Nighttime Air Temperature Research Articles

Theoretical investigation of two ideal radiative cooling materials and radiative sky water-cooling module with ideal selective radiative materials

Radiative sky cooling (RSC) is a passive approach to cool an object by emitting infrared thermal radiation to the cold outer space without any energy input and any pollutant produced. It has the potential to meet the rapidlygrowingdemandfor cooling. Radiative cooling materials and real-world applications are two concerns in RSC field. In this study, the cooling performance of two types of ideal radiative cooling materials are compared in term of the cooling power. The critical temperature at which broadband radiative materials exhibit cooling effect outside the atmospheric window is expressed as a fuction of the ambient temperature and the average atmospheric emissivity outside the atmospheric window. The temperature range for the broadband radiative materials with cooling effect outside the atmospheric window is very small, and thus selective radiative materials should be prioritized. Heat transfer processes of radiative sky water-cooling (RSWC) module with ideal selective radiative materials, which has broad application prospect, are then investigated by a theoretical model. As for ten-hour cooling process of a RSWC module under reference conditions, the temperature difference between the environment and the radiative panel rises to 5.68 °C, the temperature difference between the environment and the water rises to 3.76 °C, and the cooling power decreases from 53.5 W/m2 to 38.5 W/m2.The impact of night-time air temperature on the cooling power may reach up to 48.3 % when the atmospheric emissivity within the atmospheric window is 0.5. If the average atmospheric emissivity within the atmospheric window increases from 0.2 to 0.5 the cooling power of RSWC module decreases by 29.2 % − 39.2 %.

Sampling Error of Mean and Trend of Nighttime Air Temperature at Weather Stations Over China Using Satellite Data as Proxy

AbstractMeteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

Meteoropathy: myth or reality? Assessment of the relationship between cardiovascular diseases and weather conditions according to data from a Moscow hospital

Many patients and some doctors associate the worsening of cardiovascular diseases with meteoropathy (changes in atmospheric pressure and air temperature). Opinion about lack of weather influence on the increase in overall morbidity and the lack of related research data indicate the need to study the relationship between cardiovascular diseases and meteorological conditions.Aim. To assess the prevalence of hospitalizations with symptomatic increases in blood pressure (BP), atrial fibrillation (AF), and decompensated heart failure (HF) in a Moscow hospital depending on meteorological conditions.Material and methods. This retrospective analysis of the number of emergency hospitalizations of patients with symptomatic increases in blood pressure, AF, and decompensated HF in Moscow City Clinical Hospital № 24 was carried out in the period from October 1, 2018 to September 30, 2019. Information from the website www.weather.com was used as a source of meteorological data. The following indicators were assessed daily: daytime air temperature (t), nighttime air temperature, maximum diurnal air temperature variation, atmospheric pressure, maximum diurnal atmospheric pressure variation, amount of precipitation. To assess statistical significance, the Mann-Whitney and Kruskal-Wallis tests were used. A correlation analysis was carried out, and a prognostic model was created using multiple linear regression.Results. During the period from October 1, 2018 to September 30, 2019, 4354 patients were hospitalized at City Clinical Hospital № 24 with symptomatic BP increase and/or AF and/or decompensated HF. The highest number of hospitalizations occurred in November, December and January. The median number of admissions per day was 12 patients. When comparing the mean number of patients admitted to the hospital per day, depending on day- and nighttime air temperature, following statistically significant differences were obtained (p<0,001): with day- and nighttime air temperature ≥0о C, the number of admissions was less compared to sub-zero temperatures. Significant inverse correlations were established between the number of patients hospitalized with symptomatic BP increase, AF and HF per day, with air temperature at night (correlation coefficient r=-0,339; p<0,001), air t during the day (r=-0,316; p<0,001), temperature difference over 48 hours (r=-0,205; p<0,001); direct correlations with atmospheric pressure (r=0,106; p=0,044) and its changes (r=0,115; p=0,028) were identified. All identified relationships were of moderate strength. According to prognostic model, only nighttime air temperature turned out to be a significant indicator associated with the hospitalization rate per day. With a decrease in air temperature at night for every 7,5о C <0, one more patient is expected to be hospitalized with a symptomatic BP increase, decompensated HF, or paroxysmal AF.Conclusion. Negative air temperature in a metropolis is associated with an increase in hospitalizations for symptomatic BP increase, AF or decompensated HF. Atmospheric pressure and precipitation do not affect these conditions.

