1, FRI2) was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan, using Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellite data with a special resolution of 2 m. In the second step, the environmental factors such as sea surface temperature (SST), chlorophyll a (Chl-a) concentration, current and tide, suspended sediment concentration (SSC) in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail. The results show the following three points. (1) For the extraction of the mussel aquaculture area, FRI1 and FRI2 are complementary, and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area. As for the large yellow croaker aquaculture area extraction, FRI2 is suitable. (2) Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters. The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined. (3) This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future. the fishery ranching index (FRI1, FRI2) in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.
2024, 43(3): 87-101.
doi: 10.1007/s13131-023-2250-2
Abstract:
Antarctic sea ice is an important part of the Earth’s atmospheric system, and satellite remote sensing is an important technology for observing Antarctic sea ice. Whether Chinese Haiyang-2B (HY-2B) satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series, as other radar altimetry satellites can, needs further investigation. This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data. We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature (IST) product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images. Second, a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights. We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation. Finally, the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate (ASPeCt) ship-based observed sea ice thickness. The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable, and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m. The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products; this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.
Antarctic sea ice is an important part of the Earth’s atmospheric system, and satellite remote sensing is an important technology for observing Antarctic sea ice. Whether Chinese Haiyang-2B (HY-2B) satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series, as other radar altimetry satellites can, needs further investigation. This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data. We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature (IST) product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images. Second, a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights. We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation. Finally, the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate (ASPeCt) ship-based observed sea ice thickness. The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable, and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m. The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products; this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.
2024, 43(3): 102-114.
doi: 10.1007/s13131-023-2296-9
Abstract:
Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change. The rapid changes in Arctic sea ice have been widely concerned. However, the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series. In this study, the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data. The results indicated that Arctic sea ice was declining at a remarkably high rate of –5.4 × 104 km2/a in sea ice area (SIA) and –2.2 cm/a in sea ice thickness (SIT) during 1979 to 2020, and the reduction of SIA and SIT was the largest in summer and the smallest in winter. Spatially, compared with other sub-regions, SIA showed a sharper declining trend in the Barents Sea, Kara Sea, and East Siberian Sea, while SIT presented a larger downward trend in the northern Canadian Archipelago, northern Greenland, and the East Siberian Sea. Regarding to the seasonal trend of sea ice on sub-region scale, the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons, especially in summer and winter, i.e., the sub-regions linked to the open ocean exhibited a higher decline rate in winter; however, the other sub-regions blocked by the coastlines presented a greater decline rate in summer. For SIT, the sub-regions such as the Beaufort Sea, East Siberian Sea, Chukchi Sea, Central Arctic, and Canadian Archipelago always showed a higher downward rate in all seasons. Furthermore, a striking freeze-thaw asymmetry of Arctic sea ice was also detected. Comparing sea ice changes in different dimensions, sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension. The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period, and the rate of sea ice melting was 0.3 × 104 km2/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions, respectively. Moreover, there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004, respectively, in the horizontal/vertical dimension.
Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change. The rapid changes in Arctic sea ice have been widely concerned. However, the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series. In this study, the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data. The results indicated that Arctic sea ice was declining at a remarkably high rate of –5.4 × 104 km2/a in sea ice area (SIA) and –2.2 cm/a in sea ice thickness (SIT) during 1979 to 2020, and the reduction of SIA and SIT was the largest in summer and the smallest in winter. Spatially, compared with other sub-regions, SIA showed a sharper declining trend in the Barents Sea, Kara Sea, and East Siberian Sea, while SIT presented a larger downward trend in the northern Canadian Archipelago, northern Greenland, and the East Siberian Sea. Regarding to the seasonal trend of sea ice on sub-region scale, the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons, especially in summer and winter, i.e., the sub-regions linked to the open ocean exhibited a higher decline rate in winter; however, the other sub-regions blocked by the coastlines presented a greater decline rate in summer. For SIT, the sub-regions such as the Beaufort Sea, East Siberian Sea, Chukchi Sea, Central Arctic, and Canadian Archipelago always showed a higher downward rate in all seasons. Furthermore, a striking freeze-thaw asymmetry of Arctic sea ice was also detected. Comparing sea ice changes in different dimensions, sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension. The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period, and the rate of sea ice melting was 0.3 × 104 km2/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions, respectively. Moreover, there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004, respectively, in the horizontal/vertical dimension.
2024, 43(3): 115-126.
doi: 10.1007/s13131-023-2297-8
Abstract:
To effectively extract multi-scale information from observation data and improve computational efficiency, a multi-scale second-order autoregressive recursive filter (MSRF) method is designed. The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter (SMRF) method. The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations. Moreover, compared with the SMRF scheme, the MSRF scheme improves computational accuracy and efficiency to some extent. The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation, but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2 % compared to the SMRF scheme. On the other hand, compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed, the MSRF scheme only needs to perform two filter processes in one iteration, greatly improving filtering efficiency. In the two-dimensional experiment of sea ice concentration, the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme. This means that the MSRF scheme can achieve better performance with less computational cost, which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.
