Maximum Air Temperature Research Articles

Past and future changes in maximum air temperature and cold days in winter in Poland

Past and future changes in maximum air temperature and cold days in winter in Poland

Seasonal spatial-temporal trends of vegetation recovery in burned areas across Africa.

Africa is entering a new fire paradigm, with climate change and increasing anthropogenic pressure shifting the patterns of frequency and severity. Thus, it is crucial to use available information and technologies to understand vegetation dynamics during the post-fire recovery processes. The main objective of this study was to evaluate the seasonal spatio-temporal trends of vegetation recovery in response to fires across Africa, from 2001 to 2020. Non-parametric tests were used to analyze MODIS Normalized Difference Vegetation Index (NDVI) products comparing the following three-month seasonal periods: December-February (DJF), March-May (MAM), June-August (JJA), and September-November (SON). We evaluated the seasonal spatial trends of NDVI in burned areas by hemisphere, territory, or country, and by land cover types, and fire recurrences, with a focus on forested areas. The relationships between the seasonal spatial trend and three climatic variables (i.e. maximum air temperature, precipitation, and vapor pressure deficit) were then analyzed. For the 8.7 million km2 burned in Africa over the past 22 years, we observed several seasonal spatial trends of NDVI. The highest proportions of areas with increasing trend (p < 0.05) was recorded in MAM for both hemispheres, with 22.0% in the Northern Hemisphere and 17.4% in the Southern Hemisphere. In contrast, areas with decreasing trends (p < 0.05), showed 4.8-5.5% of burned area in the Northern Hemisphere, peaking in JJA, while the Southern Hemisphere showed a range of 7.1 to 10.9% with the highest proportion also in JJA. Regarding land cover types, 48.0% of fires occurred in forests, 24.1% in shrublands, 16.6% in agricultural fields, and 8.9% in grasslands/savannas. Consistent with the overall trend, the area exhibiting an increasing trend in NDVI values (p < 0.05) within forested regions had the highest proportion in MAM, with 19.9% in the Northern Hemisphere and 20.6% in the Southern Hemisphere. Conversely, the largest decreasing trend (p < 0.05) was observed in DJF in the Northern Hemisphere (2.7-2.9%) and in JJA in the Southern Hemisphere (7.2-10.4%). Seasonally, we found a high variability of regeneration trends of forested areas based on fire recurrences. In addition, we found that of the three climatic variables, increasing vapor pressure deficit values were more related to decreasing NDVI levels. These results indicate a strong component of seasonality with respect to fires, trends of vegetation increase or decrease in the different vegetation covers of the African continent, and they contribute to the understanding of climatic conditions that contribute to vegetation recovery. This information is helpful for researchers and decision makers to act on specific sites during restoration processes.

Yield and grain quality of spring durum wheat in the conditions of the Orenburg Cis-Urals

The article contains an analysis of the prevalence of spring durum wheat crops in the world and in the Orenburg region, and yields over a long period. There is a tendency to decrease the productivity of this crop over the studied period, primarily due to the increasing aridity of the growing season. The results of a correlation and regression analysis of the dependence of its yield on the main weather indicators are presented, while identifying their optimal values for the formation of highly productive crops of this crop. Analyzing the obtained dependencies, attention is focused on the intensity of the growing conditions of spring durum wheat in recent years in the arid zone, which do not allow realizing the potential of this crop. The calculated coefficients of the relationship between the yield of this crop and weather factors show reliable dependences on the temperature regime of May, July, the amount of precipitation in May and the relative humidity of the entire growing season. The results of the mathematical dependence of durum wheat grain quality indicators on the weather characteristics of the growing season are presented. At the same time, the optimal air temperature values in May were 13.4 °C, in June 15.0 °C, in July 23.3 °C, according to the amount of precipitation, the optimal amount for the months of vegetation was 30 mm in May and June and 36 mm in July. The above–mentioned optimal values of weather factors in May corresponded to the theoretical yield of durum wheat up to 30.0 c from 1 ha, the optimal values in June - 22.6 c from 1 ha and the optimal values in July – 18.9 c from 1 ha. Correlation coefficients and actual data on grain quality indicators allow us to note their significant variability from the conditions of the growing season and varietal specificity. A high correlation (r = 0,827-0,926) was established for the studied grain quality indicators with average relative humidity and with an average daily humidity deficit. The average relationship (r = 0.635-0.745) was obtained for the average air temperature, maximum air temperature and average daily humidity deficit. Grain quality is subject to variability from the conditions of the growing season: in harvest years, there is an increase in the vitreous content of grain to 92-93%, and in years with an increase in the temperature regime of the air, there is an improvement in the quality of gluten to the level of class II.

Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds

Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies post-wildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R2 of 0.98 and root mean square error of 0.88 °C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 °C by 2050. RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predicting post-wildfire impacts and informing climate-resilient water management strategies.

Examine the effect of various shape of absorber plate of solar air heater integrated with vortex generator

Solar energy is a noteworthy renewable energy source that may be used as a sustainable substitute for fossil fuels in many applications, including evacuated tube solar collectors, solar water heaters, and solar air heaters (SAHs). Among these, SAHs are widely utilized due to their simplicity and efficiency. In a solar air heater duct with rectangular vortex generators, this research examines the effects of various absorber plate configurations employing "computational fluid dynamics (CFD)". Heat transmission between the air and the absorber plate was examined using three different absorber plate geometries: corrugated, triangular, and trapezoidal. Additionally, variations in the geometric configurations of the triangular and trapezoidal ribbed absorber plates were considered. The air velocity was constant at 1.31 m/s in each instance, while "the absorber plate" was continually exposed to a heat flux of 815 W/m². The rate of heat transfer, outlet air temperature, temperature contours, pressure contours, velocity contours, and other critical performance metrics were investigated. The results indicate that heat transfer is significantly higher in SAH ducts with triangular and trapezoidal ribbed absorber plates compared to the corrugated plate. The maximum outlet air temperature of 325.83 K and a heat transfer rate of 595.44 W were observed in case 6, representing the highest values among all configurations studied. These results demonstrate how optimized ribbed designs may improve solar air heaters' thermal performance.

Selecting CMIP6 models to assess the impact of climate change on energy need for heating and cooling in Europe

The CMIP project has reached the end of its sixth phase, with a large set of simulations that represent a very large volume of data. The exploitation of all the simulations in impact studies would be extremely costly in computing time and storage. Moreover, these models suffer from several sources of uncertainties (coarse spatial resolution, physical parameterizations, ...) which produce large systematic biases.This study details an approach developed for the selection of a subset of simulations from the CMIP6 multi-model ensemble to assess the impact of climate change on the energy demand in Europe. At a climatic time scale, besides socio-economic factors, energy demand variability and evolution depend mainly on air temperature. The developed process of selection combines two main criteria applied to the daily mean, maximum, and minimum air temperature from all CMIP6 simulations where these variables are available. The first one consists of choosing a subset of models that represents the whole range of possible temperature changes in the future compared to the historical climate. The second criterion considers the skills of the CMIP6 historical simulations over Europe with respect to ERA5 climatology in order to discard the climate simulations with the poorest representation of the present climate. The methodology of selection allows the maximization of the diversity of climate projections among the best-performing models.

Will Olive Groves have a Future Under Climate Change Conditions in The North Aegean Sub-Region, a Mediterranean Agricultural Ecosystem of Türkiye?