Investigating Stagnant Air Conditions in Almaty: A WRF Modeling Approach

This study investigates stagnant atmospheric conditions in Almaty, Kazakhstan, a city nestled within a complex terrain. These conditions, characterized by weak local winds and inversion layers, trap pollutants within the city, particularly during winter. The Weather Research & Forecasting (WRF) model was employed to simulate atmospheric conditions using Local Climate Zone data. Verification of the model’s accuracy was achieved through comparisons with data from weather stations and the Landsat-9 satellite. The model successfully reproduced the observed daily temperature variations and weak winds during the testing period (13–23 January 2023). Comparisons with radiosonde data revealed good agreement for morning temperature profiles, while underestimating the complexity of the evening atmospheric structure. The analysis focused on key air quality factors, revealing cyclical patterns of ground-level and elevated inversions linked to mountain-valley circulation. The model effectively captured anabatic and katabatic flows. The study further examined the urban heat island (UHI) using a virtual rural method. The UHI exhibited daily variations in size and temperature, with heat transported by prevailing winds and katabatic flows. Statistical analysis of temperature and wind patterns under unfavorable synoptic situations revealed poor ventilation in Almaty. Data from three Januaries (2022/2023/2024) were used to create maps showing average daytime and nighttime air temperatures, wind speed, and frequency of calm winds.

Unpacking the nonlinear relationships and interaction effects between urban environment factors and the urban nighttime heat index

In this study, we identified the urban environmental factors affecting the nighttime heat index (NHI), which has policy implications for improving the urban thermal environment using Smart Seoul Urban Data Sensor (S-DoT) data and an interpretable machine learning methodology. S-DoT sensors are ideal for deriving the heat index at a micro-scale in space and time, unlike ground-based observations and satellite-based imagery traditionally used to measure urban temperatures. Moreover, the interpretable machine learning methodology can overcome the nonlinearity argument of the influential factors identified in previous studies. The main results of this study are as follows. First, the Sky View Factor generally has a negative association with the NHI, which means that a higher SVF is associated with a lower NHI. Conversely, the surface roughness commonly has a positive association with the NHI, which signifies that a higher surface roughness results in a higher NHI. Second, the building view factor significantly increases the NHI, and the gross floor area of the building has a positive association with the NHI regardless of its use. Third, although the center of the city has a high temperature, if it has physical environment factors similar to the outskirts of the city, the NHI there may be relatively low compared to the nighttime air temperature. This study is meaningful because it empirically shows the nonlinear relationships and interaction effects between urban environment factors and the NHI and suggests policy implications to improve the urban nighttime thermal environment.

Crowdsourcing air temperature data for the evaluation of the urban microscale model PALM—A case study in central Europe

In summertime and during heat events the urban heat island can negatively impact human health in urban areas. In the context of climate change, climate adaptation receives more attention in urban planning. Microscale urban climate modelling can identify risk areas and evaluate adaptation strategies. Concurrently, evaluating the model results with observational data is essential. So far, model evaluation is mostly limited to short-term field campaigns or a small number of stations. This study uses novel crowdsourcing data from Netatmo citizen weather stations (CWS) to evaluate the urban microscale model PALM for a hot day (Tmax ≥ 30°C) in Bochum in western Germany with anticyclonic atmospheric conditions. Urban-rural air temperature differences are represented by the model. A quality control procedure is applied to the crowdsourced data prior to evaluation. The comparison between the model and the crowdsourced air temperature data reveals a good model performance with a high coefficient of determination (R2) of 0.86 to 0.88 and a root mean squared error (RMSE) around 2 K. Model accuracy shows a temporal pattern and night-time air temperatures during the night are underestimated by the model, likely due to unresolved cloud cover. The crowdsourced air temperature data proved valuable for model evaluation due to the high number of stations within urban areas. Nevertheless, weaknesses related to data quality such as radiation errors must be considered during model evaluation and only the information derived from multiple stations is suitable for model evaluation. The procedure presented here can easily be transferred to planning processes as the model and the crowdsourced air temperature data are freely available. This can contribute to making informed decisions for climate adaptation in urban areas.

