Changes of Extreme Sea Level in 1.5 and 2.0°C Warmer Climate Along the Coast of China

Using hourly sea level data from 15 tide gauges along the Chinese coast and sea level data of three simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5), we assessed the changes and benefits of the extreme sea level of limiting warming to 1.5°C instead of 2.0°C. Observations show that the extreme sea level has risen with high confidence during the past decades along the coast of China, while the mean sea level change, especially the long-term change plays important roles in the changing process of extreme sea levels. Under the 1.5 and 2.0°C warming scenarios, the sea level will rise with fluctuations in the future, so will the return levels of the extreme sea levels. Compared with the 1.5°C warming condition, the return levels under the 2.0°C warming condition will rise significantly at all tide gauges along the Chinese coast. The results indicate that a 0.5°C warming will bring much difference to the extreme sea levels along the coast of China. It is of great necessity to limit anthropogenic warming to 1.5°C rather than 2.0°C, as proposed by the Paris Climate Agreement, which will greatly reduce the potential risks of future flood disasters along the coast of China and is beneficial for risk response management

Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model

Accurately estimating the ocean’s subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) to simultaneously estimate the ocean subsurface thermal structure (OSTS) and ocean subsurface salinity structure (OSSS) in the tropical Indian Ocean using satellite observations. The input variables include sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), eastward component of sea surface wind (ESSW), northward component of sea surface wind (NSSW), longitude (LON), and latitude (LAT). We train and validate the model using Argo data, and compare its accuracy with that of the original Convolutional Neural Network (CNN) model using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). Our results show that the CBAM-CNN model outperforms the CNN model, exhibiting superior performance in estimating thermohaline structures in the tropical Indian Ocean. Furthermore, we evaluate the model’s accuracy by comparing its estimated OSTS and OSSS at different depths with Argo-derived data, demonstrating that the model effectively captures most observed features using sea surface data. Additionally, the CBAM-CNN model demonstrates good seasonal applicability for OSTS and OSSS estimation. Our study highlights the benefits of using CBAM-CNN for estimating thermohaline structure and offers an efficient and effective method for estimating thermohaline structure in the tropical Indian Ocean

Anthropogenic Influences on 2019 July Precipitation Extremes Over the Mid–Lower Reaches of the Yangtze River

Understanding the driving factors for precipitation extremes matters for adaptation and mitigation measures against the changing hydrometeorological hazards in Yangtze River basin, a habitable area that provides water resources for domestic, farming, and industrial needs. However, the region is naturally subject to major floods linked to monsoonal heavy precipitation during May–September. This study aims to quantify anthropogenic influences on the changing risk of 2-week-long precipitation extremes such as the July 2019 extreme cases, as well as events of shorter durations, over the middle and lower reaches of Yangtze River basin (MLYRB). Precipitation extremes with different durations ranging from 1-day to 14-days maximum precipitation accumulations are investigated. Gridded daily precipitations based on nearly 2,400 meteorological stations across China are used to define maximum accumulated precipitation extremes over the MLYRB in July during 1961–2019. Attribution analysis is conducted by using the Met Office HadGEM3-GA6 modeling system, which comprises two sets of 525-member ensembles for 2019. One is forced with observed sea-surface temperatures (SSTs), sea-ice and all forcings, and the other is forced with preindustrialized SSTs and natural forcings only. The risk ratio between the exceedance probabilities estimated from all-forcing and natural-forcing simulations is calculated to quantify the anthropogenic contribution to the changing risks of the July 2019–like precipitation extremes. The results reveal that anthropogenic warming has reduced the likelihood of 2019-like 14-days heavy precipitation over the mid–lower reaches of the Yangtze River by 20%, but increased that of 2-days extremes by 30%

Understanding the compound marine heatwave and low-chlorophyll extremes in the western Pacific Ocean

The western Pacific Ocean is the global center for marine biodiversity, with high vulnerability to climate change. A better understanding of the spatiotemporal characteristics and potential drivers of compound marine heatwaves (MHWs) and low-chlorophyll (LChl) extreme events is essential for the conservation and management of local marine organisms and ecosystems. Here, using daily satellite sea surface temperature and model-based chlorophyll concentration, we find that the climatological spatial distribution of MHW-LChl events in total days, duration, and intensity exhibits heterogeneous distributions. The southwest sections of the South China Sea (WSCS) and Indonesian Seas are the hotspots for compound events, with total MHW-LChl days that are more than 2.5 times higher than in the other sub-regions. Notably, there is a trend toward more frequent (> 4.2 d/decade), stronger (> 0.5), and longer-lasting (> 1.4 d/decade) MHW-LChl occurrences in the WSCS. The occurrence of compound MHW-LChl extremes exhibits remarkable seasonal differences, with the majority of these events transpiring during winter. Moreover, there are generally statistically significant increasing trends in MHW-LChl events for all properties on both seasonal and inter-annual timescales. Furthermore, we reveal that the total days of compound MHW-LChl extremes are strongly modulated by large-scale climate modes such as the El Niño-Southern Oscillation and Dipole Mode Index. Overall, pinpointing MHW-LChl hotspots and understanding their changes and drivers help vulnerable communities in better preparing for heightened and compounded risks to marine organism and ecosystems under climate change

COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review

In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44∘ (∼ 50 km), 0.22∘ (∼ 25 km), and 0.11∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain

Testing Reanalyses in Constraining Dynamical Downscaling

Reanalysis data sets have been widely used in regional climate dynamical downscaling studies. In this study, we test the use of various reanalysis data sets in constraining dynamical downscaling by assessing the reconstruction skill of the Yellow Sea coastal winds using the COSMO model in Climate Mode (CCLM) with 7-km resolution. Four reanalysis forcing data sets are used as lateral boundary conditions and internal large-scale constraints (spectral nudging): the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set (NCEP1) is downscaled to an intermediate domain with 55-km resolution (CCLM_55km), ERA-interim reanalysis data set (ERAint), NCEP climate forecast system reanalysis data set (CFSR), and Japanese 55-year reanalysis data set (JRA55).  Several statistical analysis methods are employed to assess the modeled winds through comparison with observed offshore wind data from 2006, and it is found that the downscaled simulations yield good quality wind speed products. However, they all tend to overestimate observed low wind speeds and underestimate observed high wind speeds. Furthermore, the quality of the modeled wind direction is strongly associated with the wind speed intensities, exhibiting a much better reproduction of wind direction at strong wind speeds than at light wind speeds.  The downscaling simulations driven by ERAint, JRA55, and CFSR are consistent with each other in the reproduction of local wind speed and direction; the simulations driven by ERAint and JRA55 are slightly better for strong winds and those driven by CFSR are better for light winds. All three simulations generate local wind estimates that are superior to those of the simulation driven by CCLM_55km. This superiority reflects the better quality of the CFSR, ERAint, and JRA55 reanalyses with regard to assimilated local observations compared with the CCLM_55km hindcast, which exploits only upper-air large scale NCEP1 wind fields

Magnetic Resonance Compatibility Analysis Method of Surgical Robotic System Based on Image Quality Evaluation

To ensure the safety and precision of surgery, the robotic system applied in magnetic resonance (MR) image-guided robot-assisted surgery should be MR-compatible. In terms of this issue, a MR compatibility analysis method for surgical robotic system based on image quality evaluation is proposed in this paper, and the image sets are extended. The image quality evaluation model is constructed by combining evaluation parameters such as signal-to-noise ratio of MR images, image change factor and MR image distortion. The model can analyze the effect of the robot component and robot motion on image quality, forming a basis for image quality evaluation. The experimental results show that the image quality evaluation method can fully analyze the MR compatibility of the robotic system component, and provide an evaluative method and theoretical basis for the MR compatibility analysis of other kinds of medical robotic systems

Evaluation of Social Responsibility of Major Municipal Road Infrastructure—Case Study of Zhengzhou 107 Auxiliary Road Project

Social responsibility plays an important role in the sustainable development of major municipal road infrastructure. In this study, a major municipal road infrastructure social responsibility (MMRISR) evaluation indicator system is developed for the comprehensive evaluation of social responsibility. Questionnaires and expert interviews were used to screen the initial indicators of the proposed system. Then, 24 indicators were selected from four dimensions to establish an MMRISR evaluation indicator system. The fuzzy analytic hierarchy process was employed to calculate the weights of each indicator. Finally, the Zhengzhou 107 Auxiliary Road Project was adopted as a case study to test the reliability of the proposed evaluation system. The contribution of this study lies in the provision of a novel indicator system for the social responsibility evaluation of major municipal road infrastructures, thus improving the science of project establishment and decision-making. The proposed social responsibility system can provide an efficient decision-making tool for social responsibility governance, fundamentally promoting the sustainable development of major municipal road infrastructures and the achievement of certain sustainable development goals

The Concept of Large-Scale Conditioning of Climate Model Simulations of Atmospheric Coastal Dynamics: Current State and Perspectives

We review the state of dynamical downscaling with scale-constrained regional and global models. The methodology, in particular spectral nudging, has become a routine and well-researched tool for hindcasting climatologies of sub-synoptic atmospheric disturbances in coastal regions. At present, the spectrum of applications is expanding to other phenomena, but also to ocean dynamics and to extended forecasting. Additionally, new diagnostic challenges are appearing such as spatial characteristics of small-scale phenomena such as Low Level Jets