Diurnal Variation Research Articles

Annual cyclical variations of urban green space cold island(GSCI) effect by combining remote sensing inversion and ENVI-met simulation

Combining multiple methods offers a practical approach to studying long-term variations in the urban green space cold island(GSCI) effect. This research integrates remote sensing inversion and numerical simulation to investigate the annual cycle of the GSCI in Huachong Park, Hefei City. Initially, 59 remote sensing images from various seasons between 2010 and 2020 were retrieved using Landsat series image data and atmospheric correction methods to invert Land Surface Temperatures(LST), which preliminary identified the GSCI's annual cycle variations. Subsequently, meteorological data for Hefei from 2010 to 2020 were extracted using the Solar Terms Typical Meteorological Day(STTMD) method to obtain representative annual meteorological data. These data were then input into the ENVI-met software for numerical simulations of the study area, capturing diurnal variations of the cold island effect at 24-time points and predicting annual changes in cold island intensity. The results indicate that: (1) The GSCI exhibits an annual cycle and seasonal variations characterized by “strong in summer and weak in winter, cooler in summer and warmer in winter”; (2) A progressive relationship exists between remote sensing inversion and ENVI-met numerical simulation in studying the temporal variation of the GSCI, with the integration of these methods yielding a more comprehensive spatiotemporal analysis of the GSCI over long-term scales;(3) The STTMD method effectively simplifies representative meteorological data, progressively combining remote sensing retrievals and numerical simulations to facilitate the acquisition of comprehensive spatiotemporal variations of the green space heat effect over extended periods. These findings advance understanding of the long-term dynamics of cold island effects within urban green spaces, providing valuable insights for urban planners and environmental researchers.

Comprehensive evaluation of satellite-based precipitation products at hourly scale in Beijing

With the increase of short-duration rainstorms, accurate precipitation data with higher temporal resolution, such as hours or even minutes, has been required for urban hydrological modeling and forecasting. As a potential data source, hourly or half-hour satellite-based precipitation products (SPPs) perform differently in different metropolitan regions, so evaluation is necessary before their application. Based on the observations of 20 meteorological stations from 2015 to 2018 over a typical metropolitan region, Beijing, a comprehensive evaluation of four widely used SPPs at hourly scale was conducted. Results showed that IMERG and GSMaP had the best performance, followed by CMORPH, while PERSIANN performed worst at hourly scale; it was suggested to use data series composed of wet hours for evaluation at hourly scale with more consistent spatial distribution characteristics and reliable results; IMERG, GSMaP and CMORPH performed better during 9:00–12:00 for accuracy and 5:00–9:00 and 20:00–21:00 for detection capability while PERSIANN didn't show obvious diurnal variation. In addition, the short-duration extreme precipitation in Beijing was defined as an event with an hourly precipitation >20 mm. Overall, hourly or half-hour SPPs still need a lot of progress and our study would provide some significant references for developers and potential users.

Analysis of Fractal Properties of Atmospheric Turbulence and the Practical Applications

Atmospheric turbulence, recognized as a quintessential space–time chaotic system, can be characterized by its fractal properties. The characteristics of the time series of multiple orders of fractal dimensions, together with their relationships with stability parameters, are examined using the data from an observational station in Horqin Sandy Land to explore how the diurnal variation, synoptic process, and stratification conditions can affect the fractal characteristics. The findings reveal that different stratification conditions can disrupt the quasi-three-dimensional state of atmospheric turbulence in different manners within different scales of motion. Two aspects of practical applications of fractal dimensions are explored. Firstly, fractal properties can be employed to refine similarity relationships, thereby offering prospects for revealing more information and expanding the scope of application of similarity theories. Secondly, utilizing different orders of fractal dimensions, a systematic algorithm is developed. This algorithm distinguishes and eliminates non-turbulent motions from observational data, which are shown to exhibit slow-changing features and result in a universal overestimation of turbulent fluxes. This overestimation correlates positively with the boundary frequency between turbulent and non-turbulent motions. The evaluation of these two aspects of applications confirms that fractal properties hold promise for practical studies on atmospheric turbulence.

