Climate Indices Research Articles

A global mathematical model of climatic suitability for Plasmodium falciparum malaria

Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control. Previous work has developed mathematical models and global maps for the suitability of temperature for malaria transmission. In this paper, existing temperature-based models are extended to include two other important bioclimatic factors: humidity and rainfall. This model is combined with fine spatial resolution climatic data to produce a more biologically-realistic global map of climatic suitability for Plasmodium falciparum malaria. The climatic suitability index developed corresponds more closely than previous temperature suitability indices with the global distribution of P. falciparum malaria. There is weak agreement between the Malaria Atlas Project estimates of P. falciparum prevalence in Africa and the estimates of suitability solely based on temperature (Spearman Correlation coefficient of ρ=0.24\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho = 0.24$$\\end{document}). The addition of humidity and then rainfall improves the comparison (ρ=0.62\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho = 0.62$$\\end{document} when humidity added; ρ=0.70\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho =0.70$$\\end{document} when both humidity and rainfall added). By incorporating the impacts of humidity and rainfall, this model identifies arid regions that are not climatically suitable for transmission of P. falciparum malaria. Incorporation of this improved index of climatic suitability into geospatial models can improve global estimates of malaria prevalence and transmission intensity.

A high temporal resolution global gridded dataset of human thermal stress metrics

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension

Abstract. The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2, standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.

The Increase in Urban Heat Due to Global Warming Can be Significantly Affected by the Structure of the Land Use and Land Cover

ABSTRACTUrban populations are increasingly exposed to excessive heat. Heat distribution in the urban environment can be affected by several factors, including the spatial arrangement of land use/land cover (LULC) that is specific to a given city. This study applies a climate model with urban canopy parameterisation to downscale future climate projections and simulate the spatio‐temporal pattern of heat in the urban environment to better understand the effect of LULC structure on its distribution. Heat conditions are characterised by climate indices that are well representative in two mid‐sized Central European cities of Brno and Ostrava (Czech Republic). Our results show that the annual number of hot days (HOT), summer days (SUD), tropical nights (TRN) and warm nights (WAN) will increase significantly (p < 0.01) in the 21st century in both cities. The model also simulates a more intensive increase and a higher spatio‐temporal variability in all indices in Brno compared to Ostrava. In Brno, the annual number of HOT and TRN is projected to be more than 500% of the 1981–2010 reference period's value by the end of the 21st century under the RCP 8.5 scenario. To determine the causes of the differences in heat distribution, we applied LULC configuration metrics and correlation analysis using various geographical factors. The higher risk of urban heat in Brno compared to Ostrava can be attributed to a more homogenised and less fragmented LULC structure and to the more substantial role of altitude in the complex terrain of Brno. Other factors, such as the presence of impervious surfaces and vegetation, have a similar effect on the variability of the studied indices in both cities. Urban planners should consider the role of the LULC structure and the changes that can be made in a city when designing adaptation measures to mitigate the effects of urban heat.

The combined effect of indoor climate and psychosocial stressors on subsequent headaches: A prospective study of the general working population in Norway

This prospective panel study explores the link between indicators of poor indoor climate and psychosocial stressors in the work environment with subsequent headaches. A random sample of the Norwegian working population (18 to 66 years) was examined, using self-reported health symptoms and work exposures collected in 2009, 2013 and 2016. This study included repeated measurements of 6681 workers attending at least two consecutive interviews (16,493 observations). The results were analysed using generalized estimating equations (GEEs), adjusting for sex, birth year, occupation, education, baseline mental health and the presence of headache at baseline. Headaches were prevalent in 25.4% of cases, with odds ratios (ORs) increasing with poor indoor climate (ORcontinuous = 1.14, 95% CI: 1.08–1.19) and number of psychosocial stressors (ORcontinuous = 1.09, 95% CI: 1.04–1.14). The OR amongst individuals concurrently exposed to both poor indoor climate and psychosocial stressors was 1.49 (95% CI: 1.26–1.75). Approximately one in five headache cases were attributable to this combined exposure. Measures implemented to reduce the prevalence of headaches in the work environment should consider the combined impact of a poor indoor climate and psychosocial stressors.

