GHWR, a multi-method global heatwave and warm-spell record and toolbox

Butterfly abundance changes in England are well associated with extreme climate events

Climate change exerts significant impacts on ecosystems through extreme climate events (ECEs), which are linked to various climate variables and can occur in different seasons. However, previous studies predominantly focus on a single type of ECE within specific seasons. We address this research gap by examining four typical types of ECEs: droughts, pluvials, heatwaves, and coldwaves, which are consistently defined and identified on a daily scale using a recently proposed statistical method. Butterflies in England serve as our study subject since their life stages occur throughout the year, and we had access to a 45-year dataset encompassing 57 butterfly species. First, we analyzed the correlation between abundance changes and the severity of ECEs, revealing varying sensitivity to ECEs across different life stages. Notably, abundance changes are negatively correlated with the severity of heatwaves in the hibernation, egg, and larval stages, as well as with the severity of pluvials in the larval, pupal, and adult stages. Second, we identified the most extreme climate events (MECEs) in England in the period from 1950 to 2020 and linked them with synchronized abundance changes for all species. MECEs in the sensitive stages of butterflies are frequently (for eight out of 11 MECEs) associated with synchronized decreases in their abundance. Our findings underscore the importance of considering diverse types of ECEs across all seasons to gain insights into their potential ecosystem effects. We draw attention to the fact that our analyses are primarily data-driven, with limited ecophysiological interpretation.

Soil environmental anomalies dominate the responses of net ecosystem productivity to heatwaves in three Mongolian grasslands

Climate change is causing more frequent and intense heatwaves. Therefore, it is important to understand how heatwaves affect the terrestrial carbon cycle, especially in grasslands, which are especially susceptible to climate extremes. This study assessed the impact of naturally occurring, simultaneous short-term heatwaves on CO2 fluxes in three ecosystems on the Mongolia Plateau: meadow steppe (MDW), typical steppe (TPL), and shrub-grassland (SHB). During three heatwaves, net ecosystem productivity (NEP) was reduced by 86 %, 178 %, and 172 % at MDW, TPL, and SHB, respectively. The changes in ecosystem respiration, gross primary production, evapotranspiration, and water use efficiency were divergent, indicating the mechanisms underlying the observed NEP decreases among the sites. The impact of the heatwave in MDW was mitigated by the high soil water content, which enhanced evapotranspiration and subsequent cooling effects. However, at TPL, insufficient soil water led to combined thermal and drought stress and low resilience. At SHB, the ecosystem's low tolerance to an August heatwave was heavily influenced by species phenology, as it coincided with the key phenological growing phase of plants. The potential key mechanism of divergent NEP response to heatwaves lies in the divergent stability and varying importance of environmental factors, combined with the specific sensitivity of NEP to each factor in ecosystems. Furthermore, our findings suggest that anomalies in soil environment, rather than atmospheric anomalies, are the primary determinants of NEP anomalies during heatwaves. This challenges the conventional understanding of heatwaves as a discrete and ephemeral periods of high air temperatures. Instead, heatwaves should be viewed as chronologically variable, compound, and time-sensitive environmental stressors. The ultimate impact of heatwaves on ecosystems is co-determined by a complex interplay of environmental, biological, and heatwave features.

Assessing the global vulnerability of dryland birds to heatwaves

As global average surface temperature increases, extreme climatic events such as heatwaves are becoming more frequent and intense, which can drive biodiversity responses such as rapid population declines and/or shifts in species distributions and even local extirpations. However, the impacts of extreme climatic events are largely ignored in conservation plans. Birds are known to be susceptible to heatwaves, especially in dryland ecosystems. Understanding which birds are most vulnerable to heatwaves, and where these birds occur, can offer a scientific basis for adaptive management and conservation. We assessed the relative vulnerability of 1196 dryland bird species to heatwaves using a trait-based approach. Among them, 888 bird species are estimated to be vulnerable to heatwaves (170 highly vulnerable, eight extremely vulnerable), of which ~91% are currently considered non-threatened by the IUCN, which suggests that many species will likely become newly threatened with intensifying climate change. We identified the top three hotspot areas of heatwave-vulnerable species in Australia (208 species), Southern Africa (125 species) and Eastern Africa (99 species). Populations of vulnerable species recorded in the Living Planet Database were found to be declining significantly faster than those of non-vulnerable species (p = .048) after heatwaves occurred. In contrast, no significant difference in population trends between vulnerable and non-vulnerable species was detected when no heatwave occurred (p = .34). This suggests that our vulnerability framework correctly identified vulnerable species and that heatwaves are already impacting the population trends of these species. Our findings will help prioritize heatwave-vulnerable birds in dryland ecosystems in risk mitigation and adaptation management as the frequency of heatwaves accelerates in the coming decades.

