Heat Wave Events Research Articles

EHF based Heatwave Identification and its Impact on Urban Heat Island Intensity: A Case Study of an Indian City

Abstract. In the Indian subcontinent, the heatwaves occur mainly during the summer months from April to June. This heatwave becomes more severe when accompanied by higher summer temperatures in the country. Excess Heat Factor (EHF) is a metric used to identify heatwaves. This study identifies the heatwave using EHF and its relationship with UHI intensity in pre, post, and during heatwave event. The results show a heatwave event in the area from 10-16 June 2023 with positive EHF values. The values range from 0.433087-5.277442 during the heatwave event. To establish the link between the positive EHF value and UHI intensity, the variation of UHI intensity is monitored using MODIS data. The findings indicated that during the heatwave event, the UHI intensity and its extent grew with an increase in temperature of 2°C in the daytime and slight changes in the nighttime. This result is also validated by plotting the variation of UHI intensity using ERA-5 Land data on the grid point of the weather station (29.5°N, 79.5°E), which ranges between 0.504764 – 0.633209 during the pre-heatwave event. The intensity value kept on increasing as the heatwave event approached and reached the maximum magnitude of 0.70384 during the heatwave event. As the event finishes, intensity values decrease to 0.147939 post heatwave event. The findings provide an understanding of localized interaction of UHI intensity and the heatwave which is identified using the EHF factor.

Effect of heatwaves on mortality of Alzheimer's disease and other dementias among elderly aged 60 years and above in China, 2013–2020: a population-based study

BackgroundChina has the largest number of dementia patients in the world, posing a significant health and economic burden. Alzheimer's disease (AD) and other dementia patients face a higher risk of mortality during heatwaves, but relevant studies on this topic have been limited so far. MethodsThe study extracted data from the China Cause of Death Reporting System (CDRS) on deaths of AD and other dementia patients aged 60 years and above between 2013-2020. Using an individual-level, time-stratified, and case-crossover study design, the effects of heatwaves across nine scenarios on dementia mortality were quantified by conditional logistic regression combined with distributed lag non-linear model (DLNM). Additionally, the attributable fractions (AFs) of deaths due to heatwaves were calculated. FindingsA total of 399,036 death cases were reported caused by AD and other dementias during the study period. It was found that heatwaves significantly increased the risk of death among people with AD and other dementias. As the intensities and durations of the heatwaves increased, the lag0-7 cumulative odds ratios (CORs) of mortality increased progressively from 1·140 (95% CI: 1·118, 1·163) under the mildest heatwave to 1·459 (95% CI: 1·403, 1·518) under the most severe one, across nine heatwave scenarios examined. Additionally, under specific heatwave scenarios, sex and regions modified the mortality risk, but no significant age differences were observed. The AFs of AD and other dementia mortality due to milder heatwaves were lower compared to more severe heatwaves, ranging from 12·281% (95% CI: 10·555%, 14·015%) to 31·460% (95% CI: 28·724%, 34·124%). InterpretationThe study provided critical insights into the substantial increase in heatwave-related mortality among AD and other dementia patients during and after heatwave events. The results from our quantitative analyses will provide needed scientific evidence for policymakers and practitioners to develop relevant policies and guidelines to protect the health and well-beings of vulnerable populations in future in the context of both seasonal changes and long-term climate change. FundingThis work was supported by the Project of Prevention and Intervention on Major Diseases for Elderly in China, NCNCD [00240201307], the National Key Research and Development Program of China [2022YFC2602301, 2023YFC2308703] and the Science and Technology Fundamental Resources Investigation Program of China [2017FY101201].

The effect of climate change and temperature extremes on Aedes albopictus populations: a regional case study for Italy.

The Asian tiger mosquito, Aedes albopictus, has spread widely throughout Italy since its introduction, with significant public health implications. We examine how decadal temperature trends and sub-monthly heatwave events affect its climate-driven geographical distribution and temporal dynamics using a new regional-scale dynamical Aedes model. The model is calibrated using [Formula: see text] years of ovitrap data for Emilia-Romagna and reproduces the vector seasonality and, to a lesser extent, its inter-annual variability. Simulated vector density hotspots overlap with densely populated areas in Rome, Milan, Naples, Foggia, Catania, Palermo, Lecce, Cagliari, Genoa, Turin and large urban centres in Emilia-Romagna. Lower risk is simulated over the Central Apennine mountains and the Alps. At decadal time scale, we simulate a lengthening of the active mosquito season by 0.5-3 weeks per decade, with the vector becoming homodynamic in southern Italy. Depending on the climatic setting, heatwaves can increase or reduce vector populations and, in some locations, can temporarily decrease mosquito populations. Such decreases can be followed by a population rebound and overshoot. Given the model's skill in reproducing key spatio-temporal Ae. albopictus features, there is potential to develop an early warning system to inform control efforts at a national scale.

