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As one of the participants in the Subseasonal to Seasonal (S2S) Prediction Project, the China Meteorological Administration (CMA) has adopted several model versions to participate in the S2S Project. This study evaluates the models’ capability to simulate and predict the Madden-Julian Oscillation (MJO). Three versions of the Beijing Climate Center Climate System Model (BCC-CSM) are used to conduct historical simulations and re-forecast experiments (referred to as EXP1, EXP1-M, and EXP2, respectively). In simulating MJO characteristics, the newly-developed high-resolution BCC-CSM outperforms its predecessors. In terms of MJO prediction, the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M, and further to 24 days in EXP2. Within the first forecast week, the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill. However, during forecast weeks 2–3, EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3. Particularly at initial phases 2–3, EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection, leading to the highest prediction skill of the MJO. Our results reveal that, during the participation of the CMA models in the S2S Project, both the improved model initialization and updated model physics played positive roles in improving MJO prediction. Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.

The Beijing Climate Center Earth System Model version 1 (BCC-ESM1) is the first version of a fully coupled Earth system model with interactive atmospheric chemistry and aerosols developed by the Beijing Climate Center, China Meteorological Administration. Major aerosol species (including sulfate, organic carbon, black carbon, dust, and sea salt) and greenhouse gases are interactively simulated with a whole panoply of processes controlling emission, transport, gas-phase chemical reactions, secondary aerosol formation, gravitational settling, dry deposition, and wet scavenging by clouds and precipitation. Effects of aerosols on radiation, cloud, and precipitation are fully treated. The performance of BCC-ESM1 in simulating aerosols and their optical properties is comprehensively evaluated as required by the Aerosol Chemistry Model Intercomparison Project (AerChemMIP), covering the preindustrial mean state and time evolution from 1850 to 2014. The simulated aerosols from BCC-ESM1 are quite coherent with Coupled Model Intercomparison Project Phase 5 (CMIP5)-recommended data, in situ measurements from surface networks (such as IMPROVE in the US and EMEP in Europe), and aircraft observations. A comparison of modeled aerosol optical depth (AOD) at 550 nm with satellite observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR) and surface AOD observations from the AErosol RObotic NETwork (AERONET) shows reasonable agreement between simulated and observed AOD. However, BCC-ESM1 shows weaker upward transport of aerosols from the surface to the middle and upper troposphere, likely reflecting the deficiency of representing deep convective transport of chemical species in BCC-ESM1. With an overall good agreement between BCC-ESM1 simulated and observed aerosol properties, it demonstrates a success of the implementation of interactive aerosol and atmospheric chemistry in BCC-ESM1.

The present study examines subseasonal prediction skills and biases of the summer rainfall over eastern China in the Beijing Climate Center (BCC) Atmospheric General Circulation Model (BCC_AGCM2.2) and assesses the predictability of eastern China summer rainfall based on the multi-member forecasts. The BCC_AGCM2.2 model shows some skill in predicting summer rainfall over eastern China within the lead-times of 0–9 days. However, the subseasonal prediction skill is low on average, which is linked to the ability of the model in predicting the western Pacific subtropical high (WPSH). The low prediction skills may partially be attributed to biases in the model, including a wider meridional span and a weaker intensity of rainbelt in Yangtze River valley, earlier meridional movement, and a further northward shift of WPSH compared to that in the observations. These biases result in an obvious dry bias along the monsoon rainbelt, and a wet bias to both the north and south sides. Moreover, as a major contributor to the predictability, the Pacific-Japan pattern is not well predicted for both its spatial pattern and subseasonal evolution. The low forecast skill of summer rainfall in eastern China seems due to a dominant role of the atmospheric internal variability and a minor influence of the sea surface temperature in extratropical climate variability. Nevertheless, enhanced prediction skills under the assumption of a perfect model imply the potential to improve the prediction skill of the summer rainfall over eastern China through reducing model biases.

