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Abstract Background Depression acts as a noteworthy worldwide public health challenge. Identifying accessible biomarkers is crucial for early diagnosis and intervention. The relationship between depression in adult Americans and the neutrophil to high-density lipoprotein cholesterol ratio (NHR) was investigated in this research. Methods The relationship between NHR and depressive symptoms was analyzed utilizing National Health and Nutrition Examination Survey data from 2005 to 2018 and the Patient Health Questionnaire-9. The study included 33,871 participants with complete NHR and depression data. Adjusted multivariable logistic regression models were used to account for possible confounders, and subgroup analyses were conducted to investigate effect changes. Results Elevated NHR levels were positively correlated with a heightened risk of depression (OR = 1.03, 95% CI: 1.01–1.05, P

Abstract Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

Abstract Understanding both positive and negative impacts of climate change is essential for comprehensively assessing and well adapting to the impacts of changing climate. Conventionally, climate warming is revealed to negatively impact human activities. Here, we reveal that human beings’ performance in anaerobic sports may benefit from climate warming. Using global weather observation and athletes’ performance datasets, we show that world-top athletes’ performances in nearly all athletics anaerobic events (i.e., sprints, jumps and throws) substantially improve as ambient temperature rises. For example, 100 m performance monotonically improves by 0.26 s as ambient temperature rises from 11.8° to 36.4 °C. Using Coupled Model Intercomparison Project Phase 6 datasets, we further show that global warming can substantially improve world-top athletes’ performance in eleven of the thirteen Olympics athletics anaerobic events by 0.27%–0.88% and 0.14–0.48% under high-emission and medium-emission scenarios, respectively, during 1979–2100. Among them, the improvements for 100 m are 0.59% (0.063 s) and 0.32% (0.034 s), respectively. Mechanism analysis shows that the warmed ambient atmosphere can improve competitors’ performance through expanding the air and thus reducing the air resistance to the competitors and throwing implements for hummer throw and all the sprints, hurdling and jumps. Quantitative analysis estimates that this thermodynamic process is essential for the impacts of warmed ambient atmosphere on the performances in these events as physiological processes are.

The heart is a multicellular system, and the intercellular crosstalk mechanism is very important for the growth and development of the heart and even the organs, tissues, and cells at a distance. As a kind of extracellular vesicle, exosomes are released by different types of cells and can carry specific genetic material, endosomal proteins, cytokines, etc., which are the main material basis for mediating cell crosstalk mechanism. Among them, microRNA carried by cardiac cells-derived exosomes have highly conserved sequences and play a key role in regulating the function of organs, tissues, and cells related to cardiovascular diseases and their complications and comorbidities, which have attracted extensive attention in the medical community in recent years. Following up on the latest research progress at home and abroad, this review systematically summarized the regulatory role of microRNA from cardiac cells-derived exosomes in various cell crosstalk, including not only cardiac cells (including cardiomyocytes, fibroblasts, myofibroblast, cardiac progenitor cells, cardiac microvascular endothelial cells, cardiosphere-derived cells, etc.) but also tumor cells, bone marrow progenitor cells, and other tissue cells, in order to provide a reference for the prevention and treatment of cardiovascular diseases and their complications and comorbidities.

Tropical cyclone (TC) peripheral downdrafts and urbanization can promote extreme heatwave (HW) events in the Greater Bay Area (GBA), a highly urbanized coastal area in China. However, the roles of synoptic patterns and urbanization in the HW events remain unclear, particularly for the joint occurrences of the tropical cyclone and heatwave (TC-HW) extremes. Here, we identify three synoptic patterns closely related to TC-HW events, namely: the northeastern Taiwan TC pattern (P4), the southeastern Taiwan TC pattern (P6), and the eastern Taiwan-Philippine Sea TC pattern (P7), as these patterns could enhance HWs through strong downdrafts, strong solar radiation, and low humidity, thereby favoring the maintenance of TC-HW events. Among the three patterns, P6 is most conducive to the occurrence of TC-HW compound events in the GBA. Moreover, the urban-rural temperature disparities under the TC-HW events are unique than those on the days without TC-HW events, i.e., the daily maximum temperature at rural and suburban stations is higher than that at urban stations. This unique feature is the opposite of the urban heat island and is mainly attributed to the rural subsidence warming induced by the TCs and Foehn effects. These results indicate that the spatial distribution of HW in coastal area is substantially modulated by TCs, which is meaningful to understanding the features and underlying mechanism of compound TC-HW events and adapting to their impacts.

Abstract Taking the megacity of Beijing as an example, a short‐time Fourier transform (STFT) method was employed to extract the multi‐timescale evolution pattern of the canopy urban heat island intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the evolution of the CUHII in Beijing and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual‐scale spectrum of CUHII exhibited an increasing trend with obvious seasonal variation of the canopy urban heat island (CUHI). The intra‐daily‐scale spectrum of CUHII showed an increasing trend with the nighttime CUHI developing faster. Increasing Western Pacific Subtropical High intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII is controlled by weather system evolution, showing that the frequency of cold/heat waves (CWs/HWs) in Beijing was significantly negatively correlated with the weather‐scale spectral intensity of the CUHII. CWs and HWs can increase the CUHII for a long duration.

In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation.