Summertime climatic effects of urbanization and their impacts on human thermal comfort in Xiangjiang watershed, South-Central China

Urbanization has been a substantial force to change the natural environment, and several urbanization-related climatic issues, such as urban heat island (UHI) effect, would pose a great threat to public health. However, the climatic effects of urbanization and their possible links with human thermal comfort remain poorly understood. In this study, we examined the impact of urbanization on summertime air temperature, extreme hot events and human-perceived heat stress in Xiangjiang watershed, a typical inland highly urbanized area of China. Results showed that in the context of global warming Xiangjiang watershed experienced extensive summertime warming, especially nighttime warming in the past nearly half-century, and urbanization aggravated the warming effect in urban areas by increasing nighttime air temperature as well, creating the UHI effect. Meanwhile, all three types of extreme hot events, i.e. independent hot days (IHD), independent hot nights (IHN), and compound hot events (CHE) showed increasing trends. Urbanization significantly increased the occurrence, duration and intensity of CHE, which involved both the daytime and nighttime extreme hot temperatures. Urban expansion contributed 51.39%, 30.91% and 25.69% of the increases in occurrence, duration and intensity of CHE, respectively. Besides, increasing air temperature and extreme hot events would inevitably enhance the heat stress on people, especially in the nighttime, and urbanization exacerbated the nighttime discomfort on people with contribution to 26.92% of increases in nighttime heat stress index (HI). Nearly one quarter of summer nights in urban areas have reached very warm level of severe heat situation for human beings in the late stage (1996–2019). Urbanization-induced increases in CHE and nighttime HI caused an aggravation of human heat discomfort since people failed to relief the excess heat stress of daytime at night. This study provided new insights to understand urban climatic effect and its relation to human heat-related comfort.

How can we combine urban cooling strategies to effectively cool cities over the entire diurnal cycle?

The urban heat island effect generally peaks in the night/evening hours and, in some cases, an urban cool island effect during daytime has been reported. These patterns are observed mainly because of the widespread use of impervious and thermally massive materials (like concrete and asphalt) in the built environment and their ability to store energy during the daytime and release it at night. Unfortunately, most urban cooling strategies, such as cool (white), green, and blue spaces, provide better thermal performance during the daytime than at night. Hence, such solutions are not ideal for nighttime heat mitigation. In this study, we investigate the effect of the thermal storage capacity of existing buildings on nighttime urban air temperature for a hot arid city—Phoenix, and a hot humid city—Atlanta. The study uses regional scale atmospheric modeling to compare the nighttime urban cooling capability of thermally light buildings (Cross-laminated timber buildings in this case) with concrete buildings. The results show that the adoption of thermally light buildings reduce nighttime air temperatures, and slightly increases daytime air temperatures. On the other hand, cool roof adaption could reduce urban air temperature significantly during the daytime and slightly at night. Therefore, together with cool roofs, thermally light buildings may be able to cool the surrounding air by an average of about 1 °C throughout the diurnal cycle, providing thermal comfort and reducing cooling demand during all hours.

Quantifying Daytime Heating Biases in Marine Air Temperature Observations from Ships

Abstract Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model that quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover, and relative wind speed are then used to estimate the heating bias ship by ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations. Significance Statement Currently, the longest available record of air temperature over the oceans starts in 1880. We present an approach that enables observations of air temperatures over the oceans to be used in the creation of long-term climate records that are presently excluded. We do this by estimating the biases inherent in daytime temperature reports from ships, and adjust for these biases by implementing a numerical heat-budget model. The adjustment can be applied to the variety of ship types present in observational archives. The resulting adjusted temperatures can be used to create a more spatially complete record over the oceans, that extends further back in time, potentially into the late eighteenth century.

