Revisiting the Ocean Color Algorithms for Particulate Organic Carbon and Chlorophyll-a Concentrations in the Ross Sea

The Ross Sea is the most productive marginal sea in the Southern Ocean and plays an important role in carbon cycling. However, limited sampling of Chlorophyll-a (Chl) and particulate organic carbon (POC) concentrations from research expeditions constrains our understanding of the biogeochemical processes there. Satellites provide a useful tool for synoptic mapping of surface water properties on regional and global scales, yet the general applicability of the published algorithms in the Ross Sea is poorly known. Based on the data collected from 18 cruises in the past 20 years, we analyzed both the NASA standard and locally developed Chl and POC algorithms applicable to the Ross Sea. Our results show that Chl and POC are markedly underestimated using the NASA standard algorithms, with root mean square difference (RMSD) of 4.72 mg m−3 and 218.0 mg m−3, and mean bias of −3.48 mg m−3 and −159.1 mg m−3, for a wide range of Chl (0.42–16.3 mg m−3) and POC (46.8–812 mg m−3). Similar poor performances were also found for other algorithms applicable in the Ross Sea. We locally tuned both Chl and POC algorithms, and found that the Rrs667-based approach showed the most robust performances in retrieving both Chl and POC, with improved RMSD of 2.86 mg m−3 and 129.7 mg m−3, and limited biases. Our results show that the algal bloom signals in the Ross Sea in terms of Chl and POC are significantly greater than previously determined. More field observations will further constrain the locally tuned algorithms

Episodic subduction patches in the western North Pacific identified from BGC-Argo float data

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chen, S., Wells, M. L., Huang, R. X., Xue, H., Xi, J., & Chai, F. Episodic subduction patches in the western North Pacific identified from BGC-Argo float data. Biogeosciences, 18(19), (2021): 5539–5554, https://doi.org/10.5194/bg-18-5539-2021.Subduction associated with mesoscale eddies is an important but difficult-to-observe process that can efficiently export carbon and oxygen to the mesopelagic zone (100–1000 dbar). Using a novel BGC-Argo dataset covering the western North Pacific (20–50∘ N, 120–180∘ E), we identified imprints of episodic subduction using anomalies in dissolved oxygen and spicity, a water mass marker. These subduction patches were present in 4.0 % (288) of the total profiles (7120) between 2008 and 2019, situated mainly in the Kuroshio Extension region between March and August (70.6 %). Roughly 31 % and 42 % of the subduction patches were identified below the annual permanent pycnocline depth (300 m vs. 450 m) in the subpolar and subtropical regions, respectively. Around half (52 %) of these episodic events injected oxygen-enriched waters below the maximum annual permanent thermocline depth (450 dbar), with >20 % occurring deeper than 600 dbar. Subduction patches were detected during winter and spring when mixed layers are deep. The oxygen inventory within these subductions is estimated to be on the order of 64 to 152 g O2/m2. These mesoscale events would markedly increase oxygen ventilation as well as carbon removal in the region, both processes helping to support the nutritional and metabolic demands of mesopelagic organisms. Climate-driven patterns of increasing eddy kinetic energies in this region imply that the magnitude of these processes will grow in the future, meaning that these unexpectedly effective small-scale subduction processes need to be better constrained in global climate and biogeochemical models.This work was supported by the National Natural Science Foundation of China (NSFC) projects (grant nos. 41906159, 42030708, and 41730536), the Scientific Research Fund of the Second Institute of Oceanography MNR (grant no. 14283), and the Marine S&T Fund of Shandong Province for the Pilot National Laboratory for Marine Science and Technology (Qingdao) (grant no. 2018SDKJ0206)

A Dual-Band Model for the Vertical Distribution of Photosynthetically Available Radiation (PAR) in Stratified Waters

Based on the optical properties of water constituents, the vertical variation of photosynthetically available radiation (PAR) can be well modeled with hyperspectral resolution; the intensive computing load, however, demands simplified modeling that can be easily embedded in marine physical and biogeochemical models. While the vertical PAR profile in homogeneous waters can now be accurately modeled with simple parameterization, it is still a big challenge to model the PAR profile in stratified waters with limited variables. In this study, based on empirical equations and simulations, we propose a dual-band model to characterize the vertical distribution of PAR using the chlorophyll concentration (Chl). With an inclusive dataset including cruise data collected in the Southeast Pacific and BGC-Argo data in the global ocean, the model was thoroughly evaluated for its general applicability in three aspects: 1) estimating the entire PAR profile from sea-surface PAR and the Chl profile, 2) estimating the euphotic layer depth from the Chl profile, and 3) estimating PAR just below the sea surface from in situ radiometry measurements. It is demonstrated that the proposed dual-band model is capable of generating similar estimates as that from a hyperspectral model, thus offering an effective module that can be incorporated in large-scale ecosystem and/or circulation models for efficient calculations