The complexity of heatwaves impact on terrestrial ecosystem carbon fluxes: Factors, mechanisms and a multi-stage analytical approach

Extreme heatwaves have become more frequent and severe in recent decades, and are expected to significantly influence carbon fluxes at regional scales across global terrestrial ecosystems. Nevertheless, accurate prediction of future heatwave impacts remains challenging due to a lack of a consistent comprehension of intrinsic and extrinsic mechanisms. We approached this knowledge gap by analyzing the complexity factors in heatwave studies, including the methodology for determining heatwave events, divergent responses of individual ecosystem components at multiple ecological and temporal scales, and vegetation status and hydrothermal environment, among other factors. We found that heatwaves essentially are continuously changing compound environmental stress that can unfold into multiple chronological stages, and plant physiology and carbon flux responses differs in each of these stages. This approach offers a holistic perspective, recognizing that the impacts of heatwaves on ecosystems can be better understood when evaluated over time. These stages include instantaneous, post-heatwave, legacy, and cumulative effects, each contributing uniquely to the overall impact on the ecosystem carbon cycle. Next, we investigated the importance of the timing of heatwaves and the possible divergent consequences caused by different annual heatwave patterns. Finally, a conceptual framework is proposed to establish a united foundation for the study and comprehension of the consequences of heatwaves on ecosystem carbon cycle. This instrumental framework will assist in guiding regional assessments of heatwave impacts, shedding light on the underlying mechanisms responsible for the varied responses of terrestrial ecosystems to specific heatwave events, which are imperative for devising efficient adaptation and mitigation approaches.

Summer heat waves and their mortality risk over a 14-year period in a western region of Iran

Compared to previous decade, impact of heat waves (HWs) on mortality in recent years needs to be discussed in Iran. We investigated temporal change in added impact of summer HWs on mortality in eight cities of Iran. The pooled length of HWs was compared between 2015-2022 and 2008-2014 using random and fixed-effects of meta-analysis regression model. The temporal change in impact of HWs was evaluated through interaction effect between crossbasis function of HW and year in a two-stage time varying model. In order to pool the reduced coefficients of each period, multivariate meta-regression model, including city-specific temperature and temperature range as heterogenicity factors, was used. In addition to relative risk (RR), attributable fraction (AF) of HW in the two periods was also estimated in each city. In the last years, the frequency of all HWs was higher and the weak HWs were significantly longer. The only significant RR was related to the lowest and low severe HWs which was observed in the second period. In terms of AF, compared to the strong HWs, all weak HWs caused a considerable excess mortality in all cities and second period. The subgroup analysis revealed that the significant impact in the second period was mainly related to females and elderlies. The increased risk and AF due to more frequent and longer HWs (weak HWs) in the last years highlights the need for mitigation strategies in the region. Because of uncertainty in the results of severe HWs, further elaborately investigation of the HWs is need.

Spatio-temporal evolution of heat waves severity and expansion across the Iberian Peninsula and Balearic islands

In the current climate change scenario, heat waves have become one of the most concerning extreme climatic events, both because of their implications for human health and the economy, and because of their increase in intensity and frequency in recent decades. This work presents for the first time a climatological analysis of heat waves in the Iberian Peninsula and Balearic Archipelago (IPB) using the Excess Heat Factor index (EHF). This index considers the factor of intensity and the acclimatization process of human body in the study of heat waves. We focused on the intensity (also called severity), duration, frequency and spatial extension of heat waves in the IPB in the 1950-2020 period. The exceptional heat wave of August 2018 was approached in a similar way to further explore the usefulness of the EHF index. We found that the EHF index identified heat wave conditions 2 days earlier than indices that used only maximum temperatures. Results showed a significant increase in intensity, duration, frequency and spatial extension of heat waves for the whole IPB for 1950-2020 period. The average extent of heat waves increased by 4.0% per decade and the maximum extent by 4.1% per decade. This trend suggested a significant increase in human exposure, droughts, fire risk and energy demand in this region in the last decades.

