Avoided climate impacts of urban and rural heat and cold waves over the U.S. using large climate model ensembles for RCP8.5 and RCP4.5

Intensification of compound temperature extremes by rapid urbanization under static and dynamic Urban-rural division: A comparative case study in Hunan Province, Central-South China

Climate extremes, notably compound extremes, pose significant risks to human society and environmental systems. These extremes, heightened by urbanization-a hallmark of modern socioeconomic progression-inflict persistent, intense thermal conditions. The comprehension of urbanization's impact on compound temperature extremes, particularly in Central-South China, a region with rapid urbanization and a subtropical climate, remains limited. In addition, most existing studies relied on static urban-rural division, and few used dynamic division, with no research yet juxtaposing findings from both methods. Against this backdrop, this study provided an unprecedented assessment of urbanization's impact on both individual and compound temperature extremes in Central South China, focusing on Hunan Province during long-time period of 1960-2022 under both static and dynamic urban-rural divisions. In both urban and rural stations, a pronounced warming trend was evident across individual and compound temperature extremes. Besides, a tendency of independent day/night extremes shifting towards extremes spanning both was observed. Notably, the escalation of heat compound extreme temperature indices (ETIs) outpaces that of cold ones, with a larger urban-rural discrepancy under dynamic classifications. Urbanization intensifies temperature extremes, particularly affecting the reduction of independent cold days (30.97 %-33.94 %) and the increase in compound hot events (23.91 %-24.87 %). Interestingly, urbanization's impact is more substantial on independent daytime extremes than on independent nighttime ones. Urbanization's influence on ETIs was consistently observed under both static and dynamic classifications, with the latter revealing a more pronounced contribution (1 %-3 %), and the contribution to compound ETIs is 6 %-8 % higher than to individual ETIs. These findings emphasize the importance of considering urbanization's multifaceted impacts on climate strategies and underscore the need for adaptive infrastructure and sustainable practices to mitigate escalating climate risks.

Residential greenness for mitigating impacts of extreme heat events on depression and supporting mental health

Background

Residential green spaces (RGS) are a crucial aspect of urban life, which provide residents with a positive living environment both for mental and physical well-being. However, extreme heat events caused by global warming and local urban heat island effects are threatening the public health of rapidly growing populations. This is especially true for mental health. Depression is a mental illness that can be impacted by extreme heat events, i.e., heatwaves.

Objective

This study aimed to investigate the potential for residential green spaces (RGS) to alleviate depression by reducing heat stress sensitivity during extreme heat events.

Methods

We conducted a literature review using scientometric analysis with CiteSpace to summarize existing research on the relationships between RGS, depression, and heatwaves. We proposed a conceptual framework for the relationship between RGS and depression, and that extreme heat events may be an important contributor to depression.

Results

Our review found that RGS can provide ecosystem services that lower ambient temperatures through evaporative cooling, radiation reflection, humidity regulation, and shading. Different types of RGS, i.e., small green spaces, green roofs, green walls, and street trees, have varying cooling capacities. The mechanisms by which RGS alleviate depression during heatwaves involve green space composition, exposure, physical activity, social contacts, and cohesion. And we proposed a conceptual framework for the relationship between RGS and depression, and that extreme heat events may be an important contributor to depression.

Conclusion

We present a multidimensional RGS evaluation roadmap to inform green space design for reducing depression during heatwaves. Establishing RGS multidimensional evaluation can guide future research on leveraging RGS to build resilience against extreme heat and improve public mental health.

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Microscale models and urban heat island studies: a systematic review

Urban climate analysis usually uses data from weather stations, traverse, or satellite images. However, this methodology also has its limitations, since the series of data for climate monitoring can be scarce. Another option that has been earning attention in recent years is numeric models, which perform simulations in urban climate. Obtaining climate data is extremely important for climatology, as well as for related areas, such as urban planning, which uses this data to know how to best order the territory according to climate conditions and their projections. Our study aimed to carry out a literature review regarding urban heat island analysis methodologies, with emphasis on the use of models. We evaluated over 200 scientific documents and we used 68 in the results of this work, reporting different types of models. The results indicated that most of the works on urban climate use a more traditional methodological approach, with fieldwork, whereas studies with models have been carried out in a specific way, especially in cities in the northern hemisphere. Among the articles evaluated, the majority were published in Elsevier publisher journals, which have a more interdisciplinary approach. The most studied models were ENVI-met, SOLWEIG, PALM-4U, RayMan, and TEB. In this way, this work pointed out, unlike other works of review in urban climate methodologies, the difficulty in obtaining field data, emphasizing their importance, with regard to studies of urban heat islands and urban planning. We also conclude that the progress and development of the state of the art in numerical models are conditioned to scientific investment in the area.