The study is aimed at investigating future changes in sustainability of olive farming by means of climate change, and changes in agricultural climatic suitableness and phenology of olive tree cultivation in the North Aegean sub-region of Türkiye mainly characterised with dry summer subtropical Mediterranean climate. According to projected changes based on RCP8.5 scenario, projected warming reaches an average of 5-6°C increase indicating most negative condition on olive cultivation. According to RCP8.5 scenario annual precipitation projections, present suitable areas of olive groves will decrease in the period of 2049-2073, and almost the entire study area will be in the category of medium suitable. With respect to projected Emberger Bioclimate classification, for RCP 8.5 scenario, there will be a significant increase in dry-sub humid areas in the period of 2049-2073. This increase will cover up the coastal areas in the period of 2074-2098, and even all the study region will be very likely characterised with dry-sub humid and semi-arid Mediterranean bioclimatic types except for some coastal areas. An increase of about 6°C is expected in maximum values of maximum air temperatures during the bud swelling periods in the spring, especially after 2050 under RCP8.5 scenario. This increase in extreme maximum temperatures may cause olive trees to bloom earlier and prolong the growth period. By regarding the high vernalization requirement of main olive variety in the study area, a 6°C temperature increase may significantly decrease olive yields and will force farmers to transition to new varieties with relatively low vernalization requirements. According to both RCP scenarios, there is a possibility of extension of suitable areas for olive cultivation towards low to mid-elevation plateaus and mid-elevation slopes of mountainous areas and high plateaus particularly facing suitable aspects to lower negative effects of projected future warming and dryness.

Assessment of Future Climate Change in the Huaihe River Basin Using Bias-Corrected CMIP5 GCMs with Consideration of Climate Non-Stationarity

The Huaihe River Basin is particularly vulnerable to climate change. This paper first evaluated interpolation methods for different meteorological elements, followed by an assessment of the simulation performance of various Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Models (GCMs) for these elements. We then applied the Improved Quantile Mapping (IQM) method for bias correction of the GCMs. Finally, we analyzed the characteristics of future climate change in the Huaihe River Basin. The results show the following: (1) The radial basis function interpolation method is the most effective for rainfall, while Kriging performs best for air temperature. (2) The HadGEM2-AO model provides the most accurate rainfall simulations, MIROC-ESM best simulates maximum air temperature, and HadGEM2-ES is most effective for minimum air temperature. (3) The IQM method outperforms other approaches for bias correction of climate variables in the basin. (4) Future projections show an increase in both rainfall and air temperature, with more pronounced rises under the RCP8.5 scenario. Additionally, rainfall and maximum air temperature show considerable spatial variation across emission scenarios, while minimum air temperature consistently exhibits an upward trend.

Climate change projections in temperature and precipitation using cmip6 in Central Mexico

Knowing the spatiotemporal variability of temperature and precipitation, as well as their future projections, is essential to assess environmental risks and plan long-term mitigation and adaptation. In this study, the General Circulation Models (GCMs) HadGEM3-GC31-LL, MRI-ESM2-0 and ACCESS-ESM1-5 of the Coupled Model Intercomparison Project (CMIP6) have been used, under two Shared Socioeconomic Pathways (SPP2-4.5 and SSP5-8.5), to project the minimum and maximum air temperature and the mean annual precipitation in Atlacomulco Rural Development District (ARDD). 30 meteorological stations were used to perform regionalized climate change projections by downscaling using LARS-WG 7. The expected changes were evaluated for three time horizons: near horizon (2021–2040), mid horizon (2041–2060) and far horizon (2061–2080); compared to the historical period (1985–2017). Based on the results, the annual minimum temperature towards the far horizon is projected to increase by 2.0 ºC with SSP2-4.5, and 3.1 ºC with SSP5-8.5; while the annual maximum temperature towards the far horizon is projected to increase between 2.5 ºC with SSP2-4.5 and 3.8 ºC using SSP5-8.5. Precipitation shows an increase for SSP2-4.5 in the near and middle horizons with 15.7 and 42.5 mm.year−1, respectively, while the largest decrease is projected towards the middle horizon of SSP5-8.5 with − 26.3 mm.year−1. The findings of this study provide detailed information on climate change projections in the ARDD and can serve as a useful guide for the management of different ecosystems.

XIS-Temperature: A daily spatiotemporal machine-learning model for air temperature in the contiguous United States.

The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2023. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. The weighted root mean square error (RMSE) of predictions in site-level cross-validation for 2023 was 1.78 K for the minimum daily temperature, 1.19 K for the mean, and 1.48 K for the maximum. We obtained higher RMSEs in earlier years with fewer ground monitors. Comparing to three leading gridded temperature models in 2021 at thousands of private weather stations not used in model training, XIS had at most 60% of the mean square error for the minimum temperature and 93% for the maximum. In a national application, we report a stronger relationship between summertime minimum temperature and social vulnerability with XIS than with the other models. Thus, XIS-Temperature has potential for reconstructing important environmental exposures, and its predictions have applications in environmental justice and human health.