Spatiotemporal variations of the oxycline and its response to subduction events in the Arabian Sea

The Arabian Sea is a significant hypoxic region in world’s oceans, characterized by the most extensive oxygen minimum zones (OMZs). Both physical and biological processes can alter the vertical and horizontal distribution of dissolved oxygen within the upper ocean and affect the spatial and temporal distribution of hypoxia within the OMZ. To identify the key physical and biological factors influencing the boundaries of oxycline, we analyzed an extensive dataset collected from the biogeochemical-Argo (BGC-Argo) floats during the period of 2010–2022. In particular, we investigated the impact of physical subduction events on the oxycline. Our results shows that the upper boundary of the oxycline deepened in summer and winter, and seemed to be controlled by the mixed layer depth. In contrast, it was shallower during spring and autumn, mainly regulated by the deep chlorophyll maximum. The lower boundary of the oxycline in the western Arabian Sea was predominantly controlled by regional upwelling and downwelling, as well as Rossby waves in the eastern Arabian Sea. Subduction patches originated from the Arabian Sea High Salinity Water (ASHSW) were observed from the BGC-Argo data, which were found to deepen the lower boundary of the oxycline, and increase the oxygen inventory within the oxycline by 8.3%, leading to a partial decrease in hypoxia levels

Remote Estimation of Surface Water \u3cem\u3ep\u3c/em\u3eCO\u3csub\u3e2\u3c/sub\u3e in the Gulf of Mexico

Surface ocean partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. The demand for a clearer understanding of how, and how fast, the ocean is changing due to atmospheric CO2 absorption, requires accurate and synoptic estimation of surface pCO2. Surface ocean pCO2 is mainly controlled by four oceanic processes – thermodynamics, ocean mixing, biological activities, and air-sea CO2 exchange. Surface ocean pCO2 is therefore closely related to environmental variables that characterize each oceanic process. These variables include sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a concentration (Chl), diffuse attenuation of downwelling irradiance (Kd), and wind speed. Ocean color satellites provide a means by which the relationship between these environmental variables and surface pCO2 can be developed. Yet, remote estimation of surface pCO2 in coastal oceans has been difficult due to the dynamic and complex biogeochemical processes. To date, most of the published satellite-based pCO2 models are developed for single-process dominated regions, therefore having poor applicability in other oceanic regions. Particularly, there is no unified approach, let alone unified model, to remotely estimate surface pCO2 in oceanic regions that are dominated by different oceanic processes. This work provides solutions to these challenging issues for the remote estimation of surface pCO2 in the Gulf of Mexico (GOM), with the following objectives: 1) Develop satellite-based surface pCO2 models and data products for single-process dominated subregions of the GOM, and quantify the sensitivities of the pCO2 algorithms to the input environmental variables; 2) Quantify the oceanic processes in controlling surface pCO2 in the GOM, analyze the relationships between environmental variables and surface pCO2, and understand the mechanisms of seasonal and interannual variations of surface pCO2 and its driving factors; 3) Develop an improved SSS model and data products for most GOM waters, and quantify the sensitivities of the SSS model to the input variables; 4) Develop a unified pCO2 model and data products for the GOM waters, and quantify the sensitivities of the pCO2 model to the input environmental variables and their relationships; 5) Quantify the temperature and non-temperature effects on surface pCO2 at different latitudes, analyze the dominant controls and the corresponding the driving factors of surface pCO2. The data used in this dissertation include those from extensive cruise surveys, buoy measurements, and long-term measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS). Specifically, for single-process dominated regions, two separate algorithms are developed and validated, respectively, from MODIS measurements. One is focused on the ocean current- dominated West Florida Shelf (WFS) (Appendix A), and the other is on the river-dominated northern GOM (Appendix B). The former utilizes a multi-variate nonlinear regression approach to establish the relationship between surface pCO2 and environmental variables of SST, Chl, and Kd. The latter relies on a mechanistic semi-analytical approach (MeSAA), modified from an existing algorithm published earlier. Both algorithms show satisfactory performance, yet the latter requires SSS as the model input, which is difficult to obtain from ocean color satellite measurements. Therefore, a multilayer perceptron neural network-based (MPNN) SSS model is developed and validated, which generates SSS maps at 1-km resolution for the GOM using MODIS measurements (Appendix C). Finally, with the availability of SSS from MODIS for the GOM, a unified pCO2 algorithm is developed and validated. The machine-learning algorithm is based on a random forest regression ensemble (RFRE), which is able to estimate surface pCO2 from MODIS measurements with a Root Mean Square Error (RMSE) of \u3c 10 µatm and R2 of 0.95 for pCO2 ranging between 145 and 550 µatm (Appendix D). Using this approach, The RFRE algorithm is shown to be applicable to the Gulf of Maine (a contrasting oceanic region to GOM) after local model tuning. The results show significant improvement over other models, suggesting that the RFRE approach may serve as a template for other oceanic regions once sufficient field-measured pCO2 data are available for local model tuning. To further improve the accuracy of satellite-derived surface pCO2 from coastal oceans and to increase its capability in capturing the interannual variations of surface pCO2 resulting from anthropogenic forcing, the dominant controls of surface pCO2 over seasonal and interannual time scales need to be better understood. As such, in situ pCO2 time series data along the coasts of the United States of America at different latitudes are analyzed (Appendix E). On a seasonal time scale, surface pCO2 tends to be dominated by the temperature effect (pCO2_T) through SST and wind speed (with some exceptions) in tropical and subtropical oceans, but appears to be dominated by the non-temperature effect (pCO2_nonT) in subpolar regions. In contrast, in tropical and subtropical waters on interannual time scales, surface pCO2 is primarily moderated by the non- temperature effect (through air-sea CO2 exchange via atmospheric pCO2), but conversely dominated by the temperature effect (i.e., SST increase) in subpolar regions. The effects of biological activities (i.e., algal blooms) need to be further investigated in the future. Overall, this dissertation has developed several algorithms to estimate SSS and surface pCO2, among which the unified pCO2 algorithm for multi-processes dominated regions appears to be able to serve as a template for many other regions after local model tuning. The derived surface pCO2 data products for the GOM provide a fundamental basis to assess air-sea exchange of CO2 and understand the carbon chemistry under a changing climate