Population vulnerability to extreme cold days in rural and urban municipalities in ten provinces in Spain

BACKGROUND

The objective was to analyze whether there are differences in vulnerability to Extreme Cold Days (ECD) between rural and urban populations in Spain.

METHODOLOGY

Time series analysis carried out from January 1, 2000, through December 31, 2013. Municipalities with over 10,000 inhabitants were included from 10 Spanish provinces, classified into 42 groups by isoclimate and urban/rural character as defined by Eurostat criteria. The statistical strategy was carried out in two phases. First: It was analyzed the relationship between minimum daily temperature (Tmin) (source: AEMET) and the rate of daily winter mortality due to natural causes -CIE-10: A00 - R99- (source: National Statistics Institute). Then, It was determinated the threshold of Tmin that defines the ECD and its percentile in the series of winter Tmin (Pthreshold), which is a measure of vulnerability to ECD so that the higher the percentile, the higher the vulnerability. Second: possible explanatory variables of vulnerability were explored using Mixed Generalized Models, using 13 independent variables related to meteorology, environment, socioeconomics, demographics and housing quality.

RESULTS

The average Pthreshold was 18 %. The final model indicated that for each percentage point increase in unemployment, the vulnerability to ECD increased by 0.4 (0.2, 0.6) points. Also, with each point increase in rurality index, this vulnerability decreased by -6.1 (-2.1, -10.0) points. Although less determinant, other factors that could contribute to explaining vulnerability at the province level included minimum winter daily temperatures and the percentage of housing with poor insulation.

CONCLUSIONS

The vulnerability to ECD was greater in urban zones than in rural zones. Socioeconomic status is a key to understanding how this vulnerability is distributed. These results suggest the need to implement public health prevention plans to address ECD at the state level. These plans should be based on threshold temperatures determined at the smallest scale possible.

Temporal evolution of threshold temperatures for extremely cold days in Spain

In contrast to research on heat waves, there are no studies in recent years that analyze the temporal evolution of threshold temperatures (Tthreshold) for extremely cold days (ECD). It is unknown whether threshold temperatures have increased more quickly than the minimum daily temperature (Tmin) in recent years. The objective of this study was to analyze the temporal evolution of the minimum daily temperature (Tmin) in a group of Spanish provinces and compare it with the evolution of threshold temperatures. An ecological, retrospective time series study was carried out using daily observations between January 1, 1983 and December 31, 2018 (36 years) in 10 provinces that are representative of the different climate territories in Spain. For each representative observatory in each province, the values of Tmin were obtained for the winter months (November-March). The value of Tthreshold was determined for each province and each year, using dispersion diagrams for the pre-whitened series, with daily mortality due to natural causes displayed on the Y axis (CIEX: A00-R99) and Tmin grouped by 10 degree intervals on the X axis. To determine the temporal evolution of Tmin and Tthreshold for each province, linear models were fitted, with time as the independent variable. During the winter months, Tmin increased at an average rate of 0.2 °C/decade (IC95: 0.1-0.3), while Tthreshold remained practically constant during the period, at 0.1 °C/decade (IC95% -0.1 0.3). These values are much lower than those obtained in the case of heat, both in terms of the evolution of maximum daily temperature and that of Tthreshold. In conclusion, the fact that this trend has been maintained across time in a scenario of climate change, with a slow increase in minimum daily temperatures and constant values of Threshold, suggests a decrease in the number of ECD.

Urban heat vulnerability: A dynamic assessment using multi-source data in coastal metropolis of Southeast China