Air temperature prediction models for pavement design: a gradient boosting-based approach

ABSTRACT This article presents a machine learning-based approach for the prediction of 7-day maximum and 1-day minimum air temperatures in India. In this approach, a Gradient-boosting model is developed using latitude, longitude and altitude as model features. Raw data for this study consisted of hourly air temperature data collected over 30 years across 20,168 grid points within India. As part of the predictive methodology, cluster analysis was performed initially, which helped in obtaining homogeneous regions. Three different approaches empirical clustering, statewise clustering and k-mean clustering, were used. The model features pertaining to individual clusters were used with a gradient-boosting approach. Statistical analysis was conducted to check the accuracy of model predictions based on different clustering techniques. In all cases, predictions made at the global level resulted in poor predictions. In most cases, the results obtained with k-mean clustering showed that increasing the number of clusters improved the predictive accuracy. Furthermore, predictive accuracy with statewise or k-mean clustering was dependent on several features involved. The proposed predictive models have a very simple structure that requires the least input (i.e. geographical indicators). Hence the same can be used for faster and accurate computation of 7-day maximum and 1-day minimum air temperature.

ASSESSMENT OF THE ACCURACY OF METEOROLOGICAL DATA OBTAINED FROM VIRTUAL AND AUTOMATIC WEATHER STATIONS FOR THE CONDITIONS OF UKRAINIAN POLISSYA

The article presents a comprehensive assessment of meteorological data obtained from the virtual Visual Crossing Weather Data (VCWD) and the automatic (iMetos Base) meteorological station for the Polissya region of Ukraine. For this purpose, were selected the meteorological data which are included in the formula for calculating the reference evapotranspiration (ET0) according to the Penman-Monteith method (FAO56-PM), namely average (Tmean), maximum (Tmax) and minimum (Tmin) air temperature, dew point temperature (Tdew), average relative humidity (Rhmean), average water vapor pressure deficit (Damean), total solar radiation (Rs), average wind speed at a height of 2 m (u2 ) and daily precipitation (P). The results of the regression analysis and the calculation of the mean absolute percentage error (MAPE), root mean square error (RMSE), and standard error (SEE) demonstrate that the data on mean and maximum air temperature, as well as dew point temperature, were obtained with a high degree of accuracy from the virtual VCWD weather station. The MAPE errors are 5,6, 2,8, and 8,3%, respectively (MAPE &lt; 10%). For the minimum air temperature and average relative humidity, good accuracy is inherent, with MAPE errors of 20,0 and 13,6%, respectively (MAPE =10-20%). The data on solar radiation and water vapor pressure deficit were obtained with satisfactory accuracy, with MAPE errors of 25,0 and 45,2%, respectively (MAPE =20-50%). The data on wind speed at a height of 2 m, total monthly and daily precipitation were obtained with unsatisfactory accuracy, with MAPE errors of 62,3, 52,6, and 40103% (MAPE &gt;50%), respectively. It has been established that the values of daily precipitation (RMSE=6,0 mm) obtained from VCWD are not accurate. It is possible to use only the total precipitation for the month (RMSE=11,6 mm) or its annual values (RMSE=47,9 mm). The application of a correction factor to the obtained meteorological data increases their accuracy and reduces the errors of MAPE, RMSE and SEE. The use of various errors made it possible to comprehensively verify the obtained meteorological data. For example, the MAPE error calculates the accuracy of the meteorological indicator, while the RMSE and SEE errors indicate how the obtained value differs from the average value. In the future, the obtained meteorological indicators from the Visual Crossing Weather Data virtual meteorological station will be used to calculate the reference and actual evapotranspiration using the Penman-Monteith method (FAO56-PM) in the conditions of Polissya of Ukraine.

Characterizing Droughts During the Rice Growth Period in Northeast China Based on Daily SPEI Under Climate Change.

