Daily Maximum Temperature Research Articles

Integrating fine‐scale behaviour and microclimate data into biophysical models highlights the risk of lethal hyperthermia and dehydration

Climate change threatens biodiversity by compromising the ability to balance energy and water, influencing animal behaviour, species interactions, distribution and ultimately survival. Predicting climate change effects on thermal physiology is complicated by interspecific variation in thermal tolerance limits, thermoregulatory behaviour and heterogenous thermal landscapes. We develop an approach for assessing thermal vulnerability for endotherms by incorporating behaviour and microsite data into a biophysical model. We parameterised the model using species‐specific functional traits and published behavioural data on hotter (maximum daily temperature, Tmax > 35°C) and cooler days (Tmax < 35°C). Incorporating continuous time‐activity focal observations of behaviour into the biophysical approach reveals that the three insectivorous birds modelled here are at greater risk of lethal hyperthermia than dehydration under climate change, contrary to previous thermal risk assessments. Southern yellow‐billed hornbills Tockus leucomelas, southern pied babblers Turdoides bicolor and southern fiscals Lanius collaris are predicted to experience a risk of lethal hyperthermia on ~ 24, 65 and 40 more days year−1, respectively, in 2100 relative to current conditions. Maintaining water balance may also become increasingly challenging. Babblers are predicted to experience a 57% increase (to ~186 days year−1) in exposure to conditions associated with net negative 24 h water balance in the absence of drinking, with ~ 86 of those days associated with a risk of lethal dehydration. Hornbills and fiscals are predicted to experience ~ 84 and 100 days year−1, respectively, associated with net negative 24 h water balance, with ≤ 20 of those days associated with a risk of lethal dehydration. Integrating continuous time‐activity focal data is vital to understand and predict thermal challenges animals likely experience. We provide a comprehensive thermal risk assessment and emphasise the importance of thermoregulatory and drinking behaviour for endotherm persistence in coming decades.

Extreme heat and drought did not affect interspecific interactions between dune grasses

The frequency of extreme climatic events, such as storm and heatwaves, is predicted to increase because of climate change. Understanding interactions between species in environmental extremes plays a vital role in predicting ecosystem resilience. In this study, we examined how heat and drought combined with interspecific interactions between pioneer dune builder sand couch (Thinopyrum junceiforme) and primary foredune builder marram grass (Calamagrostis arenaria) affected growth and survival of the latter species in an embryonic dune system. In a 4-week field experiment, we transplanted marram grass within sand couch patches or on bare sediment. This plant interaction treatment was combined with a compound heat and drought treatment that was simulated with greenhouses that inhibited rainfall and increased temperatures (average daily maximum temperature +4°C). Results show that the presence of sand couch significantly reduced growth (i.e., formation of new shoots, shoot and root length and aboveground biomass) of marram grass. By contrast, the heat and drought treatment had no significant effects on growth or survival of marram grass, irrespective of species interactions. The neutral response suggests that even in its early establishment marram grass is highly heat and drought resistant. Since the competitive interaction between sand couch and establishing marram grass did not change under pressure of an extreme heat and drought event, we expect that these factors do not affect embryonic dune development.

Estimation of Future Climate Change in Low Folded Zone, Iraq with the LARS-WG and Five GMS Models under CMIP5 Scenarios

This study investigates the trends of climate change in the Low Folded Zone region of Iraq and provides future projections for daily maximum and minimum temperatures, as well as precipitation. Using the LARS-WG model and five General Circulation Models (GCMs) under three Representative Concentration Pathways (RCP) scenarios, the research aims to provide valuable insights into the anticipated changes in climate variables. The calibration and validation of the model demonstrate its capability to simulate future climatic data, with statistical indices indicating a strong correlation between observed and generated data. The projected future temperatures showed a consistent increase across all selected stations, with average annual maximum temperatures expected to rise by 1.06 to 5.48°C by the end of the twenty-first century. The highest increase in temperatures was predicted under the high-emission scenario RCP8.5. The results also indicated spatial and temporal variations in precipitation patterns, with the annual percentage increase in precipitation ranging from 7.07% to 10.75% for RCP 2.6, 0% to 2.2% for RCP4.5, and 3.7% to 6.8% for RCP8.5. The findings reveal a projected increase in annual temperatures and variable precipitation patterns, highlighting the urgency of proactive measures to address the challenges posed by climate change in the region. The results of this study are crucial for informing decision-makers and planners in developing strategies for climate change adaptation and resource management, particularly in water resource management and agricultural planning.