Extreme heat caused by global climate change has become a serious threat to the sustainable development of urban areas. Scientific assessment of the impacts of extreme heat on urban areas and in-depth knowledge of the cross-scale mechanisms of heat vulnerability forming in urban systems are expected to support policymakers and stakeholders in developing effective policies to mitigate the economic, social, and health risks. Based on the perspective of the human-environment system, this study constructed a conceptual framework and index system of "exposure-susceptibility-adaptive capacity" for urban heat vulnerability (UHV) and proposed its assessment methods. Taking Xiamen City, a coastal metropolis, as an example, spatial analysis and Geodetector were used to explore the spatial and temporal changes, spatial characteristics, and patterns of UHV under multiple external disturbances from natural to anthropological factors, and to reveal the main factors influencing UHV forming and spatial differentiation. Results showed that the exposure, susceptibility, adaptive capacity, and UHV in Xiamen City had a spatial structure of "coastal-offshore-inland". On the hot day, both the exposure and UHV showed a temporal pattern of "rising and then falling, peaking at 14:00" and a spatial pattern of "monsoonal-like" movement between coast and inland. Coastal zoning with favorable socioeconomic conditions had less magnitude of changes in UHV, where the stability of the urban system was more likely to be maintained. During the hot months, the high UHV areas were mainly distributed in the inland, while coastal areas showed low UHV levels. Further, coastal UHV was mainly dominated by "heat exposure", offshore by "comprehensive factors", and inland in the northern mountainous areas by "lack of adaptive capacity". Multi-scale urban adaptive capacity was confirmed to alter spatial distribution of exposure and reshape the spatial pattern of UHV. This study promotes the application of multi-scale vulnerability framework to disaster impact assessment, enriches the scientific knowledge of the urban system vulnerability, and provides scientific references for local targeted cooling policy development and extreme heat resilience building programs.

Dominant Factors in the Temporal and Spatial Distribution of Precipitation Change in the Beijing–Tianjin–Hebei Urban Agglomeration

Urbanization has a significant influence on precipitation, but existing studies lack the spatial and temporal heterogeneity analysis of its impact on precipitation in urban areas at different levels. This study investigates the spatial heterogeneity of precipitation and the influencing factors on six dimensions in 156 urban areas in the Beijing–Tianjin–Hebei urban agglomeration from 2000 to 2018, utilizing a mixed-methods analytical approach. The results show that the change in the natural factor layer caused by urbanization was the most important factor, affecting urban precipitation variation in summer and over the whole year, accounting for 34.5% and 10.7%, respectively. However, the contribution of the urban thermal environment in summer cannot be ignored, and the change in the urban thermal environment caused by human activities in winter is an important influencing factor. When considering the optimal combination of factors, relative humidity was shown to be significant in the spatial variations in precipitation during summer, which contributed 26.2%, followed by human activity as indicated by night-time light intensity. Over the whole year, aerosol optical depth makes the substantial contribution of 21.8% to urban precipitation change. These results provide benchmarks for improving the adaptability of urban-environment change and urban planning in the context of urbanization.

Analysis of vulnerability to heat in rural and urban areas in Spain: What factors explain Heat's geographic behavior?

INTRODUCTION

There is currently little knowledge and few published works on the subject of vulnerability to heat in rural environments at the country level. Therefore, the objective of this study was to determine whether rural areas are more vulnerable to extreme heat than urban areas in Spain. This study aimed to analyze whether a pattern of vulnerability depends on contextual, environmental, demographic, economic and housing variables.

METHODS

An ecological, longitudinal and retrospective study was carried out based on time series data between January 01, 2000 and December 31, 2013 in 42 geographic areas in 10 provinces in Spain. We first analyzed the functional relationship between the mortality rate per million inhabitants and maximum daily temperature (Tmax). We then determined the summer temperature threshold (Pthreshold) (June-September) at which increases in mortality are produced that are attributable to heat. In a second phase, based on Pthreshold, a vulnerability variable was calculated, and its distribution was analyzed using mixed linear models from the Poisson family (link = log). In these models, the dependent variable was vulnerability, and the independent variables were exposure to high temperatures, aridity of the climate, deprivation index, percentage of people over age 65, rurality index, percentage of housing built prior to 1980 and condition of dwellings.

RESULTS

Rurality was a protective factor, and vulnerability in urban areas was six times greater. In contrast, risk factors included aridity (RR = 5.89 (2.26 15.36)), living in cool summer zones (2.69 (1.23, 5.91)), poverty (4.05 (1.91 8.59)) and the percentage of dysfunctional housing (1.13 (1.04 1.24)).

CONCLUSIONS

Rural areas are less vulnerable to extreme heat than the urban areas analyzed. Also, population groups with worse working conditions and higher percentages of dwellings in poor conditions are more vulnerable.

Landscape pattern change simulations in Tibet based on the combination of the SSP-RCP scenarios

Monitoring landscape pattern change can provide spatial explicit basis for future landscape management. The future socioeconomic and climate change drivers should be systematically combined in landscape pattern monitoring, while they are often regarded as independent parameters in landscape monitoring models. This study sought to project the detailed landscape pattern change based on landscape composition and configuration in Tibet by 2030, and combined the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). The results showed area of the unused land and forest will reduce by a minimum standard of 11.42 × 10⁴ and 9.04 × 10⁴ km² from 2010 to 2030, respectively. Other land use types will increase, and the highest increase in grassland will be 9.30 × 10⁵ km². Combined SSP1 and RCP2.6 scenario show high landscape aggregation and low edge density on cultivated land, urban land and grassland in Tibet as a whole. However, in typical cultivated and urban landscape, the abovementioned rule is appeared in the combined SSP4 and RCP6.0 scenario. These findings stress the importance of systematically modeling the socioeconomic demand and climate change in landscape pattern monitoring, and using both landscape composition and configuration indexes for scenario evaluation.