The urban underlying surface may affect the thunderstorm process. However, current research on this phenomenon is still in its infancy. This paper aimed to analyze the influence of the urban underlying surface on the evolution of thunderstorm organization through ground observation and numerical simulation. The results indicated that when the thunderstorm system with strong synoptic conditions passed through the built-up area of Beijing, it exhibited obvious bifurcation and detour. The dynamic field of near-surface cold pools could serve as diagnostic indicators for understanding how the urban underlying surface affects the thunderstorm process. The large-scale compact-rise clusters in the city center could alter the movement direction and path of the cold pool outflow, thereby influencing the thunderstorm organization process. In addition to the spatial configuration of the building complex, the city size might also be an important factor influencing the thunderstorm process. This study might provide a fundamental foundation and technical support for predicting and assessing urban thunderstorm disasters.

Abstract Precipitable water vapor (PWV) is one of the crucial driving forces for tropical cyclone (TC) genesis. Under the impact of global warming, TCs have changed their way to concentrate PWV from the ambient atmosphere. This study starts with the asymmetric characteristics of PWV in the TC's north and south sides using altimetry‐satellite‐based PWV profiles paired with 367 TCs over the western Pacific Ocean between 2008 and 2020. The results suggest that the TC‐concentrated PWV has a north‐leaning and south‐leaning property over the western North Pacific (WNP) and western South Pacific (WSP), respectively. Moreover, it is observed that the radius of TC‐concentrated PWV has broadened by 5.44% (5.83%) over the WNP (WSP) from 2008 to 2020. In contrast, the PWV gradient has decreased by 23.5% (17.5%) over the WNP (WSP). Our findings highlight that the TC‐concentrated PWV can be used as an important indicator of TC responses to global warming.

The icing on power transmission lines, as a major hazard affecting the safety of electricity usage in China during winter, poses a significant challenge in systematically evaluating the weather conditions and their distribution characteristics during the icing period. Understanding the interaction between the microterrain and micrometeorology and achieving a refined analysis of the physical mechanisms during the icing process remain difficult tasks in this field. These are crucial aspects for the development of more accurate icing prediction models across southern China. Therefore, this study provides a comprehensive review and summary of the current research state and progress in the study of power transmission line icing in southern China from three perspectives: (1) large-scale circulation characteristics; (2) microphysical process, terrain–atmosphere interaction, microtopography and local micrometeorological conditions for the occurrence of icing events; and (3) numerical icing event modeling and forecasting. This study also looks ahead to the scientific issues and technological bottlenecks that need to be overcome for the prediction of ice coating on power transmission lines in southern China. The goal is to provide guidance for the causal analysis and forecasting warnings of power transmission line icing in the complex microterrain of the southern region.

Abstract This study evaluates the uncertainties of turbulent flux calculation using eddy covariance (EC) and wavelet analysis (WA) methods. First, a non‐stationary data set is concocted by adding periodic waves and random perturbations which mimic the large eddies, turbulent intermittency, and asymmetry into an observational stationary data set, and the theoretical “true” fluxes are used to quantitatively evaluate the accuracy of these methods. Results show that EC and Morlet‐wavelet generate biases ranging 50%–100% of the “true” values at different non‐stationarity grades, whereas the Mexican hat (Mexhat) wavelet has a bias of about half of them. Furthermore, there is a high correlation of the Mexhat‐derived fluxes to the benchmark values, the regression slopes of the values of these two can be improved to almost 1 by adding a correction coefficient. The results suggest the potential of using the Mexhat‐wavelet method to calculate turbulent fluxes with high accuracy under non‐stationary conditions.

Under the background of global warming, interaction between heat waves (HWs) and urban heat island (UHI) has led to trends of increase in the intensity, frequency, and duration of extreme heat events in urban areas, seriously threatening the health of urban populations. Taking Guangzhou (a tropical megacity in China) as an example, this study used automatic weather station data and ERA5 reanalysis data to explore the interaction between HWs and UHI, and to elucidate the effects of wind speed and local climate zones (LCZs) on such interaction. Results revealed obvious HWs–UHI interaction in Guangzhou, whereby HWs induces an amplification effect on UHI intensity (UHII) that was most significant at night. In the main urban area, UHII and HWs both weakened with increasing wind speed, indicating that low wind speeds contribute to increased occurrence of HWs and enhancement of UHII. Differently, in some areas peripheral to the main urban area, the UHII at medium wind speeds was stronger than that at low wind speeds, which reflect the impact of heat advection from the urban center. For different LCZs in the main urban area, the strongest UHII, highest risk of HW occurrence, and most significant HWs–UHI interaction were found in the compact mid-rise buildings and compact low-rise buildings (LCZ2 and LCZ3, respectively), followed by the compact high-rise buildings (LCZ1), which was mainly affected by the shading effect of high-rise buildings. The weakest UHII and lowest risk of HW occurrence were found in open high-rise buildings and open mid-rise buildings (LCZ4 and LCZ5, respectively), which generally have good ventilation conditions. Our findings will help to understand urban warming and its association with UHI and HW events in tropical urban regions, which has implications for rational improvement of the urban thermal environment in other tropical urban regions globally.

Mesoscale convective cloud systems have a small horizontal scale and a short lifetime, which brings great challenges to quantitative precipitation estimation (QPE) by satellite remote sensing. Combining machine learning models and geostationary satellite spectral information is an effective method for the QPE of mesoscale convective cloud, while the interpretability of machine learning model outputs remains unclear. In this study, based on Himawari-8 data, high-density automatic weather station observations, and reanalysis data over the North China Plain, a random forest (RF) machine learning model of satellite-based QPE was established and verified. The interpretation of the output of the RF model of satellite-based QPE was further explored by using the Shapley Additive Explanations (SHAP) algorithm. Results showed that the correlation coefficient between the predicted and observed precipitation intensity of the RF model was .64, with a root-mean-square error of .27 mm/h. The importance ranking obtained by SHAP model is completely consistent with the outputs of random forest importance function. This SHAP method can display the importance ranking of global features with positive/negative contribution values (e.g., current precipitation, column water vapor/black body temperature, cloud base height), and can visualize the marginal contribution values of local features under interaction. Therefore, combining the RF and SHAP methods provides a valuable way to interpret the output of machine learning models for satellite-based QPE, as well as an important basis for the selection of input variables for satellite-based QPE.