To effectively extract multi-scale information from observation data and improve computational efficiency, a multi-scale second-order autoregressive recursive filter (MSRF) method is designed. The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter (SMRF) method. The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations. Moreover, compared with the SMRF scheme, the MSRF scheme improves computational accuracy and efficiency to some extent. The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation, but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2 % compared to the SMRF scheme. On the other hand, compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed, the MSRF scheme only needs to perform two filter processes in one iteration, greatly improving filtering efficiency. In the two-dimensional experiment of sea ice concentration, the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme. This means that the MSRF scheme can achieve better performance with less computational cost, which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.
2024, 43(3): 127-138.
doi: 10.1007/s13131-023-2280-9
Abstract:
The aim of this study was to develop an improved thin sea ice thickness (SIT) retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data. This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort, Chukchi, East Siberian, Laptev and Kara seas and utilized the microwave polarization ratio (PR) at incidence angle of 40°. The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact, reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature. The relationship between the SIT and PR was found to be almost stable across the five selected regions. The SIT retrievals were then compared to other two existing algorithms (i.e., UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen) and validated against independent SIT data obtained from moored upward looking sonars (ULS) and airborne electromagnetic (EM) induction sensors. The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error (RMSE) being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data. The proposed algorithm can be used for thin sea ice thickness (<1.0 m) estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.
The aim of this study was to develop an improved thin sea ice thickness (SIT) retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data. This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort, Chukchi, East Siberian, Laptev and Kara seas and utilized the microwave polarization ratio (PR) at incidence angle of 40°. The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact, reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature. The relationship between the SIT and PR was found to be almost stable across the five selected regions. The SIT retrievals were then compared to other two existing algorithms (i.e., UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen) and validated against independent SIT data obtained from moored upward looking sonars (ULS) and airborne electromagnetic (EM) induction sensors. The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error (RMSE) being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data. The proposed algorithm can be used for thin sea ice thickness (<1.0 m) estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.
2024, 43(3): 139-154.
doi: 10.1007/s13131-023-2249-8
Abstract:
Marine oil spill emulsions are difficult to recover, and the damage to the environment is not easy to eliminate. The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments. However, the spectrum of oil emulsions changes due to different water content. Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions. Nonetheless, hyperspectral data can also cause information redundancy, reducing classification accuracy and efficiency, and even overfitting in machine learning models. To address these problems, an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established, and feature bands that can distinguish between crude oil, seawater, water-in-oil emulsion (WO), and oil-in-water emulsion (OW) are filtered based on a standard deviation threshold–mutual information method. Using oil spill airborne hyperspectral data, we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions, analyzed the transferability of the model, and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions. The results show the following. (1) The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO, OW, oil slick, and seawater. The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data and from 126 to 100 on the S185 data. (2) With feature selection, the overall accuracy and Kappa of the identification results for the training area are 91.80% and 0.86, respectively, improved by 2.62% and 0.04, and the overall accuracy and Kappa of the identification results for the migration area are 86.53% and 0.80, respectively, improved by 3.45% and 0.05. (3) The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations, with an overall accuracy of more than 80%, Kappa coefficient of more than 0.7, and F1 score of 0.75 or more for each category. (4) As the spectral resolution decreasing, the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW. Based on the above experimental results, we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data, and can be applied to images under different spatial and temporal conditions. Furthermore, we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process. These findings provide new reference for future endeavors in automated marine oil spill detection.
Marine oil spill emulsions are difficult to recover, and the damage to the environment is not easy to eliminate. The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments. However, the spectrum of oil emulsions changes due to different water content. Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions. Nonetheless, hyperspectral data can also cause information redundancy, reducing classification accuracy and efficiency, and even overfitting in machine learning models. To address these problems, an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established, and feature bands that can distinguish between crude oil, seawater, water-in-oil emulsion (WO), and oil-in-water emulsion (OW) are filtered based on a standard deviation threshold–mutual information method. Using oil spill airborne hyperspectral data, we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions, analyzed the transferability of the model, and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions. The results show the following. (1) The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO, OW, oil slick, and seawater. The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data and from 126 to 100 on the S185 data. (2) With feature selection, the overall accuracy and Kappa of the identification results for the training area are 91.80% and 0.86, respectively, improved by 2.62% and 0.04, and the overall accuracy and Kappa of the identification results for the migration area are 86.53% and 0.80, respectively, improved by 3.45% and 0.05. (3) The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations, with an overall accuracy of more than 80%, Kappa coefficient of more than 0.7, and F1 score of 0.75 or more for each category. (4) As the spectral resolution decreasing, the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW. Based on the above experimental results, we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data, and can be applied to images under different spatial and temporal conditions. Furthermore, we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process. These findings provide new reference for future endeavors in automated marine oil spill detection.
