Heat Wave Days Research Articles

Intensified Western Pacific Convection Increases the Probability of Hot Extremes in the Middle East During the Boreal Spring

AbstractUnder global warming, the convective heating over the western Pacific (WP) has exhibited a significantly intensifying trend during the boreal spring, while the surface air temperatures in the Middle East (ME) have increased more rapidly than those in other tropical regions. Are these climate phenomena of the two regions physically connected? If yes, what are the responsible dynamical mechanisms involved? Utilizing the ERA5 reanalysis data and model simulations, this study reveals a significant seesaw variation in the convection and temperature trends between WP and ME. When convective heating intensifies over the WP, the ME tends to be drier and hotter during the spring, and vice versa. A further investigation indicates that the enhanced WP convective heating can induce anticyclonic circulation anomalies in the upper and middle troposphere over the Iranian and Tibetan plateaus. These anomalous high pressures extend westward, exhibiting a barotropic structure, which leads to stronger sinking motions, reduced cloud cover, and increased surface solar radiation over the ME. Consequently, these conditions result in drier and hotter soils and an increase in heatwave days in the ME. This study provides useful information for enhancing our understanding of the role of tropical WP climate change in influencing the upstream climate conditions with a focus on the ME.

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.

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.

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.

Can combined wind and solar power meet the increased electricity load on heatwave days in China after the carbon emission peak? A case study in southern Hebei

Wind and photovoltaic (PV) power are the fastest-growing renewable energy sources; however, they are vulnerable to weather extremes. In the context of global warming, the frequency of extreme weather events, particularly heatwaves (HW), is anticipated to increase. Compared to normal summer days, HW days not only cause a rise in electricity demand but also exhibit a complementary growth in wind/PV generation. Therefore, determining whether the increase in wind/PV generation on HW days can meet the corresponding surge in electricity load is pivotal for replacing fossil fuel-based electricity. To address this question, this study conducts a case study for southern Hebei Province, which has the highest combined wind/PV installations in China, for the period 2031–2040 under the scenario that China will achieve its carbon peak (2030) and carbon neutrality goals as planned. We use recently developed load and wind power models and calibrate a boosting ensemble learning model to simulate PV generation. The results reveal that the total increase in wind/PV generation on HW days can fully meet the total increase in electricity consumption starting in 2039. However, due to the uneven distribution of wind/PV generation and load changes throughout the HW days, 9 GWh of energy storage capacity is required to ensure electricity supply in the early morning hours. Under China's climate change adaptation strategy, Hebei Province can harness wind/PV energy to address peak load demands during HW conditions and initiate climate-resilient urban development pilot projects.

Environmental Quality, Extreme Heat, and Healthcare Expenditures

Although the effects of the environment on human health are well-established, the literature on the relationship between the quality of the environment and expenditures on healthcare is relatively sparse and disjointed. In this study, the Environmental Quality Index developed by the Environmental Protection Agency and heatwave days were compared against per capita Medicare spending at the county level. A general additive model with a Markov Random Field smoothing term was used for the analysis to ensure that spatial dependence did not undermine model results. The Environmental Quality Index was found to hold a statistically significant (p &lt; 0.05), multifaceted nonlinear association with spending, as was the average seasonal maximum heat index. The same was not true of heatwave days, however. In a secondary analysis on the individual domains of the index, the social and built environment components were significantly related to spending, but the air, water, and land domains were not. These results provide initial support for the simultaneous benefits of healthcare financing systems to mitigate some dimensions of poor environmental quality and consistently high air temperatures.

Urbisphere-Berlin Campaign: Investigating Multiscale Urban Impacts on the Atmospheric Boundary Layer

Abstract For next-generation weather and climate numerical models to resolve cities, both higher spatial resolution and subgrid parameterizations of urban canopy–atmosphere processes are required. The key is to better understand intraurban variability and urban–rural differences in atmospheric boundary layer (ABL) dynamics. This includes upwind–downwind effects due to cities’ influences on the atmosphere beyond their boundaries. To address these aspects, a network of &gt;25 ground-based remote sensing sites was designed for the Berlin region (Germany), considering city form, function, and typical weather conditions. This allows investigation of how different urban densities and human activities impact ABL dynamics. As part of the interdisciplinary European Research Council Grant urbisphere, the network was operated from autumn 2021 to autumn 2022. Here, we provide an overview of the scientific aims, campaign setup, and results from 2 days, highlighting multiscale urban impacts on the atmosphere in combination with high-resolution numerical modeling at 100-m grid spacing. During a spring day, the analyses show systematic upwind-city-downwind effects in ABL heights, largely driven by urban–rural differences in surface heat fluxes. During a heatwave day, ABL height is remarkably deep, yet spatial differences in ABL heights are less pronounced due to regionally dry soil conditions, resulting in similar observed surface heat fluxes. Our modeling results provide further insights into ABL characteristics not resolved by the observation network, highlighting synergies between both approaches. Our data and findings will support modeling to help deliver services to a wider community from citizens to those managing health, energy, transport, land use, and other city infrastructure and operations.