How was the impact of heatwaves on daily hospital admissions in Portugal (2000-2018)?

Abstract Background Climate change has increased the frequency, intensity, and duration of heatwaves, posing a serious threat to public health. While the link between high temperatures and premature mortality has been extensively studied, the comprehensive quantification of heatwave impacts on morbidity remains understudied. Methods We assessed the relationship between heatwaves and daily hospital admissions at a county level in Portugal, considering all major diagnostic categories and age groups, over a 19-year period from 2000 to 2018. Our nationwide study included a comprehensive geospatial analysis, integrating over 12 million hospital admission records with heatwave events indexed by the Excess Heat Factor (EHF), covering all 278 mainland counties. To estimate the impact of heatwaves on hospital admissions, we applied negative binomial regression models at both national and county levels. Results A statistically significant overall 18·9% increase in daily hospital admissions was found during heatwave days (IRR=1·189, 95% CI: 1·179-1·198). This impact affected all age groups, with pediatric ages being the most affected (21·7%), followed by the working-age (19·7%) and elderly individuals (17·2%). All 25 Major Disease Diagnostic Categories experienced significant increases, particularly Burns (34·3%), Multiple Significant Trauma (26·8%), and Infectious and Parasitic Diseases (25·4%). Notable rises were also observed in Endocrine, Nutritional, and Metabolic (25·1%), Mental (23%), Respiratory (22·4%), and Circulatory (15·8%) diseases. Conclusions Our results provide statistically significant evidence of the association between heatwaves and increased hospitalizations across all age groups and for all major causes of disease. This is the first study to estimate the full extent of heatwaves’ impact on hospitalizations using the EHF index over a comprehensive 19-year period, encompassing an entire country, and spanning 25 disease categories during multiple heatwave events. Key messages • There is a clear association between heatwaves and increased hospital admissions across all age groups and Major Diagnostic Categories. • Our data offer crucial information to guide policymakers in effectively and efficiently allocating resources to address the profound healthcare consequences resulting from climate change.

Development of a heat mortality surveillance approach for England

Abstract Background England’s unprecedented temperatures in 2022 and 2023 highlighted the severe risks of rising global temperatures on public health. This study considers the impact of heatwave events on mortality rates, supporting the development of improved health surveillance and intervention strategies. Methods Firstly, heat-associated mortality for each heat episode was estimated by comparing observed daily mortality values with the expected baseline of daily deaths. Secondly, the historic temperature-mortality relationship was established at national and regional levels using a distributed lag non-linear model with temperature and mortality data from the previous 5 years. Finally, modelled estimates of heat-associated excess mortality were generated based on the historic temperature-mortality relationships, to assess any difference between observed and modelled estimates. Results In 2022 which saw record-breaking maximum temperatures in the UK, an estimated 2,985 (2,258-3,712) heat-associated deaths were recorded across five heat episodes, the highest annual count to date. Despite relatively cool temperatures, 2023 saw the third-highest heat-associated mortality since reporting began in 2016 of 2,295 (1,681-2,910). Comparisons between modelled and observed mortality were generally similar, but showed statistically significant discrepancies for certain heat episodes across the two summers. Conclusions Heatwaves have a rapid and clear impact on health, emphasising the need for effective early warning systems and interventions. The comparison of observed mortality with predictions from historic temperature-mortality relationships at regional level demonstrates the need for a nuanced approach to surveillance and health protection for adverse heat, with factors other than temperature also playing a critical role in health impacts. This will inform continuous improvements in England’s impact-based Heat Health Alerting system. Key messages • Heatwaves pose a significant risk to public health, as evidenced by the unprecedented excess deaths during the record-breaking temperatures in England in Summer 2022. • Comparison of observed mortality with predictions from historic data can provide insight into contextual factors modulating the health impacts of heat, and inform future surveillance and response.

