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Atmospheric rivers (ARs), responsible for extreme weather conditions, are mid-latitude systems that can cause significant damage to coastal areas. While forecasting ARs beyond two weeks remains a challenge, past research suggests potential benefits may come from properly accounting for the changes in sea surface temperature (SST) through air–sea interactions. In this paper, we investigate the impact of ARs on SST over the North Pacific by analyzing 25 years of ocean reanalysis data using an SST budget equation. We show that in the region of strong ocean modification, ocean dynamics can offset over 100% of the anomalous SST warming that would otherwise arise from atmospheric forcing. Among all ocean processes, ageostrophic advection and vertical mixing (diffusion and entrainment) are the most important factors in modifying the SST tendency response. The SST tendency response to ARs varies spatially. For example, in coastal California, the driver of enhanced SST warming is the reduction in ageostrophic advection due to anomalous southerly winds. Moreover, there is a large region where the SST shows a warming response to ARs due to the overall reduction in the total clouds and subsequent increase in total incoming shortwave radiation. Strong air-sea interactions during atmospheric rivers often lead to modest upper ocean heat changes. The authors show that interior ocean dynamics are compensating for these air-sea exchanges. These findings can help improve subseasonal forecasts. [ABSTRACT FROM AUTHOR]

In the traditional tobacco drying process, there is often a problem of uneven drying, which is closely related to drying conditions such as air velocity and temperature. To better understand the drying characteristics of tobacco, its drying kinetic performance were experimentally studied and predicted in this paper. In the drying experiment, the range of air temperature and velocity is 20–60℃ and 0.95–4.93 m/s, respectively. The results show that the effective diffusion coefficient increases with the increase of air temperature and decreases with the increase of air velocity. The effective moisture diffusivity( D eff ) ranges from 2.077 × 10–7 to 9.136 × 10–7 m2/s. Additionally, the activation energy (Ea) is between 14.292 and 21.032 kJ/mol according to Arrhenius law. Among the six commonly used empirical correlations, the logarithmic model has higher prediction accuracy, but it has a prediction deviation of more than 20% in the later stage of drying. Based on the logarithmic model and the two models, a new prediction model of tobacco drying characteristics was proposed with a maximum relative deviation error of less than 1%. [ABSTRACT FROM AUTHOR]

The sinter cooler, essential for cooling hot sintered ore to a specific temperature, has seen recent advancements with the introduction of a vertical sinter cooling furnace. This innovation aims to enhance energy efficiency, reduce emissions, and improve waste heat recovery. Despite significant research, a quantitative analysis of factors impacting its cooling and heat transfer efficiency is lacking. This study utilizes the Euler model and local non-equilibrium thermodynamic theory to identify key factors affecting the gas–solid cooperative cooling process in the vertical cooler. Through an orthogonal experimental approach, the paper determines the optimal structural and operational parameters for the furnace. Key findings include that a gas–solid ratio of 1200m^3/t, inlet air temperature of 50 ℃, cooling section height of 6m, and diameter of 13.25m maximize efficiency, achieving a weighted range normalization value of 0.962. This configuration meets sintered ore cooling requirements while optimizing waste heat recovery. The study reveals that the impact on heat transfer efficiency is influenced primarily by the gas–solid ratio, followed by the cooling section's height, diameter, and inlet air temperature. These insights are crucial for enhancing the vertical sinter cooler's design, contributing to more energy-efficient and environmentally friendly sintering processes. [ABSTRACT FROM AUTHOR]

Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. [ABSTRACT FROM AUTHOR]

Due to the increasing demand of climate change studies, traceability of measurements according to the International System of Units (SI) becomes fundamental to establish data quality and comparability in time. With the aim of guaranteeing the traceability, in October 2022, an on-site calibration of three thermometers operated by the Osservatorio meteorologico di Moncalieri was assessed. The calibration by comparison against traceable travelling standards was performed in a transportable thermal chamber manufactured by the Istituto Nazionale di Ricerca Metrologica (INRIM). Results showed that the main uncertainty contributions to the expanded uncertainty were the interpolation method, the chamber inhomogeneity and the repeatability of the sensors under calibration. This current calibration was compared with previous calibration in 2012 (on-site calibration) and in 2016 (laboratory calibration). The comparison analysis evidenced the drift effect on the thermometers and the importance of having an active calibration program to reduce this effect. Regarding the expanded uncertainty, both on-site calibrations presented the same order of magnitude and smaller than the correction values. This paper points out the advantage of performing an on-site calibration: since the calibration involves the same dataloggers and cables in the same environmental conditions, the calculated calibration curve represents more convincingly the real measurement conditions. [ABSTRACT FROM AUTHOR]

This paper discusses the radiation budget and its temporal variability over Poland. The data analysis is based on the MERRA-2 reanalysis for the years 1980–2020. During the last four decades, the enhancement of climate warming has been observed, which coincides with the changes in the radiation budget. Positive and statistically significant trends at the top of the atmosphere (TOA; 0.7 ± 0.2 W/m2/10 year) and on the Earth's surface (1.5 ± 0.2 W/m2/10 year) radiation budget (net downward flux) are mainly a consequence of changes in the amount of aerosol and greenhouse gases (GHG). According to MERRA-2, the AOD during this period decreased by − 0.19 (at 550 nm), which is 87% of the long-term (1980–2021) mean value (0.22). The reduction of AOD is due mainly to the decline of non-absorbing sulfate particles, which leads to a reduction of single-scattering albedo (SSA) by − 0.008 per decade and Angstrom exponent (AE) by − 0.06 per decade (both trends statistically significant). On the other hand, the GHG concentration increased by 4.9%/10 year and 3%/10 year, respectively, for CO2 and CH4. The total column of water vapor increased (1.3%/10 year), while ozone decreased (− 1%/10 year). Despite the fact that cloud cover and cloud optical depth (COD) decreased (− 1.8%/10 year and − 1.0%/10 year), the impact of cloud on temporal variability radiation budget is small. It can be explained by nearly compensated shortwave (SW) cooling and longwave (LW) heating effects. During the analysis period, near-surface air temperature increased by 2.0 °C. The estimated increase in SW net surface radiation (7.9 W/m2) leads to climate warming by 0.8 °C, which is a consequence mainly of the reduction of aerosol (0.4 °C) and cloud cover (0.2 °C). The impact of the change of SW radiation on air temperature is more pronounced during the warm season, while during the cold, air temperature change is controlled mainly by the variability of air mass advection. [ABSTRACT FROM AUTHOR]

