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The current college students have a lot of mental pressure due to their heavy study, life and work burdens, and the incidence of college students' problems continues to rise. Sports are considered to be one of the best ways to improve the well-being of college students. However, the mechanism of college students' well-being is still unclear. This article is intended to exploring the mechanism of action of Trait Mindfulness (TM) on well-being in college students. Four hundred and ninety six college students were tested with Mindfulness Attention Awareness Scale, Flow Experience Scale, Physical Activity Rating Scale, and Subjective Well-being Scale. College students' trait mindfulness (TM) can predict well-being, college students' trait mindfulness (TM) can have an indirect impact on well-being through flow experience, and college students' trait mindfulness (TM) has an indirect impact on well-being through sports participation. In addition, flow experience and sports participation play a sequential mediating role between college students' trait mindfulness (TM) and well-being. Flow experience and sports participation play a sequential mediating role between college students' trait mindfulness(TM) and well-being. The current research results indicate that college students get well-being in sport activities. Trait mindfulness influences the behavioral tendency of sports participation through the mediation of thinking activities and cognitive function sequences. The results of this study provide a new literature reference for expanding the theory of positive emotion expansion and well-being. In addition, this study also provides an important basis for improving college students' well-being and college education.

Sterodynamic sea level (SdynSL) is an essential component of sea level that climate models can simulate directly. Here, we disentangle the impacts of intermodel uncertainty, internal variability, and scenario uncertainty on SdynSL projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Regarding the global mean, intermodel (scenario) uncertainty dominates before (after) ∼2070, while internal variability is negligible. At the regional scale, intermodel uncertainty is the largest contributor, whereas internal variability plays a secondary role mainly in the tropical Indo‐Pacific Ocean. Scenario uncertainty becomes significant in certain regions toward the end of this century. The anthropogenic signal of global mean SdynSL emerges at the beginning of this century relative to 1971–2000. In contrast, the anthropogenic signals of regional SdynSL are likely to emerge over 70% of the global ocean by the 2090s, which could be advanced to the 2040s if model differences can be totally eliminated. Plain Language Summary Sterodynamic sea level change, that is, the sum of global mean thermosteric sea level rise and regional dynamic sea level changes due to ocean density and circulation changes, is an essential component of future sea level projections. Untangling the sources of uncertainty in SdynSL projections is fundamental for further improving projections. This study aims to separate and quantify three sources of uncertainties in global mean and regional SdynSL projections from the latest CMIP6 models: internal variability, intermodel uncertainty, and scenario uncertainty. In the near term and midterm of this century, we show that intermodel uncertainty dominates the total uncertainty in SdynSL projections at both the global and regional scales, while internal variability has a significant contribution in the tropical Indo‐Pacific Ocean. In the long term, scenario uncertainty becomes important not only for global mean SdynSL but also for regions including the northern Indian Ocean, northeastern Pacific, and the mid‐latitude Southern Ocean. Our results highlight the potential to improve SdynSL projections by eliminating model differences and provide important information for constraining uncertainty in SdynSL projections. Key Points Model differences dominate the uncertainty in global mean and regional sterodynamic sea level (SdynSL) projections by the 2070s Scenario uncertainty becomes more essential toward the end of this century for both the global mean and in certain regions SdynSL signals would emerge over 70% of the global ocean by the 2090s, or as early as the 2040s if model differences are eliminated

The effects of model resolution on the simulation of sea-level variability were analyzed based on the second-generation climate system ocean model from the State Key Laboratory of Numerical Modeling for Atmospheric Science and Geophysical Fluid Dynamics, Institute of Atmosphere Physics (LICOM2) with resolutions of 1° (LICOM2-L) and 0.1° (LICOM2-H).The interannual variability, decadal variability, and long-term trends of the dynamic sea level (DSL) are estimated using a multivariate linear regression model based on the LICOM2-L and LICOM2-H datasets during 1958–2007. The analysis reveals that the distributions of interannual and decadal variability, as well as long-term trends, are consistent between the LICOM2-L and LICOM2-H simulations in the tropics and mid-latitudes. However, differences in these variabilities are most pronounced in the regions of the western boundary currents and Antarctic Circumpolar Current, primarily due to variations in thermosteric sea level (TSSL) and halosteric sea level. In contrast, the DSL variability differences in the Southern Ocean are mainly due to the TSSL. Analyses of ocean heat content (OHC) budgets suggest that the differences between the LICOM2-L and LICOM2-H simulations are mainly in decadal variability and long-term trends. The interannual and decadal variabilities of OHC are significantly influenced by both large-scale mean advection and eddy-induced transport. The latter plays a more pronounced role in high-latitude regions and contributes notably to decadal variability and trend differences. At the equator, eddy-induced transport is the primary driver of long-term trends, accounting for 80% of the total contribution, while the large-scale mean advection contributes the remaining 20%. These findings underscore the complex interplay between mean advection and eddy processes in shaping the thermohaline structure and sea-level variability in the ocean models.