Determining influence of urban morphology on air temperature and heat index with hourly emphasis

Assessing the impact of urban morphology on micrometeorological indicators has become of great significance due to rapid urbanization. Heat Index (HI) is one such indicator that determines the combined effect of air temperature (AT) and relative humidity (RH). The present study aims to evaluate the summer time impact of urban morphology on AT and HI with an hourly emphasis in Nagpur city. AT and HI were found symmetrically distributed only during nighttime, while RH and cooling rate (CR) data presented asymmetrical data distribution throughout 24hrs. A one-way ANOVA test was conducted to confirm their variations across areas for symmetrically distributed data. This test inferred that AT and HI are functions of LCZ. This data was then correlated with all morphological parameters. AT and HI showed a strong correlation (R2 >0.7) during 23:00–06:00 h with building surface fraction, fabric density ratio, vegetation cover, albedo, sky view factor, and aspect ratio. During 23:00–06:00 h, every 10% increase in building surface fraction and albedo raises HI with 0.33 °C and 0.53 °C respectively, whereas a 10% increase in PSF and SVF reduces HI with 0.55 °C and 0.51 °C. This study concludes that nighttime AT and HI are significantly affected by urban morphology. However, daytime influence of these parameters and RH and CR throughout 24 h is inconclusive due to the asymmetrical data distribution of AT and HI. Modulation in development regulations by acknowledging the adverse effects of these morphological parameters is suggested for sustainable development.

Compound daytime and nighttime heatwaves for air and surface temperature based on relative and absolute threshold dynamic classified in Southwest China, 1980–2019

Heatwaves pose potential risks to the environment, energy, society, and public health, and compound daytime and nighttime air and surface temperature heatwaves have the most severe effects. In this study, Southwest China was taken as a case to explore the characteristics of daytime, nighttime, and concurrent daytime and nighttime heatwaves events using air and surface temperatures based on a dynamic method for classifying urban, suburban, and rural stations with 30 m resolution land use and cover data based on absolute (i.e., same threshold for the entire Southwest China) and relative thresholds (i.e., thresholds for Chongqing municipality, Sichuan, Yunnan, and Guizhou Provinces calculated separately based on built-up areas for each period). We found that heatwaves events became more frequent and more severe in urban, suburban, and rural areas based on absolute and relative thresholds for both air and surface temperatures. Overall, the positive contributions towards warming by urbanization in urban and suburban areas to different heatwaves events obviously differed among different buffer areas and the influence of urbanization was different for air and surface temperature heatwaves events based on absolute and relative thresholds. Spatially persistent compound heatwaves mainly occurred in three urban agglomerations for air temperature. This study provides an original research perspective by using air and surface temperatures during daytime, nighttime, and concurrent daytime and nighttime heatwaves to calculate indices based on absolute (fixed threshold) and relative thresholds (considering the social and economic development level of different regions) in the complex topography of the Southwest China. In addition, this study observed for the first time that the calculated heatwaves indices based on air and surface temperatures were obviously different, illustrating that results obtained using different data sources (e.g., air temperature, surface temperature, and remote sensing data) to evaluate heatwaves will differ, indicating that data sources need to be carefully considered in Southwest China and other regions.

Evaluation of planetary boundary layer schemes on the urban heat islands in the urban agglomeration over the greater bay area in South China

Urbanization of large cities exerts significant changes in surface air temperature, which subsequently lead to inadvertent local weather and climate changes. The exchanges of momentum, moisture and heat within the planetary boundary layer (PBL) impact the urban atmosphere representation. The applicability of PBL schemes in regional modeling varies with different climate regimes and underlying surface. As the first step to gaining a better forecast of urban climates, the performances of multiple PBL schemes (YSU, MRF, ACM2, BL, MYNN, UW and GBM) on reproducing the low-level urban atmosphere are evaluated over the Greater Bay Area, South China, during April–June 2018. With the aid of observations from in situ weather stations and radiosondes, the urban environmental characteristics, including surface air temperature and humidity, temperature profile, urban heat island (UHI), and PBL height, are assessed. The results show a cool/moist bias near the surface during nighttime and a warm/dry bias during daytime for all PBL schemes. The daytime bias is significant on weak-UHI days while the nocturnal bias appears to be significant on strong-UHI days. The so-called best scheme depends on the exact meteorological variables, diurnal cycles, and thermodynamic conditions that are of interest. Specifically, the MYNN and MRF schemes perform the best for the daytime and nighttime air temperature, respectively. The MRF scheme also presents the best performance in simulating the observed UHI, with a good agreement with the observed diurnal variation. The numerical exercises in this study may serve as a reference for an efficient operational way that is readily accessible to forecast air temperature in the urban agglomeration over South China.