Frost on agricultural products in spring imposes heavy financial losses to agriculture particularly in northwest of Iran's orchards. Frost early warning is an effective way in preventing frost risk in apple orchards. This paper aims to validate frost early warning system in apple orchards of northwestern Iran by predicting flowering date of apple tree and its combination with WRF (Weather Research and Forecasting) model simulations of the 2-m temperature. To this end, stepwise multiple linear regression model was used for predicting flowering date; therefore thermal variables data including mean temperature, mean maximum temperature, mean minimum temperature, last frost date, heat wave duration, heat wave intensity, GDD and maximum standard temperature data larger than 1 (Z > 1) for the period of 2007–2016 (from March 1st until flowering date) in Kahriz agrometeorological station in northwest of Iran were calculated and added to the model. Also, to evaluate the accuracy of WRF model simulations, the 72-h simulations of the 2-m air temperature for internal computational grid in northwestern Iran (West Azerbaijan Province) in 17 synoptic stations were compared to minimum air temperature observed in the stations. Results revealed that the proposed stepwise regression method is a relatively precise model with a mean absolute error of 2.7 days between the predicted and observed flowering dates. Findings were also indicative of an acceptable accuracy of 72-h minimum air temperature simulations of WRF model in the study area. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. The main aim of this paper was to determine changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 and to identify changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI). Cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020. While a decrease in the number of months with SPEI-1 below − 1 was observed in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. This is an important finding for the agriculture or forestry because it affects work management and planning. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. The increase in drought intensity was not spatially consistent over the cluster area, mostly reaching a slight decline of about − 0.1 to − 0.3, which means slight intensification of drought periods in 1991–2020. [ABSTRACT FROM AUTHOR]

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series. [ABSTRACT FROM AUTHOR]

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day. [ABSTRACT FROM AUTHOR]

In order to study the improvement of the mechanical properties of basalt fiber geogrids on semi-rigid base asphalt pavement and the prevention and control effect of reflection cracks, this paper laid the basalt fiber geogrid and test sensor in the different positions of the semi-rigid base of Yanchong Expressway experimental Sect. (625 m long) are divided into three working conditions: I, II, and III according to different reinforcement levels. Through the test, the temperature and strain at the corresponding positions of the top and bottom of the semi-rigid base course change with time, and the crack performance of the basalt fiber geogrid-reinforced semi-rigid base course is analyzed under the action of temperature and the linear regression equations between the temperature and the strain of the base course. Atmospheric temperature between substrate strain and substrate temperature is obtained. The field test results show that adding basalt fiber geogrid to the semi-rigid base can prevent reflection cracks and improve the anti-crack performance of the semi-rigid asphalt pavement, the best anti-crack effect in the middle position of the semi-rigid base. [ABSTRACT FROM AUTHOR]

Abnormal changes in air temperature cause natural disasters such as droughts, hailstorms, and storms, thereby affecting the normal lives of human beings. Consequently, timely and accurate air temperature prediction is essential for human production and livelihood. Traditional air temperature prediction methods are less accurate and less consider the spatial relationship between air temperature in different regions. In this paper, we propose a new deep learning model, convolutional long short-term memory based on channel attention and spatial autocorrelation (ConvLSTM-CASA), which focuses on the spatial correlation between ambient air temperatures and can effectively capture the interaction of air temperatures in different regions. The results show that the ConvLSTM-CASA model has an average R2 of 0.954 and MSE of 5.245 for pentad-mean temperature prediction over the Yangtze River basin. Compared with baseline forecasting models, the MSE accuracy by the ConvLSTM-CASA model improved by 72.45%, 48.95%, 48.97%, 47.79%, and 22.63% over the decision tree regression (DTR), multiple linear regression (MLR), random forests (RF), long short-term memory (LSTM), and ConvLSTM models, respectively. The ConvLSTM-CASA model is expected to outperform the ConvLSTM model over 90% of the area, suggesting robust prediction skill improvement over space. The ConvLSTM-CASA model provides new insights for data-driven pentad-mean air temperature prediction by including elaborate channel and spatial feature modeling, which aid individuals in comprehending the intricate patterns of air temperature fluctuations. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes. [ABSTRACT FROM AUTHOR]

Previous studies have reported significant uncertainty in the relationship between El Niño-Southern Oscillation (ENSO) and the winter surface air temperature (SAT) over central North America (NA). This paper uses reanalysis data and statistical methods to investigate the possible modulation effect of the Victoria mode (VM) during the preceding spring on the winter ENSO–SAT relationship. The correlation between ENSO and the winter SAT over central NA was found to be significantly positive during the positive VM phase. However, this significant correlation is almost absent during the negative VM phase. Further analysis revealed that the zonal location of the sea surface temperature anomalies (SSTA) over the tropical Pacific is an essential aspect of this VM modulation. During the positive VM phase, the dominant positive SSTA in the central-eastern tropical Pacific contribute to the formation of the negative phase of the North Pacific Oscillation (NPO)-like teleconnection, which further enhances the anomalous southerly winds and SAT over central NA. In contrast, during the negative VM phase, the Aleutian low-like atmospheric response induced by the central tropical Pacific SSTA is limited to the central North Pacific, which has a limited effect on the variation of the central NA SAT. The VM modulation mechanism is shown to be well reproduced by historical simulations of the Coupled Model Intercomparison Project Phase 6. Our results suggest that, despite the uncertainty associated with the ENSO–SAT connection, ENSO may provide prediction skills for the winter SAT over central NA during the positive VM phase. [ABSTRACT FROM AUTHOR]