High-resolution modeling and projection of heat-related mortality in Germany under climate change

Abstract Heat is a leading cause of premature deaths during summer months. Understanding the relationship between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Traditional statistical methods have been repeatedly applied to analyze heat-related mortality. However, spatially and temporally high-resolution analyses are challenging due to fragmented data archives across different agglomeration levels, especially for mortality data. We propose a neural network-based model to estimate heat-related mortality. This approach allows for high temporal and spatial resolution estimations, such as regional heat risk during specific heatwaves. Using Germany as a case study, we calculated heat-related excess mortality rates at the district level and visualized the dynamics of local health risks within a heatwave. Overall, we estimated a total of 48,000 heat-related deaths in Germany over the last decade (2014-2023), with most occurring during specific heatwave events. For example, in 2023, the heatwave from July 7-14 contributed approximately 28% (1100 deaths) to the total of about 3,900 heat-related deaths for the entire year. This estimation is consistent with previously published reports from the Robert Koch Institute (RKI), considering the resolution differences in the input data. In addition, we combined our model with shared socio-economic pathways (SSPs) of future climate change, assuming a static population and baseline mortality rate. The results suggest that heat-related risk in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat. Our approach is a valuable tool for the monitoring of regional heat-related risks as well as scenario-based risks projections for the future. The results of the model can help develop climate-driven public health strategies, aiding in the identification of local risks during heatwaves and in long-term resilience planning. Key messages • High-resolution modeling of heat-related mortality helps us to develop targeted adaptation and prevention plans. • The majority of heat-related mortality occurs during heatwaves in summer.

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

AbstractAccurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

Global warming level indicators of climate change and hotspots of exposure

Abstract In the 21st century, a growing population will be exposed to various hazards caused by a warming climate. Here we present a new database of 12 climate change indicators with a total of 42 variants at different global warming levels (GWLs) (1.2 °C–3.5 °C), which is based on global climate and hydrological model data from the Coupled Model Intercomparison Project round 6 and the Inter-Sectoral Impact Model Intercomparison Project round 3b at 0.5° spatial resolution. It comprises of indicators relating to temperature and precipitation extremes, heatwave events, and hydrological variability. To facilitate the comparison of hazards from different indicators, including for an audience without a scientific background, we have developed a bivariate hazard score which is applied on the grid cell level and incorporates statistics on both the absolute hazard (e.g. low or high precipitation) and the relative change under global warming compared to the reference period (e.g. a large change from low to high precipitation). Additionally, we calculate exposed land area and population through the 21st century for a large set of countries and regions by combining this score with gridded projections of population from the Shared Socioeconomic Pathways (SSPs). The datasets are intended for use by the wider research community and analysts seeking digestible climate hazard and exposure data summarized by GWLs. To illustrate potential uses of the data, in a preliminary analysis we find that even at 1.5 °C large parts of the land area and population face substantial unavoidable risks from multiple indicators, with 86% of the world’s population exposed to at least three indicators with at least medium hazard using the population projections for SSP2 in the year 2050. This picture only worsens with increasing warming, as the land areas facing the highest number of impacts coincide with some of the most densely populated parts of the world.

Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India

This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.

High-resolution modeling and projection of heat-related mortality in Germany under climate change.

Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels. Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate changescenarios. We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014-2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7-14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions. Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning.

Combining the uncombinable: corporate memories, ethnobiological observations, oceanographic and ecological data to enhance climatic resilience in small-scale fisheries