Remote Estimation of Surface Water \u3cem\u3ep\u3c/em\u3eCO\u3csub\u3e2\u3c/sub\u3e in the Gulf of Mexico

Surface ocean partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. The demand for a clearer understanding of how, and how fast, the ocean is changing due to atmospheric CO2 absorption, requires accurate and synoptic estimation of surface pCO2. Surface ocean pCO2 is mainly controlled by four oceanic processes – thermodynamics, ocean mixing, biological activities, and air-sea CO2 exchange. Surface ocean pCO2 is therefore closely related to environmental variables that characterize each oceanic process. These variables include sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a concentration (Chl), diffuse attenuation of downwelling irradiance (Kd), and wind speed. Ocean color satellites provide a means by which the relationship between these environmental variables and surface pCO2 can be developed. Yet, remote estimation of surface pCO2 in coastal oceans has been difficult due to the dynamic and complex biogeochemical processes. To date, most of the published satellite-based pCO2 models are developed for single-process dominated regions, therefore having poor applicability in other oceanic regions. Particularly, there is no unified approach, let alone unified model, to remotely estimate surface pCO2 in oceanic regions that are dominated by different oceanic processes. This work provides solutions to these challenging issues for the remote estimation of surface pCO2 in the Gulf of Mexico (GOM), with the following objectives: 1) Develop satellite-based surface pCO2 models and data products for single-process dominated subregions of the GOM, and quantify the sensitivities of the pCO2 algorithms to the input environmental variables; 2) Quantify the oceanic processes in controlling surface pCO2 in the GOM, analyze the relationships between environmental variables and surface pCO2, and understand the mechanisms of seasonal and interannual variations of surface pCO2 and its driving factors; 3) Develop an improved SSS model and data products for most GOM waters, and quantify the sensitivities of the SSS model to the input variables; 4) Develop a unified pCO2 model and data products for the GOM waters, and quantify the sensitivities of the pCO2 model to the input environmental variables and their relationships; 5) Quantify the temperature and non-temperature effects on surface pCO2 at different latitudes, analyze the dominant controls and the corresponding the driving factors of surface pCO2. The data used in this dissertation include those from extensive cruise surveys, buoy measurements, and long-term measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS). Specifically, for single-process dominated regions, two separate algorithms are developed and validated, respectively, from MODIS measurements. One is focused on the ocean current- dominated West Florida Shelf (WFS) (Appendix A), and the other is on the river-dominated northern GOM (Appendix B). The former utilizes a multi-variate nonlinear regression approach to establish the relationship between surface pCO2 and environmental variables of SST, Chl, and Kd. The latter relies on a mechanistic semi-analytical approach (MeSAA), modified from an existing algorithm published earlier. Both algorithms show satisfactory performance, yet the latter requires SSS as the model input, which is difficult to obtain from ocean color satellite measurements. Therefore, a multilayer perceptron neural network-based (MPNN) SSS model is developed and validated, which generates SSS maps at 1-km resolution for the GOM using MODIS measurements (Appendix C). Finally, with the availability of SSS from MODIS for the GOM, a unified pCO2 algorithm is developed and validated. The machine-learning algorithm is based on a random forest regression ensemble (RFRE), which is able to estimate surface pCO2 from MODIS measurements with a Root