The paper presents the results of cobalt corrosion tests in air at different temperature and humidity values. It was demonstrated that corrosion losses in cobalt at temperatures of 20 and 30 °C and relative humidity of 70, 80, and 95% are insignificant. However, the rate of cobalt corrosion sharply increases at an air temperature of 50 °C, especially at 95% relative humidity. A conclusion about the intensity of the cobalt corrosion processes under these conditions can also be derived from the sample appearance: after testing at 50 °C and 70% humidity, the samples turn black, while at 50 °C and 95% humidity they become covered with black, oily, and easily crumbling flakes, which represent the products of cobalt corrosion. It was found that corrosion film formed on the cobalt surface consists of the eutectic Co–CoO mixture of variable composition and CoO·Co(OH)2·H2O complex oxide. At increased corrosion test temperatures, the surface film of hydrated cobalt oxide becomes thicker, develops microcracks, and leads to the formation of cobalt hydroxide flakes, which exhibit weak adhesion to the substrate and crumble. [ABSTRACT FROM AUTHOR]

This paper selects three typical observation sites in Hengshui city, Hengshui Lake wetland, and youth woodland along the river, and uses non-contact infrared temperature measurement equipment to carry out high-temperature continuous observation of four kinds of underlay surfaces, namely, asphalt, grassland, woodland, and wetland, to compare the temporal characteristics of the surface temperature of each kind of underlay surface and its relationship with meteorological factors, and to establish the multivariate linear regression equations for the four kinds of maximum surface temperatures of underlay surfaces based on a variety of meteorological factors. Regression equations were established, and the main results were as follows: ①The daily maximum temperature, daily average temperature, and daily minimum temperature change curves of asphalt underlay were significantly higher than those of other underlay, and the change trends of grassland, woodland, and wetland were the same, and the curves were close to each other. ②The maximum and minimum temperatures of the four types of underlayment were ranked as asphalt > wetland > forestland > grassland. ③The maximum surface temperatures of the four types of underlayment were positively correlated with the daily maximum air temperature and solar radiation, with correlation coefficients around 0.9, and negatively correlated with the daily total cloudiness and the daily maximum relative humidity, with correlation coefficients above 0.5. ④The four types of sub surface maximum temperature forecasts are well fitted to the observed values, with correlation coefficients of 0.70 or more, and the error results are within the acceptable range, which can meet the needs of high-temperature forecasting, among which the grassy subsurface has the best fit, with a correlation coefficient of 0.90.The results have certain reference significance for knowing thermal environment of different urban underlying surfaces, while. providing scientific evidence for the development of refined urban meteorological forecasting services. [ABSTRACT FROM AUTHOR]

In order to accurately and easily predict the variation of the average temperature of the concrete rectangular hollow pier. Firstly, the temperature distribution of the concrete rectangular hollow pier of Changjiahe Special Bridge was observed for 212 days. Then, based on the observation data, the functional relationship between the average concrete hollow pier temperature and the outside air temperature, and the air temperature inside the hollow pier was studied. Finally, based on this functional relationship, a prediction model for the range of variation of the mean temperature of the hollow pier was given and verified. The results of the study show that: the change of external shade temperature can be regarded as the superposition of different cyclic changes and random changes; the change rule of the average temperature of the concrete hollow pier is the same as that of the air temperature, both presenting day-by-day cyclic and step changes; the linear correlation coefficients between the daily maximum and daily minimum average hollow pier temperature and the daily average air temperature are R = 0.980 and R = 0.973, respectively; the daily average air temperature, the daily average air temperature inside the pier and daily average hollow pier temperature are R = 0.980, R = 0.998; the daily variation of the average hollow pier temperature and the daily variation of the air temperature are approximately linear, with a correlation coefficient of R = 0.899; assuming that the average temperature of the concrete hollow pier is a folding change, based on the above relationship, a method of predicting the time-by-time average temperature of the concrete hollow piers is proposed, as well as two methods of predicting the average temperature of the test piers. Average temperature change range method. Comparing the predicted values with the measured values, it is found that the predicted values are in good agreement with the measured values. [ABSTRACT FROM AUTHOR]

In this study, we report observations of propagating radial carpet beams (RCBs) through a turbulent atmosphere at ground level with a 120 m path length. RCBs are a class of nondiffracting, accelerating, self-healing, and self-amplifying beams, and generated in the diffraction of a plane wave from amplitude/phase radial gratings having different spoke numbers. Observations were made at different times of the day. The intensity profile of an RCB becomes complicated when the number of grating spokes used to generate the beam is large, and includes high intensity spots, called main intensity spots (MISs), which are symmetrically placed at the central area around the beam axis and whose number is equal to (twice) the number of spokes of the amplitude (phase) grating used to generate the beam. With the aid of a telescope and a CCD camera, successive frames of the intensity pattern of the RCBs having different levels of structural complexity are recorded at the end of the path. For the data recorded at different times of the day, we calculate the variance of displacements of MISs along the radial direction. We observe that displacements of the MISs increase with increasing mean temperature of the air; on the other hand, as the complexity of the beam intensity pattern increases, the displacements of the MISs decrease. In order to compare the resilience of different RCBs and a well-known structured beam against atmospheric turbulence, we investigate deformation of the intensity profiles of a Laguerre–Gaussian (LG) beam having a topological charge 20 and different RCBs at the end of the path. It is shown that under the same turbulence condition, highly complex RCBs are more resilient to the destructive effects of the atmospheric turbulence. In particular, for the RCBs generated with gratings having 30 spokes and more, the number of MISs of the received intensity patterns is changed by less than 1% even when the turbulence strength is high. But for the LG beam, its intensity ring is clearly broken in different places, which makes it impossible to follow its maximum intensity in the radial direction [ABSTRACT FROM AUTHOR]