Large model structural uncertainty in global projections of urban heat waves

Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Here, we use an urban climate emulator combined with large ensemble global climate simulations to show that, at the urban scale a large proportion of the variability results from the model structural uncertainty in projecting UHWs in the coming decades under climate change. Omission of this uncertainty would considerably underestimate the risk of UHW. Results show that, for cities in four high-stake regions - the Great Lakes of North America, Southern Europe, Central India, and North China - a virtually unlikely (0.01% probability) UHW projected by single-model ensembles is estimated by our model with probabilities of 23.73%, 4.24%, 1.56%, and 14.76% respectively in 2061-2070 under a high-emission scenario. Our findings suggest that for urban-scale extremes, policymakers and stakeholders will have to plan for larger uncertainties than what a single model predicts if decisions are informed based on urban climate simulations.

Late lactation in small mammals is a critically sensitive window of vulnerability to elevated ambient temperature

Predicted increases in global average temperature are physiologically trivial for most endotherms. However, heat waves will also increase in both frequency and severity, and these will be physiologically more important. Lactating small mammals are hypothesized to be limited by heat dissipation capacity, suggesting high temperatures may adversely impact lactation performance. We measured reproductive performance of mice and striped hamsters (Cricetulus barabensis), including milk energy output (MEO), at temperatures between 21 and 36 °C. In both species, there was a decline in MEO between 21 and 33 °C. In mice, milk production at 33 °C was only 18% of that at 21 °C. This led to reductions in pup growth by 20% but limited pup mortality (0.8%), because of a threefold increase in growth efficiency. In contrast, in hamsters, MEO at 33 °C was reduced to 78.1% of that at 21 °C, yet this led to significant pup mortality (possibly infanticide) and reduced pup growth by 12.7%. Hamster females were more able to sustain milk production as ambient temperature increased, but they and their pups were less capable of adjusting to the lower supply. In both species, exposure to 36 °C resulted in rapid catastrophic lactation failure and maternal mortality. Upper lethal temperature was lowered by 3 to 6 °C in late lactation, making it a critically sensitive window to high ambient temperatures. Our data suggest future heat wave events will impact breeding success of small rodents, but this is based on animals with a long history in captivity. More work should be performed on wild rodents to confirm these impacts.

Exploring the Driving Factors of the Spatiotemporal Variation of Precipitation in the Jing–Jin–Ji Urban Agglomeration from 2000 to 2015

Urbanization has a significant impact on urban precipitation. Existing studies on precipitation pay more attention to the impact of natural and meteorological factors, and the research on the impact of urbanization on the spatial patterns of precipitation is still very deficient. Based on geographic detection, this study quantitatively analyzed the dominant, interaction, and sensitivity factors that affect precipitation changes in more than 150 urban units in Jing–Jin–Ji (Beijing–Tianjin–Hebei) during the process of urbanization. The research findings show the following: ① The dominant factors have seasonal differences in terms of the precipitation variation in Jing–Jin–Ji. The leading factors in summer were the change of radiation and relative humidity. The dominant factors in winter were the changes in radiation, relative humidity, and wind speed. On the annual scale, the dominant factors were the changes in relative humidity, aerosol optical depth, radiation, and wind speed. ② Whether in summer, in winter, or on the annual scale, urbanization can enhance the explanatory power of spatial variation of urban precipitation through interaction with natural/meteorological factors, and all the dominant interaction factors show a nonlinear enhancement trend. ③ The night light intensity and urban heat island can greatly amplify the explanatory power of other factors, thus becoming the most sensitive factor in urbanization precipitation changes. The above research can provide a theoretical basis for the formulation of urban climate policies and urban planning.