Compared with the urban boundary layer, air pollution–surface energy budget interactions in farmland areas are relatively sparse. To investigate the roles of local aerosol and transported dust pollution on the surface energy balance over farmland, a field experiment was conducted during spring 2016 in eastern China. The heavy pollution episodes during the measurement period were typically accompanied by easterly winds, low wind speeds, and high relative humidity. On polluted days, relative to clean days, both downward and upward shortwave radiation decreased by 46%, while downward and upward longwave radiation increased by 4% and 1%. The combined effects of the four radiation components resulted in a 33% reduction in net radiation on polluted days, and thus, the surface energy budget changed. Polluted days had 15% (58%) less sensible (latent) heat flux than clean days. This resulted in a higher daytime Bowen ratio on polluted days (0.83) compared to clean days (0.44). In a case analysis of dust pollution, high wind speeds and continuous high pressure induced the outbreak of the dust pollution episodes. Compared to local pollution events, the latent (sensible) heat flux was reduced more (less) than that in transported dust pollution events. Generally, this work has important implications for understanding the surface energy balance across land–atmosphere interactions over farmland regions, which are experiencing frequent aerosol pollution from local emissions and transboundary dust events.

Abstract Objective d-Serine is a crucial endogenous co-agonist of N-methyl-d-aspartate receptors (NMDARs) in the central nervous system and can affect the function of the brain derived neurotrophic factor (BDNF) system, which plays an essential role in modulating synaptic plasticity. The current study aimed to systematically evaluate the role and mechanisms of d-serine in depressive behavior in nucleus accumbens (NAc). Methods d-Serine concentration in the chronic social defeat stress (CSDS) model in NAc was measured using high-performance liquid chromatography (HPLC). The antidepressant-like effects of d-serine were identified using forced swim test (FST) and tail suspension test (TST) in control mice and then assessed in CSDS model. We applied social interaction and sucrose preference tests to identify the susceptibility of CSDS model. Western blotting was further performed to assess the changes of BDNF signaling cascade in NAc after CSDS and d-serine treatment. The BDNF signaling inhibitor (K252a) was also used to clarify the antidepressant-like mechanism of d-serine. Moreover, d-serine effects on synaptic plasticity in NAc were investigated using electrophysiological methods. Results d-Serine concentration was decreased in depression susceptible mice in NAc. d-Serine injections into NAc exhibited antidepressant-like effects in FST and TST without affecting the locomotor activity of mice. d-Serine was also effective in CSDS model of depression. Moreover, d-serine down-regulated the BDNF signaling pathway in NAc during CSDS procedure. Furthermore, BDNF signaling inhibitor (K252a) enhanced the antidepressant effects of d-serine. We also found that d-serine was essential for NMDARs-dependent long-term depression (LTD). Conclusion d-Serine exerts antidepressant-like effects in mice mediated through restraining the BDNF signaling pathway and regulating synaptic plasticity in NAc.

Warm-sector heavy rainfall in South China is a frequent type of precipitation in summer in the Pearl River Delta region. The complexity of the mechanisms involved in the triggering of convection, especially the effects of urbanization, has greatly increased the uncertainty of numerical simulations of warm-sector heavy rainfall. In this study, five new surface parameters with five new anthropogenic heat (AH) parameters were constructed and coupled with the urban canopy model (UCM) of the Weather Research and Forecasting model, version 4.1, based on the local climate zone system over the Pearl River Delta Urban Agglomeration (PRDUA). Taking a typical warm-sector heavy rainfall process that occurred in the PRDUA on 20 April 2019 as an example, five groups of experiments involving different schemes were compared and analyzed, revealing that the precipitation simulated using the localized UCM with the new AH parameters was the best (closest to observations). The localized UCM successfully simulated the increase in 2 m temperature and sensible heat flux and the resultant thermal forcing in urban areas, which promoted the convergence of low-level southerly winds with water vapor and the lifting of the lower-layer warm and humid water vapor to the upper layers in the urban center, leading to a significant increase of precipitation. The improved AH parameters enhanced the anthropogenic heat and its vertical conduction in urban areas, but contributed only marginally to the convergence of 10 m winds. Compared with observations from wind profile radar, it was found that the localized UCM enhanced the accuracy of the simulated horizontal wind field convergence at upper and lower levels, while the improved AH parameters enhanced the accuracy of the simulated low-level jet intensity and vertical movement, which are important drivers for the spatial variation in warm-sector heavy rainfall over the PRDUA. The current findings will be helpful for improving the model skill in simulating warm-sector heavy rainfall over high-density urban areas, as well as enhancing understanding of the impact mechanism of urbanization on the occurrence and development of warm-sector heavy rainfall.