Diurnal variation in the association between the scan frequency of isCGM and CGM metrics: post hoc analysis of the ISCHIA study

Diurnal variation in the association between the scan frequency of isCGM and CGM metrics: post hoc analysis of the ISCHIA study

Variations in the Abundance, Biodiversity, and Assemblage Structure of Larval Fish in the Restricted Waters of the Wang-an Light Fishery off Penghu, Taiwan

This study examined the abundance, biodiversity, and assemblage structure of larval fish inside and outside the restricted fishing zone of the Wang-an light fishery, which is located off Penghu, Taiwan. A total of 97 taxa belonging to 48 families of larval fish were identified through plankton tows conducted during the present investigation. Upeneus japonicus from the Mullidae family emerged as the dominant species in the surveyed area. Diurnal variation in larval fish abundance was evident, with higher abundance observed at night-time than during daytime. However, no significant difference was observed between the larval fish abundance inside and outside the restricted zone. Moreover, fish egg abundance and larval fish biodiversity did not exhibit notable differences between sampling locations or times. Cluster analysis revealed the presence of two groups of seasonal species. The major species in the spring group were Abudefduf vaigiensis and Pomacentrus coelestis, which prefer a low-temperature environment. The major species in the summer group was U. japonicus, which prefers a high-temperature environment. The seasonal succession of water masses under the influence of monsoon activity likely contributed to the observed grouping patterns of larval fish assemblages in the study area. These patterns were closely linked to the marine environmental conditions in this area. The study outcomes provide scientific insights and recommendations regarding the importance and benefits of establishing a restricted fishing zone for sustaining fishery resources.

Evaluation of Scikit-Learn Machine Learning Algorithms for Improving CMA-WSP v2.0 Solar Radiation Prediction

This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to improve the solar radiation prediction made by the CMA-WSP v2.0. It is found that the performance of the solar radiation forecasting from the CMA-WSP v2.0 is closely related to the weather conditions, with notable diurnal fluctuations. The mean absolute percentage error (MAPE) produced by the CMA-WSP v2.0 is approximately 74% between 11:00 and 13:00. However, the MAPE ranges from 193% to 242% at 07:00–08:00 and 17:00–18:00, which is greater than that observed at other daytime periods. The MAPE is relatively low (high) for both sunny and cloudy (overcast and rainy) conditions, with a high probability of an absolute percentage error below 25% (above 100%). The forecasts tend to underestimate (overestimate) the observed solar radiation in sunny and cloudy (overcast and rainy) conditions. By applying machine learning models (such as linear regression, decision trees, K-nearest neighbors, random forests regression, adaptive boosting, and gradient boosting regression) to revise the solar radiation forecasts, the MAPE produced by the CMA-WSP v2.0 is significantly reduced. The reduction in the MAPE is closely connected to the weather conditions. The models of K-nearest neighbors, random forests regression, and decision trees can reduce the MAPE in all weather conditions. The K-nearest neighbor model exhibits the most optimal performance among these models, particularly in rainy conditions. The random forest regression model demonstrates the second-best performance compared to that of the K-nearest neighbor model. The gradient boosting regression model has been observed to reduce the MAPE of the CMA-WSP v2.0 in all weather conditions except rainy. In contrast, the adaptive boosting (linear regression) model exhibited a diminished capacity to improve the CMA-WSP v2.0 solar radiation prediction, with a slight reduction in MAPE observed only in sunny (sunny and cloudy) conditions. In addition, the input feature selection has a considerable influence on the performance of the machine learning model. The incorporation of the time series data associated with the diurnal variation of solar radiation as an input feature can further improve the model’s performance.