In spring over the southern Bay of Bengal (BOB), a vortex commonly develops, followed by the Asian summer monsoon onset. An analysis of relevant data and a case study reveals that the BOB monsoon onset vortex is formed as a consequence of air–sea interaction over BOB, which is modulated by Tibetan Plateau forcing and the land–sea thermal contrast over the South Asian area during the spring season. Tibetan Plateau forcing in spring generates a prevailing cold northwesterly over India in the lower troposphere. Strong surface sensible heating is then released, forming a prominent surface cyclone with a strong southwesterly along the coastal ocean in northwestern BOB. This southwesterly induces a local offshore current and upwelling, resulting in cold sea surface temperatures (SSTs). The southwesterly, together with the near-equatorial westerly, also results in a surface anticyclone with descending air over most of BOB and a cyclone with ascending air over the southern part of BOB. In the eastern part of central BOB, where sky is clear, surface wind is weak, and ocean mixed layer is shallow, intense solar radiation and low energy loss due to weak surface latent and sensible heat fluxes act onto a thin ocean layer, resulting in the development of a unique BOB warm pool in spring. Near the surface, water vapor is transferred from northern BOB and other regions to southeastern BOB, where surface sensible heating is relatively high. The atmospheric available potential energy is generated and converted to kinetic energy, thereby resulting in vortex formation. The vortex then intensifies and moves northward, where SST is higher and surface sensible heating is stronger. Meanwhile, the zonal-mean kinetic energy is converted to eddy kinetic energy in the area east of the vortex, and the vortex turns eastward. Eventually, southwesterly sweeps over eastern BOB and merges with the subtropical westerly, leading to the onset of the Asian summer monsoon.

For the routine target detection algorithm in the underwater complex environment to obtain the image of the existence of blurred images, complex background and other phenomena, leading to difficulties in model feature extraction, target miss detection and other problems. Meanwhile, an improved YOLOv7 model is proposed in order to improve the accuracy and real-time performance of the underwater target detection model. The improved model is based on the single-stage target detection model YOLOv7, incorporating the CBAM attention mechanism in the model, so that the feature information of the detection target is weighted and enhanced in the spatial dimension and the channel dimension, capturing the local relevance of feature information, making the model more focused on target feature information, improved detection accuracy, and using the SPPFCSPC module, reducing the computational effort of the model while keeping the model perceptual field unchanged, improved inference speed of the model. After a large number of comparison experiments and ablation experiments, it is proved that our proposed ACFP-YOLO algorithm model has higher detection accuracy compared with Efficientdet, Faster-RCNN, SSD, YOLOv3, YOLOv4, YOLOv5 models and the latest YOLOv7 model, and is more accurate for target detection tasks in complex underwater environments advantages.

The El Niño-Southern Oscillation (ENSO) ensemble prediction skills of the Beijing Climate Center (BCC) climate prediction system version 2 (BCC-CPS2) are examined for the period from 1991 to 2018. The upper-limit ENSO predictability of this system is quantified by measuring its “potential” predictability using information-based metrics, whereas the actual prediction skill is evaluated using deterministic and probabilistic skill measures. Results show that: (1) In general, the current operational BCC model achieves an effective 10-month lead predictability for ENSO. Moreover, prediction skills are up to 10–11 months for the warm and cold ENSO phases, while the normal phase has a prediction skill of just 6 months. (2) Similar to previous results of the intermediate coupled models, the relative entropy (RE) with a dominating ENSO signal component can more effectively quantify correlation-based prediction skills compared to the predictive information (PI) and the predictive power (PP). (3) An evaluation of the signal-dependent feature of the prediction skill scores suggests the relationship between the “Spring predictability barrier (SPB)” of ENSO prediction and the weak ENSO signal phase during boreal spring and early summer.

High quality underwater images and videos are important for exploitation tasks in the underwater environment, but the complexity of the underwater imaging environment makes the quality of the acquired underwater images generally low. To correct the chromatic aberration and enhance the sharpness of underwater images in order to improve the quality of underwater images, we based on the differential compensation proposed a Differential Attenuation Compensation (DAC) method. The underwater image is contrast stretched to improve the contrast of the image, as well as the underwater image is denoised, for the red channel with serious loss of detail information we choose the blue and green channels with more detail information to compensate for this, and finally the image is restored through the grayscale world to obtain more realistic colors. Our method is qualitatively and quantitatively compared with multiple state-of-the-art methods in the public underwater image dataset, underwater image enhancement benchmark (UIEB) and enhancing underwater visual perception (EUVP), demonstrating that the underwater images processed by our method better resolve the problems of chromatic aberration and blur, with more realistic color, detail and better underwater image quality evaluation indicators