El Niño enhances snow-line rise and ice loss on the Quelccaya Ice Cap, Peru

Abstract. Tropical glaciers in the central Andes are vital water resources and crucial climate indicators, currently undergoing rapid retreat. However, understanding their vulnerability to the combined effects of persistent warming, the El Niño and La Niña climate phenomena, and interannual fluctuations remains limited. Here, we automate the mapping of key mass balance parameters on the Quelccaya Ice Cap (QIC) in Peru, one of the largest tropical ice caps. Using Landsat's near-infrared (NIR) band, we analyze snow cover area (SCA) and total area (TA) and calculate the accumulation area ratio (AAR) and equilibrium-line altitude (ELA) over nearly 40 years (1985–2023). Between 1985 and 2022, the QIC lost ∼58 % and ∼37 % of its SCA and TA, respectively. We show that the QIC's reduction in SCA and rise in ELA are exacerbated by El Niño events, which are strongly correlated with the preceding wet season's Oceanic Niño Index (ONI). Further, expansion in the QIC's SCA is observed during all La Niña years, except during the 2021–2022 La Niña. Although this is a singular event, it could indicate a weakened ability for SCA recovery and an accelerated decline in the future, primarily driven by anthropogenic warming.

Chlorophyll-a and suspended matter variability in a data-scarce coastal-estuarine ecosystem

Analyzing the variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) in estuarine and coastal environments is crucial for understanding ecosystem health, guiding environmental management decisions, and evaluating climate change impacts. Satellite remote sensing offers a powerful tool for this analysis due to its extensive spatial and temporal coverage. Although several algorithms exist for complex coastal and estuarine waters, long-term datasets such as GlobColor's Ocean Color (OC5) and neural network (NN) algorithms are frequently used for robust variability analysis. This study uses the GlobColor NN algorithm to investigate the seasonal and inter-annual variability of Chl-a and TSM in a data-scare region, namely the Meghna estuary in Bangladesh and its adjacent coastal fringe. The other algorithm (i.e. OC5), while offers the longest time series, cannot be used in this region due to the high number of invalid pixels. Therefore, this study examines different environmental factors (i.e. sea surface temperature (SST), photosynthetically active radiation (PAR), rainfall, zonal (ZWC) and meridional (MWC) wind components, and ocean currents) and climatic indices (i.e., El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) to understand their influence on the seasonal and inter-annual variability of Chl-a and TSM derived from the GlobColor NN algorithm. Empirical orthogonal function analysis identifies the seasonal signal as dominant in the study region. The seasonal cycle of Chl-a is influenced by factors including MWC, TSM, SST, and rainfall. In contrast, TSM seasonal variations are primarily driven by rainfall and MWC. Post-monsoon Chl-a inter-annual fluctuations are mainly linked to TSM inter-annual variability, with secondary influences from monsoon rainfall and the winter ENSO index. Inter-annual changes in TSM are primarily associated with the winter ENSO index and monsoon rainfall. This research elucidates the primary mechanisms influencing Chl-a and TSM variability in the Meghna estuary and its adjacent coast, thus advancing the understanding of the dynamics in the study region. The information obtained through this study is valuable for scientists, policymakers, and stakeholders involved in the sustainable management of the Meghna estuary and its coastal resources, particularly in the context of climate change.

Variability of UCP1 and UCP3 uncoupling protein genes in relation to climate in indigenous populations of Siberia and the Far East