Urban heat health risk assessment in Singapore to support resilient urban design — By integrating urban heat and the distribution of the elderly population

Elderly people are more vulnerable than other age groups to the increasing urban heat risk caused by both climate change and urbanization. This study developed a systematic method to assess the urban heat health risk to the aging population in high-density tropical cities. Based on the assessment, the study identifies high heat health risk districts, diagnoses specific reasons in individual districts, and suggests specific solutions. First, we applied an existing GIS model to quantitatively evaluate urban heat island (UHI) intensity, which is influenced by both longwave radiation and anthropogenic heat. Second, the urban heat health risk to the elderly in individual districts was weighted by UHI intensity, density and proportion of the elderly population in different districts. The integrated mapping results showed that the old HDB neighborhoods in the Central Region are at the highest risk, with a higher UHI intensity, density and proportion of the elderly population than other districts. Based on the analysis, we suggest both UHI mitigation strategies, social support, and facilities for the elderly population at Singapore HDB neighborhoods. Research outputs allow different stakeholders to understand various issues of individual neighborhoods regarding the urban heat health risk to the elderly and to develop corresponding solutions. • A new method is developed to assess heat health risks on aging population, by heat, exposure, and vulnerability. • GIS overlapping map is used to identify specific heat health risk reasons at districts. • Nighttime air temperature is used to assess urban heat health issue, rather than land surface temperature. • Older neighborhoods in the Central Region of Singapore are at the highest risk. • Both adaptation and mitigation recommendations are provided to develop systematic and specific strategies.

Extreme tolerance for nocturnal emergence at low body temperatures in a high-latitude lizard: implications for future climate warming.

High-latitude lizards live in environments where ambient air temperature at night is frequently below retreat temperatures, which likely has implications for nocturnal emergence and activity. However, patterns of lizard activity at night under current temperate climates are poorly understood, a situation that limits our understanding of potential effects of climate change. We investigated patterns of nocturnal emergence and activity in the cold-adapted, viviparous gecko (Woodworthia 'Otago/Southland'). We measured operative environmental temperature (T e) available to geckos that emerged at night and simultaneously assessed nighttime emergence activity using time-lapse trail cameras. Also, we assessed field body temperature (T b) of emerged geckos of various life history groups at night using thermography to understand how current weather conditions affect field T b of emerged geckos. Our results show that Te , nocturnal emergence activity and field-active T b increased with nighttime air temperature. Nocturnal emergence was highest in spring and summer but also occurred in autumn and (unexpectedly) in winter. Geckos were active over a broad range of T b down to 1.4°C (a new record low for lizards) and on rock surfaces typically warmer than air temperature or T b. We conclude that this nocturnal, high-latitude lizard from the temperate zone is capable of activity at low winter temperatures, but that current climate limits emergence and activity at least in autumn and winter. Activity levels for cool-temperate reptiles will probably increase initially as climates warm, but the consequences of increased nocturnal activity under climate change will probably depend on how climate change affects predator populations as well as the focal species' biology.