The Indo-Pacific warm pool (IPWP) has warmed and expanded substantially over the past decades, which has significantly affected the hydrological cycle and global climate system. It is unclear how the IPWP will change in the future under anthropogenic (ANT) forcing. Here, we quantify the human contribution to the observed IPWP warming/expansion and adjust the projected IPWP changes using an optimal fingerprinting method based on Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. We find that more than 95% rapid warming and 85% expansion of the observed IPWP are detected and attributable to human influence. Furthermore, human activities affect IPWP warming through both greenhouse gases and ANT aerosols. The multiple model ensemble mean can capture the ANT warming trend and tends to underestimate the ANT warming trend. After using the observation constraint, the IPWP warming is projected to increase faster than that of the ensemble mean in the 21st Century, and the Indian Ocean warm pool is projected to expand more than previously expected. The rapid warming and expansion of IPWP over the rest of the 21st century will impact the climate system and the life of human beings.

Changes in climate extremes pose far-reaching consequences to ecological processes and hydrologic cycles in alpine ecosystems of the arid mountain regions. Therefore, regional assessments in various climates and mountain regions are needed for understanding the uncertainties of the change trends for extreme climate events. The objective of this study was to assess the spatial distribution and temporal trends of extreme precipitation and temperature events responses to global warming on the arid mountain regions of China. Results found that temperature extremes exhibited a significant warming trend, consistent with global warming. Warming trend in autumn and winter were greater than in spring and summer. Besides, precipitation extremes also exhibited statistically increase trend, such as number of days with heavy precipitation and rain day precipitation, etc. The distribution of the number of rainy days was showed a significant increasing trend in many sites, indicating that the increase of rain day precipitation mainly contributed by the increase of single precipitation event duration and moderate-rain days. The greater increasing trend of extreme climate events mainly existed in higher altitudes. This results lend an evidence to earlier predictions that the climate in northwestern China is changing from cold-dry to warm-wet.

This paper introduces the Flexible Global Ocean‐Atmosphere‐Land System Model: Grid‐Point Version 3 (FGOALS‐g3) and evaluates its basic performance based on some of its participation in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) experiments. Our results show that many significant improvements have been achieved by FGOALS‐g3 in terms of climatological mean states, variabilities, and long‐term trends. For example, FGOALS‐g3 has a small (−0.015°C/100 yr) climate drift in 700‐yr preindustrial control (piControl) runs and smaller biases in climatological mean variables, such as the land/sea surface temperatures (SSTs) and seasonal soil moisture cycle, compared with its previous version FGOALS‐g2 during the historical period. The characteristics of climate variabilities, for example, Madden‐Julian oscillation (MJO) eastward/westward propagation ratios, spatial patterns of interannual variability of tropical SST anomalies, and relationship between the East Asian Summer Monsoon and El Niño–Southern Oscillation (ENSO), are well captured by FGOALS‐g3. In particular, the cooling trend of globally averaged surface temperature during 1940–1970, which is a challenge for most CMIP3 and CMIP5 models, is well reproduced by FGOALS‐g3 in historical runs. In addition to the external forcing factors recommended by CMIP6, anthropogenic groundwater forcing from 1965 to 2014 was incorporated into the FGOALS‐g3 historical runs. Plain Language Summary The sixth phase of the Coupled Model Intercomparison Project (CMIP6) is a crucial support for the sixth Assessment Report of Intergovernmental Panel on Climate Change (IPCC AR6) and will also provide important foundation for research in climate change in the next few years. This paper gives the description of FGOALS‐g3 model, its experiment configures, and the experiments conducted according to the experimental design of CMIP6 and evaluates the preliminary performance of model simulation. This work offers references to CMIP6 data users and provides enormous output data sets for assessing and understanding climate change. Key Points This paper describes FGOALS‐g3 and its experiment design for CMIP6 Historical, preindustrial, and scenario simulations are evaluated Climate drift is small in preindustrial simulation, and mean climate and climate variabilities at different temporal scales are realistic in historical runs

A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the scheduling optimization problem for ship plane block flow line involves dual resource constraints, including work teams and spare parts supply limitations. This can be seen as a Dual Resource Constrained Blocked Flow Shop Scheduling Problem (DRCBFSP). This paper presents a scheduling optimization method for this kind of problem to minimize the maximum completion time. To address the dual constraints, chromosomes are encoded as a two-dimensional array composed of positive integers representing the assembly order of blocks and the allocation of work teams. An improved Grey Wolf Optimization Algorithm (IGWO) is proposed to solve the problem, and the Rank Order Value (ROV) rule is used to transform the discrete scheduling solution with the continuous individual position vector. The IGWO algorithm also incorporates nonlinear search factors, dynamic inertia weight factors, and Gaussian mutation perturbation strategies to enhance its development and exploration capabilities. The experimental results suggest that the mathematical model and the IGWO algorithm established in this paper can effectively solve the DRCBFSP encountered in ship block building.