Mean Square Error (RMSE) of \u3c 10 µatm and R2 of 0.95 for pCO2 ranging between 145 and 550 µatm (Appendix D). Using this approach, The RFRE algorithm is shown to be applicable to the Gulf of Maine (a contrasting oceanic region to GOM) after local model tuning. The results show significant improvement over other models, suggesting that the RFRE approach may serve as a template for other oceanic regions once sufficient field-measured pCO2 data are available for local model tuning. To further improve the accuracy of satellite-derived surface pCO2 from coastal oceans and to increase its capability in capturing the interannual variations of surface pCO2 resulting from anthropogenic forcing, the dominant controls of surface pCO2 over seasonal and interannual time scales need to be better understood. As such, in situ pCO2 time series data along the coasts of the United States of America at different latitudes are analyzed (Appendix E). On a seasonal time scale, surface pCO2 tends to be dominated by the temperature effect (pCO2_T) through SST and wind speed (with some exceptions) in tropical and subtropical oceans, but appears to be dominated by the non-temperature effect (pCO2_nonT) in subpolar regions. In contrast, in tropical and subtropical waters on interannual time scales, surface pCO2 is primarily moderated by the non- temperature effect (through air-sea CO2 exchange via atmospheric pCO2), but conversely dominated by the temperature effect (i.e., SST increase) in subpolar regions. The effects of biological activities (i.e., algal blooms) need to be further investigated in the future. Overall, this dissertation has developed several algorithms to estimate SSS and surface pCO2, among which the unified pCO2 algorithm for multi-processes dominated regions appears to be able to serve as a template for many other regions after local model tuning. The derived surface pCO2 data products for the GOM provide a fundamental basis to assess air-sea exchange of CO2 and understand the carbon chemistry under a changing climate

Phytoplankton Blooms Expanding Further Than Previously Thought in the Ross Sea: A Remote Sensing Perspective

Accurate and robust measurements from ocean color satellites are critical to studying spatial and temporal changes of surface ocean properties. Satellite-derived Chlorophyll-a (Chl) is an important parameter to monitor phytoplankton blooms on synoptical scales, particularly in remote seas. However, the present NASA standard Chl algorithm tends to strongly underestimate the Chl in the Ross Sea. Based on a locally-tuned Chl algorithm in the Ross Sea and using the data record from MODIS between 2002 and 2020, here we investigated the spatial expansion of phytoplankton blooms in the Ross Sea. Our results show the geometric areas of the phytoplankton blooms could reach (7.20 ± 2.8) × 104 km2 on average, which was ~3.1 times that of those identified using the NASA default Chl algorithm. Spatially, blooms were frequently identified on the shelf of the Ross Sea polynya with a typical chance of ≥80%. In the context of climate change and global warming, the general decrease and interannual dynamics of sea ice cover tends to affect solar light penetration and surface seawater temperature, which were found to regulate the spatial expansion of the phytoplankton blooms over the years. Statistical analyses showed that the spatial coverages of the phytoplankton blooms were significantly correlated with sea surface temperature (Spearman correlation coefficient R = 0.55, at p p p 10 years) trends over the study period. The stronger phytoplankton blooms than those previously observed may indicate larger carbon sequestration, which needs to be investigated in the future. More valid satellite observations under cloud covers will further constrain the estimates

Environmental Controls of Surface Water pCO2 in Different Coastal Environments: Observations from Marine Buoys