Sea surface carbon dioxide during early summer at the Tuandao nearshore time series site

To investigate air-sea CO2 flux at the Qingdao nearshore site and its temporal variations, a high-resolution continuous observation of surface carbon dioxide partial pressure (pCO2) was carried out at Zhongyuan Pier near Tuandao from May 25 to July 8, 2019. It was observed that during this period, surface pCO2 varied between ∼490 and ∼690 μatm, mainly associated with sea surface temperature. Surface pCO2 also displayed substantial diurnal variations, with an average amplitude of 64 ± 21 μatm, largely dominated by biological activities. During the observational period, this site acted as a source of atmospheric CO2, releasing 361 mmol CO2 m−2. The notable diurnal variations in air-sea CO2 flux, such as the observed average amplitude of 10.9 mmol m−2 d−1 in this study, pose a challenge for accurately estimating the air-sea CO2 flux in coastal regions without high-resolution observations.

Diurnal cycles of rain storms over South China during the presummer rainy season

The variation characteristics of rain storms (RSs) are closely related to complex internal physical processes and external dynamic and thermodynamic conditions. Using a 17-year observational rainfall dataset from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), the diurnal cycles of various RS characteristics and their responses to the South China Sea Summer Monsoon (SCSSM) are investigated in South China (SC) during the presummer rainy season (PSRS). Before SCSSM outbreaks, the RSs in the western inland region of SC (WSC) have stronger precipitation intensities and convective features due to orographic lifting, especially at night, while those in the eastern inland region of SC (ESC) have higher RS occurrence frequencies with higher moisture transport, especially in the afternoon. The onset of the SCSSM leads to abundant moisture and sufficient convective available potential energy over the entire SC area, resulting in significant increases in the occurrence frequency, convective fraction, and precipitation top height of RSs. In the WSC, the weakened upward motion and smaller convective inhibition limit RS development, resulting in weaker rainfall intensity. In the ESC, a particularly unstable atmosphere and enhanced low-level moisture transport in the afternoon lead to a larger three-dimensional size, more active convection, and greater precipitation intensity in the RSs. These results offer fresh insight into the diurnal variation in rainfall during the PSRS and can be effectively used to evaluate numerical model simulations.

Strong upwards transport of HONO in daytime over urban area of Beijing, China

Strong upwards transport of Nitrous acid (HONO) in daytime over urban area of Beijing was observed based on combined observations of HONO, NOx (NO and NO2), nitrate, and PM2.5 at two heights (90 m and 528 m) on the highest building of Beijing (528 m above ground). The mean HONO at the 528 m (0.26 ppb) was lower than that at the 90 m (0.54 ppb), and a clear difference in diurnal variation of HONO between the two heights was observed. HONO at the 90 m showed two peaks in the morning rush hour and mid-night, but decreased sharply in daytime (e.g., from 0.62 ppb at 08:00 to 0.34 at 14:00); while the decreasing trend of HONO in daytime significantly weakened at the 528 m (e.g., from 0.26 ppb at 08:00 to 0.27 at 14:00).With PBL development in the morning, HONO in low layer was upwards transported to the 528 m, which compensated partly HONO loss via photolysis and resulted in a relatively stable concentration at the 528 m in daytime. A positive relationship of the bulk Richardson number (Ri) in 0–500 m with the difference of HONO between the two heights during daytime (08:00–18:00) confirmed the above analyses. HONO budget analysis indicated that a strong unknown HONO source existed at the 528 m in daytime, which was negative correlated to the Ri. These results further confirmed that vertical transport of HONO from low layer was a potential HONO source at the 528 m. Moreover, the contribution of photolysis of particulate nitrate significantly increased at the 528 m. Its contribution in total HONO sources increased from 11.9 % at the 90 m to 16.0 % at the 528 m.

Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation in several important trace gas and aerosol pollutants including nitrogen dioxide (NO2), which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50 % of the column amount, especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low-Earth-orbit satellite observation. We use GEMS data in combination with TROPOspheric Monitoring Instrument (TROPOMI) satellite and Pandora ground-based remote sensing measurements and Multi-Scale Infrastructure for Chemistry and Aerosols (Version 0, MUSICAv0) 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be key to including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new Earth Venture Instrument-1 Tropospheric Emissions: Monitoring Pollution (TEMPO) data over North America, and later over Europe with Sentinel-4.