The global food production system is increasingly strained by abrupt and unpredictable weather events, which hinder communities' ability to adapt to climate variations. Despite advances in meteorological predictions, many communities lack the academic knowledge or infrastructure to interpret these complex models. This gap highlights the need for solutions that make climate forecasts more accessible and actionable, especially for communities reliant on natural resources. This study explores the potential of enhancing seasonal climate forecasts by integrating local ecological knowledge (LEK) with scientific data. Specifically, we combined ethnobiological information gathered between 2022 and 2024 with existing oceanographic and ecological data to create an ethnobiological calendar for four fishing cooperatives. An ethnographic approach was used to understand the population's ethnobiological knowledge and their perceptions of marine heatwaves and climate change impacts. Coastal monitoring data was collected using moorings that recorded temperature over a 14-year period (2010–2024). To characterize giant kelp dynamics, we used an existing dataset of multispectral Landsat images, which estimates the surface canopy biomass of giant kelp forests. Ecological monitoring was conducted annually every summer from 2006 to 2023 to record the in situ abundance of ecologically and economically important invertebrate and fish species. Combining oceanographic, ecological, and ethnographic data, allowed for alligning fishers' observations with recorded marine heatwave events and ecological shifts. Our findings revealed that these observations closely matched documented marine heatwave data and corresponding ecological changes. The integration of LEK with scientific oceanographic data can significantly improved our understanding of dynamic climate regimes, offering contextually relevant information that enhances the reliability and utility of seasonal climate forecasts. By incorporating yearly data into an ethnobiological calendar, we promote more inclusive, community-based approaches to environmental management, advocating for the integration of LEK in climate adaptation efforts, emphasizing its crucial role in strengthening resilience strategies against climatic shocks.

Highly inhomogeneous interactions between background climate and urban warming across typical local climate zones in heatwave and non-heatwave days

Abstract Urban heat island (UHI) in conjunction with heatwave (HW) leads to exacerbation of thermal stress in urban areas. Prior research on UHI and HW has predominantly concentrated on examining the thermal conditions at the surface and near-surface, with few investigations extending to the radiative and dynamical interactions of UHI and HW, particularly with a focus on the inhomogeneities across local climate zones (LCZs). Here, we analyse the temperature disparity between HW and non-HW conditions across LCZs in the Sydney area by quantifying the contributions of individual radiative and dynamical processes using the coupled surface-atmosphere climate feedback-response analysis method (CFRAM). Three moist HW events in 2017, 2019, and 2020 are simulated using the Weather Research and Forecasting (WRF) model coupled with the single-layer urban canopy model (SLUCM). It is found that the maximum surface and 900 hPa temperature difference between HW and non-HW days may reach up to 10 K, with the increased net solar radiation during HWs being comparable to the typical level of anthropogenic heat flux in urban areas. It is also found that the reduction of clouds, the presence of vapour, and the increase of sensible heat contribute to the warming effect to various degrees, with the contribution of clouds being the most dominant. Conversely, the generation of dry convection and the increase of latent heat flux lead to cooling effects, with the latter being more dominant and capable of causing up to 10 K surface temperature difference between LCZ1 (compact high-rise) and LCZ9 (sparsely built). The differences in the contributions of climate feedback processes across different LCZs become more evident during more severe and humid HWs. These findings underscore the necessity of implementing LCZ-tailored heat mitigation strategies.

The characterization, mechanism, predictability, and impacts of the unprecedented 2023 Southeast Asia heatwave

In April and May 2023, Southeast Asia (SEA) encountered an exceptional heatwave. The Continental SEA was hardest hit, where all the countries broke their highest temperature records with measurements exceeding 42 °C, and Thailand set the region’s new record of 49 °C. This study provides a comprehensive analysis of this event by investigating its spatiotemporal evolution, physical mechanisms, forecast performance, return period, and extensive impacts. The enhanced high-pressure influenced by tropical waves, moisture deficiency and strong land-atmosphere coupling are considered as the key drivers to this extreme heatwave event. The ECMWF exhibited limited forecast skills for the reduced soil moisture and failed to capture the land-atmosphere coupling, leading to a severe underestimation of the heatwave’s intensity. Although the return period of this heatwave event is 129 years based on the rarity of temperature records, the combination of near-surface drying and soil moisture deficiency that triggered strong positive land-atmosphere feedback and rapid warming was extremely uncommon, with an occurrence probability of just 0.08%. These analyses underscore the exceptional nature of this unparalleled heatwave event and its underlying physical mechanisms, revealing its broad impacts, including significant health repercussions, a marked increase in wildfires, and diminished agricultural yields.

Heatwave events and concurrent ozone concentrations between 2006 and 2022 at two sites in southern and northern Spain.