Abstract Extreme torrential rainfall events are low‐probability events. A “China‐Record Extremely Heavy Rainfall” (CREHR) event with rain rate of 201.9 mm/hr occurred on 20 July in Zhengzhou in North China. Using high‐density meteorological observations, ERA5 reanalysis data, remote sensing data from China's FY‐4A satellite, and numerical simulation, we revealed that sufficient warm–humid airflows were continuously transported to the Zhengzhou area via multiple pathways that were mainly modulated by the large‐scale western Pacific subtropical high to the north along with the anomalies of the westerly belt and a meso‐scale binary typhoon system. At the local scale, under the combined action of the vertical circulation that caused low‐level convergence and high‐level divergence, and the rapid uplift of water vapor related to the blocking by mountainous terrain, this CREHR event was eventually triggered. Particularly, our present work shows the roles of both typhoon Cempaka and meso‐scale convective systems to south of Henan province can not be ignored, producing a strong super‐thick water vapor transport layer. The existence of the binary typhoon raises the level of uncertainty involved in the water vapor transport of this CREHR event.

BackgroundDepression is a possible influence factor for the increased risk of incident atrial fibrillation (AF). Although several investigations have assessed their association, the results are still controversial. Therefore, we conducted a meta-analysis to evaluate the association between depression or using antidepressants and AF.MethodsWe systemically performed the literature retrieval from two electronic databases PubMed and EMBASE until March 2022 to extract relevant data. The hazard ratios (HRs) and odds ratios (OR) from included studies with 95% confidence intervals (CIs) were adjusted into the risk ratio (RR) and pooled by using the random-effects model.ResultsTotally 9 studies about the associations between depression or antidepressants and incident AF risk were included in this meta-analysis. Among them, 5 studies specifically analyzed the impact of antidepressants on the risk of AF. The outcomes of our analysis indicated that depression or depressive symptoms could increase AF risk (RR = 1.15, 95% CI, 1.03–1.27, P < 0.01). In addition, the use of antidepressants can also increase AF risk (RR = 1.16, 95% CI, 1.07–1.25, P < 0.001). These results remained unchanged when we remove the source of heterogeneity or adjust the analysis model into the fixed-effects model.ConclusionsBased on existing investigations, both depression and the use of antidepressants are closely related to the increase of incident AF risk.

Indoor air pollution contributes significantly as a world-wide environmental issue, impacting health and livelihood. To quantify benefits of filtration on indoor air quality, it is essential to understand the relationships between the various factors impacting the concentrations of indoor air pollutants. This work uses a mass-conserving 2-box model, high-frequency observations of aerosol number concentration, and a home-made, low-cost, 3-layer non-woven fabric filter, powered by a standard ventilation fan to quantify the effectiveness aerosol reduction in multiple indoor environments. The data shows that aerosol loading is effectively reduced under both steady-state and extreme event conditions, although there are significant and important differences between simultaneous observations both indoor and outside. To obtain a proper accounting, the following must be considered: the usage or not of the fan filter, whether windows are opened or closed, the state of outdoor air is pollution, and the strength and duration of indoor emissions. The experiments are applied in residential indoor environments in four cities in eastern (Xuzhou), central (Zhoukou), and southern (Zhuhai and Shunde) China. Photographic evidence of the altered fan filter state under both conditions show that while usually dark/black aerosols dominate, there are conditions when yellow aerosols also dominate. The observations are based on multiple, independent, continuous low-cost sensors which have been calibrated against a GRIMM-180 over the number concentration range from 0.3 to 1.0 microns, and yield a removal rate due to the fan-filter of 46%, 80%, 81%, and 36% respectively across the four cities. A corresponding rate to return from an extreme event to steady-state, is computed outdoors and indoors respectively from: 14.−44. minutes, 6.6–21. minutes, 16.−33. minutes, and 24.−58. minutes. The most important factors contributing to the removal efficiency and decay gradient are observed as keeping windows closed and reducing leakiness, the apartment/classroom size, and the outdoor air pollution loading.

Studying the long-term variations in warm and cold events and their causes under global warming is important for understanding urban climate change, planning, and green development, as well as for disaster prevention and mitigation. In this study, taking the megacity of Nanjing in China as an example, we analyzed the trends and characteristics of the daily average temperature, daily maximum temperature, daily minimum temperature, and warm and cold events from 1960 to 2021, and their association with synoptic weather patterns (SWPs) and urbanization. The results showed that, over the past 62 years, the maximum/average/minimum temperatures in Nanjing have trended upward significantly (at the urban station they increased at rates of 0.17, 0.34, and 0.67 °C/decade), with the minimum temperatures being the most significant. In spring, the warming rate of the average temperature was the greatest, reaching 0.45 °C/decade. All other seasons had their highest warming rate in their minimum temperatures, reaching 0.38 °C/decade, 0.73 °C/decade, and 0.67 °C/decade in the summer, autumn, and winter, respectively. The extreme high temperatures showed a decreasing trend until the mid-1980s, closely related to the decrease in the two SWPs with prevailing southwesterly winds (Types 1 and 2), while a significant increasing trend was apparent thereafter, mainly related to the increase in the SWPs with prevailing southeasterly winds (Types 3 and 4). The number of warm days was strongly positively correlated with extreme high temperatures during the study period, and about 91% of the warm day interannual variation can be explained by extreme high temperature variation. The extreme low temperatures showed a significant decreasing trend. The number of cold nights was strongly and positively correlated with extreme low temperatures, and about 85% of the cold night interannual variation can be explained by extreme low temperature variation. The effect of urbanization was basically positive, contributing the most to the average temperatures and second-most to the minimum temperatures, with warming contributions of 26.5% and 20.9%, respectively, and an insignificant contribution to the maximum temperatures. The effect of urbanization on extreme high temperatures was not significant, but the contribution of warming to extreme low temperatures reached 27.9%. Our results have important implications for future urban climate prediction, as well as for impact assessment and decision making in urban planning.