Cross-seasonal and diurnal variation in physical contact between sub-adult pigs

Social touch is an important aspect of social relationships and has a major influence on development and health. However, in many species the occurrence and function of social touch is unknown. Pigs have frequent physical contact but this behaviour is largely unexplored. The aim of this study was to investigate the undisturbed variation in physical contact between pigs, and to assess diurnal and seasonal influences. A stable group of ten Puławska sub-adult female pigs was observed for 406 h across four seasons during the day and night, by continuous observations on an individual level (406 h / pig). They were housed indoors (112 m2 pen) on straw bedding. The ethogram distinguished the amount of surface contact between pigs when lying, the orientation to others, social nosing, other non-agonistic social behaviour and agonistic behaviours. Resting location within the pen was recorded 48 times per pig, and dominance relationships were calculated from agonistic interactions. Data were analysed with mixed models accounting for repeated observations. Pigs spent on average 40 % of their time lying in body contact, most often lying with their extremities in contact (71 %), followed by lying in partial body contact (16 %) or full body contact (13 %) and were frequently nosing the head and body of others. The duration of lying in any type of contact, as well as agonistic behaviour, was influenced by season, with the longest durations in autumn. In summer, the duration of partial body contact was lowest, but full body contact was unaffected by season. Nosing behaviour remained constant throughout the year. Pigs lay in contact more during the night, while showing more social behaviours during the day. Pigs mostly lay in nonparallel orientation or head-to-head, but clearly least in the head-to-tail orientation. Season influenced the frequency of lying head-to-head and head-to-tail, but not lying nonparallel. The coefficient of variation of the lying location was influenced by season. Allo-grooming, mounting, and nudging were shown infrequently and mostly by specific individuals. In conclusion, when at a large space allowance, pigs spend nearly half of their time in body contact, even during the higher temperatures in summer. This shows that social touch, including affiliative behaviour, has an important role in pigs’ social life, and remains largely unaffected by external influences.

Diurnal Variation in Summer Precipitation and the Characteristics of Precipitation Events in the Western Tarim Basin, China

The Tarim Basin in the western part of Northwest China (NWC) is the largest inland basin in the world and one of the most arid regions in the middle latitudes. In recent years, heavy precipitation events have occurred frequently in this region, especially in the western Tarim Basin (WTB), due to the climate change. Based on the hourly precipitation data from 2010 to 2022, the diurnal variation in summer precipitation and the characteristics of precipitation events with different durations in WTB have been analyzed. The results mainly show that (1) the diurnal variations in the precipitation amount (PA), precipitation frequency (PF) and precipitation intensity (PI) mainly present a unimodal pattern, but the times of maximum value do not coincide. The peak value of PA and PF appears between 01:00 and 03:00 BJT (Beijing Time), while the valley value appears around 18:00 BJT, yet the peak value of PI appears between 20:00 and 23:00 BJT with no obvious valley value. (2) There are some differences in the diurnal variation characteristics of precipitation among different summer months and different regions. (3) During the past decade, the precipitation structure in WTB has been continuously adjusted, and short-duration- and long-duration-precipitation-dominant periods appear alternately. On the whole, short-duration precipitation has been more frequent in summer, accounting for 70% of the total precipitation events and 40% of the total accumulated precipitation amount. These results can help us to better understand the refined physical characteristics of precipitation events and enhance our understanding of the local climate in the WTB under the background of climate change.