The motley drivers of heat and cold exposure in 21st century US cities

We use a suite of decadal-length regional climate simulations to quantify potential changes in population-weighted heat and cold exposure in 47 US metropolitan regions during the 21st century. Our results show that population-weighted exposure to locally defined extreme heat (i.e., "population heat exposure") would increase by a factor of 12.7-29.5 under a high-intensity greenhouse gas (GHG) emissions and urban development pathway. Additionally, end-of-century population cold exposure is projected to rise by a factor of 1.3-2.2, relative to start-of-century population cold exposure. We identify specific metropolitan regions in which population heat exposure would increase most markedly and characterize the relative significance of various drivers responsible for this increase. The largest absolute changes in population heat exposure during the 21st century are projected to occur in major US metropolitan regions like New York City (NY), Los Angeles (CA), Atlanta (GA), and Washington DC. The largest relative changes in population heat exposure (i.e., changes relative to start-of-century) are projected to occur in rapidly growing cities across the US Sunbelt, for example Orlando (FL), Austin (TX), Miami (FL), and Atlanta. The surge in population heat exposure across the Sunbelt is driven by concurrent GHG-induced warming and population growth which, in tandem, could strongly compound population heat exposure. Our simulations provide initial guidance to inform the prioritization of urban climate adaptation measures and policy.

Characterizing the role of socioeconomic pathways in shaping future urban heat-related challenges

Urban dwellers worldwide are increasingly affected by more frequent and intense extreme temperature events, ongoing urbanization, and changes in socioeconomic conditions. Decades of research have shown that vulnerability is a crucial determinant of heat-related risk and mortality in cities, yet assessments of future urban heat-related challenges have largely overlooked the contribution of changes in socioeconomic conditions to future heat-related risk and mortality. The scenario framework for climate change research, made up of socioeconomic scenarios (Shared Socioeconomic Pathways - SSPs) combined with climate scenarios (Representative Concentration Pathways - RCPs), facilitates the integration of socioeconomic scenarios into climate risks assessments. In this study, we used Greater Houston (Texas) as a case study to implement the scenario framework at the intra-urban scale. Integrating locally extended SSPs along with a range of sectoral modelling approaches, we combined projections of urban extreme heat - which account for SSP-specific urban heat islands - with projections of future population and vulnerability. We then produced estimates of future heat-related risk and mortality for 2041-2060 (2050s) summers at Census tract level, for multiple combinations of climate and socioeconomic scenarios. Using a scenario matrix, we showed that the projected ~15,738-24,521 future summer excess mortalities compared to 1991-2010 are essentially driven by population growth and changes in vulnerability, with changes in climatic conditions alone being of little influence. We outline methods to apply the new scenario framework at intra-urban scale and to better characterize the contribution of socioeconomic pathways to future urban climate risks. This socio-climatic approach provides comprehensive estimates of future climate risks in urban areas, which are essential for adaptation planning under climatic and socioeconomic uncertainty.

Population dynamics modify urban residents' exposure to extreme temperatures across the United States

Exposure to extreme temperatures is one primary cause of weather-related human mortality and morbidity. Global climate change raises the concern of public health under future extreme events, yet spatiotemporal population dynamics have been long overlooked in health risk assessments. Here, we show that the diurnal intra-urban movement alters residents' exposure to extreme temperatures during cold and heat waves. To do so, we incorporate weather simulations with commute-adjusted population profiles over 16 major U.S. metropolitan areas. Urban residents' exposure to heat waves is intensified by 1.9° ± 0.7°C (mean ± SD among cities), and their exposure to cold waves is attenuated by 0.6° ± 0.8°C. The higher than expected exposure to heat waves significantly correlates with the spatial temperature variability and requires serious attention. The essential role of population dynamics should be emphasized in temperature-related climate adaptation strategies for effective and successful interventions.

Heat risk assessment for the Brussels capital region under different urban planning and greenhouse gas emission scenarios

Urban residents are exposed to higher levels of heat stress in comparison to the rural population. As this phenomenon could be enhanced by both global greenhouse gas emissions (GHG) and urban expansion, urban planners and policymakers should integrate both in their assessment. One way to consider these two concepts is by using urban climate models at a high resolution. In this study, the influence of urban expansion and GHG emission scenarios is evaluated at 100 m spatial resolution for the city of Brussels (Belgium) in the near (2031-2050) and far (2081-2100) future. Two possible urban planning scenarios (translated into local climate zones, LCZs) in combination with two representative concentration pathways (RCPs 4.5 and 8.5) have been implemented in the urban climate model UrbClim. The projections show that the influence of GHG emissions trumps urban planning measures in each period. In the near future, no large differences are seen between the RCP scenarios; in the far future, both heat stress and risk values are twice as large for RCP 8.5 compared to RCP 4.5. Depending on the GHG scenario and the LCZ type, heat stress is projected to increase by a factor of 10 by 2090 compared to the present-day climate and urban planning conditions. The imprint of vulnerability and exposure is clearly visible in the heat risk assessment, leading to very high levels of heat risk, most notably for the North Western part of the Brussels Capital Region. The results demonstrate the need for mitigation and adaptation plans at different policy levels that strive for lower GHG emissions and the development of sustainable urban areas safeguarding livability in cities.