In the context of global warming and rapid urbanization, heat waves (HW) are becoming more frequent, which is amplifying canopy urban heat island (CUHI) via various driving mechanisms. While the roles of local circulation and urban morphology remain unclear in the synergistic interaction between HW and CUHI. By utilizing the data from high-density automatic weather stations in the Beijing megacity, this article explored spatiotemporal patterns of the interactions between HW and CUHI. The average daily CUHII during HW periods exhibited a significant increase of 59.33% compared to the non-heat wave (NHW) periods. Mountain-valley breeze significantly modulated the spatiotemporal patterns of CUHI intensity (CUHII). In particular, on an urban scale, the turning mountain-valley breeze caused horizontal transport of heat inner-city, resulting in the north-south asymmetric pattern of urban excess warming during HW periods. On a street scale, the amplified CHUII was closely associated with urban morphology in the inner city, especially for the vertical characteristics of buildings. During the mountain breeze phase, the amplification of CUHII in the high-rise street zone was significantly stronger than that in the low-rise street zone. During the valley breeze phase, the amplification of CUHII in high-rise street zones exhibits weaker effects in the afternoon compared to the low-rise street areas, while demonstrating stronger amplification during the nighttime. Our findings provide scientific insight to understand the driving mechanisms of urban excess warming and mitigating the escalating risks associated with extreme high-temperature events over megacities in the transitional zone of mountains and plains. [ABSTRACT FROM AUTHOR]

Mesopause is the zone of minimum temperature in Earth's atmosphere. Temperature variation in this region is one of the important responsible factors for chemical and physical changes including spatiotemporal variability in water vapor content. Twenty-two years of monthly temperature and water vapor data were used from Sounding of the Atmosphere using Broadband Emission Radiometry. Eight months per year (excluding transitional months) were selected for temporal analysis. Spatially the region is classified into four parts including Northern, and Southern Poles. Long-term variations in water vapor and temperature in the selected domains of time and space as well as at equinoxes and solstices are presented. A decreasing, and increasing trend in temperature and water vapor respectively was observed during the study period. Yearly averaged temperature and water vapor content showed that 2002 was the hottest year (193K) and had minimum water vaper content (0.89ppmv) and 2018 was the coldest year (187K) and had maximum water vapor content (1.14ppmv). June and July were the coldest months and January and December were hotter months throughout the year over the North Pole and Equator. The vertical gradient of temperature and water vapor (80 to 100km) changes with space and time however, has a strong negative relation in all selected locations and seasons. Around the equinoxes, the monthly average distribution of mesopause temperature was highest (191K), followed by winter solstice and then summer solstice. The decreasing trend in temperature and an increasing trend in water vapor can be an early warning indication for future climate change. [ABSTRACT FROM AUTHOR]

The effect of urban barriers may have a significant impact on the patterns of thunderstorm processes and lightning activity, but there is still a lack of comprehensive mechanical explanations. The observational analysis carried out in this study found that cloud-to-ground (CG) lighting activity tends to cluster around the outer boundaries of the mega cities, while, on the opposite, CG gathers within the small-sized city. When a squall line originating from a type of mesoscale convective system (MCS) known as '0713' passed through the built-up area, the barrier effect of the rough underlying surface contributed to the separation of the cold pool. This led to weakening of vertical airflow and breaking of the convergence line, ultimately triggering the bifurcation of the thunderstorm. Simulation results complement these observations. When buildings outside the 5 Ring Road (RR) are replaced with bare soil, the separation of the cold pool is minimized. Furthermore, the density of the buildings also influenced the strength of the barrier effect. Therefore, the specific urban morphologies were identified as a critical factor in modulating cloud-to-ground (CG) lightning activity and the organization process of thunderstorms. This study offers a fundamental foundation and technical support for predicting and assessing urban cloud-toground (CG) lightning risks. It holds significant implications for understanding excess urban warming, its prediction and assessment, and the resulting thermal risk, influenced by factors such as ventilation, sea breezes, and the geophysical environment in coastal cities. [ABSTRACT FROM AUTHOR]

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management. [ABSTRACT FROM AUTHOR]

Tripterygium wilfordii Hook. f. (TwHF) is a Chinese botanical drug containing a large number of metabolites. The discovered and recognized anti-inflammatory and immune-regulating effects have made it attract more and more attentions in trials and clinical researches. The extraction and processing of TwHF for pharmaceuticals is a manifestation of the role of traditional Chinese medicine. However, TwHF is toxic. Optimization of TwHF preparations has become a requirement for the development of TwHF pharmaceuticals. Our article introduces the main preparations of TwHF on the Chinese market and their characteristics. In particular, we summarize the clinical applications and influential mechanisms of TwHF and its preparations in kidney diseases. Considering that nephropathy is closely related to immune inflammation and TwHF is a botanical drug with a high number of metabolites, the application of TwHF in kidney diseases may be much more complicated. By revealing the role and mechanisms of TwHF in kidney diseases, this study aims to provide more insights to basic and clinical studies about nephropathy.