High-Resolution WRF Modeling of Wind and Thermal Regimes with LCZ in Almaty, Kazakhstan

This study evaluates the effectiveness of the Weather Research and Forecasting (WRF) model in simulating high-resolution atmospheric conditions for Almaty, Kazakhstan, a city prone to stagnant winter air. While the previously used Bougeault and Lacarrere scheme for parameterizing the planetary boundary layer was applied in high-resolution modeling, the number of vertical levels was increased, and a detailed local climate zones (LCZs) map was included. Ground-based observations from meteorological stations and monitoring stations, remote sensing data, and radiosonde measurements are used to verify the model. Comparison results with ground-based observations show that the WRF model with the LCZ map provides a better representation of the wind and thermal regimes of Almaty compared to the three-class land use map, including in high resolution. A good correspondence of wind direction is demonstrated by comparing the modeling results with pollutant transport plumes recorded by remote sensing data. In addition, a good correlation was found between land surface temperature from satellite data and air temperature simulated by WRF with a resolution of 333 m. A comparison of simulated data and aerological measurements confirmed that downscaling did not have a significant impact on boundary layer calculations. Analysis of turbulent processes showed that the adopted model effectively describes the attenuation and dissipation of turbulent kinetic energy and reflects the typical diurnal variations of meteorological processes in the atmosphere of Almaty in the anticyclonic winter period. The results of high-resolution WRF modeling can form the basis for the development of a hybrid system capable of modeling atmospheric processes at the building level.

Streamflow diurnal fluctuation and driving mechanism of headwater stream in a semi-humid mountainous region

Diurnal fluctuation in streamflow during rainless periods provides valuable signals for the calibration of hydrological models and comprehension of hydrological processes. Previous research has primarily focused on this phenomenon in the context of single-factor analysis in the Mediterranean, Maritime, and Alpine climates of Middle Europe and Western US. This study is based on the Xitaizi Experimental Watershed (XEW), a typical mountainous headwater watershed situated in the semi-humid monsoon North China Plain. Seven years of high-frequency observation data were used to investigate the characteristics of streamflow diurnal fluctuation, its driving mechanisms, and the propagation pathways of these fluctuation signals through hillslope-riparian-stream. The results are as follows: (1) Streamflow exhibits diurnal fluctuation during rainless periods throughout the year, with variations up to 36% of the daily mean and distinct patterns in rainy (peak at night) and dry seasons (peak in the afternoon). Choosing the proper timing for daily discharge observation can reduce bias due to fluctuations. (2) In rainy seasons, the impact of discharge fluctuation is significant, particularly in years with lower discharge. Meanwhile, the soil moisture in both the hillslope and the riparian zone, and the groundwater level in the riparian zone exhibit a daytime decline and a nighttime rise. The groundwater level in the hillslope fluctuates with a lag time of approximately 6 to 9 hours. (3) Numerical simulations and cross-correlation analysis demonstrate that the dominant driving mechanism of diurnal fluctuations in rainy seasons is the diurnal evapotranspiration variation in the riparian zone, while the influence of aquifer hydraulic conductivity is insignificant.

Seasonality in synthetic average wind speed

There is a growing demand for computer-generated realistic high-fidelity wind speed data for various applications in the wind industry. Such data should capture the non-stationary dynamics of real-world wind time series, as well as be consistent with the statistical descriptors – the probability density function and power spectral density – of the observed wind speed. However, complying with the statistical descriptors is not a guarantee that the seasonality will be correctly reproduced in synthetic data. The seasonality, characterized by the average diurnal and seasonal variations, is driven by the periodicities embedded in diurnal and annual harmonic series respectively. Those periodicities are determined by the long-term orbital forcing components, which establish the insolation for a given latitude and longitude. We show that average diurnal and seasonal variations can be visualized as the output of comb filters, whose fundamental frequencies match the diurnal and annual fundamental frequency respectively. The aforementioned theoretical findings are readily reproduced in synthetic wind speed, generated by a non-parametric data-driven statistical model, based on the phase-randomized Fourier transform. The model, tested on both 10-min and 1-min resolution real-world datasets, yields average non-stationarities in synthetic wind speed with the accuracy close to the computing precision.