Impact of refined 2D/3D urban morphology on hourly air temperature across different spatial scales in a snow climate city

Urban morphology profoundly affects the air temperature, and a better understanding of their relationships is crucial for improving outdoor comfort and urban planning. However, how their complicated associations change across different times and spaces is not clear. This study compared the influence of the refined urban 2D/3D morphology on hourly air temperature in a snow climate city by using high spatial resolution GF-2 images, correlation analysis and multiple regression models at different spatial scales. Results indicated that: (1) the air temperature differences among the sites were much larger during nighttime (4.14 °C) as compared to daytime (2.22 °C). The spatial pattern of the hourly air temperature had similar distributions except at the hottest time of day. (2) The relationships between air temperature and urban morphology had an obvious spatial-temporal dependency. Overall, their connections became stronger as the spatial scale increased and tended to become stale at the 500 m scale; the effect of urban characteristics on average nighttime, lowest, and daily air temperature was larger than daytime air temperature, and there was almost no correlation between the highest air temperature and urban morphology. (3) The effects of 2D and 3D urban morphology on air temperature vary depending on the selection of spatial scales and the choice of different indicator of air temperature. Typically, the building height and volume had the strongest warming effect. The 3D metrics performed better in predicting air temperature than 2D metrics at the 500 m scale, while the influence of 2D features was stronger at the 1000 m scale. Regardless of the spatial analysis scale, 3D features had stronger correlations with average daily, daytime, and nighttime air temperature. Based on our results, 3D urban morphology indications, such as building height and volume, should be emphasized at the 500 m scale for urban planning. Arranging buildings and vegetation in a reasonable way could generate a better thermal comfort environment for city dwellers.

Cooling effect of the pocket park in the built-up block of a city: a case study in Xi'an, China.

Pocket parks, the green infrastructures with small sizes and flexible layouts, are suitable for thermal environment improvement in the urban built-up block with limited green space. To quantify the relationship between pocket parks and the thermal environment in western China, two parks in the built-up block of Xi'an were selected. By field measurement, the cooling effect could be extended 100m from the park boundary, connecting two parks. Furthermore, the road and greening within the block demonstrate significant influence on the cooling diffusion by regression analysis. Based on ENVI-met simulation, the ratio of the tree and the grass, the layout of the tree and the grass, and the layout of the paving were analyzed at different proportions of greening and paving of the park. Finally, a combination of the daytime physiological equivalent temperature (PET) and nighttime air temperature (AT) was proposed to choose the optimum layouts: the trees concentrated in the center and the pavement with more roads. Results can provide insights for designing pocket parks based on the thermal environment improvement.

Trends in synoptic heat events in four Minnesota urban areas through the 21st century

Extreme heat is often overlooked as a public health concern in Minnesota, where intraseasonal summer variability limits acclimatization to oppressive heat conditions. Specific categories of synoptic-scale air masses are linked to summer excess mortality and elevated health risk in the Midwestern United States, particularly within urban areas. Between 1948 and 2019, Minnesota's four largest urban areas have experienced decreased nighttime cooling, while warmer and more humid air masses have increased in frequency at the expense of cooler and drier ones. We used downscaled CMIP5 climate projections for 21st-century Minnesota, under RCP4.5 and RCP8.5 emissions scenarios, to generate daily synoptic classifications and evaluate projected frequency and character trends in the highest-risk air masses. Projections show dramatic increases in both the frequency and temperature of days within the Dry Tropical category, neither of which have changed significantly thus far across Minnesota's historical record. Frequency and duration of consecutive-day episodes of excess heat, as identified either by synoptic classifications or by the Excess Heat Factor, are likewise expected to increase more substantially in the future than they have in the past. Other projected trends, such as rising dew point temperatures and nighttime air temperatures, represent continuations of already existing historical trends.

Long-Term Resilience of Primary Sex Ratios in a Species with Temperature-Dependent Sex Determination after Decades of Climate Warming.

AbstractSpecies with environmental sex determination (ESD) have persisted through deep time, despite massive environmental perturbation in the geological record. Understanding how species with temperature-dependent sex determination (TSD), a type of ESD, persist through climate change is particularly timely given the current climate crisis, as highly biased sex ratios and extinction are predicted. Since 1982, we have studied primary sex ratios of a reptile with TSD (Chelydra serpentina). Primary sex ratios remained unchanged over time, despite warming in the environment. Resilience of the primary sex ratio occurred via a portfolio effect, realized through remarkable intra-annual variation in nest-level sex ratios, leading to a relatively consistent mean annual sex ratio. Intra-annual variation in nest-level sex ratios was related to variation in egg burial depth coupled with large clutch sizes, creating thermal gradients in the nest and promoting mixed-sex clutches. Furthermore, both locally and globally, sustained increases in nighttime air temperature contribute more to warming than increases in daily maximum temperature, but development rate was affected more strongly by maximum daily air temperature, conferring additional resilience to overall warming. Our study suggests that some TSD species may be resilient to warming and provides an example of how ESD may persist under environmental change.