In this study, the effects of oceanic mesoscale eddies on the looping path of the Kuroshio intrusion (KI) were symmetrically investigated by composite analysis using merged satellite data. We found that the mesoscale eddies propagating from the east have a significant impact on the looping path over a time scale of 30–60 days. Cyclonic eddies (CEs) enhance the looping path, but anticyclonic eddies decrease it. We also found that strong eddies do not have strong effects on the looping path. For instance, strong CEs induce the strong surface intrusion of the Kuroshio, but the looping currents are weak due to the presence of the strong Luzon Cold Eddy in the South China Sea, which tends to prevent loop formation. The complicated relationship between eddies and the path of the KI results in a nonsignificant correlation coefficient between the KI and eddy activities in the western Pacific.

The Flexible Global Ocean-Atmosphere-Land System model, Spectral Version 2 （FGOALS-s2） was used to simulate realistic climates and to study anthropogenic influences on climate change. Specifically, the FGOALS-s2 was integrated with Coupled Model Intercomparison Project Phase 5 （CMIP5） to conduct co- ordinated experiments that will provide valuable scientific information to climate research communities. The performances of FGOALS-s2 were assessed in simulating major climate phenomena, and documented both the strengths and weaknesses of the model. The results indicate that FGOALS-s2 successfully overcomes climate drift, and realistically models global and regional climate characteristics, including SST, precipita- tion, and atmospheric circulation. In particular, the model accurately captures annual and semi-annual SST cycles in the equatorial Pacific Ocean, and the main characteristic features of the Asian summer monsoon, which include a low-level southwestern jet and five monsoon rainfall centers. The simulated climate variabil- ity was further examined in terms of teleconnections, leading modes of global SST （namely, ENSO）, Pacific Decadal Oscillations （PDO）, and changes in 19th-20th century climate. The analysis demonstrates that FGOALS-s2 realistically simulates extra-tropical teleconnection patterns of large-scale climate, and irregu- lar ENSO periods. The model gives fairly reasonable reconstructions of spatial patterns of PDO and global monsoon changes in the 20th century. However, because the indirect effects of aerosols are not included in the model, the simulated global temperature change during the period 1850 2005 is greater than the observed warming, by 0.6℃. Some other shortcomings of the model are also noted.

Multidecadal sea surface temperature covariance in the southern Atlantic and Indian Oceans within the 20°–45°S band (termed the SAIOs) has been detected, but the origins of these variations remain uncertain. This study focuses on the decade‐long warming surge (0.43°C/decade) during 1965–1975 and the stalled warming pace (0.05°C/decade) thereafter. Observations and models overall suggest the essence of external forcing in controlling the warming rates of these two periods. Specifically, the 1963 eruption of Mount Agung caused an abrupt cooling of the SAIO, and the recovery of radiative heating during the following decade led to rapid surface warming. The persistent positive trend of the Southern Annular Mode since the late‐1970s has retarded the SAIO warming, primarily by enhancing surface wind speed and latent heat release. Although internal variability can also affect the SAIO through atmospheric teleconnections, its role is overall secondary during the two periods. Plain Language Summary Sea surface temperatures (SSTs) in the southern Atlantic and Indian Oceans (SAIOs) show prominent decade‐to‐decade variations, with notable impacts on global and regional climates. However, the origins of these multidecadal variations are largely unknown. While previous studies overall stressed internal climate variability, this study suggests essential effects of external drivers on the rapid warming of 1965–1975 and the subsequent slowdown. Specifically, atmospheric aerosols from the 1963 eruption of Mount Agung caused an abrupt drop in surface solar radiation, and the recovery of solar heating in the following decade led to accelerated surface warming in the SAIO. Since the late 1970s, the human‐induced Antarctic ozone hole drove a positive trend in the Southern Annular Mode. This trend enhanced the surface winds over the SAIO and attenuated the SST warming. In addition, impacts from internal variability, such as the Interdecadal Pacific Variability, are also considerable during the two periods. Key Points The sea surface temperature (SST) warming rate of the southern Atlantic and Indian Ocean (SAIO) shows multidecadal variations, with a short‐term surge during 1965–1975 and decelerated warming thereafter The 1963 eruption caused a dip in the SAIO SST, and the subsequent dissipation of volcanic aerosols led to accelerated warming of 1965–1975 The Interdecadal Pacific Variability and Southern Annular Mode also modulate the warming rate of the SAIO through their signatures in atmospheric circulation