Introduction. Several studies have demonstrated a correlation between variations in genes regulating human uncoupling proteins (UCP) and environmental factors. However, information on the intra- and interpopulation diversity of allele and genotype frequencies of UCP1 and UCP3 within the territory of Russia remains insufficient. This study aims to investigate the intra- and inter-ethnic variability of genetic determinants of activity of uncoupling proteins UCP1 and UCP3, and to evaluate genotype-environment associations in the populations residing in the northeastern region of Russia. Materials and methods. Genotyping was conducted on a total of 1,698 biological specimens collected from individuals representing 22 population groups of European Russia, Siberia, and the Russian Far East. Geographical and climatic data spanning from 1940 to 2023 were gathered for each sample collection locality. Results and discussion. The spatial distribution of genetic determinants affecting the activity of UCP1 and UCP3 uncoupling proteins demonstrates a correlation with the severity of natural conditions. Three regression models of allele frequencies were constructed using climatic characteristics as predictors. All models are statistically significant (p<0.05 in all cases) and explain 39%, 36%, and 64% of the variability in UCP1 (rs6536991, rs1800592) and UCP3 (rs1800849) allele frequencies, respectively. These correlations revealed by the models confirm the adaptability of UCP genes in the indigenous population groups of Northern Eurasia. Conclusion. The results of this study are consistent with the assumptions reported in scientific literature and significantly enhance existing knowledge. We observed correlations between allele and genotype frequencies of UCP1 (rs6536991, rs1800592) and UCP3 (rs1800849) genes and geographic latitude, elevation above sea level, as well as climatic indicators such as annual average rainfall, range of surface air temperatures, and the Bodman “weather severity” index. Further investigation involving a larger number of ethnic and territorial groups is necessary to better understand the factors influencing UCP gene variability formation.

Filling in Munk’s ‘orbital gap’ in climate and sea-level variability

Abstract This study explores the linkage between lunar precessions, solar activity, plus their interactions, and the interannual variability of climate indices and Australian sea level. The focus is on variability longer than the lunar nodal cycle (18.6 years), but shorter than millennia, also known as orbital gap. Climate indices include the Pacific Decadal Oscillation, El Niño-Southern Oscillation, the Southern Oscillation Index, the Indian Ocean Dipole, and the Southern Annular Mode. Long-term (> 100 years) sea-level data were examined at Fremantle (Western Australia) and Fort Denison (Sydney, Eastern Australia). Periodicities associated with gravitational and radiational forces were fitted to 3-yr- and 5-yr-filtered records of each climate index and of sea-level records. All fits, 10 for climate indices and 12 for sea level, explained the variance of all filtered records remarkably well (>60%). Findings suggest that lunar precessions and their interactions with solar activity may be connected to interannual variations in oceanic mixing. Mixing leads to climate variability through transport of heat, mass and solutes in the atmosphere and ocean, and ultimately relates to interannual sea-level variability. This study represents the possibility of astronomic effects on Earth’s environmental variables, beyond the internal variability of the ocean-atmosphere system.

Effects of Climate Change on the Future Attractiveness of Tourist Destinations in Greece

Climate change is a major challenge for the global tourism sector, affecting destinations worldwide. Greece, known for its scenery and abundant cultural history, is particularly vulnerable to these impacts. Tourism is a key driver of Greece’s economy, yet climate change threatens both natural environments and cultural sites. To evaluate the impacts of climate change on different types of Greek tourism (beach, sightseeing, winter tourism in mountainous areas), the widely used Holiday Climate Index (beach and urban versions) alongside three additional climatic indices customized for Greek climatic conditions, namely the Urban Climate Comfort Index, the Beach Utility Index, and the Mountainous Winter Climate Index were utilized for top tourist destinations of Greece. The results indicate that urban tourism may face challenges during peak summer months due to rising temperatures, but the shoulder seasons (April–May and September–October) will offer improved conditions, potentially extending the tourist season. For beach tourism, favorable conditions are expected to increase from April to October, with significant gains in June and September. Winter tourism in mountainous areas, especially snow-dependent activities like skiing, is at risk due to the declining snow availability. Overall, the study highlights both the challenges and opportunities posed by climate change for Greece’s tourism sector. It emphasizes the importance of adaptation strategies, including infrastructural improvements and promoting alternative activities, to minimize negative impacts and enhance the future attractiveness of Greek tourism.

