Description: Growing evidence suggests that ocean acidification (OA) may affect animal behaviors such as feeding. Although gustation plays a crucial role in evaluating the quality and palatability of food and ultimately influences whether or not teleosts consume the food, the potential impact of OA on gustation-mediated feeding behavior remains unknown. In this study, gustation mediated-feeding behavior, as indicated by the consumption rate (CR) and swallowing rate (SR) of agar pellets with or without feed upon OA exposure was investigated in black sea bream (Acanthopagrus schlegelii). Results showed that the exposure to acidified seawater led to significant reductions in the CR and SR of feed-containing agar pellets. In addition, the in vivo contents of three neurotransmitters and expression of genes from the gustatory signal transduction pathway were all significantly suppressed by the OA treatment. In general, the data obtained indicated that OA may hinder the gustation-mediated feeding behavior of A. schlegelii by disrupting gustatory signal transduction, which may aggravate the issue of food shortage for wild populations of black sea bream.

Description: Ensuring that oocytes are fertilized by a single sperm during broadcast spawning is crucial for the fertilization success of many marine invertebrates. Although the adverse impacts of ocean acidification (OA) on various marine species have been revealed in recent years, its impact on polyspermy and the underlying mechanisms involved remain largely unknown. Therefore, in the present study, the effect of OA on polyspermy risk was assessed in a broadcast spawning bivalve, Tegillarca granosa. In addition, the impacts of OA on the two polyspermy blocking processes, the fast block (membrane depolarization) and the permanent block (cortical reaction), were investigated. The results show that the exposure of oocytes to two future OA scenarios (pH 7.8 and pH 7.4) leads to significant increases in polyspermy risk, about 1.70 and 2.38 times higher than the control, respectively. The maximum change in the membrane potential during oocyte membrane depolarization markedly decreased to 15.79% (pH 7.8) and 34.06% (pH 7.4) of the control value. Moreover, the duration of oocyte membrane depolarization was significantly reduced to approximately 63.38% (pH 7.8) and 21.91% (pH 7.4) of the control. In addition, cortical granule exocytosis, as well as microfilament migration, were significantly arrested by OA treatment. Exposure to future OA scenarios also led to significant reductions in the ATP and Ca2+ content of the oocytes, which may explain the hampered polyspermy blocking. Overall, the present study suggests that OA may significantly increase polyspermy risk in T. granosa by inhibiting membrane depolarization and arresting cortical granule exocytosis.

Description: Due to the construction and operation of cascade reservoirs, the natural hydrological regimes have inevitably been altered and most fish spawning grounds have been destroyed, thus causing ecological impacts on riverine ecosystems and fish reproduction. An effective way to restore fish habitats and fulfill their spawning requirements is to generate an appropriate ecological flow regime guided by Natural-based solutions (NbS) that can help restore valuable functions provided by nature. With a focus on one major existing spawning ground for four major Chinese carps (FMCC) in the Hongshui River (HSR), China, this study first evaluated temporal variations of flow regimes and their ecological responses in multiple time scales by employing several eco-hydrological indicators. A fuzzy logic-based one- and two-dimensional-coupled fish habitat model was then built to estimate a suitable ecological flow to ensure the existence of continuous and connected habitats. Furthermore, an improved fluctuation identification algorithm was proposed to quantitatively identify naturally effective high-flow processes to trigger fish spawning behaviors following NbS guidance. Thus, suitable ecological flow regimes were established based on the notion of thresholds of flow discharge while meeting the requirements for water temperature during the spawning period of FMCC. The results recommended high-flow processes characterized with thresholds of flow indicators while mimicking natural flow regimes to be generated to stimulate FMCC spawning in different periods in HSR. The findings of this study provide actionable insights into the conservation of fish in dammed rivers and such NbS-oriented practices can create achievable strategies for reservoir operations.

Description: The surface heating on the Tibetan Plateau (TP) exerts large influence on the Asian summer monsoon. Drivers of TP surface heat include tropical forcings and other climate modes in middle and high latitude of the Northern Hemisphere, however whether the climate modes in Southern Hemisphere influence the TP surface heat is rarely studied. Using multiple source data diagnosis and numerical experiment from an atmospheric general circulation model (AGCM), we found that the Antarctic Oscillation (AAO) in May can efficiently modulate the subsequent surface heat source over the TP in June. When the AAO is in positive phases, a northeastward propagating atmospheric Rossby wave train originates from the Amundsen Sea low. As a part of this wave train, a pair of anomalous cyclone and anticyclone in the Southern Hemisphere accelerates the surface southeasterlies between them, accompanied by cold sea surface temperature (SST) anomaly in the equatorial middle and eastern Indian Ocean induced by the wind evaporation-SST feedback. Due to the large thermal inertia, in June, the cold SST anomaly stimulates anticyclone anomalies over the western TP and eastern Arabian Sea, which increase the moisture transportation toward the TP and are conducive to the formation and maintenance of the precipitation over the middle and eastern TP. In contrast, the surface heat source, which is dominated by the upward sensible heat flux, is reduced substantially. This result indicates that the AAO can be one of the precursors of the TP heat source, and may help improve the prediction of the Asian summer monsoon.