Povezanost med vročinskimi valovi in obiski na Internistični prvi pomoči Univerzitetnega kliničnega centra Ljubljana v obdobju 2013–2017

Background: Heatwaves have a severe impact on the number of deaths and emergency department visits, making them the most hazardous natural disaster due to climate change in many regions of the world. Our aim was to assess the short-term association between emergency department visits and heat waves from 2013 to 2017 to see whether any difference in the number of visits occurred at the Emergency Department in Ljubljana University Medical Centre during that period. Methods: The design of our study is a retrospective cross-sectional epidemiologic study. We estimated relative risks (RR) for a number of ED visits during heatwave days and reference period days for the observed diagnoses (all causes, diseases of the circulatory system, respiratory system, endocrine diseases, diseases of the digestive system and genitourinary diseases) sex, and age (from 19 to 74 years old, 75 years old and older), as well as 99.6% confidence intervals (CI) and excess ED visits in percent (%) associated with heatwaves occurring in the years from 2014 to 2017. Data were analyzed for the patients arriving at the Emergency Department of the Ljubljana University Medical Centre during summer time, from May to September. The region covers around 550,000 people and is considered an urban and rural area. Results: The results of our study showed that there was no statistically significant increase in number of ED visits during heatwaves for all causes in all observed years. We noticed even statistically significant protective phenomena between heatwaves and the number of ED visits. These happened in the years 2015 and 2017 for both sexes and circulatory system diseases, in 2016 for men, circulatory system diseases, and in 2015 for respiratory system diseases. We showed a statistically significant association between heatwaves and the number of visits in the ED for endocrine diseases in 2013. Conclusion: Despite the increased heat load in the last observed years, the number of ED visits in Ljubljana has remained stable or even decreased. Further studies on how each intervention affects the behavior of the observed population are needed to clarify whether public health measures and inter-sectoral cooperation impact the number of ED visits.

Causes on the 2018 record-breaking heatwave in South Korea: Compound impacts from recurrent large-scale atmospheric teleconnections

Abstract This study analyzed the causes of the extreme heatwave in East Asia in 2018, which brought a record-breaking event in South Korea in terms of the number of hot days and the intensity in history. Long-lasting atmospheric patterns were observed during the 2018 heatwave, and the patterns were similar to those known as the Pacific-Japan (PJ), Circum-global Teleconnection (CGT), and Arctic Oscillation (AO) patterns identified in a previous study. In 2018, all three patterns appeared to exhibit a strong positive phase. In particular, the PJ and AO Indices showed the highest values within the analysis period, having a major impact on the heatwave. According to a quantitative analysis through multi-linear regression (MLR) of how much each of the three patterns affects the heatwave, all three patterns of PJ, CGT, and AO have significant effects on the heatwave in Korea. The statistically reconstructed heatwave days (HWD) time series with the three indices by MLR account for 51% of the total variability at the interannual timescale. Especially in 2018, most of the occurred HWD could be restored using the three indices. Also, in 1994, when the three indices had a high peak, the number of HWDs was the second-longest ever, while in 1993, when all three indices had a low peak, the heatwave was the lowest. This study quantitatively examined whether the large-scale extreme heatwave in 2018 greatly increased because of PJ, CGT, and AO. This study suggests that extreme heatwave can occur again if three apparently independent atmospheric patterns appear at once in the future.