How do driving factors affect the diurnal variation of land surface temperature across different urban functional blocks? A case study of Xi'an, China

A comprehensive and in-depth understanding of the formation mechanisms of the urban thermal environment is the basis for thermal environment regulation, however, there is insufficient knowledge regarding how driving factors influence daytime and nighttime land surface temperature (LST) within urban functional blocks (UFBs). We selected Xi'an, China as a case study, integrating remote sensing data including ECOSTRESS, Landsat-8, and Gaofen-1, along with geographic data including road network, areas of interest, points of interest, building footprint, and mobile phone signaling. It divided 10 types of UFBs, inverted daytime and nighttime LST, calculated 5 types of driving factors, and finally analyzed the contribution and marginal effects of driving factors on day-night LST using boosted regression tree. The results showed that LST and its driving factors differed significantly in different times and UFBs. Industrial blocks and urban villages had higher LST in the daytime, while residential blocks, commercial blocks, and public service blocks had higher LST in nighttime. Industrial blocks were the dominant blocks that drove the overall LST up during the day, while residential blocks were the dominant blocks at night. Location distance (-) and population density (+) affected LST in all UFBs during day and night, NDVI (-), building density (+), and floor area ratio (-) were key factors for most UFBs during daytime, and NDVI (+), surface albedo (-), and point density of interest (+) were key factors during nighttime. Most of the driving factors had significant influence thresholds, but there were small differences across UFBs. This paper aims to elucidate the mechanisms by which driving factors influence urban LST during day and night across different UFBs, thereby providing new support for more targeted thermal environment regulation and diurnal trade-offs at the block scale.

A travel time matrix data set for the Helsinki region 2023 that is sensitive to time, mode and interpersonal differences, and uses open data and novel open-source software

Travel times between different locations form the basis for most contemporary measures of spatial accessibility. Travel times allow to estimate the potential for interaction between people and places, and is therefore a vital measure for understanding the functioning, sustainability, and equity of cities. Here, we provide an open travel time matrix dataset that describes travel times between the centroids of all cells in a grid (N = 13,132) covering the metropolitan area of Helsinki, Finland. The travel times recorded in the dataset follow a door-to-door approach that provides comparable travel times for walking, cycling, public transport and car journeys, including all legs of each trip by each mode, such as the walk to a bus stop, or the search for a parking spot. We used the r5py Python package, that we developed specifically for this computation. The data are sensitive to diurnal variations and to variations between people (e.g. slow and fast walking speed). We validated the data against the Google Directions API and present use cases from a planning practice. The five key principles that guided the data set design and production – comparability, simplicity, reproducibility, transferability, and sensitivity to temporal and interpersonal variations – ensure that urban and transport planners, business and researchers alike can use the data in a wide range of applications.

Evolution Characteristics of PM2.5 and O3 and Their Synergistic Effects on Atmospheric Compound Pollution in Tangshan

The concentrations of atmospheric pollutants PM2.5, O3, SO2, NO2, and CO together with the meteorological factors of temperature （T）, relative humidity （RH）, wind speed, and other relevant data in Tangshan from 2015 to 2021 were collected to study the variation characteristics of PM2.5 and O3 at different periods in Tangshan City in the past seven years and their influencing factors, to discuss the contributions of air mass transport to PM2.5 and O3 pollution, and to reveal the synergistic influence mechanism of PM2.5 and O3 on atmospheric compound pollution by using correlation analysis and backward trajectory cluster analysis techniques. The results showed that PM2.5 concentrations in Tangshan decreased year by year from 2015 to 2021, whereas O3 concentration showed a unimodal trend, with the peak appearing in 2017. Both PM2.5 and O3 concentrations showed obvious seasonal variation trends； PM2.5 was characterized by the highest concentration in winter and the lowest concentration in summer, whereas O3 was characterized by the highest concentration in summer and the lowest concentration in winter. In addition, the diurnal variation in PM2.5 showed a bimodal distribution, with the peak occurring during the morning and evening on weekdays, and O3 showed a unimodal distribution, with the peak value appearing during the period with strong ultraviolet radiation in the afternoon. PM2.5 had a significant positive correlation with SO2, NO2, and CO, whereas O3 had a significant positive correlation with radiation and temperature. Under the different pollution conditions, PM2.5 and O3 were affected by air mass transports from different directions. Being impacted by various factors, the synergistic effect of PM2.5 and O3 on atmospheric compound pollution showed an obvious negative effect in winter, whereas there was an obvious positive effect in spring, summer, and autumn. Under the backgrounds of different pollutions, when the concentration of PM2.5 exceeded 150 μg·m-3, the synergistic effect of PM2.5 and O3 showed an obvious negative effect.