Spatiotemporal Distribution of Heatwave Hazards in the Chinese Mainland for the Period 1990-2019

Heatwaves occur frequently in summer, severely harming the natural environment and human society. While a few long-term spatiotemporal heatwave studies have been conducted in China at the grid scale, their shortcomings involve their discrete distribution and poor spatiotemporal continuity. We used daily data from 691 meteorological stations to obtain torridity index (TI) and heatwave index (HWI) datasets (0.01°) in order to evaluate the spatiotemporal distribution of heatwaves in the Chinese mainland for the period of 1990-2019. The results were as follows: (1) The TI values rose but with fluctuations, with the largest increase occurring in North China in July. The areas with hazard levels of medium and above accounted for 22.16% of the total, mainly in the eastern and southern provinces of China, South Tibet, East and South Xinjiang, and Chongqing. (2) The study areas were divided into four categories according to the spatiotemporal distribution of hazards. The "high hazard and rapidly increasing" and "low hazard and continually increasing" areas accounted for 8.71% and 41.33% of the total, respectively. (3) The "ten furnaces" at the top of the provincial capitals were Zhengzhou, Nanchang, Wuhan, Changsha, Shijiazhuang, Nanjing, Hangzhou, Haikou, Chongqing, and Hefei. While the urbanization level and population aging in the developed areas were further increased, the continuously increasing heatwave hazard should be fully considered.

Increased moist heat stress risk across China under warming climate

Heatwaves have afflicted human health, ecosystem, and socioeconomy and are expected to intensify under warming climate. However, few efforts have been directed to moist heat stress (MHS) considering relative humidity and wind speed, and moist heat stress risk (MHSR) considering exposure and vulnerability. Here we showed MHS and MHSR variations across China during 1998-2100 using China Meteorological Administration Land Data Assimilation System datasets, the 6th Coupled Model Intercomparison Project (CMIP6) merged datasets, Gross Domestic Product, population and leaf area index. We detected increased MHS across China under different Shared Socioeconomic Pathways (SSPs). Specifically, the historical MHS occurred mostly during mid-July to mid-August. We found increasing trends of 0.08%/year, 0.249%/year, and 0.669%/year in the MHS-affected areas under SSP126, SSP245, and SSP585, respectively. Furthermore, we observed the highest increasing rate of MHSR in Northwest and Southwest China, while the MHSR across Northeast and North China under SSP126 shifted from increasing to decreasing trends. Noteworthy is that the increasing trend of MHSR under SSP585 is 1.5-2.6 times larger than that under SSP245, especially in North and South China. This study highlights spatiotemporal evolutions of MHS and MHSR and mitigation to moisture heat stress in a warming climate.

Spatiotemporal Evolution and Drivers of Total Health Expenditure across Mainland China in Recent Years

A substantially growing health expenditure has become an important global issue. Thus, how and why health expenditure is rising should be urgently investigated in systematic research. The Bayesian space-time model and the Bayesian least absolute shrinkage and selection operator (LASSO) model were employed in this study to investigate the spatiotemporal trends and influence patterns of total health expenditure per capita (THEPC) and total health expenditure (THEE) as a share of the gross domestic product (GDP) on the Chinese mainland from 2009 to 2018. The spatial distribution of THEE as a share of GDP in mainland China has shaped a distinct geographical structure with the characteristic of 'west high/east low'. Its local increasing trends formed a geographical structure that exhibited a 'north high/south low' feature. The heterogeneity of the influence patterns of health expenditure was observed from east to west across China. Natural environmental factors, such as air pollution and green coverage, along with changes in dietary structures, have increasingly influenced the growth of health expenditures.

Spatio-Temporal Variation of Extreme Heat Events in Southeastern Europe

Many studies in the last few years have been dedicated to the increasing temperatures and extreme heat in Europe since the second half of the 20th century because of their adverse effects on ecosystems resilience, human health, and quality of life. The present research aims to analyze the spatio-temporal variations of extreme heat events in Southeastern Europe using daily temperature data from 70 selected meteorological stations and applying methodology developed initially for the quantitative assessment of hot weather in Bulgaria. We demonstrate the suitability of indicators based on maximum temperature thresholds to assess the intensity (i.e., magnitude and duration) and the tendency of extreme heat events in the period 1961–2020 both by individual stations and the Köppen’s climate zones. The capability of the used intensity-duration hot spell model to evaluate the severity of extreme heat events has also been studied and compared with the Excess Heat Factor severity index on a yearly basis. The study provides strong evidence of the suitability of the applied combined approach in the investigation of the spatio-temporal evolution of the hot weather phenomena over the considered domain.