Biennial variability of boreal spring surface air temperature over India

In this study, we report significant biennial variability or oscillation (BO) in the boreal spring (March–May) Surface Air Temperature (SAT) over India and unravelled the causative mechanisms. The positive phase of the BO exhibit significant seasonal warming over India, whereas seasonal cooling is observed during the negative phase of BO. Heat wave days are more during the positive phase of BO compared to negative or neutral phase. The positive (negative) phase of BO is generally coherent with the central (eastern) Pacific warming (cooling) years. The anomalous low-level divergence associated with low-level anticyclonic circulation induces less cloudiness and intense surface solar radiation ovr India during the positive phase, favouring surface warming. The evolution of some positive and/or negative phases of BO without any large scale forcing from the equatorial Pacific suggested the possibility of alternate pathways. The strong anomalous upper-level (at 200 hPa) anticyclonic circulation provoked by mid-latitude Rossby waves is found contributing to the positive phase, thereby highlighting the role of dominant mid-latitude pathways in the biennial SAT variability in addition to El Niño forcing. The sinking motion associated with persistent high, and the associated adiabatic compression also supported surface heating during the positive phase of BO. On the other hand, the mid-latitude Rossby wave induced upper-level cyclonic circulation is found contributing to the negative phase. The sinking motion associated with persistent high, and the associated adiabatic compression also supported surface heating during the positive phase of BO. In contrast, negative soil temperature anomalies and high latent heat flux release to the atmosphere supported surface cooling during the negative phase.

Understanding the differences in the contribution and impact of urbanization on urban warming during heatwave and non-heatwave periods in China

Heatwaves (HW) present a significant threat to human health, making it essential to understand HW trends in China and the interaction between urbanization and HW is crucial for effective climate change adaptation. This study aims to quantify the differences in the contribution and impact of urbanization on urban warming during HW and non-heatwave (NHW) periods in China. By utilizing remote sensing impervious surface data, 2, 421 meteorological stations were dynamically classified into urban and rural stations from 1978 to 2017. The study identified concurrent heatwave (CHW) and NHW periods in both urban and rural areas, exploring the differences in urbanization effect between these periods. The results revealed that most regions in China were experiencing increasingly frequent and severe HW, with the number of HW events and total HW days rising by 0.3 events and 2.2 days per decade, respectively. During CHW periods, the average temperature difference between urban and rural areas increased by 0.33 °C, while the effect of urbanization on urban warming rose by 0.21 °C per decade, compared to NHW period. This indicates a synergistic enhancement of population exposure risk due to HW and urbanization. Over 60% of cities experienced a steeper linear trend in urban temperatures compared to rural areas during CHW, alongside reduced wind speeds and decreased precipitation. Implementing targeted adaptation and mitigation strategies is crucial to mitigating the increased risk of population exposure during HW events.

Study of Heat Wave Using High‐Resolution Real Time Meso‐Scale Analysis Over India

AbstractThe applicability and accuracy of high‐resolution Real‐Time Meso‐scale Analysis (RTMA) system is assessed over India for the first time. The RTMA is a high‐spatial (2.5 km) and temporal resolution analysis system for near‐surface weather conditions. It is used to simulate near‐surface air temperature over India during the Heatwave (HW) period 12th to 20th April 2023. The verification analysis of temperature using the GLDAS gridded temperature shows reasonable improvement in the analysis from RTMA by capturing regional features compared to first‐guess. The spatial and temporal verification using the IMD station observations also confirms the value addition of RTMA in capturing the locations of recorded highest Tmax and their daily variations. The location‐specific heat stress analysis of RTMA shows high skill over many locations during HW days. Heat stress regions have been accurately brought out in the RTMA. Hence, the RTMA can be used for now‐casting and severe weather monitoring.

Is heat wave a predictor of diarrhoea in Dhaka, Bangladesh? A time-series analysis in a South Asian tropical monsoon climate.

While numerous studies have assessed the association between temperature and diarrhoea in various locations, evidence of relationship between heat wave and diarrhoea is scarce. We defined elevated daily mean and maximum temperature over the 95th and 99th percentiles lasting for at least one day between March to October 1981-2010 as TAV95 and TAV99 and D95 and D99 heat wave, respectively. We investigated the association between heat wave and daily counts of hospitalisations for all-cause diarrhoea in Dhaka, Bangladesh using time series regression analysis employing constrained distributed lag-linear models. Effects were assessed for all ages and children aged under 5 years of age. Diarrhoea hospitalisation increased by 6.7% (95% CI: 4.6%- 8.9%), 8.3% (3.7-13.1), 7.0 (4.8-9.3) and 7.4 (3.1-11.9) in all ages on a TAV95, TAV99, D95 and D99 heat wave day, respectively. These effects were more pronounced for under-5 children with an increase of 13.9% (95% CI: 8.3-19.9), 24.2% (11.3-38.7), 17.0 (11.0-23.5) and 19.5 (7.7-32.6) in diarrhoea hospitalisations on a TAV95, TAV99, D95 and D99 heat wave day, respectively. At lags of 3 days, we noticed a negative association indicating a 'harvesting' effect. Our findings suggest that heat wave was a significant risk factor for diarrhoea hospitalisation in Dhaka. Further research is needed to elucidate the causal pathways and identify the preventive measures necessary to mitigate the impacts of heat waves on diarrhoea. Given that no heat wave definitions exist for Dhaka, these results may help to define heat waves for Dhaka and trigger public health interventions including heat alerts to prevent heat-related morbidity in Dhaka, Bangladesh.