Hotter-than-usual days are becoming more common, such that heat waves are expected to increase in intensity, frequency, duration, and spatial extent in Spain. Within this framework, this paper looks at the combined effects of extreme temperatures and air pollution in two cities in Spain, Córdoba and Valladolid, over the period 2006-2022. Synoptic patterns and air mass movement were analysed during the eleven coincident heat waves at both locations in order to study what impact orography and local meteorology have on ozone concentrations. Weak flow conditions were the most frequent synoptic pattern in the Iberian Peninsula during heat waves. Moreover, west and local circulations characterised the main air trajectories at low levels (500m agl), while southwest maritime advections and African air mass transport were more frequent at higher levels (1500 and 3000m agl) in Córdoba and Valladolid, respectively. On average, maximum ozone values were higher in Córdoba (105.1µgm-3) than in Valladolid (80µgm-3) and were strongly correlated with extreme temperatures at both locations (r up to 0.8, p-value < 0.05). Mean temperature in Córdoba was 31.9°C, with the maximum value reaching 43.7°C, while temperatures in Valladolid were lower (28.3°C and 37.3°C, respectively). Calculation and assessment of some indices helped to understand the impact of extreme events. Caution actions based on the Heat Index characterised heat wave periods. Moderate risk was the general Air Quality Health Index feature recorded and reached a very high risk of unhealthy air quality in the June 2022 event in Córdoba.

Compound and cascading droughts and heatwaves decrease maize yields by nearly half in Sinaloa, Mexico

The impact of droughts and heatwaves on agriculture losses has been exacerbated by the occurrence of compound and cascading events. Here we present a study that evaluates the impact of these events both as singly and as compound and cascading on maize yield in Sinaloa Mexico from 1990 to 2022, using the WOFOST crop model. Drought and heatwave events were identified using the Standardized Precipitation Index and threshold method, respectively. Results show that yield reduction (25%) is found during extreme drought events, emphasizing the vulnerability of maize farming to unfavorable drought conditions. While heatwaves alone did not show a significant impact on maize yields, the compound and cascading droughts and heatwaves amplified the loss of maize yields by up to 44% compared to normal conditions. This study highlights the need for adaptive strategies in agriculture to sustain food security during extreme events, especially in the context of multi hazard framework.

Current summer heat waves impair rainbow trout (Oncorhynchus mykiss) spermatogenesis: Implications for future fish farming management practices in South Europe

Freshwater environments provide different ecosystem services for human well-being, and are one of the most important natural sources of healthy animal protein. Climate change and extreme temperature events, among other factors, represent a growing concern for their sustainable use. In contrast to the research effort to elucidate how global warming affects these aquatic environments, few studies pay attention to the potential effects of heat waves, a current phenomenon with direct effects on contemporaneous organisms. Here, the potential effects of a single heat wave event on rainbow trout (Oncorhynchus mykiss) males, mimicking a recorded environmental event, have been evaluated by means of biometric, redox and stress response, plasma testosterone, histopathology and transcriptomic analyses. Environmental monitoring showed how, during the last 4 years, mean water temperature increased (2–4 °C; from April to October), heat wave duration progressively increased (from 15 to 100 days), and mean intensities ranged 1.7–3.8 °C with a maximum of 8.0 °C. An experimentally recreated heat wave lasting only 16 days, with mean and maximum intensities of 2.7 and 6.7 °C, induced a decrease in body weight and total antioxidant status, but increased blood plasma cortisol levels and redox enzyme activity. The induced heat wave decreased testosterone plasma levels and sperm quality, arrested spermatocyte type I and II differentiation and increased spermatozoa apoptosis. These effects were correlated with a differential expression of 1156 genes (116 up- and 1040 down-regulated genes) in exposed fish when compared to Control fish, reflecting an alteration in cell apoptosis and spermatozoa maturation processes. These results not only increase concern about current heat wave events, recreating a real scenario, but also unveil for the first time the particular molecular mechanisms of subsequent spermatogenesis disruption, opening new avenues for designing urgent and necessary strategies to mitigate the effects of extreme climate change conditions on freshwater aquaculture.

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.