Spatiotemporal Variation Analysis of the Fine-Scale Heat Wave Risk along the Jakarta-Bandung High-Speed Railway in Indonesia

As a highly important meteorological hazard, heat waves notably impact human health and socioeconomics, and accurate heat wave risk identification and assessment are effective ways to address this issue. The current spatial scale of heat wave risk assessment is relatively coarse, hardly meeting fine-scale heat wave risk assessment requirements. Therefore, based on multi-source fine-scale remote sensing data and socioeconomic data, this paper evaluates the heat wave risk along the Jakarta-Bandung high-speed railway, obtains the spatial distribution of heat wave risk in 2005, 2014 and 2019, and analyzes spatiotemporal risk variations over the past 15 years. The results show that most high-risk areas were affected by high-temperature hazards. Over time, the hazard, exposure, vulnerability and risk levels increased by 25.82%, 3.31%, 14.82% and 6.97%, respectively, from 2005–2019. Spatially, the higher risk in the northwest is mainly distributed in Jakarta. Additionally, a comparative analysis was conducted on the risk results, and the results showed that the 100-m scale showed more spatial differences than the kilometer scale. The research results in this paper can provide scientific advice on heat wave risk prevention considering the Jakarta-Bandung high-speed railway construction and regional economic and social development.

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

BACKGROUND

Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.

OBJECTIVES

This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.

METHODS

SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.

RESULTS

The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.

CONCLUSION

Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Declining Oxygen Level as an Emerging Concern to Global Cities

Rising CO₂ concentration and temperatures in urban areas are now well-known, but the potential of an emerging oxygen crisis in the world's large cities has so far attracted little attention from the science community. Here, we investigated the oxygen balance and its related risks in 391 global large cities (with a population of more than 1 million people) using the oxygen index (O_I), which is the ratio of oxygen consumption to oxygen production. Our results show that the global urban areas, occupying only 3.8% of the global land surface, accounted for 39% (14.3 ± 1.5 Gt/yr) of the global terrestrial oxygen consumption during 2001-2015. We estimated that 75% of cities with a population more than 5 million had an O_I of greater than 100. Also, cities with larger O_I values were correlated with more frequent heatwaves and severe water withdrawals. In addition, cities with excessively large O_I values would likely experience severe hypoxia in extremely calm weather. Thus, mitigation measures should be adopted to reduce the urban O_I in order to build healthier and more sustainable cities.

Intensity-duration-frequency relationship of WBGT extremes using regional frequency analysis in South Korea

The risk levels of heat-related extreme events need to be estimated for prediction and real-time monitoring to mitigate their impacts on air quality, public health, the ecosystem, and critical infrastructure. Many countries have adopted meteorological variable base thresholds for assessing the risk level of heat-related extreme events. These thresholds provide an approximate risk level for a specific event but do not consider its intensity and duration in the risk assessment. The current study provides a statistical tool to assess the risk of heat-related extreme events while concurrently considering their intensities and durations based on the wet-bulb globe temperature (WBGT). To this end, the intensity-duration-frequency (IDF) relationship of the extreme WBGT in South Korea was derived. Regional frequency analysis was employed to understand the IDF relationship. Return levels of heat-related extreme events in South Korea were calculated and their characteristics were investigated based on the annual maximum WBGT observations. The results showed that the IDF relationship could provide the risks of heat-related extreme events while concurrently considering their intensities and durations. The extreme WBGT in South Korea was used to categorize two regions such as coastal and inland based on their statistical characteristics. The return levels of the annual maximum WBGT events were found to vary largely by location. The return levels corresponding to 32 °C with 3-h duration for stations in the coastal and inland regions ranged from 1- to 100-years and 3- to 1000-years, respectively. Mean values of return levels for heatwave events in Seoul, Incheon, Daejon, Gwangju, Daegu, and Busan were 2.8-, 8.4-, 15.3-, 2.8-, 1.6-, and 2.2-years, respectively. The return levels of heatwaves for the warmer cities are smaller than those for cooler cities. The return levels of the heatwave events in South Korea showed a significant increasing trend in several cities, supporting the notion that the impact of heatwave events on South Korea might become more severe in the future.