The Impact of Street Trees on Temperature Reduction in a Nature-Based Climate Adaptation Program in George Town, Malaysia

Nature-based solutions have been promoted as an effective strategy to address climate impacts, including urban temperature reduction. In this paper, we analyze the impacts of the introduction of street trees on temperature (Universal Thermal Climate Index, UTCI) for three different dates, 2000, 2023, and 2050. A 3D model was developed in Rhinoceros software for a part of George Town, on Penang Island. Four different sections of streets were simulated after integration of the model with the Grasshopper plug-in, where a parametric system was built for temperature measurements based on simulations in the Ladybug and Honeybee plug-ins. The tree species used were selected from a pool of tree species commonly planted in urban settings in Malaysia that have low and medium sensitivity to climate impacts. The results show a maximum reduction of 7 °C between 2000 and 2050, achieved on a street with an NW–SE orientation that was planted with three rows of trees. The minimum UTCI reduction achieved was 3 °C, between 2023 and 2050, in a street with NW–SE orientation that was planted with one tree row. The two streets with a SW–NE orientation showed a 5 °C temperature reduction between 2023 and 2050. Both streets have only one row of trees but different species and sizes, with the bigger trees reducing the temperature in a slightly larger area. The results show the importance of introducing and safeguarding street trees to reduce urban temperatures in the country, potentially keeping temperatures below life-threatening levels, thereby safeguarding urban health, while also reducing costs of energy consumption. Solar orientation, the number of tree rows, and their distribution impact the outcomes. The findings provide useful guidance for climate-conscious urban planning practices in Malaysia.

Quantifying the impact of vertical greenery systems (VGS) on Mediterranean urban microclimate during heat wave events

This research investigates the potential of vertical greenery systems (VGS) to modulate the outdoor microclimate in a real-scale Mediterranean climate street canyon with respect to a non-vegetated control during four heat waves and summer conditions. The cooling effect of VGS on air temperature was assessed using an identical set of sensors in both canyons. The effect on thermal comfort was evaluated using radiation fluxes, Mr.T tool, and the ENVI-met model that was validated against measured data.The VGS cooling effect during the heat waves was up to 0.36–2.04°C, and its duration was significantly extended. The correlation (R2 = 0.75) between the cooling effect, relative humidity, and wind direction, highlights that warmer and drier conditions increase the VGS cooling effect during heat waves.VGS improved the mean radiant temperature (Tmrt) above the canyon (3.9–4.1°C) and at the pedestrian level (2.21–2.8°C), while the physiological equivalent temperature (PET) and universal thermal climate index (UTCI) decreased by an average of 0.67–1.07 °C and 0.63–0.8 °C, respectively. Shaded walls improved the average Tmrt (11.7°C), PET (3.2°C), and UTCI (2.7°C), reflecting the importance of casting shadow in the urban environment.

Temperature variations and frozen zone migration: assessing the influence of climate change on fatigue cracking in roadway asphalt pavements – a case study of seven provinces in China

Both temperature fluctuations and migrations in frozen zone distribution resulting from climate change can influence fatigue cracking in asphalt pavement. This study focused on seven provinces in China, utilising climate prediction data from 2021 to 2100 to analyse temperature variations and frozen zone migrations over the next 80 years. The study calculated the fatigue cracking of asphalt pavements on freeways and secondary roadways under the influence of these two factors. Additionally, a Fatigue Cracking Climate Index (FCCI) was introduced to reflect the extent of climate change impact on fatigue cracking. Research findings indicate that the rising temperatures associated with climate change adversely affect fatigue cracking. Conversely, the migration of frozen zones tends to alleviate this adverse impact. Overall, fatigue cracking is gradually exacerbated under the influence of climate change. In the region of interest, a majority of the asphalt pavements experience negative effects from climate change on fatigue cracking.

Projected changes of thermal, rainfall and drought indices in Morocco from high resolution climate models

Abstract Over the past decade, global changes in temperature, average and extreme precipitation have been observed many times. The increase in temperature or even the change in precipitation systems is directly affect extreme weather events, for example, heat waves, heavy rainfall, storms, and droughts are becoming more frequent and more powerful worldwide due to climate change caused by several factors including human activity. This is why there is a necessity to monitor future changes in climate extremes. The objective of this paper is to evaluate the future changes in thermal and rainfall extremes projected by the four climate models over time horizon 2041-2060 relative to the 1981-2010 reference period, based on the 5 climate indices, including Tmm, Tx90p, WSDI, PRCPTOT and SPI. The projections are given under the RCP 4.5 medium scenario. The analysis in thermal aspect shows that Morocco could experience by 2041-2060, a significant warming, has manifested by an increase in average temperature. Future changes in rainfall indices show that the spatial distribution of rainfall in Morocco could change significantly by 2041-2060 compared to the reference period 1971-2010. The four climate models agree on a decrease in annual precipitation that could affect most of the Kingdom. In the same way to dryness, the future changes of the SPI index that characterizes the drought, indicates a move to a dry climate.