Heat wave impacts on crop‐pest dynamics are dependent upon insect ontogeny and plant resistance

AbstractHeat waves, brief periods of unusually high temperatures, are damaging to agroecosystems and are increasing in frequency and intensity due to climate change. Despite growing appreciation for the threat that heat waves pose to agricultural sustainability, we have a poor understanding of what determines their impact on agroecological interactions in the field. Here, we report the results of a field experiment that examined how heat waves and their timing interact with crop pest resistance to influence the interactions between potato (Solanum tuberosum) and its most damaging pest, the Colorado potato beetle (CPB; Leptinotarsa decemlineata). We used open‐top chambers and ceramic heaters to generate heat wave conditions in field plots with pest‐resistant and pest‐susceptible potato varieties at four CPB developmental stages. We then assessed CPB performance, leaf herbivory, and tuber yield. The neonate‐stage heat wave reduced larval survival by 10%, but the surviving larvae were 18% larger and developed 15% faster. However, these effects occurred only on the susceptible variety; both larval survival and growth were unaffected by the heat wave in the pest‐resistant variety. Moreover, the neonate‐stage heat wave reduced adult survival by 15%, suggesting negative carry‐over effects of early‐life heat exposure. Heat wave events after the neonate stage had no effects on CPB performance, crop damage, or tuber yield. Our results indicate that timing and pest resistance in crops are essential for understanding the impacts of extreme heat events on crop‐pest dynamics. Agroecological pest management in an increasingly variable and extreme climate will likely benefit from the development of strategies that account for the seasonal timing of potential heat events and from the continued use of crop varieties bred for pest resistance, which our results suggest may dampen the impacts of extreme temperatures on crop‐pest interactions.

Forecasting the impact of marine heat waves on farmed bivalves Nodipecten nodosus and Magallana gigas

Marine heat waves are considered a threat to the cultivation of commercially important species because exposure to thermal stress may lead to mass mortalities of organisms. Investigating the upper thermal limits of marine species and their capacity for shifting these limits can contribute to securing the sustainability of aquaculture activities as exposure to heat extremes is increasing in frequency. The scallop Nodipecten nodosus and oyster Magallana [Crassostrea] gigas are two bivalve species commercially farmed in Brazil and production of spat is undertaken mainly at two hatcheries located in different regions subjected to distinct climate and seawater temperature conditions over the year. This study investigated the upper thermal tolerance of populations of N. nodosus and M. gigas sourced from temperate and tropical farming areas. Groups of N. nodosus and M. gigas farmed under warmer temperature regimes exhibited lower mortality rates when exposed to elevated temperatures compared to groups sourced from cooler waters and consequently displayed higher values of lethal temperature 50 (LT50). N. nodosus exhibited superior thermal tolerance than was previously known, however, commercial cultivation of this species is still at risk of mass mortalities depending on the duration and intensity of forecasted heat waves in both cultivation regions. M. gigas showed a high capacity to endure acute thermal stress at the resting stage of the reproductive cycle. To enhance the aquaculture sector's resilience to climate change, we recommend the development of cultivation methods that account for marine and atmospheric heat wave events, alongside continuous monitoring of abiotic factors at farming sites.

The flash droughts across the south-central United States in 2022: Drivers, predictability, and impacts

A rare subseasonal-to-seasonal phenomenon – two consecutive flash drought events interrupted by a period of recovery – occurred across eastern Oklahoma, Arkansas, and southern Missouri, spanning the summer and early fall of 2022. These flash drought events (the first in June–July, the second in August–September) led to severe (D2) and extreme (D3) drought conditions via the United States Drought Monitor across much of the region following the first period of rapid drought intensification, and extreme (D3) and exceptional (D4) drought conditions by the end of the second event. A notable driver of both flash drought events included a persistent upper-level ridge either centered over or shifted west and upstream of the flash drought region, leading to broad-scale subsidence and reduced mid-level moisture which acted to limit precipitation development and increase evaporative demand. In addition, several heatwave events developed during the warm season in 2022 and either (1) acted to drive flash drought development via increased evaporative demand or (2) were enhanced by land surface desiccation and land-atmosphere feedbacks following rapid drought intensification. Furthermore, S2S composite forecasts predicted drought development for both events. However, only 20% of the ensembles predicted rapid drought development associated with flash drought for the first event and 16% of the ensembles predicted rapid drought development during the second event. This result highlights a key challenge in S2S prediction of rapidly developing drought conditions versus that of more conventional and slower drought development. The ensembles that did predict rapid drought intensification were associated with the forecasting of positive 500 hPa geopotential height anomalies over the south-central United States (first event) or an amplified wave pattern centered over the west-central United States (second event). Lastly, the compounding effects of two flash droughts in a single warm season led to substantial impacts on agricultural, environmental, and hydrologic sectors across the region.