Description: Accurate prediction of the East Asian summer monsoon (EASM) is beneficial to billions of people’s production and lives. Here, a convolutional neural network (CNN) and transfer learning are used to predict the EASM. The results of the constructed CNN regression model show that the prediction of the CNN regression model is highly consistent with the reanalysis dataset, with a correlation coefficient of 0.78, which is higher than that of each of the current state-of-the-art dynamic models. The heat map method indicates that the robust precursor signals in the CNN regression model agree well with previous theoretical studies and can provide the quantitative contribution of different signals for EASM prediction. The CNN regression model can predict the EASM one year ahead with a confidence level above 95%. The above method can not only improve the prediction of the EASM but also help to identify the involved physical predictors.

Description: The multiple scale atmospheric or oceanic prediction has been a challenging issue due to different dynamical and thermodynamic processes involved and the uncertainty inherent in highly nonlinear and stochastic forcing, such as the prediction of Asian summer monsoon (ASM) precipitation. The seamless prediction was proposed to address this issue. The idea behind this approach is that temporal averaging reduces the spread of the prediction ensemble but retains the approximate first moment, which effectively increases the signal-to-noise ratio of the prediction. In this study, the superiority of the seamless prediction was assessed for the ASM precipitation at timescales from days to weeks. The hindcasts from the European Center for Medium-Range Weather Forecasts in the Subseasonal to Seasonal ensemble dataset were used for the detailed assessment. Results show significant advantages by the seamless approach in evaluating the ASM precipitation prediction on timescales ranging from two days to three weeks. Furthermore, the source of the average window predictability was analyzed for two lead times with significantly superior prediction accuracy: lead times of 4d4d and 3w3w. For 4d4d, the Madden–Julian Oscillation is dominant, while the role of the El Niño–Southern Oscillation phase becomes substantial for 3w3w. These results offer a detailed analysis for the prediction of ASM precipitation within a framework of seamless prediction, benefiting further interpretation of seamless prediction of ASM precipitation.

Description: In this study, a new index based on the potential vorticity (PV) framework is proposed for the quantification of the Tibetan Plateau (TP) surface thermodynamic and dynamic forcing. The results show that the derived TP surface PV (SPV) includes the topographical effect, near-surface absolute vorticity, and land–air potential temperature differences. The climatological annual cycle of the SPV suggests that the TP transitions from a cooling to a heating source in April. The SPV reaches a maximum from June to August, which is consistent with the evolution of the Asian summer monsoon precipitation. Further analysis suggests that the intensified SPV in the boreal summer results in a low-level cyclonic circulation anomaly associated with increased precipitation over the southeastern slope of the TP and South China and decreased precipitation over the Indian Ocean. In winter, the intensified SPV is associated with local cold air and divergence at the TP surface.

Description: The Dst index is one of the most widely used statistical descriptors for monitoring and forecasting global space environmental disturbances such as geomagnetic storms. In order to improve the quality of the index to accommodate the trends in data fusion from joint cross-platform observations , a quantitative analysis of the impact arising from observatory layout was conducted, which was considered to be the largest factor in previous studies. Using data from the INTERMAGNET and Geomagnetic Network of China databases, the 50 strongest magnetic storms in the 24th solar cycle were selected for analysis. Based on the traditional Dst index derivation method and a Bland–Altman statistical plot, a quantitative comparison was undertaken to study the changing characteristics of the Dst index during the process of decreasing the longitude interval of the observing network by increasing the number of observatories, one by one. The results showed that, before the number of observatories reached seven，each additional observatory typically brought a deviation of -7 nT to the local peak, suggesting that the ideal observing network could consist of six stations uniformly distributed around the world . In addition, the hourly value and the minute value of the Dst may show a large difference of up to 15 nT. Combined with the pilot observations, such as the solar wind acquired by satellites, further quantitative analysis is expected to improve the accuracy of the Dst index prediction based on deep learning methods, thus better providing an important reference and instruction for space environment monitoring and space working tasks.

Description: The economic and population growth in India accompanied by increasingly frequent and severe disasters, such as floods and forest fires, raises concerns about the future. In recent decades, satellite technology has become an important tool for monitoring the Earth's surface, including natural hazards and land surface changes. Our USAID (United States Agency for International Development) project, REmote Sensing for Forest Renewal, Ecosystem Services, and Sustainable Hydrological Management (REFRESH), aims to improve forest sustainability in India with the help of satellite geodetic and remote sensing data. Here we report the progress for the physical science component of the REFRESH Project. Our team has developed a deep-learning model for classifying 10 types of Land Use/Land Cover (LULC) to detect long-term changes in forest areas over India. With collaboration from Indian partners, we validate our classification results using their in-situ land cover data. We used PlanetScope daily images at 3–5 m spatial resolution to monitor the evolutions of floods and forest fires over India. Other satellite data products include in-land water level virtual stations over rivers, dams, and lakes using three decades of satellite radar altimetry, daily-sampled GRACE/GRACE-FO Level 1B gravimetry data to monitor monsoonal floods and seasonal droughts, spaceborne lidar and GEDI for mapping canopy changes, and deep-learning downscaled satellite gravimetry measured total water storage anomalies over entire India. Finally, we plan to generate spaceborne GNSS-Reflectometry and NASA’s CYGNSS for water/land classification data products over India. The data products can be retrieved, visualized, and analyzed on an elaborate observation portal.