Dust- versus cloud-radiation impacts on the diurnal temperature range for long-lasting dust weather over the Taklimakan Desert

The diurnal temperature range (DTR), determined by the difference between daily maximum and minimum air temperature, is an important index of global climate change. The dust direct radiative effect on DTR in cloudy conditions in the Taklimakan Desert (TD) area is still unclear. Based on springtime ground observation data from 2002 to 2013, the DTR variation in dust/cloudy days and the DTR-visibility relationship in dust days in TD were analyzed and summarized. The mean DTR was proportional to the mean visibility, with a slow DTR increase in the southwestern TD caused by cloud-induced radiation when visibility was 4–10 km; the DTR difference between cloudy and cloudless dust days was more significant (1.8–4.2 °C) when visibility was >6 km. The simulation of a long-lasting dust-cloud coexisting event (4 days) with the factor separation technique showed that both pure dust- and cloud-radiation impacts reduced DTR but their synergistic radiation impact mostly increased DTR in TD. Both simulated and observed results indicated that when the daily visibility of dust weather increased from 4-6 km to 6–10 km, the important pure dust impact on DTR weakened whereas the cloud impact became significant.

Variability of ENSO teleconnections indices, and its impacts on moroccan agriculture

In Morocco, drought periods have become more frequent and severe due to climate change, which has had a substantial effect on freshwater supply and agricultural output. The El Niño-Southern Oscillation (ENSO) and the influence of climatic variability were the main subjects of the current study, which made use of daily precipitation, maximum and minimum temperature data collected at 50 locations around the nation from 1990 to 2022. The study analysed Palmer Drought Severity Index (PDSI) over Morocco to evaluate the impact of climate indices such as the Oceanic Niño Index (ONI) and the North Atlantic Oscillation (NAO) on drought severity. It also investigated the changes in precipitation, temperature, and extreme event-related indices. Rainfall is used as a proxy for El Nino and La Nina, supported by a negative and significant correlation between Oceanic Nino Index (ONI) and rainfall. Attempted to understand the impact of the NAO on Morocco's agricultural production, particularly during the winter months. During negative NAO phases, winter temperatures can lead to reduced wheat yields by 35–45% during the El Niño years and decreased by 4–7% during the La Niña years compared to the actual yield. Relying on the NAO plays a crucial role in modulating the precipitation patterns, which implies that crop requires adequate soil moisture with no extreme temperature during the growing season in order to produce higher yield. ENSO events can indeed lead to these extreme conditions in selected locations, potentially impacting crop yields.

Summer High-Altitude Diurnal Rainfall Change in the Three Rivers Source Region and Associated Mechanism

Abstract The Tibetan Plateau (TP), a crucial area influencing global climatic patterns and water resources, is experiencing a unique climatic paradox, particularly evident in the Three Rivers Source Region (TRSR). A striking summer asymmetry in the increases of near-surface temperature and precipitation is observed from 1989 to 2018: the rate of daily minimum temperature (0.64°C decade−1) surpasses the daily maximum temperature (0.52°C decade−1), while the daytime precipitation intensity (0.40 mm day−1 decade−1) increases at a faster rate compared to nighttime (0.30 mm day−1 decade−1). Despite these trends, the summer mean nighttime precipitation intensity consistently remains higher than the daytime average. Notably, this pattern is accompanied by an increasing trend of moisture transport during both daytime and nighttime in TRSR. This paper deciphers the thermodynamic and dynamic processes behind this trend. The daytime warmth not only alters the stability of atmosphere but also modulates convective inhibition (CIN), thereby reshaping precipitation mechanics and potentially dampening or delaying daytime convection. Thermodynamically, a shift from unstable to stable anomalies in the summer troposphere suppresses precipitation development. The combination of increased CIN during those period leads to fewer but more intense rainy days. Dynamically, the shift from a consistent downward motion anomaly throughout troposphere to an upward motion anomaly becomes dominant during nighttime, exhibiting a similar transition but only below 500 hPa during daytime. These findings reveal the complex interplay between thermodynamics, dynamics, and precipitation, highlighting the need for refined climatic models that can accurately simulate these summer diurnal processes.

Excess mortality related to high air temperature: Comparison of the periods including 1994 and 2018, the worst heat waves in the history of South Korea.