In agricultural production, droughts occurring during the crucial growth periods of crops hinder crop development, while the daily-scale standardized precipitation evapotranspiration index (SPEI) can be applied to accurately identify the drought characteristics. In this study, we used the statistical downscaling method to obtain the daily precipitation (Pr), maximum air temperature (Tmax) and minimum air temperature (Tmin) during the rice growing season in Heilongjiang Province from 2015 to 2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6, to study the spatial and temporal characteristics of drought during the rice growing season in cold region and the effect of climate change on drought characteristics. The potential evapotranspiration (PET0) was calculated using the regression correction method of the Hargreaves formula recommended by the FAO, and the daily SPEI was calculated to quantitatively identify the drought classification. The Pearson correlation coefficient was used to analyze the correlation between the meteorological factors (Pr, Tmax, Tmin), PET0 and SPEI. The results showed that: (1) Under 3 SSP scenarios, Pr showed an increasing trend from the northwest to the southeast, Tmax showed an increasing trend from the northeast to the southwest, and higher Tmin was mainly distributed in the east and west regions. (2) PET0 indicated an overall interannual rise in the three future SSP scenarios, with higher values mainly distributed in the central and western regions. The mean daily PET0 values ranged from 4.8 to 6.0 mm/d. (3) Under SSP1-2.6, rice mainly experienced mild drought and moderate drought (-0.5 ≥ SPEI > -1.5). The predominant drought classifications experienced were mild, moderate, and severe drought under SSP2-4.5 and SSP8.5 (-0.5 ≥ SPEI > -2.0). (4) The tillering stage experienced the highest drought frequency and drought intensity, with the longest drought lasting 24 days. However, the heading flower stage had the lowest drought frequency and drought intensity. The drought barycenter was mainly in Tieli and Suihua. (5) The PET0 was most affected by the Tmax, while the SPEI was most affected by the Pr. This study offers a scientific and rational foundation for understanding the drought sensitivity of rice in Northeast China, as well as a rationale for the optimal scheduling of water resources in agriculture in the future.

Climate Anomalies of Maize Drought Level based on Land Water Balance in Gorontalo Province, Indonesia

Background: The complexity of the distribution patterns of drought and soil water balance across various regions raises questions about how the mechanism of drought events responds to climate anomalies. The research aims to determine the climate anomaly pattern of maize drought levels based on land water balance with FAO Penman Monteith evapotranspiration value estimates in Gorontalo district, Indonesia. This research was carried out from April to August 2020. Methods: The research location was in Limboto subdistrict, Gorontalo Province, Indonesia. The material in this research is climate data from 1997 to 2016 (20 years) including rainfall, solar radiation, maximum and minimum air temperature, exposure time, air humidity and wind speed. The tools in this research are sample rings, Belgi drills, GPS, documentation tools. The method used is the drought index analysis method and the water adequacy index based on the FAO Penman Monteith evapotranspiration method. Result: El Nino and La Nina climate anomaly patterns occur every 5 to 7 year recurring period. The highest level of drought with strong drought status occurred during the El Nino anomaly in 1997-1998 for 8 months and this triggered a decrease in harvested area and corn production with a coefficient of reduction in vulnerable production category. La Niña climate anomaly years 1999 and 2007 had an impact on low accumulation potential of water loss with highest level of drought weak status and this triggered an increase in harvested area and corn production with a coefficient of reduction in very resistant production category.

Design and simulation of a simple greenhouse dryer and Peltier thermoelectric module under independent and combined operating conditions

Abstract India is a suitable country for cultivating several food produce, and most of them are seasonal. To make the food produce available in off seasons and to increase their shelf life there is vital need for food processing. Rapid urbanization has imposed a great demand for food due to very less availability of fresh food produce. There is a huge wastage in the food produce as the cultivation land is located far from the end user and time delay due to transportation. There are several food processing methods of which dehydration and cooling play a major role. A simple solar greenhouse dryer and Peltier thermoelectric cooling was designed and studied using COMSOL and EJS tool. The performance study integrating both systems was analysed and reported. The models were considered to work solely on solar energy. Solar thermal energy for dehydration and solar photo voltaic for operating the Peltier device. Studies on performance of the designed systems were carried out along with scope for performance improvement. The simulation studies revealed a 1.439 oC increase in drying chamber temperature by integrating the heat parameter of the Peltier thermoelectric cooler. At 40 K temperature difference the Peltier thermo electric module attained optimal performance. From the analysis using EJS tool, the maximum and average air temperatures inside the dryer were found to be 65 oC and 57 oC, the COP and cooling capacity of the Peltier thermoelectric model were high at 800 W/m2 solar radiation.