Detailed Comparison of the Operational Characteristics of Energy-Conserving HVAC Systems during the Cooling Season

To provide useful information concerning energy-conserving heating, ventilation, and air-conditioning (HVAC) systems, this study used EnergyPlus to analyze in detail their operational characteristics and energy performance. This study also aimed to understand the features of the systems under consideration by investigating the dry-bulb temperature, relative humidity, and airflow rate at major nodes in each system’s schematic. Furthermore, we analyzed the indoor environment created by each HVAC system, as well as examining the cooling energy consumptions and CO2 emissions. The HVAC systems selected for this study are the variable air volume (VAV) commonly used in office buildings (base-case model), constant air volume (CAV), under-floor air distribution (UFAD), and active chilled beam (ACB) with dedicated outdoor air system (DOAS). For the same indoor set-point temperature, the CAV’s supply airflow was the highest, and VAV and UFAD were operated by varying the airflow rate according to the change of the space thermal load. ACB with DOAS was analyzed as being able to perform air conditioning only with the supply airflow constantly fixed at a minimum outdoor air volume. The primary cooling energy was increased by about 23.3% by applying CAV, compared to VAV. When using the UFAD and ACB with DOAS, cooling energy was reduced by 11.3% and 23.1% compared with VAV, respectively.

Will there be cold-related mortality in Spain over the 2021-2050 and 2051-2100 time horizons despite the increase in temperatures as a consequence of climate change?

INTRODUCTION

Global warming is resulting in an increase in temperatures which is set to become more marked by the end of the century and depends on the accelerating pace of greenhouse gas emissions into the atmosphere. Yet even in this scenario, so-called "cold waves" will continue to be generated and have an impact on health.

OBJECTIVES

This study sought to analyse the impact of cold waves on daily mortality at a provincial level in Spain over the 2021-2050 and 2051-2100 time horizons under RCP4.5 and RCP 8.5 emission scenarios, on the basis of two hypotheses: (1) that the cold-wave definition temperature (T _threshold) would not vary over time; and, (2) that there would be a variation in T _threshold.

MATERIAL AND METHODS

The results of a retrospective study undertaken for Spain as a whole across the period 2000-2009 enabled us to ascertain the cold-wave definition temperature at a provincial level and its impact on health, measured by reference to population attributable risk (PAR). The minimum daily temperatures projected for each provincial capital considering the above time horizons and emission scenarios were provided by the State Meteorological Agency. On the basis of the T _threshold definition values and minimum daily temperatures projected for each province, we calculated the expected impact of low temperatures on mortality under the above two hypotheses. Keeping the PAR values constant, it was assumed that the mortality rate would vary in accordance with the available data.

RESULTS

If T _threshold remained constant over the above time horizons under both emission scenarios, there would be no cold-related mortality. If T _threshold were assumed to vary over time, however, then cold-related mortality would not disappear: it would instead remain practically constant over time and give rise to an estimated overall figure of around 250 deaths per year, equivalent to close on a quarter of Spain's current annual cold-related mortality and entailing a cost of approximately €1000 million per year.

CONCLUSION

Given that cold waves are not going to disappear and that their impact on mortality is far from negligible and is likely to remain so, public health prevention measures must be implemented to minimise these effects as far as possible.

Development of high-resolution daily gridded temperature datasets for the central north region of Egypt

This study developed 0.05° × 0.05° land-only datasets of daily maximum and minimum temperatures in the densely populated Central North region of Egypt (CNE) for the period 1981-2017. Existing coarse-resolution datasets were evaluated to find the best dataset for the study area to use as a base of the new datasets. The Climate Prediction Centre (CPC) global temperature dataset was found to be the best. The CPC data were interpolated to a spatial resolution of 0.05° latitude/longitude using linear interpolation technique considering the flat topography of the study area. The robust kernel density distribution mapping method was used to correct the bias using observations, and WorldClim v.2 temperature climatology was used to adjust the spatial variability in temperature. The validation of CNE datasets using probability density function skill score and hot and cold extremes tail skill scores showed remarkable improvement in replicating the spatial and temporal variability in observed temperature. Because CNE datasets are the best available high-resolution estimate of daily temperatures, they will be beneficial for climatic and hydrological studies.