Climate change has caused extreme weather events, including frequent summer heat waves. We examined how the effects of high air temperatures on mortality have changed between the two study periods (1991-1995 and 2015-2019), including 1994 and 2018, the worst heat wave years in the meteorological history of South Korea. Temperature data from the Korea Meteorological Administration and mortality data from Statistics Korea were used in this study. We used distributed lag nonlinear models to estimate the cumulative relative risks (CRRs) to determine the association between daily maximum temperature in summer (June to September) and mortality. CRRs were estimated for each province and pooled using a random-effects meta-analysis for all provinces. Maximum temperature and annual average days in heat wave were 37.7°C and 11.8 in 1991-1995 and 38.3°C and 18.8 in 2015-2019. The slope of the CRR for mortality increases with increasing temperature and has been steeper in the past than in recent years and steeper in those over 65 than in those under 65. Excess mortality has recently declined compared with that in the past. The impact of high summer temperatures on mortality changed between the two periods, suggesting improved population resilience.

Risk Factors for Spotted Fever Group Rickettsioses in Kilimanjaro Region, Tanzania.

Knowledge gaps exist on risk factors for spotted fever group rickettsioses (SFGR) in sub-Saharan Africa. We sought to identify SFGR risk factors in Kilimanjaro Region, Tanzania. We recruited febrile patients presenting at 2 hospitals in Moshi from February 2012 through May 2014. Standardized clinical and risk factor questionnaires were administered. SFGR exposure was defined as a Rickettsia africae immunofluorescence antibody reciprocal titer ≥64, and acute SFGR as a ≥4-fold rise between paired sera. Logistic regression was used to identify associations. Of 1190 participants providing ≥1 serum sample, the median age was 21.8 (range, 0.3-100.2) years, 646 (54.3%) were female, and 650 (54.6%) had SFGR exposure. Of 731 participants with paired sera, 67 (9.2%) had acute SFGR. On multivariable analysis, odds of acute SFGR were higher in the age group 0-2 years (adjusted odds ratios [aORs] for older age groups, <0.36; P < .011), rural residence (aOR, 4.1; P = .007), and areas with maximum daily temperature <26°C (aORs for higher temperature groups, <0.42; P < .035). Odds of SFGR exposure were higher in those working in the garden (aOR, 1.8; P = .010) and seeing a dog (aOR, 1.5; P = .010). Odds of SFGR exposure were lower in the age group 0-2 years (aORs for older age groups, >1.5; P < .026), female sex (aOR, 0.62; P < .001), and being from the Chaga tribe (aOR, 0.68; P = .003). Those aged <2 years, rural residents, and persons residing in areas with cooler temperatures had increased odds of SFGR. Our results identify groups for further research on tick exposure and for targeted prevention interventions.

Changing climate risks for high-value tree fruit production across the United States

Abstract Climate change poses growing risks to global agriculture including perennial tree fruit such as apples that hold important nutritional, cultural, and economic value. This study quantifies historical trends in climate metrics affecting apple growth, production, and quality, which remain understudied. Utilizing the high-resolution gridMET dataset, we analyzed trends (1979-2022) in several key metrics across the U.S. – cold degree days, chill portions, last day of spring frost, growing degree days (GDD), extreme heat days (daily maximum temperature &gt;34°C), and warm nights (daily minimum temperatures &gt;15°C). We found significant trends across large parts of the U.S. in all metrics, with the spatial patterns consistent with pronounced warming across the western states in summer and winter. Yakima County, WA, Kent County, MI, Wayne County, NY—leading apple-producers—showed significant decreasing trends in cold degree days and increasing trends in GDD and warm fall nights. Yakima county, with over 48,870 acres of apple orchards, showed significant changes in five of the six metrics - earlier last day of spring frost, fewer cold degree days, increasing GDD over the overall growth period, and more extreme heat days and warm nights. These trends could negatively affect apple production by reducing the dormancy period, altering bloom timing, increasing sunburn risk, and diminishing apple appearance and quality. Large parts of the U.S. experience detrimental trends in multiple metrics simultaneously that indicate the potential for compounding negative impacts on the production and quality of apples and other tree fruit, emphasizing the need for developing and adopting adaptation strategies.

Has There Been a Recent Warming Slowdown over North China?

The warming slowdown observed between 1998 and 2012 has raised concerns in recent years. To examine the temporal and spatial variations in annual mean temperature (Tmp) as well as 12 extreme temperature indices (ETIs), and to assess the presence of a warming slowdown in North China (NC), we analyzed homogenized daily observational datasets from 79 meteorological stations spanning 1960 to 2020. Additionally, we investigated the influences of 78 atmospheric circulation indices (ACIs) on ETIs during the period of warming slowdown. To compare temperature changes, the study area was divided into three parts based on topographic conditions: Areas I, II, and III. The results revealed significant warming trends in Tmp and the 12 ETIs from 1960 to 2020. Comparing the time frames of 1960–1998, 2012–2020, and 1998–2012, both Tmp and the 12 ETIs displayed a cooling trend in the latter period, confirming the existence of a warming slowdown in NC. Notably, indices derived from daily maximum temperature exhibited higher cooling rates during 1998–2012, with winter contributing most significantly to the cooling trend among the four seasons. The most pronounced warming slowdown was observed in Area I, followed by Area III and Area II. Furthermore, our attribution analysis of ACIs concerning the temperature change indicated that the Asia Polar Vortex Area Index may have had the greatest influence on ETIs from 1960 to 2016. Moreover, the weakening of the Tibet Plateau Index Band and the Asian Latitudinal Circulation Index, and the strengthening of the Eurasian Latitudinal Circulation Index, were closely associated with ETIs during the warming slowdown period in NC. Through this research, we aim to deepen our understanding of climate change in NC and offer a valuable reference for the sustainable development of its natural ecology and social economy.