Color centers in wide band-gap semiconductors, which have superior quantum properties even at room temperature and atmospheric pressure, have been actively applied to quantum sensing devices. Characterizing the quantum properties of the color centers in the semiconductor materials and ensuring that these properties are uniform over a wide area are key issues for developing quantum sensing devices based on color centers. In this article, we have developed an optics design protocol optimized for evaluating the quantum properties of color centers and have used this design approach to develop a new microscopy system called columnar excitation fluorescence microscope (CEFM). The essence of this system is to maximize the amount of fluorescence detection of polarized color centers, which is achieved by large-volume and uniform laser excitation along the sample thickness with sufficient laser power density. This laser excitation technique prevents undesirable transitions to undesirable charge states and undesirable light, such as unpolarized color center fluorescence, while significantly increasing the color center fluorescence. This feature enables fast measurements with a high signal-to-noise ratio, making it possible to evaluate the spatial distribution of quantum properties across an entire mm-size sample without using a darkroom, which is difficult with typical confocal microscope systems. [ABSTRACT FROM AUTHOR]

The Antarctic Peninsula (West Antarctica) marine ecosystem has undergone substantial changes due to climate-induced shifts in atmospheric and oceanic temperatures since the 1950s. Using 25 years of satellite data (1998-2022), this study presents evidence that phytoplankton biomass and bloom phenology in the West Antarctic Peninsula are significantly changing as a response to anthropogenic climate change. Enhanced phytoplankton biomass was observed along the West Antarctic Peninsula, particularly in the early austral autumn, resulting in longer blooms. Long-term sea ice decline was identified as the main driver enabling phytoplankton growth in early spring and autumn, in parallel with a recent intensification of the Southern Annular Mode (2010-ongoing), which was observed to influence regional variability. Our findings contribute to the understanding of the complex interplay between environmental changes and phytoplankton responses in this climatically key region of the Southern Ocean and raise important questions regarding the far-reaching consequences that these ecological changes may have on global carbon sequestration and Antarctic food webs in the future. Using 25 years of satellite data, this study presents evidence that phytoplankton biomass and bloom phenology in the West Antarctic Peninsula are significantly changing as a response to anthropogenic climate change. These findings raise important questions regarding the effect of these ecological changes on global carbon sequestration and Antarctic food webs in the future. [ABSTRACT FROM AUTHOR]

There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R2 > 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R2 > 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress. [ABSTRACT FROM AUTHOR]

İstanbul is the largest city located in the Mediterranean Basin and has a medium to high risk of climate change and future climate risks. Changes in temperature and other weather variables have had significant impacts on İstanbul. In this context, there is a need for studies on the issues of climate monitoring and climate change vulnerability to reduce the adverse impacts. The aim of this study is to investigate the temperature trends of synoptic weather station in İstanbul Atatürk Airport between 1973 and 2023 to have a general idea about how the temperature has changed over the last half-century and to establish statistically whether a trend is significant or not. The values of minimum (Tmin), maximum (Tmax) and mean (Tmean) temperature related parameters were estimated. Annual, monthly and seasonal temperature trends are also analyzed. The findings of this study indicate a significant (p < 0.001) rise in the mean air temperature (Tmean) of İstanbul over the past 51 years (1973–2023), with an annual warming trend of 0.06 °C. The strongest increasing trend in seasonal mean air temperatures has been observed in the summer season, with an increase of 0.08 °C per year. The trend analysis also shows a statistically non-significant increase in yearly average minimum temperature (Tmin) between 1973 and 2023, with a rate of 0.04 °C per year. However, the annual maximum temperature (Tmax) has shown no changes. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster. The multi-layer perceptron methods developed demonstrate high accuracy in predicting visibility, with R values of 0.90 for Warsaw and an RMSE of 1.52 km for Zabrze. Key findings reveal that air temperature and relative humidity significantly impact visibility, alongside PM1 concentration and specific heavy metals such as Rb, Vi, and Cd in Warsaw and Cr, Vi, and Mo in Zabrze. Cluster analysis underscores the localized and complex nature of visibility determinants, highlighting the substantial but previously underappreciated role of heavy metals. Integrating the k-means clustering and GSA methods emerges as a powerful tool for unravelling complex mechanisms of chemical compound changes in particulate matter and air, significantly influencing visibility development. [ABSTRACT FROM AUTHOR]

Possible local influence of an extreme marine heatwave is investigated on unprecedentedly hot summer around northern Japan in 2023. Sea-surface temperatures (SSTs) and subsurface ocean temperatures around northern Japan were also unprecedentedly high in the summer. This was especially the case off the east coast of Japan, where cool Oyashio water was replaced with much warmer water due to a striking poleward meander of the Kuroshio Extension persistent from the spring. Particularly amplified near-surface air temperature anomalies and even stronger warm anomalies in the subsurface ocean suggest that the marine heatwave acted to sustain the atmospheric heatwave. Anomalous upward of latent and sensible heat fluxes from the warmed sea surface are indicative of local oceanic impact. The warm SST anomalies reduced the lower-tropospheric stratification to maintain unfavourable condition for low-level cloud formation, which in turn led to increased surface insolation for further SST warming as positive feedback. The increased moisture in the warmed lower troposphere contributed to the enhanced surface downward longwave radiation. This enhanced greenhouse effect acted not only as positive feedback on the warm SST anomalies that increased evaporation but also as a contributor for the extreme warmth over northern Japan landmass. [ABSTRACT FROM AUTHOR]

The complex meso- and microclimatic heterogeneity inherent to mountainous regions, driven by both topographic and biotic factors, and the lack of observations, poses significant challenges to using climate models to predict and understand impacts at various scales. We present here a six-year dataset (2017–2022) of continuous climatic measurements collected at five elevations from 983 m to 2705 m above sea level in the Val Mazia - Matschertal valley in the Italian Alps. The measurements include the air temperature, relative humidity, wind speed and direction, solar radiation, soil properties, precipitation, and snow height. Collected within the European Long-Term Ecological Research program (LTER), this dataset is freely available in an open access repository. The time series may be valuable for the validation of regional climate models, atmospheric exchange modelling, and providing support for hydrological models and remote sensing products in mountain environments. Additionally, our data may be useful for research on the influence of elevation on ecological processes such as vegetation growth, plant composition, and soil biology. Beyond its utility in advancing such fundamental research, meteorological monitoring data contribute to informed socio-political decisions on climate adaptation strategies, land management, and water resource planning, enhancing the safety and resilience of mountain communities and biodiversity. [ABSTRACT FROM AUTHOR]