Time series of in situ surface seawater partial pressure of CO2 (pCO2) data collected between 2005 and 2017, together with other environmental variables from field or satellite measurements, along the coasts of the United States of America and its territories at different latitudes, are analyzed to separate the temperature effect from the remaining non-temperature effects (i.e., biological and other physical effects) on driving surface pCO2. Similar to the findings in the open ocean, on seasonal time scales, the temperature effect (pCO2_T) tends to override the non-temperature effect (pCO2_nonT) in modulating surface pCO2 in tropical and subtropical oceanic waters. However, the balance between pCO2_T and pCO2_nonT tends to shift towards pCO2_nonT in temperate zone waters, with a few exceptions in some specific oceanic environments. On interannual time scales, both atmospheric pCO2 and surface pCO2 show significant increasing trends over short time scales (i.e., \u3c10 years) except for a few outliers. In tropical and subtropical waters, the interannual changes of surface pCO2 are mainly controlled by the non-temperature effect (through air-sea CO2 exchange). In temperate regions, these changes are primarily driven by the temperature effect (through increased SST). Considering that temperature is commonly included in remote sensing algorithms of surface pCO2, this study suggests that, to better capture the seasonal and interannual signals in surface pCO2 from satellites, atmospheric pCO2 must be considered in the surface pCO2 remote sensing algorithms especially in tropical and subtropical waters. The non-temperature effect on surface pCO2, especially the biological effect (e.g., algal blooms), needs further investigation

Phytoplankton Blooms Expanding Further Than Previously Thought in the Ross Sea: A Remote Sensing Perspective

Accurate and robust measurements from ocean color satellites are critical to studying spatial and temporal changes of surface ocean properties. Satellite-derived Chlorophyll-a (Chl) is an important parameter to monitor phytoplankton blooms on synoptical scales, particularly in remote seas. However, the present NASA standard Chl algorithm tends to strongly underestimate the Chl in the Ross Sea. Based on a locally-tuned Chl algorithm in the Ross Sea and using the data record from MODIS between 2002 and 2020, here we investigated the spatial expansion of phytoplankton blooms in the Ross Sea. Our results show the geometric areas of the phytoplankton blooms could reach (7.20 ± 2.8) × 104 km2 on average, which was ~3.1 times that of those identified using the NASA default Chl algorithm. Spatially, blooms were frequently identified on the shelf of the Ross Sea polynya with a typical chance of ≥80%. In the context of climate change and global warming, the general decrease and interannual dynamics of sea ice cover tends to affect solar light penetration and surface seawater temperature, which were found to regulate the spatial expansion of the phytoplankton blooms over the years. Statistical analyses showed that the spatial coverages of the phytoplankton blooms were significantly correlated with sea surface temperature (Spearman correlation coefficient R = 0.55, at p < 0.05), sea surface wind speed (R = 0.42, at p < 0.05), and sea ice concentration (R = −0.84, at p < 0.05), yet without significant long-term (>10 years) trends over the study period. The stronger phytoplankton blooms than those previously observed may indicate larger carbon sequestration, which needs to be investigated in the future. More valid satellite observations under cloud covers will further constrain the estimates

Simulation of Drainage Capacity in a Coastal Nuclear Power Plant under Extreme Rainfall and Tropical Storm

To ensure the safety of coastal nuclear power plants, accurately simulating water depth due to flooding resulting from heavy rainfall and tropical storms is important. In this paper, a combined model is developed to analyze and simulate the drainage capacity in a coastal nuclear power plant under the combined action of extreme rainfall and wave overtopping. The combined model consist of a surface two-dimensional flood-routing model, a pipe network model, and an offshore wave model. The method of predictive correction calculation is adopted to calculate the node return flow. The inundated water depth varying with time for different design rainstorm return periods (p = 0.1 and 1%) was simulated and analyzed by the combined model. The maximum inundated water depth is calculated for the important entrances of the workshop. The model was validated and calibrated with the data of the rainfall, outflow discharge, and flow velocity measured on 23 June 2016 in plant. Modeling indicates that the simulated depths are consistent with the observed depths. The results show that the water depths in the left and right of the nuclear power plant are 0.2⁻0.4 m and 0.3⁻0.8 m, respectively. The water depth increases of Monitoring Point 22 are the largest in different design rainstorm return periods (p = 0.1 and 1%), which increase by 16% for a rainstorm once every thousand years compared to events occurring once in one hundred years. The main factor influencing water accumulation is wave overtopping, and the seawall, revetments, and pipe system play an important role in decreasing the inundated water depth. Through scientific analysis, a certain decision-making basis has been provided for flood disaster management and a certain security guarantee has also been provided for regional sustainable development