Case Study: Impact Analysis of Roof-Top Green Infrastructure on Urban System Sustainability in San José, CA

This paper presents results from a case study focusing on analyzing impacts of Green Infrastructure (GI) on sensible and latent heat fluxes, urban microclimate and the subsequent water–energy nexus components of an urban infrastructure system. The case study, focusing on the campus of a public university in San José, CA, aimed to quantify the pre- and post-conditions for a hypothetical GI implementation, which is in support of San José State University’s (SJSU) robust sustainability initiatives, which are also aligned with Silicon Valley’s broader strategic goals. The results revealed that a reduction of 0.3 °C in the average daily peak maximum temperature on campus could be achieved by the GI implementation. Air-conditioning related energy use was projected to decrease by 1.28%, monthly water use by 7052 m3, and it would result in an estimated reduction of approximately 2800 kWh in the water–energy nexus. In addition to lowering the campus’s carbon footprint, GI therefore offers significant economic and environmental benefits in terms of reductions in the urban air temperature, energy usage and water demand. This study provides valuable information for policy makers and low impact development water infrastructure managers considering GI implementation.

Heat Waves and Adverse Health Events Among Dually Eligible Individuals 65 Years and Older

Extensive research has found the detrimental health effects of heat waves. However, a critical gap exists in understanding their association with adverse health events among older dually eligible individuals, who may be particularly susceptible to heat waves. To assess the association between heat waves and adverse health events among dually eligible individuals 65 years and older. This retrospective time-series study assessed the association between heat waves in warm months from 2016 to 2019 and zip code tabulation area (ZCTA)-day level adverse health events. Dually eligible individuals 65 years and older who were continuously enrolled in either a Medicare fee-for-service plan or a Medicare Advantage plan with full Medicaid benefits from May to September in any given year were identified. All ZCTAs in the US with at least 1 dually eligible individual in each study year were included. Data were analyzed from September 2023 to August 2024. Heat waves, defined as 3 or more consecutive extreme heat days (ie, days with a maximum temperature of at least 90 °F [32.2 °C] and in the 97th percentile of daily maximum temperatures for each ZCTA during the study period). Daily counts of heat-related emergency department visits and heat-related hospitalizations for each ZCTA. The study sample included 5 448 499 beneficiaries 65 years and older in 28 404 ZCTAs across 50 states and Washington, DC; the mean (SD) proportion of female beneficiaries and beneficiaries 85 years and older in each ZCTA was 66% (7%) and 20% (8%), respectively. The incidence rate for heat-related emergency department visits was 10% higher during heat wave days compared to non-heat wave days (incidence rate ratio [IRR], 1.10; 95% CI, 1.08-1.12), and the incidence rate of heat-related hospitalizations was 7% higher during heat wave days (IRR, 1.07; 95% CI, 1.04-1.09). There were similar patterns in other adverse health events, including a 4% higher incidence rate of death during heat wave days (IRR, 1.04; 95% CI, 1.01-1.07). The magnitude of these associations varied across some subgroups. For example, the association between heat waves and heat-related emergency department visits was statistically significant only for individuals in 3 of 9 US climate regions: the Northwest, Ohio Valley, and the West. In this time-series study, heat waves were associated with increased adverse health events among dually eligible individuals 65 years and older. Without adaptation strategies to address the health-related impacts of heat, dually eligible individuals are increasingly likely to face adverse outcomes.