Although many severe pollution events in Central and East China have been analyzed in recent years, the heavy PM2.5 pollution episode happened on persistent foggy days from January 13 to 18, 2018 was unique, characterized by explosive increase and sharp decrease in PM2.5 (particles with kinetic equivalent diameter less than or equal to 2.5 microns) concentration. Based on hourly data of ground level meteorological parameters, PM2.5 data and CALIPSO-based (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) aerosol data, combined with ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data and radiosonde temperature profile, a comprehensive analysis was conducted to reveal the meteorological reasons for the evolution of the episode at horizontal and vertical scales. The PM2.5 concentration experienced four stages: a slow-increase phase, rapid-increase phase, rapid-decrease phase, and rebound phase. Results show that because Central and East China (CEC) were located at the back of a high-pressure system, humid southerly winds and near surface inversion (NSI) were responsible for the slow accumulation of pollutants. The rapid-increase phase was attributed to pollution transport at both ground level and in the lower troposphere because of weak cold air invasion. The significant subsidence at 500 hPa and 700 hPa intensified the NSI and led to dense fog. In that case, corresponding to the supersaturated atmosphere, the particles entered the fog droplets and were scavenged partly by deposition at night and were resuspended on the next day when the atmosphere was unsaturated. Our findings provide convincing evidence that surface PM2.5 rapid-decrease phase and the rebound phase were closely associated with dense fog process.

It is important to quantify changes in the local meteorological observational environment (MOE) around weather stations if we are to obtain accurate assessments of the regional warming of the surface air temperature (SAT) in relation to urbanization bias. Current studies often use two-dimensional parameters (e.g., the land surface temperature, land use/land cover and the normalized difference vegetation index) to characterize the local MOE. Most of the existing models of the relationship between urbanization bias in SAT series and MOE parameters are linear regression models, which ignore the non-linear driving effect of MOE changes on SAT series. By contrast, there is a lack of three-dimensional parameters in the characterization of the morphological features of the MOE. Changes in the MOE related to urbanization lead to uncertainties in the contribution of SAT series on different scales and we need to introduce vertical structure indexes to enrich the three-dimensional spatial morphology of MOE parameters. The non-linear response of urbanization bias in SAT series to three-dimensional changes in the MOE and its scale dependence should be explored by coupling computational fluid dynamics model simulations with machine learning.

Using the ERA5 (the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts) data and the T-PCA (Principal Component Analysis in T-mode) objective classification method to classify the 850-hPa geopotential height, we summarize four conceptual models of large-scale synoptic weather types over East Asia. By combining this with the daily precipitation observation data of 36 meteorological stations in Guangdong, South China, during summer (June to August) of 2014–2018, we found that summer precipitation in Guangdong Province is closely related to the position of the northwestern Pacific subtropical high and the strong upward motion of the warm airflow over the Pearl River Delta. It is further revealed the regulation effect of different weather patterns on summer precipitation in Guangdong Province and their urban–rural differences. More specifically, both urban and rural areas have a decreasing proportion of light rainfall and an increasing proportion of heavy and torrential rainfall, which are mainly regulated by the trend of frequency changes of four different weather types: Type 1 (47.39%) and Type 2 (32.39%) days are decreasing year by year, modulating the trend of light rainfall, while Type 3 (13.26%) and Type 4 (6.96%) days are steadily increasing, dominating the trend of heavy rainfall. In addition, it was further found that the frequency of light rainfall is decreasing more significantly in cities compared to that in rural areas, while the proportion of heavy and stormy rainfall is increasing more significantly, which is closely related to the effects of rapid urbanization.

A micro-pulse lidar system incorporating differential absorption lidar (DIAL) and coherent Doppler wind lidar (CDWL) is proposed and demonstrated. Due to the high signal-to-noise ratio (SNR) of the superconducting nanowire single-photon detector (SNSPD), the DIAL channel achieves high sensitivity in CO2 measurement. Meanwhile, the CDWL channel is used to obtain the horizontal wind field. In the process of the optimization and calibration of the DIAL receiver, specifically, mode scrambling and temperature control of the connecting fiber between the telescope and the SNSPD enhance the stability and robustness of the system. Horizontal scanning of the CO2 concentration and the wind field is carried out in a 6 km range over a scanning span of 60° with a radial resolution of 150 m and 15 s. The results show that the hybrid lidar system captures the spatial distribution of CO2 concentration and the wind field simultaneously. The horizontal net CO2 flux in a radius of 6 km is estimated by integrating the CO2 concentration and the wind transport vector, indicating different characteristics of horizontal net CO2 fluxes in an industrial area, a university campus, and a park. During most of the experiment, CO2 flux remained positive in the industrial area, but balances fell to nearly zero on the campus and in the park. The horizontal net fluxes averaged over 24 h in the three areas are 3.5 × 105 ppm·m2·s−1, 0.7 × 105 ppm·m2·s−1, and 0.1 × 105 ppm·m2·s−1.

Large-scale modifications to urban underlying surfaces owing to rapid urbanization have led to stronger urban heat island (UHI) effects and more frequent urban heat wave (HW) events. Based on observations of automatic weather stations in Beijing during the summers of 2014–2020, we studied the interaction between HW events and the UHI effect. Results showed that the UHI intensity (UHII) was significantly aggravated (by 0.55°C) during HW periods compared to non-heat wave (NHW) periods. Considering the strong impact of unfavorable weather conditions and altered land use on the urban thermal environment, we evaluated the modulation of HW events and the UHI effect by wind speed and local climatic zones (LCZs). Wind speeds in urban areas were weakened due to the obstruction of dense high-rise buildings, which favored the occurrence of HW events. In detail, 35 HW events occurred over the LCZ1 of a dense high-rise building area under low wind speed conditions, which was much higher than that in other LCZ types and under high wind speed conditions (< 30 HW events). The latent heat flux in rural areas has increased more due to the presence of sufficient water availability and more vegetation, while the increase in heat flux in urban areas is mainly in the form of sensible heat flux, resulting in stronger UHI effect during HW periods. Compared to NHW periods, lower boundary layer and wind speed in the HW events weakened the convective mixing of air, further expanding the temperature gap between urban and rural areas. Note that LCZP type with its high-density vegetation and water bodies in the urban park area generally exhibited, was found to have a mitigating effect on the UHI, whilst at the same time increasing the frequency and duration of HW events during HW periods. Synergies between HWs and the UHI amplify both the spatial and temporal coverage of high-temperature events, which in turn exposes urban residents to additional heat stress and seriously threatens their health. The findings have important implications for HWs and UHII forecasts, as well as for scientific guidance on decision-making to improve the thermal environment and to adjust the energy structure.

