Mangroves, coastal salt marshes and seagrass beds, as the typical coastal blue carbon ecosystems, have been widely recognized for their remarkable capacity in carbon storage. Vegetation carbon pool and sediment (or soil) carbon pool were considered to be the major carbon pools within the coastal blue ecosystems and their variations determined the overall carbon sequestration of the ecosystems. From a perspective of carbon pool interactions, this study summarized the previous research work based on literature review, including the interactions within various vegetation carbon pools and within various sediment carbon pools, as well as the interactions between vegetation and sediment carbon pools. Interspecific competition, allochthonous carbon input and biogeomorphology were found to be the key to understand the carbon pool interactions. Finally, a perspective on the current state-of-the-art of blue carbon pool study is offered, with challenges and suggestions for future directions.
Artificial intelligence in oceanography has demonstrated a great potential with the explosive growth of ocean observation data and numerical model products. This article first reviews the history of ocean big data development, and then introduces in detail the current status of artificial intelligence in oceanography applications including identifying ocean phenomenon, forecasting ocean variables and phenomenon, estimating dynamic parameters, correcting forecast errors, and solving dynamic equations. Specifically, this article elaborates the research on the intelligent identification of ocean eddies, internal waves and sea ice, the intelligent prediction of sea surface temperatures, El Ni?o-Southern Oscillation, storm surges, waves and currents, the intelligent estimation of ocean turbulence parameterization for numerical models, and the intelligent correction of waves and current forecast errors. In addition, it discusses the recent progress of applying physical mechanism fusion and Fourier neural operator for solving ocean dynamic equations. This article is based on the current status of artificial intelligence in oceanography and aims to provide a comprehensive demonstration of the advantages and potential of applying artificial intelligence methods in the field of oceanography. With the two emerging research hotspots: digital twin oceans and artificial intelligence large models, the future development direction of artificial intelligence provides enlightenment and reference for interested scientists and researchers.
Costal ocean receives a bunch of carbon materials and nutrients from terrestrial sources, relates a lot of carbon-involving interactions. Meanwhile, it is normal that sedimentary reservoir-cap systems with good trap conditions beneath coastal ocean, these entrapments have potentials to storage CO2. This review focuses on the coastal ocean as the research object, and introduces the carbon cycle processes in coastal ocean, their factors which could influence CO2 fluxes in the carbon cycle processes, and the potential carbon storage mechanisms of the coastal marine sedimentary basins. From the perspective of “carbon peaking and carbon neutrality”, the significance of coastal oceans for “Ocean Negative Carbon Emission (ONCE)”, its potential promotion paths, carbon storage potentials in sedimentary basins and the problems faced by coastal oceans are discussed. Overall, the costal ocean is one of the important blue carbon sink areas. In the coastal marine seawater system, improving the reaction efficiency of microbial carbon pump and carbonate carbon pump have positive significance for CO2 negative emissions; The suitable reservoir-cap systems for CO2 storage beneath coastal ocean can not only provide extra spaces, but also guarantee the safety for CO2 storage. In the future, the main research directions should be to inhibit the conversion process of carbon materials to CO2 in coastal oceans and ensure the safety of CO2 storage in sedimentary reservoirs, these could provide theoretical basis and technical guarantee for CO2 negative emissions.
Carbon stock variation observation forms the basis for coastal saltmarsh blue carbon sink accounting. In order to accurately estimate the carbon sequestration rate of coastal saltmarshes over a short-term scale (seasonal to annual), this study carried out field observations and sample collections within a coastal saltmarsh on the south bank of Hangzhou Bay, covering different seasons of 2022. This study was primarily based on high-resolution surface monitoring by Surface Elevation Table (SET) systems. The results revealed a seasonal plant growth pattern between March and September for both the native species Scirpus mariqueter and the exotic species Spartina alterniflora. In terms of belowground biotic carbon stock changes, over the growing season, the carbon stock increase for Scirpus mariqueter reached 11 g C·m-2 whilst this value was 56 g C·m-2 for Spartina alterniflora. The SET data indicated a sedimentation rate of 13.02 cm·a-1 within the Spartina alterniflora saltmarsh, higher than that of the Scirpus mariqueter saltmarsh, 12.30 cm·a-1. Calculating the sedimentation rate data with sediment bulk density and organic carbon content, the sediment carbon accumulation rate of Scirpus mariqueter saltmarsh was estimated to be 460 g C·m-2·a-1, lower than 588 g C·m-2·a-1 of the Spartina alterniflora saltmarsh. Combining the biotic carbon stock increase and sediment carbon stock increase, the carbon sequestration rate for the Spartina alterniflora saltmarsh was found to be 644 g C·m-2·a-1, higher than the value of Scirpus mariqueter saltmarsh, 471 g C·m-2·a-1. Thus, the difference in carbon sequestration abilities of native and exotic species should be considered for future coastal blue carbon management.