Physical processes associated with onset of the 1998 Asian summer monsoon were examined in detail using multi-source datasets. We demonstrated that strong ocean-atmosphere-land interaction in the northern Indian Ocean and tropical Asian area during spring is a fundamental factor that induces the genesis and development of a monsoon onset vortex over the Bay of Bengal （BOB）, with the vortex in turn triggering onset of the Asian summer monsoon. In spring, strong surface sensible heat- ing over India and the Indochina Peninsula is transferred to the atmosphere, forming prominent in situ cyclonic circulation, with anticyclonic circulations over the Arabian Sea and northern BOB where the ocean receives abundant solar radiation. The corresponding surface winds along the North Indian Ocean coastal areas cause the ocean to produce the in situ offshore cur- rents and upwelling, resulting in sea surface temperature （SST） cooling. With precipitation on the Indochina Peninsula in- creasing from late April to early May, the offshore current disappears in the eastern BOB or develops into an onshore current, leading to SST increasing. A southwest-northeast oriented spring BOB warm pool with SST 〉31℃forms in a band from the southeastern Arabian Sea to the eastern BOB. In early May, the Somali cross-equatorial flow forms due to the meridional SST gradient between the two hemispheres, and surface sensible heat over the African land surface. The Somali flow overlaps in phase with the anticyclone over the northern Arabian Sea in the course of its inertial fluctuation along the equator. The con- vergent cold northerlies on the eastern side of the anticyclone cause the westerly in the inertial trough to increase rapidly, so that enhanced sensible heat is released from the sea surface into the atmosphere. The cyclonic vorticity forced by such sensible heating is superimposed on the inertial trough, leading to its further increase in vorticity strength. Since atmospheric inertial motion is destroyed, the flow deviates from the inertial track in an intensified cyclonic curvature, and then turns northward to- ward the warm pool in the northern BOB. It therefore converges with the easterly flow on the south side of the anticyclone over the northern BOB, forming a cyclonic circulation center east of Sri Lanka. Co-located with the cyclonic circulation is a generation of atmospheric potential energy, due to lower tropospheric heating by the warm ocean. Eventually the BOB mon- soon onset vortex （MOV） is generated east of Sri Lanka. As the MOV migrates northward to the warm pool it develops quickly such that the zonal oriented subtropical high is split over the eastern BOB. Thus, the tropical southwesterly on the southern and eastern sides of the MOV merges into the subtropical westerly in the north, leading to active convection over the eastern BOB and western Indochina Peninsula and onset of the Asian summer monsoon.

The leaves of the Chinese cabbage which is most widely consumed come in a wide variety of colors. Leaves that are dark green can promote photosynthesis, effectively improving crop yield, and therefore hold important application and cultivation value. In this study, we selected nine inbred lines of Chinese cabbage displaying slight differences in leaf color, and graded the leaf color using the reflectance spectra. We clarified the differences in gene sequences and the protein structure of ferrochelatase 2 (BrFC2) among the nine inbred lines, and used qRT-PCR to analyze the expression differences of photosynthesis-related genes in inbred lines with minor variations in dark-green leaves. We found expression differences among the inbred lines of Chinese cabbage in photosynthesis-related genes involved in the porphyrin and chlorophyll metabolism, as well as in photosynthesis and photosynthesis-antenna protein pathway. Chlorophyll b content was significantly positively correlated with the expression of PsbQ, LHCA1_1 and LHCB6_1, while chlorophyll a content was significantly negatively correlated with the expression PsbQ, LHCA1_1 and LHCA1_2. Our results provide an empirical basis for the precise identification of candidate genes and a better understanding of the molecular mechanisms responsible for the production of dark-green leaves in Chinese cabbage.

Ocean exploration has always been an important strategic direction for the joint efforts of all mankind. Many countries in the world today are developing their own underwater autonomous explorers to better explore the seabed. Vision, as the core technology of autonomous underwater explorers, has a great impact on the efficiency of exploration. Different from traditional tasks, the lack of ambient light on the seabed makes the visual system more demanding. In addition, the complex terrain on the seabed and various creatures with different shapes and colors also make exploration tasks more difficult. In order to effectively solve the above problems, we combined the traditional models to modify the structure and proposed an algorithm for the super-resolution fusion of enhanced extraction features to perform semantic segmentation of seabed scenes. By using a structurally reparameterized backbone network to better extract target features in complex environments, and using subpixel super-resolution to combine multiscale feature semantic information, we can achieve superior ocean scene segmentation performance. In this study, multiclass segmentation and two-class segmentation tests were performed on the public datasets SUIM and DeepFish, respectively. The test results show that the mIoU and mPA indicators of our proposed method on SUIM reach 84.52% and 92.33%mPA, respectively. The mIoU and mPA on DeepFish reach 95.26% and 97.38%, respectively, and the proposed model achieves SOTA compared with state-of-the-art methods. The proposed model and code are exposed via Github 1 .