Integrating weather observations and local-climate-zone-based landscape patterns for regional hourly air temperature mapping using machine learning

Air temperature is a crucial variable of urban meteorology and is essential to many urban environments, urban climate and climate-change-related studies. However, due to the limited observational records of air temperature and the complex urban morphology and environment, it might not be easy to map the hourly air temperature with a fine resolution at the surface level within and around cities via conventional methods. Thus, this study employed machine learning (ML) algorithms and meteorological and landscape data to develop hourly air temperature mapping techniques and methods at the 1-km resolution over a multi-year warm seasons period. Guangdong Province, China was selected for the case study. Random forest algorithm was employed for the hourly air temperature mapping. The validation results showed that the hourly air temperature maps exhibit good accuracy from 2008 to 2019, with mean R2, root mean square error (RMSE) and mean absolute error (MAE) values of 0.8001, 1.4821 °C and 1.0872 °C, respectively. The importance assessment of the driving factors showed that meteorological factors, especially relative humidity, contributed the most to the air temperature mapping. Simultaneously, landscape factors also played a non-negligible role. Further analysis revealed that the maps steadily maintained high accuracy at nighttime (20:00–7:00), which is essential for investigating nighttime urban climate conditions, especially the urban heat island effect. Moreover, a correlation existed between the nighttime air temperature changes and urban morphology represented by the local climate zones. Air temperatures tended to fall more slowly in the core of metropolitan areas than in the urban fringe. Using ML, this study reliably improves the spatial refinement of hourly air temperature mapping and reveals the spatially explicit air temperature patterns in and around cities at different times in a day during the warm seasons. Moreover, it provides a novel valuable and reliable dataset for air-temperature-related implementation and studies.

Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forest

• A new nighttime LST downscaling method using local linear forest (LLF) was proposed. • Feature-selected ASTER nighttime LSTs were identified as important input kernels. • The LLF-based model predicted LST with high linearity, especially for both extremes. • DLST revealed more distinct characteristics of urban climate than MODIS LST. Spatial downscaling effectively produces high spatiotemporal resolution land surface temperature (LST) in urban areas. Although nighttime LST is an essential indicator in urban thermal research, few LST downscaling studies have focused on nighttime in fine resolution. This study proposed a novel approach using local linear forest (LLF) to downscale 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) nighttime LSTs to 250 m spatial resolution in three cities: Rome, Madrid, and Seoul. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) to select a set of past clear-sky ASTER LSTs (ALST) which showed a high spatial correlation with the target MODIS LST. Downscaling models were then developed using input kernels of the selected ALSTs and eight auxiliary variables: normalized difference vegetation index (NDVI), elevation, slope, built-up area percentage, road density, population density, wind speed, and distance from the built-up weighted center of the study area. Three schemes were evaluated: scheme 1 (S1) using only auxiliary variables as input kernels with a random forest (RF) model; scheme 2 (S2) using selected ALSTs and auxiliary variables as input kernels with an RF model; and scheme 3 (S3) using input kernels as in S2 but with the LLF model. Validation was performed using bias-corrected ALSTs for seven reference dates in the three cities. LLF-based S3 showed the highest accuracy with an average correlation coefficient (R) ∼ 0.94 and Root Mean Square Error (RMSE) ∼ 0.64 K while maintaining the dynamic range of the original LST at the finer resolution. The downscaled LST (DLST) based on S3 effectively depicted the nocturnal thermal spatial pattern in greater detail than the other two schemes did. The S3-based DLST also showed a relatively high spatial correlation with the in-situ nighttime air temperature within the cities. When compared to the original 1 km LST, S3-based DLST showed larger surface urban heat island intensity for the urban-type surfaces and a higher temporal correlation with nighttime air temperature.