Joint Global Ocean Flux Study (JGOFS)

Potential response of dark carbon fixation to global warming in estuarine and coastal waters

Dark carbon fixation (DCF), through which chemoautotrophs convert inorganic carbon to organic carbon, is recognized as a vital process of global carbon biogeochemical cycle. However, little is known about the response of DCF processes in estuarine and coastal waters to global warming. Using radiocarbon labelling method, the effects of temperature on the activity of chemoautotrophs were investigated in benthic water of the Yangtze estuarine and coastal areas. A dome-shaped thermal response pattern was observed for DCF rates (i.e., reduced rates at lower or higher temperatures), with the optimum temperature (Topt ) varying from about 21.9 to 32.0°C. Offshore sites showed lower Topt values and were more vulnerable to global warming compared with nearshore sites. Based on temperature seasonality of the study area, it was estimated that warming would accelerate DCF rate in winter and spring but inhibit DCF activity in summer and fall. However, at an annual scale, warming showed an overall promoting effect on DCF rates. Metagenomic analysis revealed that the dominant chemoautotrophic carbon fixation pathways in the nearshore area were Calvin-Benson-Bassham (CBB) cycle, while the offshore sites were co-dominated by CBB and 3-hydroxypropionate/4-hydroxybutyrate cycles, which may explain the differential temperature response of DCF along the estuarine and coastal gradients. Our findings highlight the importance of incorporating DCF thermal response into biogeochemical models to accurately estimate the carbon sink potential of estuarine and coastal ecosystems in the context of global warming.

Correspondence between DOM molecules and microbial community in a subtropical coastal estuary on a spatiotemporal scale

Dissolved organic matter (DOM) changes in quantity and quality over time and space, especially in highly dynamic coastal estuaries. Bacterioplankton usually display seasonal and spatial variations in abundance and composition in the coastal regions, and influence the DOM pool via assimilation, transformation and release of organic molecules. The change in DOM can also affect the composition of bacterial community. However, little is known on the correspondence between DOM molecules and bacterial composition, particularly through a systematic field survey. In this study, the spatiotemporal signatures of microbial communities and DOM composition in the subtropical coastal estuary of Xiamen are investigated over one and half years. The co-occurrence analysis between bacteria and DOM suggested microorganisms likely transformed the DOM from a relatively high (>400 Da) to a low (<400 Da) molecular weight, corresponding to an apparent increase in overall aromaticity. This might be the reason why microbial transformation renders "dark" organic matter visible in mass spectrometry due to more efficient ionization of microbial metabolites, as well as photodegradation processes. K- and r-strategists exhibited different correlations with two-size categories of DOM molecules owing to their different lifestyles and responses to environmental nutrient conditions. A comparison of the environmental variables and DOM composition with the microbial communities showed that the environmental/DOM variations played a more important role in shaping the microbial communities than vice versa. This study sheds light on the interactions between microbial populations and DOM molecules at the spatiotemporal scale, improving our understanding of microbial roles in marine biogeochemical cycles.

ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products

Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R² increasing by 3.7–46.4%. Among them, the LHF estimation using the ANN method increased the R² of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m⁻². Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003–2007. The ocean LHF fusion products estimated from ANN methods were 10–30 W m⁻² lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.