The apparent optimum air temperature for vegetation photosynthesis (Topt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m2/day). To date, most models use biome-specific Topt (Topt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific Topt (Topt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the Topt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global Topt-site data products from 2000 to 2019. The Topt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used Topt-biome. Therefore, using this global dataset of Topt-site estimates might significantly affect GPP simulation in current ecosystem models. [ABSTRACT FROM AUTHOR]

Diffuse light is produced by clouds and aerosols in the atmosphere. Exploring the effects of diffuse light on ecosystem productivity is important for understanding the terrestrial carbon (CO2) cycle. Here, 2 years of gross ecosystem primary productivity (GEP) from a (winter) wheat cropland in China was assessed using eddy covariance technology to explore the effects of diffuse photosynthetic active radiance (PAR) on wheat GEP. Wheat GEP increased significantly and positively along with diffuse PAR. In addition, wheat GEP was significantly affected by total PAR, air temperature, and vapor press deficit in different diffuse PAR fraction (fDIF) change stages. Because significant autocorrelations existed among the controlling factors, a path analysis was used to quantify the effects of diffuse light on GEP. Diffuse PAR was the primary and secondary importance factors affecting GEP with direct path coefficients of 0.54 and 0.48, respectively, in different fDIF change stages. A multilayer canopy model revealed that the middle and lower canopy levels intercepted more light when diffuse PAR increased. This resulted in the photosynthetic enhancement of middle and lower canopy levels, which contributed approximately 65% and 35%, respectively, to the increase in photosynthesis for the entire canopy (~ 30.5%). Overall, our study provided new evidence regarding the importance of diffuse light for CO2 uptake in agroecosystems, which is important for predicting the responses of ecosystem CO2 budgets to future climate-related light changes. [ABSTRACT FROM AUTHOR]

In this study, bare and ionic liquid-modified ZnO nanoparticles have been fabricated using microwave irradiation method. The fabricated nanoparticles were characterized by different techniques, viz. XRD, FT-IR, FESEM, and UV–Visible spectroscopy, and were explored as adsorbent for effective sequestration of azo dye (Brilliant Blue R-250) from aqueous media. Various factors affecting the adsorption efficiency of synthesized nanoparticles (bare/ionic liquid-modified) such as concentration of dye, pH of reaction media, dose of nanoparticles, and reaction time were thoroughly investigated with varying experimental conditions; on a magnetic stirrer and in a sonicator. The results exhibited a high adsorption efficiency of ionic liquid-modified nanoparticles for removal of dye as compared to the bare one. Also, an enhanced adsorption was observed via sonication in comparison with magnetic stirring. Different isotherms such as Langmuir, Freundlich, and Tempkin were elaborated. Evaluation of adsorption kinetics showed a linear pseudo-second-order equation for adsorption process. The exothermic and spontaneous nature of adsorption was further confirmed by thermodynamic investigations. As per the results obtained, it is suggested that the fabricated ionic liquid-modified ZnO nanoparticles could successfully remediate the toxic anionic dye from aqueous media. Hence, this system can be utilized for large-scale industrial applications. [ABSTRACT FROM AUTHOR]

We applied a perfect prognosis approach to downscale four meteorological variables that affect photovoltaic (PV) power output using four machine learning (ML) algorithms. In addition to commonly investigated variables, such as air temperature and precipitation, we also focused on wind speed and surface solar radiation, which are not frequently examined. The downscaling performance of the four variables followed the order of: temperature > surface solar radiation > wind speed > precipitation. Having assessed the dependence of the downscaling accuracy on the scaling factor, we focused on a super-resolution downscaling. We found that the convolutional neural network (CNN) generally outperformed the other linear and non-linear algorithms. The CNN was further able to reproduce extremes. With the rapid transition from coal to renewables, the need to evaluate low solar output conditions at a regional scale is expected to benefit from CNNs. Because weather affects PV power output in multiple ways, and future climate change will modify meteorological conditions, we focused on obtaining exemplary super-resolution application by evaluating future changes in PV power outputs using climate simulations. Our results confirmed the reliability of the CNN method for producing super-resolution climate scenarios and will enable energy planners to anticipate the effects of future weather variability. [ABSTRACT FROM AUTHOR]

Several developing countries in Asia are relying on agricultural practices in which climate change is a challenging concern. The trends change in atmospheric parameters and vegetation indices are questioned and vague. Consequently, the trend variations of the environmental characteristics were introduced in the first decade of 21 century to better understand the trend changes and envisage the relationship between the atmospheric parameters and the vegetation cover. The Moderate Resolution Imaging Spectroradiometer and the Atmospheric Infrared Sounder provide several atmospheric parameters, e.g., Air Temperature, Aerosol Optical Depth (AOD), and Carbon Dioxide (CO2). In addition, NDVI was estimated and aimed at the role of the interactions among the atmospheric parameters based on statistical analysis. The results showed that AOD and NDVI were air temperature dependent; meanwhile, AOD, NDVI, and air temperature were exchanging and influencing one another. Furthermore, the average minimum air temperature of the region increased during this period and carbon dioxide was a time-dependant parameter that was rising at a steep slope, which may lead to environmental crises. The trend analysis outlines what policies, programs, and procedures should be used to balance the ecosystem and mitigate the climatic negative effects. The Current research strengthens on the social awareness and the international cooperation to reduce socio-economic and political harm and loss in the face of climate change. [ABSTRACT FROM AUTHOR]

Predictive estimates of changes in the climatological boundary of the permafrost zone as a function of the average annual air temperature in the Bolshezemelskaya tundra under various global economy scenarios until the middle of the XXI century have been obtained. The permafrost climatological boundary shift in the northeasterly direction observed in the period from 1950 to 2010 was determined from the threshold average annual air temperature. According to the adjusted scenario forecasts obtained using the climate model, it will continue in the coming decades under any global economy scenario and is an inevitable consequence of the anthropogenic influence on the climate. The study results are important for assessing the prospects and development of a long-term observation network created to monitor the permafrost state and greenhouse gas fluxes in the Russian Federation. [ABSTRACT FROM AUTHOR]