Modeling the lagged and nonlinear effects of weather conditions on abundance of Culex tarsalis mosquitoes in Saskatchewan, Western Canada using a bi-dimensional distributed lag nonlinear model.

The establishment of West Nile Virus (WNV) competent vectors continues to pose a major public health challenge in Canada, especially in the south. While studies have examined the association between weather conditions and the abundance of mosquitoes over trap weeks, there is limited research on the effects of weather conditions on the abundance of Culex tarsalis (Cx. tarsalis) mosquitoes for a lapse of time beyond the trap week in Saskatchewan, Western Canada. To address this gap, we analyzed provincially available weekly mosquito trap and co-incident meteorological station data in Saskatchewan from 2010 to 2021 using a bi-dimensional distributed lag and nonlinear model. Data indicate that 171,141 Cx. tarsalis mosquitoes were trapped across much of Saskatchewan, from 2010 to 2021. Cx. tarsalis were found to be most abundant between weeks 26 and 35 (July and August) and peaked in weeks 30 and 31. Based on the WNV-positive pools, mosquito infection rates increased from week 23 to 36. While weekly average maximum air temperatures between 20 °Cand 30 °C were associated with more Cx. tarsalis across all lags (0 - 8 weeks), higher weekly average minimum air temperatures had a strong and immediate effect that diminished over longer lags. Higher weekly average rainfall amounts (> 20 mm) were associated with fewer Cx. tarsalis mosquitoes across all lags, while average weekly rainfall between 8 and 20 mm was strongly associated with a high abundance of Cx. tarsalis mosquitoes over longer lags (5 -7 weeks). Additionally, increasing wind speed was associated with lower abundance of Cx. tarsalis across all lags. Findings identified nonlinear lag associations for weekly average maximum air temperature and rainfall, but linear associations for weekly average minimum air temperature and wind speed. Identified lags and thresholds for temperature, rainfall, and wind speed at which mosquito abundance peaked could help to inform public health authorities in timing of vector control measures to prevent WNV transmission.

Experimental investigation of thermal micro-environments and local thermal sensations in enclosed and semi-enclosed localized heating systems

Traditional building heating warms entire rooms, often leaving some dissatisfied with uneven warmth. Recently, the personalized heating system has addressed this by providing targeted warmth, enhancing comfort and satisfaction. The personalized heating system in this study is a new enclosed personalized heating system consisting of a semi-enclosed heating box and an insulated chair covered with a thick blanket. The study compares the heating effects of semi-enclosed and enclosed localized heating systems on the body and examined changes in subjects’ thermal sensations. Due to the lower heat loss of the enclosed personalized heating system compared to the semi-enclosed version, it created thermal micro-environments with higher ambient temperatures. The maximum air temperature increase within the enclosed system was twice that of the semi-enclosed system, with the heating film surface temperature rising by up to 6.87 ℃. Additionally, the temperature of the skin could increase by as much as 6.19 ℃, allowing individuals to maintain thermal neutrality even when the room temperature dropped as low as 8 ℃. A two-factor repeated measures analysis of variance revealed differences in temperature sensitivity across various body regions, with the thighs showing a notably higher response under high-power heating conditions. The corrective energy and power requirements of the enclosed personalized heating system also made it more energy-efficient than other personalized heating systems, with a minimum value reaching 6.07 W/K.