Role of Strong Sea Surface Temperature Diurnal Variation in Triggering the Summer Monsoon Onset Over the Bay of Bengal in a Climate Model

AbstractThe earliest Asian summer monsoon onset (SMO) occurs in the Bay of Bengal (BoB), heralding the coming of the rainy season. In late April or early May, the strong sea surface temperature (SST) diurnal variation accompanied by ocean surface warming triggers the SMO. However, this observed diurnal cycle intensity cannot be reasonably simulated by state‐of‐the‐art climate models, resulting in a spurious delayed SMO. To address this issue, the SST diurnal cycle parameterized by a diagnostic sublayer scheme was incorporated into a climate model named FIO‐ESM v2.0. The large diurnal amplitude of SST contributes to surface warming and changes atmospheric circulation. Consequently, the high‐pressure anomaly at high levels and an inverted trough at low levels promote more convective activity, triggering an earlier SMO. Our findings improve the ability of climate models in simulating the evolution of the Asian monsoon system.

Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau

Abstract. The planetary boundary layer (PBL) over the Tibetan Plateau (TP) exerts a significant influence on regional and global climate, while its vertical structures of turbulence and evolution features remain poorly understood, largely due to the scarcity of observations. This study examines the vertical profile of and daytime variation in the turbulence dissipation rate (ε) in the PBL and free troposphere over the TP using the high-resolution (6 min and 120 m) measurements from a radar wind profiler (RWP) network, combined with hourly data from ERA5 during the period from 1 September 2022 to 31 October 2023. Observational analyses show that the magnitude of ε below 3 km under all-sky conditions exhibits a large spatial discrepancy over the six RWP stations over the TP. Particularly, the values of ε at Minfeng and Jiuquan over the northern TP and at Dingri (alternately Tingri) over the southern TP are roughly an order of magnitude greater than those at Lijiang, Ganzi (alternately Garzê), and Hongyuan over the eastern TP. This could be partially attributed to the difference in land cover across the six RWP stations. In terms of the diurnal variation, ε rapidly intensifies from 09:00 local standard time (LST) to 14:00 LST and then gradually levels off in the late afternoon. Under clear-sky conditions, both ε and the planetary boundary layer height (zi) are greater compared with cloudy-sky conditions, which could be due to the cooling effect of clouds, which reduces the solar irradiation reaching the surface. In the lower PBL (0.3 ≤ z/zi ≤ 0.5), where z is the height above ground level, the dominant influential factor in the development of turbulence is the surface–air temperature difference (Ts−Ta). By comparison, in the upper PBL (0.6 ≤ z/zi ≤ 1.0), both Ts−Ta and vertical wind shear (VWS) affect the development of turbulence. Above the PBL (1.0 < z/zi ≤ 2.0), the shear production resulting from VWS dominates the variation in turbulence. Under cloudy-sky conditions, the reduced Ts−Ta and weakened surface sensible heat flux tend to inhibit the turbulent motion in the PBL. On the other hand, the strong VWS induced by clouds enhances the turbulence above the PBL. The findings obtained here underscore the importance of the RWP network in revealing the fine-scale structures of the PBL over the TP and gaining new insight into the PBL evolution.