Mangroves, salt marshes, and seagrass beds are known as the three major coastal blue carbon ecosystems (CBCEs), which play important roles in marine biodiversity maintenance, water purification, nutrient recycling, carbon sequestration and storage. Guangdong Province and Guangxi Zhuang Autonomous Region in China, where the CBCEs are widely distributed, were selected as the research areas, to investigate the spatial relationship between CBCEs and the marine ecological redlines (MERs). The CBCE conservation gap was analyzed, and the recommended priority conservation areas outside the MERs were proposed. The CBCE distribution data obtained from the satellite images in 2019, combined with field survey and UAV remote sensing data collected during 2020 and 2021 showed that the CBCEs in Guangdong totaled 14 481.39 hm2 (mangroves 11 928.87 hm2, salt marshes 1 258.00 hm2, seagrass beds 1 294.52 hm2), whereas the CBCEs in Guangxi totaled 11 751.30 hm2 (mangroves 10 171.70 hm2, salt marshes 1 450.36 hm2, seagrass beds 129.24 hm2). 62.13% of the CBCEs in Guangdong and 59.88% in Guangxi were covered by the MERs. The distribution areas and protection ratios of mangroves and seagrass beds in Guangdong were both larger than those in Guangxi, while the distribution area and protection ratio of salt marshes in Guangxi were larger than that in Guangdong. As to the 3 types of CBCEs in Guangdong, 62.13% of the mangroves, 38.16% of the salt marshes and 85.41% of the seagrass beds were under protection. For the CBCEs in Guangxi, 61.44% of the mangroves, 49.58% of the salt marshes and 52.99% of the seagrass beds were protected. This research suggests the coast from Rongmujiang Bay to Maowei Sea, the areas of Tieshan Bay, Leizhou Bay, Zhelin Bay and other related locations as the recommended priority conservation areas outside the MERs.
Coastal bays are greatly affected by human activities and natural changes, and the influence mechanism of variation in seawater carbon source and sink patterns is extremely complex. Due to the small spatial scale of the bay, it is necessary to use wide-bands high-spatial resolution satellite remote sensing for monitoring the air-sea CO2 flux. Compared with the traditional kilometer-level ocean color satellite data, the retrieval of the sea surface partial pressure of CO2 (pCO2), the key parameter to calculate air-sea CO2 flux, is extremely challenging in small-scale bays. Taking Xiangshan Bay in Zhejiang Province in autumn as an example, a satellite retrieval algorithm for sea surface pCO2 was proposed based on the in situ pCO2 data and Sentinel-2 satellite images in the past five years, using the machine learning method of support vector machine (SVM). The algorithm validation results showed a good performance with R2 of 0.92 and RMSE of 23.23 μatm, and the satellite-derived results were consistent with the in situ values. On this basis, the satellite products of pCO2 in Xiangshan Bay in autumn from 2017 to 2021 (September to November) were produced. The results revealed that the pCO2 of Xiangshan Bay showed a decreasing trend from the top of the bay to the mouth of the bay, with an average value of 514.56 μatm, of which the average pCO2 in the inner bay was 551.94 μatm and the average pCO2 in the outer bay was 477.19 μatm, which implied that Xiangshan Bay was a source of atmospheric CO2 as a whole. There was no significant trend change of pCO2 in autumn in the past five years. Combined with the analysis of in situ data of multiple parameters, it was found that the sea surface pCO2 of autumn in Xiangshan Bay in 2021 was jointly regulated by physical mixing and biological activities. Sea surface temperature (SST) had a good positive correlation with pCO2, which was mainly reflected by the thermodynamic equilibrium of carbonate system. In addition, the normalized pCO2(NpCO2) with average temperature had a good negative correlation with seawater salinity and dissolved oxygen saturation. The relationship between NpCO2 and salinity resulted from the exchange of sea water inside the bay and offshore coastal water under tidal effect. Long-time series satellite data analysis also confirmed that sea surface pCO2 had a relatively consistent trend with the average tide height inside and outside the bay, and this trend was stronger in the outside bay than that in the inner bay. In this study, a set of pCO2 remote sensing retrieval methods in the small-scale bay was constructed, which laid a good foundation for the subsequent long-time series satellite monitoring of sea-air CO2 fluxes.
Marine gas hydrate deposits are significant temporal reservoirs for hydrocarbons migrating from deep sources. This is crucial to our understanding of ocean carbon cycling. The cold seep, a geological process regarding gas leakage from deep or shallow sources, is usually linked with gas hydrate decomposition. In this thesis, we reviewed the latest applications of in situ monitoring and detecting methods regarding the leakage plumes, migration pathways, and seafloor geomorphologies associated with gas hydrate and cold seep systems, primarily including vessel-and land-based gas plume measurements, surface ocean-lower atmosphere hydrocarbon emission detections, seafloor visualization techniques, and in situ observation networks. The integrated applications of these in situ observation methods provide a nuanced view of the temporal and spatial variability of hydrate and cold seep systems, facilitate understanding of the fate of hydrocarbons, and expand our knowledge of cold-seep biota in a watery desert.
Based on the mesoscale atmospheric model WRF and the regional ocean model ROMS, a two-way coupled WRF-ROMS air-sea model was constructed to simulate the super typhoon Mangkhut in 2018. The results showed that the simulation results of the coupled air-sea model were better than those of the only atmospheric or ocean model, and the error of the typhoon track obtained from the coupled model was within 60 km, which was in good agreement with the best track. Compared with the observation results, the simulation results of wind speed and sea level pressure in the coupled model were better than others model. Based on the simulation results of the coupled air-sea model, the spatial and temporal distribution of the wind field, pressure field, sea surface flow field, and storm surge under the super typhoon Mangkhut were further analyzed. The results showed that: (1) In terms of spatial distribution, after the typhoon entered the South China Sea, the radius of the seven-level wind circle was larger behind the right side of the typhoon; the cyclonic flow field showed a significant Ekman effect with the typhoon wind field, and the flow direction was 45° from the wind direction. The wind field, pressure field, wind-generated flow field and water gain distribution all had obvious asymmetry, and the typhoon intensity, flow velocity and water gain were greater on the right side of the typhoon path than on the left side. (2) In terms of time distribution, the distribution of the wind field and the pressure field were similar and synchronized with the typhoon center, while the wind-driven flow field and storm surge were three hours behind the typhoon track.