Understanding the trend of seasonal rainfall in the context of climate change is crucial for the maintenance of regional water resources management. The present study examines the seasonal rainfall trend in the Mahi River basin, India by using the wavelet transform and clustering method. Daily gridded rainfall data (0.25° × 0.25° spatial resolution) for 51 grids from 1970 to 2020 have been taken from India Meteorological Department (IMD). The monthly, seasonal, and annual statistic has been analyzed for 51 years. We have also plotted the relationship between precipitation trend–elevation. After that, homogeneous precipitation regions are delineated with hierarchical clustering analysis. Results reveal that seasonal precipitation over the basin clusters into 4 subregions for monsoon and 3 subregions for winter, pre-monsoon, and post-monsoon seasons. After the regionalization of the subregions, the periodicity and the inter-seasonal relationship were analyzed using continuous wavelet transform (CWT). In addition, it was clear that the cross-correlation between pre-monsoon and monsoon seasons had a significant periodic change in the last 30 years over the basin. The basin is located adjacent to the Arabian sea, which makes the basin more sensitive to a natural event like ENSO, it can cause changes in ocean temperatures and atmospheric circulation patterns that affect precipitation patterns during the study period. It is expected that some natural phenomena like monsoon variability, low-level jet streams, and many more also have an impact on the rainfall patterns of the basin. Thus, the understanding of seasonal precipitation variation provides a practical reference for water resources management, agricultural planning, and a forecast of precipitation in different regions and river basins of India which may give a better climate change indication. [ABSTRACT FROM AUTHOR]

Aims: Quantitative characterization of the time-lag effect between canopy temperature and atmospheric temperature and its controlling factors in the agricultural ecosystem may contribute to a higher inversion accuracy of soil water content using canopy-air temperature information. Methods: Tc of winter wheat were continuously monitored, and the data of such environmental factors as solar radiation (Rs), atmospheric temperature (Ta), relative humidity (RH) and soil water content (SWC) were simultaneously collected. Results: Hysteresis existed between Tc and Ta over the diel cycles, and different weather and irrigation levels did not change the direction of the time lag loop. the key driver regulating the diel hysteresis pattern between Tc and Ta varied under different weather: on rainy days, key driver was Rs while on cloudy and sunny days, the key driver was RH. the multiple regression model indicated that together Rs, Ta, RH, and SWC explained 58 ± 10% of the variation of time-lag effect. Path analysis showed on rainy days the key driver (Rs and RH) could enhance the time-lag effect through other indirect factors (Ta and SWC); on cloud days the key driver (RH and SWC) could inhibit the time-lag effect through other indirect factors (Ta); On sunny days this mutual inhibition was further significant. Conclusions: These findings indicated a dynamic process of time-lag effect between Tc and Ta with different weather and different irrigation levels. This study contributes to the understanding of the time-lag effect and its driving factors and this analysis provides the basis for further improvement in monitoring crop water deficit. [ABSTRACT FROM AUTHOR]

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 "Resilient Cooling for Buildings", are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves. [ABSTRACT FROM AUTHOR]

The sensitivity strength of air temperature (T) to surface soil temperature (sST) (namely β hereafter) constitutes a significant factor in how global climate models quantify changes in the climate. This study examines how this sensitivity is represented in the CMIP6 models. Results show regional differences and even contrasts in the β trends at interannual scales between climate models and two reanalysis products during 1980–2014. At high latitudes in the Northern Hemisphere (NH), β is stronger in the CMIP6 models than in the reanalysis data. Additionally, the β trends differ between the CMIP6 and reanalysis data, which may be related to the different precipitation (PR) and soil water availability (PR-ET) trends between the models. In the regions of increasing β intensity at high latitudes in the NH, sST is more sensitive to PR and PR-ET. Consequently, increasing PR and PR-ET leads to slower sST changes, potentially making β intensity stronger in CMIP6 models. However, in the reanalysis data, decreases in PR and PR-ET accelerate sST changes, leading to a weakening of the β intensity. A resulting implication is that β, based on high-emission scenarios, shows a stronger trend during 2015–2100, although this trend could potentially be overestimated. The findings contribute to a better understanding of the sensitivity of T to sST and facilitate the assessment of energy exchange between the land surface and the atmosphere in climate models. [ABSTRACT FROM AUTHOR]

Canopy temperature is generally accepted as an indirect but rapid, accurate, and large-scale indicator of crop water status and is, therefore, proposed to monitor irrigation needs. Crop Water Stress Index (CWSI) is the most widely used among the existing thermal-based indicators, and its links with water stress have been demonstrated. When calculating CWSI using the empirical approach, the differential between canopy and air temperature is normalized by two thresholds, also known as baselines. The Non-water stress baseline (NWSB) in the empirical approach is calculated as the relationship between Tc–Ta (°C) and the vapor pressured deficit (VPD, kPa) for well-irrigated crops. The baselines display different slopes depending on the species, which have a significant impact on the computed CWSI. This study analyzed the resulting errors on CWSI due to the measurement errors of critical inputs needed for its calculation. Six crop species were selected according to their NWSB with slopes that range from − 0.5 to − 3 °C·kPa−1 and used for this analysis, assuming measurement errors ranging 0.2–1 °C for Ta, 0.25–2 °C for Tc, and 5–10% for relative humidity (RH). It was concluded that the effects observed on CWSI are heavily dependent on the slope of the NWSB and therefore vary across species. The calculation was very sensitive to the bias in air and canopy temperature. These errors were maximal as the slope of the NWSB was less steep. When the VPD ranged from 2 to 6.6 kPa, an error of 1 °C in measuring the air temperature affected CWSI between 28 and 83% in orange, which is the species displaying the minimum slope (− 0.5 °C kPa−1). On the contrary, crops with steeper baseline slopes such as squash (− 3 °C kPa−1) showed errors ranging between 2 and 8% for the same VPD interval. This differences among the different crops species considered in this study may be related to the contrasting coupling of the species to the atmosphere, that determines the influence of vapor pressure on the transpiration rate. This study highlights the importance of reliable climatic data and the need for accurate calibrated thermal sensors to calculate CWSI accurately. [ABSTRACT FROM AUTHOR]