Temporal changes in precipitation and correlation with large climate indicators in the Hengshao Drought Corridor, China

Understanding the lagged correlations between regional precipitation and large scale climate indicators (LCI) is crucial for water management and drought risk assessment. Copula functions are commonly used to quantify the relationship between precipitation and these factors. However, existing studies often neglect monsoon inland areas and lack quantification of the lagged correlations of precipitation at the monthly scale. To address these limitations, this study constructed a 3-dimensional vine copula-based model to assess the impact of large scale climatic change on regional precipitation. The analysis focused on the Hengshao Drought Corridor (HDC), the driest region in Hunan Province, China. The results indicate that precipitation in this region is influenced primarily by the Pacific Ocean’s circulation pattern, particularly the Niño1 + 2 and Niño3 indices, with a 2 ∼ 3 month lag. The R-Vine Copula provided the best fit when identifying lagged correlations, but due to its fitting center, it did not perform as well as the C-Vine Copula in generating stochastic simulated precipitation. The study revealed that the HDC receives more precipitation when the Niño1 + 2 and Niño3 are asynchronous, but less precipitation when they are synchronous, making the area more susceptible to drought events. These findings have implications for the HDC and other monsoon inland regions worldwide, providing insights to cope with climate extremes and develop appropriate strategies.

A Bayesian Model for 20th Century Antarctic Sea Ice Extent Reconstruction

AbstractAntarctic sea ice, a key component in the complex Antarctic climate system, is an important driver and indicator of the global climate. In the relatively short satellite‐observed period from 1979 to 2022 the sea ice extent has continuously increased (contrasting a major decrease in Arctic sea ice) up to a dramatic decrease between 2014 and 2017. Recent years have seen record sea ice lows in February 2022–February 2023. We use a statistical ensemble reconstruction of Antarctic sea ice to put the observed changes into the historical context of the entire 20th century. We propose a seasonal Vector Auto‐Regressive Moving Average (VARMA) model fit in a Bayesian framework using regularized horseshoe priors on the regression coefficients to create a stochastic ensemble reconstruction of monthly Antarctic Sea ice extent from 1900 to 1979. This novel model produces a set of 2,500 plausible sea ice extent reconstructions for the sea ice by sector that incorporate the autocorrelation structure of sea ice over time as well as the dependence of sea ice between the sectors. These fully observed reconstructions exhibit plausible month‐to‐month changes in reconstructed sea ice as well as plausible interactions between the sectors and the total. We reconstruct an overall higher sea ice extent earlier in the 20th century with a relatively sharp decline in the 1970s. These trends agree well with previous reconstructions of Antarctic sea ice based on ice core data, whaling locations, and climatological data, as well as early satellite observations in the reconstruction period.

Forecasting the climate-conflict risk in Africa along climate-related scenarios and multiple socio-economic drivers

This study investigates how climate change might impact economic development in the future through its effects on violence, addressing the gap in research on long-term conflict risk assessment. Using geocoded data (1°resolution) on climate and socio-economic indicators covering 1990-2050, we employ a forecasting recursive model to examine the probability and intensity of different types of conflict, under various socio-economic and climate scenarios. Our analysis reveals that climate change has both direct and indirect effects on violence, highlighting the key role of the agricultural channel, the spillover across neighbouring areas and the socio-economic context. These findings offer new insights into adaptation strategy and provide implications for the need to jointly account for the complex interactions between climate conditions, socio-economic factors, and conflict dynamics.

