中华人民共和国国务院

The Arctic, which is highly sensitive to climate change, is experiencing unprecedented transformations. Cyclones, as extreme weather events, significantly impact sea ice through dynamic and thermodynamic forcings. This study quantifies the relative roles of dynamic, atmospheric thermodynamic, and oceanic thermodynamic forcings on sea ice changes from Arctic cold-season cyclones (1993–2020) throughout distinct phases of cyclone passage (4 days prior to the day of, 1–4 days after, and 5–7 days after cyclone passage) in the northern Barents Sea and the southwestern Kara Sea. It is found that sea ice concentration decreases significantly before cyclone passage and gradually increases thereafter. Across all phases, dynamic forcing dominates the sea ice response, while atmospheric thermodynamic forcing plays a secondary role. Oceanic thermodynamic forcing contributes minimally and is primarily active in regions with pronounced Atlantic Water influence. Notably, we identify a new oceanic thermodynamic mechanism: cyclones reduce the distance between mixed layer depth and warm-layer upper boundary (WL-MLD), enhancing upward ocean heat flux and then influencing sea ice. In the northern Barents Sea, dynamic forcing reduces sea ice effective thickness by 0.57 cm/day before cyclone passage and increases it by 0.68 cm/day after it passes, which, like all following values, is defined as the difference between the “cyclone’’ and “non-cyclone’’ scenarios. Before cyclone passage, atmospheric thermodynamic forcing suppresses thickness increase by 0.49 cm/day, while oceanic thermodynamic forcing mitigates thickness loss by 0.17 cm/day. In the southwestern Kara Sea, dynamic forcing leads to a reduction of 0.79 cm/day before cyclone passage, followed by an increase of 0.42 cm/day after passage.

The ocean bottom seismograph (OBS) is a powerful device deployed on the seafloor for acquiring marine seismic data, capable of detecting the multi-scale Earth’s interiors from submarine sediments to the mantle. Due to the frequent use of free-fall deployment, it is challenging to accurately track its precise position. Additionally, the internal crystal oscillator clock of the OBS has limited accuracy, resulting in clock drift for long-term work on the seabed. To improve the reliability of OBS detections, it is crucial to calculate the precise OBS location and time correction. Focusing on accurately determining OBS position and timing, this study developed a positioning method that integrates time correction based on the Markov Chain Monte Carlo (MCMC) algorithm, utilizing travel times of direct water waves triggered by two-dimensional (2-D) shot lines or three-dimensional (3-D) airgun arrays. This newly developed method can simultaneously estimate accurate OBS location and time correction, incorporating bathymetric data into the inversion procedures to improve sampling efficiency and enhance the reliability of the final results. Synthetic tests with appropriate noise levels are performed independently to evaluate the feasibility and reliability of our method, indicating that it is robust enough to determine OBS location and time correction precisely. Finally, we use travel-time data recorded at three OBSs deployed in the Southwest Indian Ridge to relocate locations and calculate time corrections. The results exhibit high consistency when using 2-D and 3-D shot data, indicating that high-resolution bathymetric data plays a fingerprint role in inversion to evaluate precise OBS location and time correction.

Ship detection is crucial for both military and civilian applications and is a key use of polarimetric SAR (PolSAR). While convolutional neural networks (CNNs) enhance PolSAR ship detection with powerful feature extraction, existing approaches still face challenges in discriminating targets from clutter, detecting multi-scale objects in complex scenes, and achieving real-time detection. To address these issues, we propose a Mamba-CNN hybrid Multi-scale ship detection Network driven by a Dual-perception feature of Doppler and Scattering. First, at the input feature level, a Dual-perception feature of Doppler and Scattering (DDS) is introduced, effectively differentiating ship and clutter pixels to enhance the network’s ship discrimination. Specifically, Doppler characteristics distinguish between moving and stationary targets, while scattering characteristics reveal fundamental differences between targets and clutter. Second, at the network architecture level, a Mamba-CNN hybrid Multi-scale ship detection Network (MCMN) is designed to improve multi-scale ship detection in complex scenarios. It uses a Multi-scale Information Perception Module (MIPM) to adaptively aggregate multi-scale features and a Local-Global Feature Enhancement Module (LGFEM) based on Mamba for long-range context modeling. MCMN remains efficient through feature grouping, pointwise and depthwise convolutions, meeting real-time requirements. Finally, extensive experiments on the GF-3 and SSDD datasets demonstrate the superiority of DDS and MCMN. DDS effectively distinguishes ships from clutter across scenarios. As an input feature, it boosts average F1-score and AP by 4.3% and 4.3%, respectively, over HV intensity, and outperforms other polarization features. MCMN achieves state-of-the-art results, improving AP by 1.2% and 0.8% on the two datasets while reducing parameters by 1.29M, FLOPs by 1.5G, and inference time by 59.2%.

Understanding the response of the Earth system to varying concentrations of carbon dioxide (CO 2 ) is critical for projecting possible future climate change and for providing insight into mitigation and adaptation strategies in the near future. In this study, we generate a dataset by conducting an experiment involving carbon dioxide removal (CDR)—a potential way to suppress global warming—using the Chinese Academy of Sciences Earth System Model version 2.0 (CAS-ESM2.0). A preliminary evaluation is provided. The model is integrated from 200–340 years as a 1% yr −1 CO 2 concentration increase experiment, and then to ~478 years as a carbon dioxide removal experiment until CO 2 returns to its original value. Finally, another 80 years is integrated in which CO 2 is kept constant. Changes in the 2-m temperature, precipitation, sea surface temperature, ocean temperature, Atlantic meridional overturning circulation (AMOC), and sea surface height are all analyzed. In the ramp-up period, the global mean 2-m temperature and precipitation both increase while the AMOC weakens. Values of all the above variables change in the opposite direction in the ramp-down period, with a delayed peak relative to the CO 2 peak. After CO 2 returns to its original value, the global mean 2-m temperature is still ~1 K higher than in the original state, and precipitation is ~0.07 mm d −1 higher. At the end of the simulation, there is a ~0.5°C increase in ocean temperature and a 1 Sv weakening of the AMOC. Our model simulation produces similar results to those of comparable experiments previously reported in the literature.