Evaluation of Ocean Forecasting in the East China Sea

The accuracy of the initial condition of a global ocean forecasting system and its prediction skill was evaluated against in situ temperature, salinity and satellite salinity observations during the winter of 2015 and the summer of 2016 for the East China Sea. The ocean forecasting system demonstrates better skill for the Yangtze River estuary and the East China Sea during winter time than during summer time. During winter time, the root-mean-square error (RMSE) of the initial fields of the system for salinity is 1.90 psu, and the correlation is 0.56. The model has a salty bias of 0.29 psu. The salinity RMSE reduces with increasing distance from the coast. In contrast, the RMSE for temperature is 0.76°C, and the correlation is as high as 0.95. There is no bias between model temperature and observation. During summer time, the accuracy and forecast skill of the global ocean forecasting system are very poor. The RMSE for salinity is 3.14 psu, and the correlation is 0.28. The model has a salty bias of 0.95 psu. The RMSE for temperature is 7.22°C, and the model has a warm bias as high as 5.52°C

Validation and Application of SMAP SSS Observation in Chinese Coastal Seas

Using sea surface salinity (SSS) from the Soil Moisture Active Passive (SMAP) mission from September 2015 to August 2016, the spatial distribution and seasonal variation in SSS in the Chinese coastal seas were investigated. First, in situ salinity observation over Chinese East Sea was used to validate SMAP observation. Then, the SSS signature of the Yangtze River fresh water was analyzed using SMAP data and the river discharge data. The SSS around the Yangtze River estuary in the Chinese East Sea, the Bohai Sea and the Yellow Sea is significantly lower than that of the open ocean. The SSS of Chinese coastal seas shows significant seasonal variation, and the seasonal variation in the adjacent waters of the Yangtze River estuary is the most obvious, followed by that of the Pearl River estuary. The minimum value of SSS appears in summer while maximum in winter. The root-mean-squared difference of daily SSS between SMAP observation and in situ observation is around 3 psu in both summer and winter, which is much lower than the annual range of SSS variation. The path of fresh water from SMAP and in situ observation is consistent during summer time

A new merged dataset of global ocean chlorophyll-a concentration for better trend detection

Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS (Sea-viewing Wide Field-of-view Sensor) in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve climate change related studies, it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI (Ocean Colour Climate Change Initiative), which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the inter-mission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the SeaWiFS, MODIS (Medium-spectral Resolution Imaging Spectrometer), MERIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), and OLCI (Ocean and Land Colour Instrument) with inter-mission differences corrected in this work. The fitness of the dataset on long-term Chla trend study was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. The results suggest that our dataset can be used for long-term series analysis and trend detection. We also provide the global trend map in Chla over 23 years (1998–2020) and present a significant positive global trend with 0.67% ± 0.37%/yr

An Inherent Optical Properties Data Processing System for Achieving Consistent Ocean Color Products From Different Ocean Color Satellites

We used field measurements and multimission satellite data to evaluate how well an inherent optical properties (IOPs) data processing system performed at correcting the residual error of the atmospheric correction in satellite remote sensing reflectance (R-rs) and how well the system simultaneously minimized intermission biases between different remote sensing systems. We developed the IOPs data processing system as a semianalytical algorithm called IDAS. Our results show that IDAS generates accurate and consistent IOPs products from two ocean color missions: Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer Aqua (MODISA). Specifically, with "high-quality" SeaWiFS and MODISA R-rs data, IDAS provided temporally consistent IOPs products for the oligotrophic open ocean resulting in an annual mean intermission difference of less than 3%, which is significantly lower than what a quasi-analytical algorithm (QAA) provided. We used IDAS to generate a long time series of b(b)(555) from the Northwest Atlantic Subtropical Gyre using SeaWiFS (1998 to 2002) and MODISA (2003 to 2017) images. Our results show that the IDAS-derived annual b(b)(555) decreased monotonically by 2.81% per decade from 1998 to 2017. Comparing the IDAS-generated annual trend for b(b)(555) to the same data processed with the QAA algorithm, we found that the QAA results differed because of impacts of the residual errors of the atmospheric correction and intermission biases. The differences in the annual trends existed despite the same temporal changing patterns of in situ particulate organic carbon existing in the Sargasso Sea and in the satellite chlorophyll-a concentration in the Northwest Atlantic Subtropical Gyre

Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques

Oceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth’s climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in the entire Indian Ocean. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We utilized the Extremely Randomized Trees (ERT) model with the best performance, inputting complete reanalysis data and spatiotemporal information to reconstruct a four-dimensional DO dataset of the Indian Ocean during 1980–2019. The evaluation results showed that the ERT-based DO dataset was superior to the DO simulations in Earth System Models across different time and space. Furthermore, we assessed the spatiotemporal variations in reconstructed DO dataset. DO decline and oxygen-minimum zone (OMZ) expansion were prominent in the Arabian Sea, Bay of Bengal, and Equatorial Indian Ocean. Through correlation analysis, we found that temperature and salinity changes related to solubility primarily control the oxygen decrease in the middle and deep sea. However, the complicated factors with solubility change, vertical mixing, and circulation govern the oxygen increase in the upper and middle sea. Finally, we conducted a volume integral to estimate the oxygen content in the Indian Ocean. Overall, a deoxygenation trend of −141.5 ± 15.1 Tmol dec−1 was estimated over four decades, with a slowdown trend of −68.9 ± 31.3 Tmol dec−1 after 2000. Under global warming and climate change, OMZ expanding and deoxygenation in the Indian Ocean are gradually mitigating. This study enhances our understanding of DO dynamics of the Indian Ocean in response to deoxygenation

Remote estimation of chlorophyll concentration in clear, turbid, and productive waters: a generalized approach

In-situ data of simultaneously measured normalized water-leaving radiance and chlorophyll concentratio