The increase in the world population and the decrease in clean water resources increase people's interest in water purification technologies. Many industries, such as the textile industry, leather industry, cosmetics and food industry, color their products with substances such as dyes and pigments. In this study, a cheap, useful, innovative, environmentally friendly, and sustainable adsorbent was developed for the removal of Methylene Blue (MB), one of the dyes that is harmful to the environment. In fruit tree cultivation, in addition to the product, very high amounts of by-products/waste (branches, bark, leaves, fruit seeds, fruit shells, etc.) are produced. In this direction, walnut tree and olive tree wastes were immobilized with chitosan, made magnetic (m-WCH and m-OCH), and the adsorption of MB on the developed adsorbents was examined in a batch system. Characterization of the synthesized biocomposite adsorbents was performed by FT-IR, SEM, EDX and XRD analyzes. It has been thoroughly described how the pH solution of the MB dye compares to the pHPZC of the adsorbent surface. The pHPZC values for m-WCH and m-OCH were 5.2 and 5.5 respectively. MB adsorption of biocomposites depends on the pH of the environment (3–8), amount of adsorbent (2–10 gL−1), contact time (5–360 min), temperature (25–55 °C) and initial dye concentration (10–250 ppm) was examined as a function. The obtained data were evaluated with kinetic and isotherm models. Using adsorption equilibrium data obtained from MB adsorption studies using m-WCH and m-OCH biocomposite adsorbents, their suitability to Langmuir, Freundlich, Stachard, Dubinin–Radushkevich and Temkin models was examined. The empirical data of MB adsorption by m-WCH and m-OCH showed agreement with the Langmuir isotherm model. The maximum adsorption capacity for MB by m-WCH and m-OCH was estimated to be 85.47 mg g−1 and 53.48 mg g−1, respectively. The result showed that a higher adsorption selectivity on m-WCH compares to m-OCH. Among the kinetic models applied, the pseudo-second-order kinetic model was identified with the highest regression coefficients. In the light of these results, it was determined that there are new promising adsorbents of natural origin, with higher adsorption capacity, lower cost, and alternatives to commercially used adsorbents in the removal of MB from aqueous media. [ABSTRACT FROM AUTHOR]

Taking gas and the heat transfer process between gas and plate as the research object, the mathematical model of heat transfer in one working cycle by moving variable temperature air mass under the action of sound field is established, which provides a new idea for understanding thermoacoustic effect. The influence factors in the model are analyzed and it is found that the amplitude of the air mass has a significant influence on the heat transfer, and the heat transfer of the air mass in one working cycle is proportional to the square of the amplitude. In a certain working environment, the thermoacoustic refrigerator has a critical operating frequency, and only when the operating frequency is greater than the critical frequency can refrigeration be realized. The critical operating frequency is independent of the amplitude and increases with the increase of the stack temperature gradient. With the pressure belly point as the reference position, the greater the distance from the reference position, the greater the critical operating frequency. On this basis, the idea of short plate overlapping is put forward and the formation mechanism of temperature difference between two ends of plate overlapping is explained. [ABSTRACT FROM AUTHOR]

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes. [ABSTRACT FROM AUTHOR]

This study applies three widely used land models (SSiB, CLM, and Noah-MP) coupled in a regional climate model to quantitatively assess their skill in preserving the imposed ± 5 °C anomalies on the initial land surface and subsurface temperature (LST/SUBT) and generating the 2-m air temperature (T2m) anomalies over Tibetan Plateau (TP) during May–August. The memory of the LST/SUBT initial anomalies (surface/soil memory) is defined as the first time when time series of the differences in daily LST/SUBT cross the zero line during the simulation, with the unit of days. The memory of the T2m anomalies (T2m memory) is defined in the same way. The ensemble results indicate that the simulated soil memory generally increases with soil depth, which is consistent with the results based on the observations with statistic methods. And the soil memory is found to change rapidly with depth above ~ 0.6–0.7 m and vary slowly below it. The land models have fairly long soil memories, with the regional mean 1.0-m soil memory generally longer than 60 days. However, they have short T2m memory, with the regional means generally below 20 days. This may bring a big challenge to use the LST/SUBT approach on the sub-seasonal to seasonal (S2S) prediction. Comparison between the three land models shows that CLM and Noah-MP have longer soil memory at the deeper layers (> ~ 0.05 m) while SSiB has longer T2m/surface memories and near-surface (≤ ~ 0.05 m) soil memory. As a result, it is difficult to say which land model is optimal for the application of the LST/SUBT approach on the S2S prediction. The T2m/surface/soil memories are various over TP, distinct among the land models, and different between the + 5 °C and − 5 °C experiment, which can be explained by both changes in the surface heat fluxes and variances in the hydrological processes over the plateau. [ABSTRACT FROM AUTHOR]

Today, the biggest issue appears to be the increase in drought in some regions brought on by global warming, which has greatly increased the significance of water management. In light of evaporation's effect on drought, this research intends to evaluate the effectiveness of hybrid machine learning (ML) models, such as the Gradient Boosting Machines (GBM) technique paired with Empirical Mode Decomposition (EMD), Robust Empirical Mode Decomposition (REMD), Ensemble Empirical Mode Decomposition (EEMD), and Variational Mode Decomposition (VMD) signal decomposition techniques, for monthly evaporation prediction models in the Southeast Anatolia Project Area. In the design of the models, 80% of the data was used for training and 20% for testing. Furthermore, tenfold cross-validation was applied to solve the overfitting problem, which negatively affected the forecast performance. In the model setup, various combinations of precipitation, average air temperature, minimum air temperature, maximum air temperature, wind speed, actual air pressure, relative humidity, and solar time variables are presented to artificial intelligence models as input. The study revealed that the GBM methodology in combination with the signal decomposition methods REMD, EMD, EEMD, and VMD generally allowed for more accurate evaporation estimations than the GBM model alone. The study's results are essential in relation to agricultural production, irrigation planning, water resources management studies, and hydrological modeling studies in the region. [ABSTRACT FROM AUTHOR]