Relationships Among Global Climate Indices, Rainfall Patterns, and Crop Productivity in the Southern Part of Java, Indonesia

Relationships Among Global Climate Indices, Rainfall Patterns, and Crop Productivity in the Southern Part of Java, Indonesia

Effect of heat stress in the first 1000 days of life on fetal and infant growth: a secondary analysis of the ENID randomised controlled trial

The intersecting crises of climate change, food insecurity, and undernutrition disproportionately affect children. Understanding the effect of heat on growth from conception to 2 years of age is important because of mortality and morbidity implications in the near term and over the life course. In this secondary analysis, we used longitudinal pregnancy cohort data from the Early Nutrition and Immunity Development (ENID) randomised controlled trial in West Kiang, The Gambia, which occurred between Jan 20, 2010, and Feb 10, 2015. The ENID trial assessed micronutrient supplementation in the first 1000 days of life starting from 20 weeks' gestation, during which anthropometric measurements were collected prospectively. We used multivariable linear regression to assess the effect of heat stress (defined by Universal Thermal Climate Index [UTCI]) on intrauterine growth restriction based on length-for-gestational age Z score (LGAZ), weight-for-gestational age Z score (WGAZ), and head circumference-for-gestational age Z score (HCGAZ) at birth, and assessed for effect modification of supplement intervention on the relationship between heat stress and infant anthropometry. We used multivariable, multilevel linear regression to evaluate the effect of heat stress on infant growth postnatally based on weight-for-height Z score (WHZ), weight-for-age Z score (WAZ), and height-for-age Z score (HAZ) from 0 to 2 years of age. Complete data were available for 668 livebirth outcomes (329 [49%] female infants and 339 [51%] male infants). With each 1°C increase in mean daily maximum UTCI exposure, in the first trimester, we observed a reduction in WGAZ (-0·04 [95% CI -0·09 to 0·00]), whereas in the third trimester, we observed an increase in HCGAZ (0·06 [95% CI 0·00 to 0·12]), although 95% CIs included 0. Maternal protein-energy supplementation in the third trimester was associated with reduced WGAZ (-0·16 [-0·30 to -0·02]) with each 1°C increase in mean daily maximum UTCI exposure, while no effect of heat stress on WGAZ was found with either standard care (iron and folate) or multiple micronutrient supplementation. For the postnatal analysis, complete anthropometric data at 2 years were available for 645 infants (316 [49%] female infants and 329 [51%] male infants). Postnatally, heat stress effect varied by infant age, with infants aged 6-18 months being the most affected. In infants aged 12 months exposed to a mean daily UTCI of 30°C (preceding 90-day period) versus 25°C UTCI, we observed reductions in mean WHZ (-0·43 [95% CI -0·57 to -0·29]) and mean WAZ (-0·35 [95% CI -0·45 to -0·26]). We observed a marginal increase in HAZ with increasing heat stress exposure at age 6 months, but no effect at older ages. Our results suggest that heat stress impacts prenatal and postnatal growth up to 2 years of age but sensitivity might vary by age. In the context of a rapidly warming planet, these findings could have short-term and long-term health effects for the individual, and immediate and future implications for public child health. Wellcome Trust.

Less power when more is needed. Climate-related current and possible future problems of the wind energy sector in Poland.

Heat waves, combined with an increasing share of weather-dependent renewable energy sources, lead to disruptions in the operation of energy systems. This paper estimates how energy consumption in Poland changed during four heat waves (2010-2022). It also analyzes how often wind conditions may occur in the summer, that interrupt energy production by land-based wind farms. Moreover, authors are looking at future changes of climate indices responsible for increased energy consumption and possible reduction of energy production in wind farms. During the 2010 heatwave, the increase in energy consumption during the hottest days did not exceed 4%. Later summer demand increased; in 2019, it was 8% higher than during the 2015 heatwave. In 2016-2022, energy consumption at the average daily air temperature above 26°C was over 8% higher on working days than at 18°C. Recently, even in regions with favorable wind conditions, the number of summer days with low wind speed (2 ms-1) exceeds 14. For Poland, the number of days with low wind speeds will increase by about ten. Therefore, the increasing demand for energy on hot days, combined with their more frequent occurrence, may have a negative impact on the stability of the energy system in the future.