Future changes in extremes across China based on NEX-GDDP-CMIP6 models

This paper evaluates the NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP) CMIP6 models’ performance in simulating extreme climate indices across China and its eight subregions for the period 2081–2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The models effectively reproduce the spatial patterns of extreme high temperatures, especially in northern China. They show enhanced capabilities in accurately simulating the maximum daily maximum temperature (TXx) and the number of high temperature days (T35). They improve the cold bias of the TXx index in Northwest China and warm bias in South China. In terms of precipitation, the models demonstrate strong performance, evidenced by significant spatial correlations in total wet day precipitation (PTOT) simulations. They reduce the biases of PTOT and simple daily intensity (SDII) compared to CMIP6 models. Regionally, they enhance PTOT accuracy along southern coasts and in Yunnan, better captures very heavy precipitation days (R20) in the Southwest region, max 5-day precipitation (RX5D) in North China and Southwest region, and SDII in the Northeast region and Yunnan. Under SSP5-8.5 scenario, significant impacts include increased TXx in Northwest China, more heatwave days in Southwest China, and more T35 in South China. Extreme precipitation will become more frequent in South and East China, with the greatest intensity increases in Southwest China (SWC1). North China will see fewest consecutive dry days (CDD) indices, while consecutive wet days (CWD) will prominently rise in SWC1.

Increasing heat waves frequencies over India during post-El Niño spring and early summer seasons

The increasing frequency of extreme Heat Waves (HWs) has generated significant societal impacts in recent years. This study used different observational datasets to investigate the HW characteristics over India during the post-El Niño spring and early summer seasons (April to June; AMJ). Analysis suggests that HW days are more prevalent over India, predominantly increased in south-central and northwest India, during the decaying phase of strong El Niño years. It is found that anomalous anticyclone circulation accompanied by high pressure extending from the Western North Pacific region towards the Bay of Bengal and India is responsible for enhanced HW days and intensity during the AMJ of strong El Niño decay years. This anomalous anticyclone-induced downdraft reduces the specific humidity in the lower troposphere, leading to decreased cloud cover over India. As a result, shortwave radiation is enhanced at the surface, which causes abnormal HWs over India. During the decaying phase of strong El Niño years, the HW days over India contributed to an increase in the frequency of Discomfort Index hours (above 28 °C), maximum temperatures exceeding 40 °C (hours per day), and Universal Thermal Climate Index days above 38 °C and 46 °C during the spring and early summer months, especially in the East Coast, central, and northwestern parts of India. Thus, proper prediction of large-scale atmospheric circulation over the Indo-western Pacific region during El Niño can help to predict the HW conditions three months in advance. This would help to implement suitable adaptation measures and put into practice strong mitigation policies to limit the increased risk of such events during AMJ of El Niño decay years.

Heatwaves in Vietnam: Characteristics and relationship with large‐scale climate drivers

AbstractThis study analyses the spatio‐temporal variability of heatwave characteristics and their association with large‐scale climate drivers across seven climatic sub‐regions in Vietnam, including the Northwest (R1), Northeast (R2), Red River Delta (R3), North Central (R4), South Central (R5), Central Highlands (R6) and the South (R7). The analysis is based on observed daily maximum temperatures from 102 meteorological stations, spanning the period 1980–2020. The obtained results reveal diverse heatwave patterns across the country. Amongst the seven climatic sub‐regions of Vietnam, the R3 and R4 sub‐regions experienced more frequent heatwaves and a higher number of heatwave days, but shorter durations. In contrast, other sub‐regions had fewer heatwave events and heatwave days but experienced longer‐lasting heatwaves. The intensity of heatwave events varies amongst sub‐regions, with the highest value in the R4 sub‐region, and the lowest in R7. Notably, the R1–R5 sub‐regions are affected by heatwaves over larger areas, compared to others. Additionally, the findings confirm that the lagged influence of El Niño–Southern Oscillation (ENSO) is the primary climatic driver of heatwave characteristics in Vietnam. Generally, heatwaves tend to occur more frequently in the years following El Niño events than after La Niña events. This observation provides opportunities for developing a system of seasonal predictions of heatwaves in Vietnam. The impact of ENSO on the number of heatwave events and heatwave days is evident in five out of seven sub‐regions, with less impact in the R2 and R7 sub‐regions. However, it does not significantly affect heatwave intensity.