The effect of urbanization on surface air temperature (SAT) is one of the most important systematic biases in SAT series of urban stations. Correcting this so-called urbanization bias has the potential to provide accurate basic data for long-term climate change monitoring and research. In the western region of the Yangtze River Delta, 42 meteorological stations with site-relocation history from 2009 to 2018 were selected to analyze the statistical characteristics of the differences in comparative site-relocation daily average SAT. The annual average differences in comparative site-relocation SAT series between the old and the new stations (SATDON) were used to characterize the impact of urbanization bias on the air temperature observation series. Using remote sensing technology, spatial datasets of land-use, landscape, and geometric parameters of the underlying surface in the 5-km buffer zone around the station were established as the observed environmental factors of the site, and the differences in these observed environmental factors (DOEFs) between the old and the new stations were calculated to indicate the change induced by urbanization. Next, multiple linear regression models of SATDON and DOEFs were constructed, showing that the error range of the model for simulated SATDON was 3.66–18.21%, and the average error was 10.09%. Finally, this new correction method (NCM) and conventional correction method (CCM) were applied to the correction of the urbanization bias of SAT series at Hefei station. After comparison, it is found that the NCM could reveal clear contributions of the rapid and slow stages of the urbanization process and resultant environmental changes around the stations to the observed SAT. In summary, the NCM based on remote sensing technology can more reasonably and effectively correct the urbanization bias caused by local human activities, as well as reduce the error caused by the selection of reference stations via the conventional correction method.

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system.

Rwanda has experienced high temperature rising phenomena over the last decades and hence, highly vulnerable to climate change. This paper examined the spatial and temporal variations of daily maximum and minimum surface air temperature (Tmin and Tmax) and diurnal temperature range (DTR). It studied variables at monthly, seasonal and annual time-scales from 1961 to 2014. The study applied various statistical methods such as ordinary least-square fitting, Mann-Kendall, Sen’ slope and Sequential Mann-Kendall statistical test to the new reconstructed ENACTS dataset that cover the period from 1983 to 2014 while pre-1983s recorded data from 24 meteorological stations have been added to complete the lengthiness of ENACTS data. The January to February season did not show a significant trend at seasonal time-scales. The authors decided only to consider March-to-May, June-to-August and October-to-December seasons for further analyses. Topography impacts on temperature classified stations into three regions: region one (R1) (1,000–1,500 m), region two (R2) (1,500–2,000 m) and region three (R3) (≥2,000 m). With high confidence, the results indicate a significant positive trend in both Tmin and Tmax in all three regions during the whole study period. However, the magnitude rate of temperatures change is different in three regions and it varies in seasonal and annual scale. The spatial distributions of Tmax and Tmin represent a siginificant warming trend over the whole country notably since the early 1980s. Surprisingly, Tmin increased at a faster rate than Tmax in R3 (0.27 vs. 0.07°C/decade in March-to-May) and (0.29 vs. 0.04°C/decade in October-to-December), resulting in a significant decrease in the DTR. This is another confirmation of warming in Rwanda. The mutation test application exhibited most of the abrupt changes in the seasonal and annual Tmax and Tmin trends between 1984 and 1990. The present work mainly focus on the spatial and temporal variability of Tmin, Tmax and DTR in Rwanda and their relationship with elevation change, leaving a gap in other potential cause factors explored in the future.

Abstract Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

To analyze the effectiveness of the thermal assessment test, the simulation method of the ground test in the arc-heated wind tunnel is studied. Based on the solution of the thermochemical nonequilibrium Navier-Stokes equations, the flowfield around the spherical cylinder is simulated in the flight and ground test conditions, and the difference in the high enthalpy flowfield between the flight and ground test conditions is investigated. The flight parameters and ground test conditions are selected according to the criterion that the total enthalpy and the stagnation point heat flux of the fully catalytic cold wall (calibrated heat flux) are similar. The flowfield for different temperature boundaries and different catalytic walls is solved under the same free stream conditions, and the stagnation point heat flux and oxygen atom mass fraction are compared and analyzed. It is found that the heat flux on the fully catalytic wall for the radiation balance temperature boundary in the ground test is lower than that in the corresponding flight condition, but the difference is not obvious on the noncatalytic wall. In addition, the oxygen atom mass fraction after the shock wave in the ground test is higher than that in the corresponding flight condition. To make the stagnation point heat flux and oxygen atom mass fraction after the shock wave similar to those of the flight, the simulation method of the arc-heated wind tunnel test needs to be adjusted.