Urbanization Effects on Human-Perceived Temperature Changes in the North China Plain

Urbanization and associated land use changes significantly alter the energy and radiation balance, land surface characteristics, and regional climates, posing challenges to natural ecosystems and human society. The combined effects of changes in air temperature (T), relative humidity (RH), and wind speed (WS) profoundly influence human-perceived temperature and the corresponding human thermal comfort, especially in urban areas with large population. This study analyzes the spatiotemporal changes in human-perceived temperatures in the North China Plain, represented by heat index (HI) in summer and wind chill temperature (WCT) in winter, and quantifies the effects of urbanization on temperature changes, based on the observational data of 56 meteorological stations during 1976–2016. The results show a significant warming trend, with human-perceived temperatures increasing faster than T. The warming trend in WCT is higher than that in HI, indicating more thermal discomfort in summer and more thermal comfort in winter. However, the warming trend moderately slows after 1996, partly due to the global surface warming hiatus. Urban areas experience stronger warming trends than non-urban areas, demonstrating the notable effects of urbanization. For the entire study area, urbanization and associated urban land expansion accelerate the increase in HI by 26% and the increase in WCT by 17%.

Mortality attributable to high temperatures over the 2021-2050 and 2051-2100 time horizons in Spain: Adaptation and economic estimate

BACKGROUND

In recent years, a number of studies have been conducted with the aim of analysing the impact that high temperatures will have on mortality over different time horizons under different climate scenarios. Very few of these studies take into account the fact that the threshold temperature used to define a heat wave will vary over time, and there are practically none which calculate this threshold temperature for each geographical area on the assumption that there will be variations at a country level.

OBJECTIVE

To analyse the impact that high temperatures will have on mortality across the periods 2021-2050 and 2051-2100 under a high-emission climate scenario (RCP8.5), in a case: (a) where adaptation processes are not taken into account; and (b) where complete adaptation processes are taken into account.

MATERIAL AND METHODS

Based on heat-wave definition temperature (T_threshold) values previously calculated for the reference period, 2000-2009, for each Spanish provincial capital, and their impact on daily mortality as measured by population attributable risk (PAR), the impact of high temperatures on mortality will be calculated for the above-mentioned future periods. Two hypotheses will be considered, namely: (a) that T_threshold does not vary over time (scenario without adaptation to heat); and, (b) that T_threshold does vary over time, with the percentile to which said T_threshold corresponds being assumed to remain constant (complete adaptation to heat). The temperature data were sourced from projections generated by Coupled Model Intercomparison Project (CMIP5) climate models adapted to each region's local characteristics by the State Meteorological Agency (Agencia Estatal de Meteorología/AEMET). Population-growth projections were obtained from the National Statistics Institute (Instituto Nacional de Estadística/INE). In addition, an economic estimate of the resulting impact will be drawn up.

RESULTS

The mean value of maximum daily temperatures will rise, in relation to those of the reference period (2000-2009), by 1.6⁰C across the period 2021-2050 and by 3.3⁰C across the period 2051-2100. In a case where there is no heat-adaptation process, overall annual mortality attributable to high temperatures in Spain would amount to 1414 deaths/year (95% CI: 1089-1771) in the period 2021-2050, rising to 12,896 deaths/year (95% CI: 9852-15,976) in the period 2051-2100. In a case where there is a heat-adaptation process, annual mortality would be 651 deaths/year (95% CI: 500-807) in the period 2021-2050, and 931 deaths per year (95% CI: 770-1081) in the period 2051-2100. These results display a high degree of heterogeneity. The savings between a situation that does envisage and one that does not envisage an adaptive process is €49,100 million/year over the 2051-2100 time horizon.

CONCLUSION

A non-linear increase in maximum daily temperatures was observed, which varies widely from some regions to others, with an increase in mean values for Spain as a whole that is not linear over time. The high degree of heterogeneity found in heat-related mortality by region and the great differences observed on considering an adaptive versus a non-adaptive process render it necessary for adaptation plans to be implemented at a regional level.