Climatology of Wet-Bulb Globe Temperature and Associated Heat Waves in the U.S. Great Plains

Abstract Extreme heat such as that seen in the United States and Europe in summer 2022 can have significant impacts on human health and infrastructure. The Occupational Safety and Health Administration (OSHA) and the U.S. Army use wet-bulb globe temperature (WBGT) to quantify the impact of heat on workers and inform decisions on workload. WBGT is a weighted average of air temperature, natural wet-bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat-related disruptions. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with a peak WBGT about 1°F higher. The high- and extreme-risk categories in the southern U.S. Great Plains (USGP) have increased from 5 days per year to 15 days from 1960 to 2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery.

Heat-related illness among workers in British Columbia, Canada: Extreme hot weather in 2021 compared to 2001-2020.

British Columbia (BC), Canada, experienced an unprecedented summer with record-breaking high temperatures in 2021. Yet the health impact has not been examined in occupational settings. This study aimed to characterize occupational heat-related illness (HRI) among BC workers estimated by incidence rates and associations between heatwaves and HRI, compare risks from 2021 and prior summers of 2001-2020, and assess differential impacts on worker groups by demographics and occupations. We identified HRI from workers' compensation claims that occurred between June and August from 2001-2021 in BC. Incidence rates were calculated using working population estimates from Statistics Canada's Labour Force Survey. A time-stratified case-crossover design with conditional Poisson regression was used to examine the impact of heatwaves on occupational HRI. All analyses were stratified by year (2021 versus 2001-2020), age, sex, and occupation. Of the 521 claims identified, 107 (21%) occurred in 2021. Incidence rates for 2021 and prior summers were 3.97 [95% confidence interval (CI) 3.26-4.80] and 0.93 (95% CI 0.85-1.03) claims per 100 000 workers, respectively. This difference represents a 327% increase. Rates were higher in health occupations in 2021 versus 2001-2020. During 2001-2021, the risk of HRI during heatwave days was 4.33 (95% CI 2.98-6.27) times that during non-heatwave days, and the risk was higher among middle-aged workers and workers in trades, transport, and equipment operations. The 2021 heatwaves had greater impact on younger and female workers than those from prior summers. Heat is a crucial workplace hazard. Prevention strategies should prioritize at-risk workers and not be limited to heatwaves.

Projected Increase in Heatwaves under 1.5 and 2.0 °C Warming Levels Will Increase the Socio-Economic Exposure across China by the Late 21st Century

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes.

Mean Summer Land Temperatures in the Southern California Coastal Zone: Connections With Ocean Processes

AbstractThe cooling effect of the ocean on the Southern California coastal zone is investigated using a high‐resolution (4‐km) gridded surface meteorological data set (gridMET) of daily maximum temperature (Tmax), with focus on summer mean conditions, taken as the July–August–September (JAS) average. An empirical orthogonal function analysis reveals a coastal mode of JAS temperature covariability, distinct from a more energetic inland mode, that captures Tmax averaged across the Southern California coastal plain. The coastal mode temperature correlates significantly with, and has similar amplitude to, regional sea surface temperature (SST). High (low) summer land and sea surface temperatures, as well as inversion layer temperature differences, are associated with decreases (increases) of northerly coastal wind speeds and coastal cloudiness. The number of extreme heat days on land increases as regional SST increases (4.3 days °C−1), with heat wave days 10 times more likely during peak warm versus cool coastal mode years. The coastal zone was notably warmer and heat wave days peaked during the well documented marine heat wave events of 2014/15 and 2018 off Southern California. The marine variability associated with the coastal mode also has strong expression off the Baja California peninsula, presumably due to strong covarying winds in that area. As in previous studies, higher ocean temperatures are attributed to weaker summer winds, with associated reductions in ocean surface heat loss, coastal upwelling, and cloudiness.