The macro- and microphysical properties of clouds can reflect their vertical physical structure and evolution and are important indications of the formation and development of precipitation. We used four-year merged CloudSat-CALIPSO-MODIS products to distinguish the macro- and microphysical properties of precipitating and non-precipitating clouds over central-eastern China during the warm season (May–September). Our results showed that the clouds were dominated by single- and double-layer forms with occurrence frequencies > 85%. Clouds with a low probability of precipitation (POP) were usually geometrically thin. The POP showed an increasing trend with increases in the cloud optical depth, liquid water path, and ice water path, reaching maxima of 50%, 60%, and 75%, respectively. However, as cloud effective radius (CER) increased, the POP changed from an increasing to a decreasing trend for a CER > 22 μm, in contrast with our perception that large particles fall more easily against updrafts, but this shift can be attributed to the transition of the cloud phase from mixed clouds to ice clouds. A high POP > 60% usually occurred in mixed clouds with vigorous ice-phase processes. There were clear differences in the microphysical properties of non-precipitating and precipitating clouds. In contrast with the vertical evolution of non-precipitating clouds with weaker reflectivity, precipitating clouds were present above 0 dBZ with a significant downward increase in reflectivity, suggesting inherent differences in cloud dynamical and microphysical processes. Our findings highlight the differences in the POP of warm and mixed clouds, suggesting that the low frequency of precipitation from water clouds should be the focus of future studies.

Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPPMOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m−2 d−1. Therefore, it was deemed reliable to upscale GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m−2) than that for wheat (532 g C m−2). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPPMOD (GPPRF minus GPPMOD) was found to be 2.5–3.25 g C m−2 d−1 for rice and 0.75–1.25 g C m−2 d−1 for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.

Evaluation of the cloud seeding effect is a challenge due to lack of directly physical observational evidence. In this study, an approach for directly observing the cloud seeding effect is proposed using a 1548 nm coherent Doppler wind lidar (CDWL). Normalized skewness was employed to identify the components of the reflectivity spectrum. The spectrum detection capability of a CDWL was verified by a 24.23-GHz Micro Rain Radar (MRR) in Hefei, China (117°15′ E, 31°50′ N), and different types of lidar spectra were detected and separated, including aerosol, turbulence, cloud droplet, and precipitation. Spectrum analysis was applied as a field experiment performed in Inner Mongolia, China (112°39′ E, 42°21′ N ) to support the cloud seeding operation for the 70th anniversary of China’s national day. The CDWL can monitor the cloud motion and provide windshear and turbulence information ensuring operation safety. The cloud-precipitation process is detected by the CDWL, microwave radiometer (MWR) and Advanced Geosynchronous Radiation Imager (AGRI) in FY4A satellites. In particular, the spectrum width and skewness of seeded cloud show a two-layer structure, which reflects cloud component changes, and it is possibly related to cloud seeding effects. Multi-component spectra are separated into four clusters, which are well distinguished by spectrum width and vertical velocity. In general, our findings provide new evidence that the reflectivity spectrum of CDWL has potential for assessing cloud seeding effects.

Synoptic weather conditions can modulate short-term variations in the marine biophysical environment. However, the impact of large-scale synoptic circulation patterns (LSCPs) on variations in chlorophyll-a (chl-a) and sea surface temperature (SST) in the South China Sea (SCS) remains unclear. Using a T-mode principal component analysis method, four types of LSCP related to the Northwest Pacific subtropical high are objectively identified over the SCS for the summers of 2015–2018. Type 1 exhibits a lower chl-a concentration of −3 offshore of southern Vietnam with respect to the other three types. For Type 2, the high chl-a concentration zone (>0.3 mg m−3) along the coast of Guangdong exhibits the widest areas of coverage. The offshore chl-a bloom jet (>0.3 mg m−3) formed in southern Vietnam is the most obvious under Type 3. Under Type 4, the high chl-a concentration zone along the coast of Guangdong is the narrowest, while the chl-a concentration in the middle of the SCS is the lowest (−3). These type differences are mostly caused by the various monsoon circulations, local ocean mesoscale processes and resultant differences in localized precipitation, wind vectors, photosynthetically active radiation and SST. In particular, precipitation over land helps to transport nutrients from the land to the shore, which is conducive to the increase of chl-a. However, precipitation over ocean will dilute the upper seawater and reduce chl-a. Typhoons pump the deeper seawater with nutrients to the surface, and therefore make a positive contribution to chl-a in most offshore areas; however, they also disturb shallower water and hinder the growth of phytoplankton, making a negative contribution near the coast of Guangdong. In general, our findings will provide a better understanding of wind pump impact: the responses of marine biophysical environments to LSCPs.

The ocean surface and subsurface biophysical responses and their feedbacks to super typhoon Nida were comprehensively investigated based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data. Nida experienced two Category 5 stages: a rapid intensification stage that was fast moving along a straight-line track, and a rapid weakening stage that was slowly moving along a sharp-left sudden-turning track. During the straight-line stage, Nida caused an average sea surface temperature (SST) cooling of 1.44 °C and a chlorophyll-a (chl-a) concentration increase of 0.03 mg m−3. During the sudden-turning stage, cyclonic sudden-turning induced a strong cold cyclonic eddy (SSHA < −60 cm) by strong upwelling, which caused the maximum SST cooling of 6.68 °C and a long-lasting chl-a bloom of 0.6 mg m−3 on the left-hand side of the track, resulting in substantial impacts on the ocean ecological environment. Furthermore, the enhanced ocean cold wake and the longer air–sea interaction in turn decreased the average inner-core SST of 4 °C and the corresponding enthalpy flux of 780 W m−2, which induced a notable negative feedback to the typhoon intensity by weakening it from Category 5 to Category 2. The left bias response and notable negative feedback are special due to sharp-left sudden-turning of typhoon. Comparing with the previously found slow translation speed (~4 m s−1) of significant ocean response, the negative feedback requires even more restriction of translation speed (−1) and sharp sudden-turning could effectively relax restrictions by making equivalent translation speed lower and air-sea interaction time longer. Our findings point out that there are some unique features in ocean–typhoon interactions under sudden-turning and/or lingering tracks comparing with ordinary tracks.