Land-atmosphere feedback exacerbated the mega heatwave and drought over the Yangtze River Basin of China during summer 2022

In the summer of 2022, a record-breaking heatwave and drought event occurred in the Yangtze River (YR) Basin of China, causing great damage to the society and ecosystem. However, the role of land-atmosphere (LA) interactions in driving and reinforcing this event has not been fully studied. In this study, using air temperature, soil moisture (SM), surface sensible heat fluxes, surface latent heat fluxes and radiation fluxes data from ERA5, we analyze the process of this event and reveal the contribution of the LA feedbacks. The results indicate that during the 2022 YR Basin heatwave and drought event, the regional average maximum air temperature and SM reached unprecedented levels of 2.7 standard deviations (SDs) and −3.5 SDs, respectively, compared to the climatology from 1980 to 2021. In August 2022, SM rapidly declined, pushing the region into a rare "dry" state. The dry soil increased the sensitivity of daily maximum air temperature to SM, intensifying the occurrence of heatwaves in the area. Simultaneously, increased downward solar radiation reached surface and most of that converted to sensible heat fluxes due to low soil moisture limitations leading to elevated air temperatures. While similar events have been reported multiple times in regions like Europe and western North America, their occurrence in the "moist" region of the YR Basin of China is exceptionally rare, which suggests an increasing likelihood of such extreme events in this region. Land-atmosphere interactions play an increasingly crucial role in exacerbating extreme conditions, and therefore, more studies such as this are needed for improving predictability of extreme events on a sub-seasonal time scale.

Combining Multi-Source Satellite Data with a Microclimate Model to Analyze the Microclimate of an Urban Park

Cities concentrate many people, and studies have shown that resultant urban heat islands can be intense. Urban parks can function as “cool islands” that mitigate heat island effects. This study used the microclimate model ENVI-met 5.1 to assess the cooling effect of Panyu Park in the center of Shanghai, China. The primary objectives were to increase the diversity of data sources and to conduct a microclimate analysis. Two scenarios were examined: the actual park and no park. The results indicated that (1) the integration of satellite technology enhanced the data sources for ENVI-met and thereby increased the efficiency of urban modeling and (2) the simulated results for the park correlated well with the actual data observed at weather stations. The presence of the park resulted in a decrease in the maximum air temperature by 0.1 °C at 1.4 m above ground, a decrease in the wind speed by 1.67 m/s, a maximum increase of 0.2% in relative humidity, and a reduction of 1.94 in the Predicted Mean Vote. The results demonstrated the applicability of multi-source satellite data in microclimate research, saved time on data collection, and provided valuable information for studies undertaken in areas where the collection of field data is challenging and/or historical data are unavailable.

Dust Content Modulation and Spring Heat Waves in Senegal (2003–2022)

The population of Senegal faces health challenges related to desert dust and heat waves (HWs). This study aims to (a) update the documentation of HWs in Senegal, expanding on the work of Sambou et al. (2019); (b) investigate the modulation of dust indicators during HWs; and (c) assess the distinct impacts of dust content on night-time and daytime HWs. We use [i] the daily maximum air temperature (Tx), minimum air temperature (Tn), and apparent temperature (Ta) from 12 stations in the Global Surface Summary of the Day (GSOD) database and [ii] the Dust Aerosol Optical Depth (Dust AOD), particulate matter (PM) concentrations, 925 hPa wind, and Mean Sea Level Pressure (MSLP) from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. HWs are defined for each station in spring as periods when Tx, Tn, or Ta exceeds the 95th percentile for at least three consecutive days. Three homogeneous zones from the Atlantic coast to inland Senegal are identified using hierarchical cluster analysis: Zone 1 (Saint-Louis, Dakar-Yoff, Ziguinchor, and Cap Skirring), Zone 2 (Podor, Linguère, Diourbel, and Kaolack), and Zone 3 (Matam, Tambacounda, Kédougou, and Kolda). Our results show that Zone 1 records the highest number of HWs for Tx, Tn, and Ta, while Zone 3 experiences more HWs in terms of Tn and Ta than Zone 2. The influence of dust is notably stronger for HWs linked to Tn and Ta than for those related to Tx. Analysis of the mechanisms shows that the presence of dust in Senegal and its surrounding regions is detected up to four days before the onset of HWs. These findings suggest that dust conditions associated with spring HWs in Senegal may be better distinguished and predicted.