Projected trends in high-mortality heatwaves under different scenarios of climate, population, and adaptation in 82 US communities

Some rare heatwaves have extreme daily mortality impacts; moderate heatwaves have lower daily impacts but occur much more frequently at present and so account for large aggregated impacts. We applied health-based models to project trends in high-mortality heatwaves, including proportion of all heatwaves expected to be high-mortality, using the definition that a high-mortality heatwave increases mortality risk by ≥20 %. We projected these trends in 82 US communities in 2061-2080 under two scenarios of climate change (RCP4.5, RCP8.5), two scenarios of population change (SSP3, SSP5), and three scenarios of community adaptation to heat (none, lagged, on-pace) for large- and medium-ensemble versions of the National Center for Atmospheric Research's Community Earth System Model. More high-mortality heatwaves were expected compared to present under all scenarios except on-pace adaptation, and population exposure was expected to increase under all scenarios. At least seven more high-mortality heatwaves were expected in a twenty-year period in the 82 study communities under RCP8.5 than RCP4.5 when assuming no adaptation. However, high-mortality heatwaves were expected to remain <1 % of all heatwaves and heatwave exposure under all scenarios. Projections were most strongly influenced by the adaptation scenario- going from a scenario of on-pace to lagged adaptation or from lagged to no adaptation more than doubled the projected number of and exposure to high-mortality heatwaves. Based on our results, fewer high-mortality heatwaves are expected when following RCP4.5 versus RCP8.5 and under higher levels of adaptation, but high-mortality heatwaves are expected to remain a very small proportion of total heatwave exposure.

Classifying heatwaves: Developing health-based models to predict high-mortality versus moderate United States heatwaves

Heatwaves are divided between moderate, more common heatwaves and rare "high-mortality" heatwaves that have extremely large health effects per day, which we define as heatwaves with a 20% or higher increase in mortality risk. Better projections of the expected frequency of and exposure to these separate types of heatwaves could help communities optimize heat mitigation and response plans and gauge the potential benefits of limiting climate change. Whether a heatwave is high-mortality or moderate could depend on multiple heatwave characteristics, including intensity, length, and timing. We created heatwave classification models using a heatwave training dataset created using recent (1987-2005) health and weather data from 82 large US urban communities. We built twenty potential classification models and used Monte Carlo cross-validations to evaluate these models. We ultimately identified several models that can adequately classify high-mortality heatwaves. These models can be used to project future trends in high-mortality heatwaves under different scenarios of a changing future (e.g., climate change, population change). Further, these models are novel in the way they allow exploration of different scenarios of adaptation to heat, as they include, as predictive variables, heatwave characteristics that are measured relative to a community's temperature distribution, allowing different adaptation scenarios to be explored by selecting alternative community temperature distributions. The three selected models have been placed on GitHub for use by other researchers, and we use them in a companion paper to project trends in high-mortality heatwaves under different climate, population, and adaptation scenarios.

Scenario dependence of future changes in climate extremes under 1.5 °C and 2 °C global warming

The 2015 Paris Agreement aims to limit global warming below 2 °C and pursue efforts to even limit it to 1.5 °C relative to pre-industrial levels. Decision makers need reliable information on the impacts caused by these warming levels for climate mitigation and adaptation measures. We explore the changes in climate extremes, which are closely tied to economic losses and casualties, under 1.5 °C and 2 °C global warming and their scenario dependence using three sets of ensemble global climate model simulations. A warming of 0.5 °C (from 1.5 °C to 2 °C) leads to significant increases in temperature and precipitation extremes in most regions. However, the projected changes in climate extremes under both warming levels highly depend on the pathways of emissions scenarios, with different greenhouse gas (GHG)/aerosol forcing ratio and GHG levels. Moreover, there are multifold differences in several heavily polluted regions, among the scenarios, in the changes in precipitation extremes due to an additional 0.5 °C warming from 1.5 °C to 2 °C. Our results demonstrate that the chemical compositions of emissions scenarios, not just the total radiative forcing and resultant warming level, must be considered when assessing the impacts of global 1.5/2 °C warming.

Aging Will Amplify the Heat-related Mortality Risk under a Changing Climate: Projection for the Elderly in Beijing, China

An aging population could substantially enhance the burden of heat-related health risks in a warming climate because of their higher susceptibility to extreme heat health effects. Here, we project heat-related mortality for adults 65 years and older in Beijing China across 31 downscaled climate models and 2 representative concentration pathways (RCPs) in the 2020s, 2050s, and 2080s. Under a scenario of medium population and RCP8.5, by the 2080s, Beijing is projected to experience 14,401 heat-related deaths per year for elderly individuals, which is a 264.9% increase compared with the 1980s. These impacts could be moderated through adaptation. In the 2080s, even with the 30% and 50% adaptation rate assumed in our study, the increase in heat-related death is approximately 7.4 times and 1.3 times larger than in the 1980s respectively under a scenario of high population and RCP8.5. These findings could assist countries in establishing public health intervention policies for the dual problems of climate change and aging population. Examples could include ensuring facilities with large elderly populations are protected from extreme heat (for example through back-up power supplies and/or passive cooling) and using databases and community networks to ensure the home-bound elderly are safe during extreme heat events.