Heat-related mortality at the beginning of the twenty-first century in Rio de Janeiro, Brazil

Temperature record-breaking events, such as the observed more intense, longer-lasting, and more frequent heat waves, pose a new global challenge to health sectors worldwide. These threats are of particular interest in low-income regions with limited investments in public health and a growing urban population, such as Brazil. Here, we apply a comprehensive interdisciplinary climate-health approach, including meteorological data and a daily mortality record from the Brazilian Health System from 2000 to 2015, covering 21 cities over the Metropolitan Region of Rio de Janeiro. The percentage of absolute mortality increase due to summer extreme temperatures is estimated using a negative binomial regression modeling approach and maximum/minimum temperature-derived indexes as covariates. Moreover, this study assesses the vulnerability to thermal stress for different age groups and both genders and thoroughly analyzes four extremely intense heat waves during 2010 and 2012 regarding their impacts on the population. Results showed that the highest absolute mortality values during heat-related events were linked to circulatory illnesses. However, the highest excess of mortality was related to diabetes, particularly for women within the elderly age groups. Moreover, results indicate that accumulated heat stress conditions during consecutive days preferentially preceded by persistent periods of moderate-temperature, lead to higher excess mortality rather than sporadic single hot days. This work may provide directions in human health policies related to extreme climate events in large tropical metropolitan areas from developing countries, contributing to altering the historically based purely reactive response.

Spatiotemporal Distribution and Risk Assessment of Heat Waves Based on Apparent Temperature in the One Belt and One Road Region

Heat waves seriously affect the productivity and daily life of human beings. Therefore, they bring great risks and uncertainties for the further development of countries in the One Belt and One Road (OBOR) region. In this study, we used daily meteorological monitoring data to calculate the daily apparent temperature and annual heat wave dataset for 1989–2018 in the OBOR region. Then, we studied their spatiotemporal distribution patterns. Additionally, multi-source data were used to assess heat wave risk in the OBOR region. The main results are as follows: (1) The daily apparent temperature dataset and annual heat wave dataset for 1989–2018 in the OBOR region at 0.1° × 0.1° gridded resolution were calculated. China, South Asia and Southeast Asia are suffering the most serious heat waves in the OBOR region, with an average of more than six heat waves, lasting for more than 60 days and the extreme apparent temperature has reached over 40 °C. Additionally, the frequency, duration and intensity of heat waves have been confirmed to increase continuously. (2) The heat wave risk in the OBOR region was assessed. Results show that the high heat wave risk areas are distributed in eastern China, northern South Asia and some cities. The main conclusion is that the heat wave risk in most areas along the OBOR route is relatively high. In the process of deepening the development of countries in the OBOR region, heat wave risk should be fully considered.

Heat wave intensity and daily mortality in four of the largest cities of Spain

In the current context of climate change, heat waves have become a significant problem for human health. This study assesses the effects of heat wave intensity on mortality (natural, respiratory and cardiovascular causes) in four of the largest cities of Spain (Barcelona, Bilbao, Madrid and Seville) during the period between 1990 and 2014. To model the heat wave severity the Excess Heat Factor (EHF) was used. The EHF is a two-component index. The first is the comparison of the three-day average daily mean temperature with the 95th percentile. The second component is a measure of the temperatures reached during the three-day period compared with the recent past (the previous 30 days). The city-specific exposure-response curves showed a non-linear J-shaped relationship between mortality and the EHF. Overall city-specific mortality risk estimates in natural causes for 1st vs. 99th percentile increases range from the highest mortality risk with 2.73 (95% CI: 2.34-3.18) in Seville to a risk of 1.78 (95% CI: 1.62-1.97) and 1.78 (95% CI: 1.45-2.19) in Barcelona and Bilbao, respectively. When we compare our results with risk estimates for the analyzed Spanish cities in other studies, the heat wave related mortality risks seem to be clearly higher. Furthermore, it has been demonstrated that different heat wave days of the same event do not present the same degree of severity/intensity. Thus, the intensity of a heat wave is an important mortality risk indicator during heat wave days. Due to the low number of studies on the EHF as a heat wave intensity indicator and heat-related mortality and morbidity, further research is required to validate its application in other geographic areas and focus populations.

Heat wave Intensity Duration Frequency Curve: A Multivariate Approach for Hazard and Attribution Analysis

Atmospheric warming is projected to intensify heat wave events, as quantified by multiple descriptors, including intensity, duration, and frequency. While most studies investigate one feature at a time, heat wave characteristics are often interdependent and ignoring the relationships between them can lead to substantial biases in frequency (hazard) analyses. We propose a multivariate approach to construct heat wave intensity, duration, frequency (HIDF) curves, which enables the concurrent analysis of all heat wave properties. Here we show how HIDF curves can be used in various locations to quantitatively describe the likelihood of heat waves with different intensities and durations. We then employ HIDF curves to attribute changes in heat waves to anthropogenic warming by comparing GCM simulations with and without anthropogenic emissions. For example, in Los Angeles, CA, HIDF analysis shows that we can attribute the 21% increase in the likelihood of a four-day heat wave (temperature > 31 °C) to anthropogenic emissions.