Primary production in the Sea of Okhotsk is largely supported by dissolved iron (dFe) transported by the Amur river, indicating the importance of dFe discharge from terrestrial environments. However, little is known about the mechanisms of dFe discharge into the Amur river, especially in terms of long-term change in dFe concentration. In the Amur river, extreme increase in dFe concentration was observed between 1995 and 1997, the cause of which remains unclear. As a cause of this iron anomaly, we considered the impact of permafrost degradation. To link the permafrost degradation to long-term variation in dFe concentration, we examined the changes in annual air temperature (Ta), accumulated temperature (AT), and net precipitation for three regions (northeast, south, and northwest) of the basin between 1960 and 2006. Ta and AT were relatively high in one out of every few years, and were especially high during 1988–1990 continuously. Net precipitation in late summer (July to September) has increased since 1977 and has stayed positive until 2006 throughout the basin. Most importantly, we found significant correlations between Ta and late summer dFe concentration with a 7-year lag (r = 0.54–0.69, p < 0.01), which indicate a close relationship between high Ta in year Y and increased late summer dFe concentration in year Y + 7. This correlation was the strongest in northeastern Amur basin where permafrost coverage is the highest. Similar 7-year lag correlation was also found between AT in the northeastern basin and late summer dFe concentration (r = 0.51, p < 0.01). Based on our findings, we propose the following hypothesis as a cause of iron anomaly. (1) Increased net precipitation since 1977 has increased soil moisture, which created suitable conditions for microbial dFe generation; (2) permafrost degradation during the warm years of 1988–1990 promoted iron bioavailability and led to the intensive dFe generation in the deeper part of the active layer; and (3) dFe took approximately 7 years to reach the rivers and extremely increased dFe concentration during 1995–1997. This is the first study to suggest the time-lagged impact of permafrost degradation on iron biogeochemistry in the Amur river basin. [ABSTRACT FROM AUTHOR]

Canopy temperature (Tc) measurements are increasingly being used to compute crop thermal indices for water stress estimation and improved irrigation management. Conventionally monitoring crop thermal response requires maintenance of a well-watered crop from which non-stressed canopy temperature (Tcns) is measured as a reference for thermal index computation. This study alternatively evaluated the performance of 36 weather data driven model combinations to predict peak time (12:00–17:00 h) Tcns in maize grown in semi-arid climates at the West Central Research, Extension, and Education Center (WCREEC) in North Platte, NE, and at the Limited Irrigation Research Farm (LIRF) in Greeley, CO. Data-driven models considered were multilinear regression (MLR), forward feed neural network (NN), recurrent neural network (RNN), multivariate adoptive regression splines (MARS), random forest (RF), and k-nearest neighbor (KNN). For each of these models, the following weather data combinations were tested: average air temperature (Ta), average relative humidity (RH), wind speed (U2), and solar radiation (Rs) (combination 1); RH, U2, Rs (combination 2), Ta, RH, Rs (combination 3); Ta, RH (combination 4); RH, Rs (combination 5); and Ta, Rs (combination 6). Ranking the performance of weather data × model combinations across both climate sites showed that MARS model with combination 1 was a better predictor of Tcns with R2 of 0.866 and RMSE value of 0.966 °C at WCREEC and R2 of 0.910 and RMSE value of 0.693 °C at LIRF. The performance of site specific (localized) and generalized model combinations was compared and indicated that cross site prediction of Tcns was primarily determined by weather data combinations, rather than model specificity. [ABSTRACT FROM AUTHOR]

The high-temperature corrosion behavior of type 310 heat-resistant steel used for superheater tubes in waste-to-energy boilers was investigated to determine the breakdown behavior of the protective Cr-rich oxide scale in the initial stages of corrosion by conducting ash-embedded corrosion tests using combustion ash collected from an actual boiler. The corrosion tests were performed in air at a specimen temperature of 460 °C and an atmospheric temperature of 685 °C. After 24 h, the specimen surfaces were entirely covered with brown corrosion products, but there was almost no mass loss. However, the corrosion mass loss increased after 60 h. After 1–12 h, the specimen surfaces were partially covered with brown corrosion products, but in other areas, the interference color or a yellow hue remained, where no breakdown had occurred. Examination of the cross-sectional microstructures of these breakdown-free areas revealed that a protective Cr-rich oxide scale was formed on the surface after 1 h of corrosion, and sodium ferrite was distributed on the surface of the Cr-rich oxide scale. Although the breakdown of the Cr-rich oxide scale was not complete, iron chloride formation occurred at the substrate subsurface under the Cr-rich oxide scale, and the latter was partially exfoliated from the substrate in SUS310S. Cl2 gas was also generated during the formation of sodium chromate. Therefore, despite the protective nature of the Cr-rich oxide scale, partial formation of chromate may cause chloride formation on the substrate followed by exfoliation of the scale. After scale breakdown, the Cl2 gas caused intergranular or localized internal corrosion, and when the internal corrosion had spread to some extent, the Cr-rich oxide scale was formed again. This cycle led to the corrosion of the heat-resistant steel. [ABSTRACT FROM AUTHOR]

Based on the spectral analysis results obtained for air temperature and ice cover on the East Siberian Sea shelf reconstructed for the past five thousand years, empirical forecast models have been constructed to record the "natural trend scenario" of these climatic parameters in the coming centuries. Based on the forecast construction results, the duration of the ice-free period, as well as the near-surface air temperature, will tend to increase in the future. The studies conducted make it possible to suggest the anthropogenic influence on the temperature and ice conditions in the studied region in the industrial period. Taking into account this factor, it can be assumed that the studied natural phenomena will have a higher rate than is indicated by the models. [ABSTRACT FROM AUTHOR]