Transparent, Sprayable Plastic Films for Luminescent Down‐Shifted‐Assisted Plant Growth

AbstractThe world's steadily growing population and global heating due to climate change are a threat to food security. To meet this challenge, novel technologies are needed to increase crop production in a sustainable way. In this work, the use of luminescent down‐shifting (LDS) materials based on molecular Eu3+‐containing polyoxotitanates for plant growth enhancement is investigated. Using a systematic design strategy to optimize down‐shifting properties, conversion of the ultraviolet spectral range to the photosynthetically active radiation (PAR) is achieved with quantum yields as high as 68%. The prototype Eu3+‐compound can be incorporated into water‐based acrylic varnish that can be spray‐coated onto existing greenhouses. Comparing coated with uncoated greenhouses, basil plants produce 9% more leaf dry weight per plant, and a highly significant 10% increase in individual leaf dry weight. The coating reduces the amount of transmitted PAR by 8% but has advantageous effects on diffuse radiation and in reducing the internal mean temperature. Although there is some uncertainty as to the contribution of down‐shifting, with the bulk of the increase probably being due to higher diffused light and the reduction in maximum daily temperatures, this study establishes a model for the design of LDS paints for real‐world agricultural applications.

Assessing the Effects of Climate Change on Human Security: Case of Nyahururu Sub-county, Laikipia County, Kenya

Climate change poses a significant threat to human security by exacerbating societal challenges, potentially leading to widespread despair. The primary objective of this study was to assess the effects of climate change on human security in Nyahururu Sub-County. The study adopted a concurrent triangulation research design. The study targeted the residents (household heads) of Nyahururu Sub-County, including farmers, pastoralists, ranchers and traders, who were distributed across the 4 locations/administrative units: Gituamba, Marmanet, Ngarua and Nyahururu. According to the Kenya National Bureau of Statistics, Nyahururu Sub-County had an estimated population of approximately 154,704 individuals and a total of 44,073 households as of the 2019 census. The study was conducted between 2021 and 2024. The study used both quantitative and qualitative methods. Data collection involved questionnaires, key informant interviews and focus group discussions, with stratified random sampling for household heads and purposive sampling for key informants. Data were analyzed using frequencies, percentages and thematic analysis. The study found that Nyahururu Sub- County experiences fluctuations in both temperature and precipitation levels from 1989 to 2023, with mean daily maximum temperatures ranging from 22.8°C to 24.4°C and total annual precipitation varying between 665.1 mm and 771.7 mm. These trends indicate climate variability rather than a consistent long-term change in either temperature or precipitation. The study recommends targeted interventions to enhance community capacity, strengthen governance structures and promote sustainable livelihoods to mitigate the impacts of climate change on human security.

Associations between extreme heat and hospital length of stay among those hospitalised with cardiovascular disease in Greater London: a population-based longitudinal study between 2007–2019

BackgroundIt is unclear whether a relationship exists between extreme heat and hospital length of stay in the UK. Using healthcare and climate data this study explores the effects of extreme heat on hospital length of stay for individuals hospitalised with cardiovascular disease, a common heat-sensitive condition, in Greater London. MethodsThis longitudinal study included patients registered to General Practices in Greater London (8·908 million inhabitants), hospitalised with cardiovascular disease during summer months of 2007–19. Administrative healthcare data was derived from Clinical Practice Research Datalink and Hospital Episode Statistics. Climate data from the Meteorological Office. The study exposure was extreme heat on day of hospital admission, defined using maximum daily temperature as per the UK Health Security Agency (low-level [28–31·9°C]; medium-level [32–39·9°C]; high-level [≥40°C]). The study outcome was hospital length of stay (days). In main analyses, linear fixed-effects models were used with fixed-effects for practice-by-calendar-day and practice-by-year and inclusion of covariates (daily rainfall, age, sex, deprivation, primary diagnosis, comorbidity). Findings24 435 hospital admissions were included. There were 72 low-level heat days, 11 medium-level heat days and 0 high-level heat days. There was no evidence of association between extreme heat and length of stay in unadjusted (0·03; 95% CI –0·10 to 0·15), and fully adjusted models (0·03; 95% CI –0·09 to 0·16). During medium-level heat, there was a significant association in unadjusted models (–0·27; 95% CI –0·50 to –0·03), and models adjusted by rainfall (–0·26; 95% CI –0·50 to –0·03), age (–0·25; 95% CI –0·45 to –0·04), sex (–0·25; 95% CI –0·46 to –0·05) and deprivation (–0·25; 95% CI –0·45 to –0·04). However, accounting for primary diagnosis (–0·15; 95% CI –0·44 to 0·14) and comorbidity (–0·17; 95% CI –0·46 to 0·12), there was no longer a significant association. InterpretationThis first UK-based study did not identify an association between low-level or medium-level heat on day of hospital admission and hospital length of stay. Further work is needed to evaluate the effects of extreme heat on the UK health system. FundingRP discloses funding from the Oxford Martin School as part of the Future of Cooling Programme. This funding allowed access to the data and paid for research assistant time to support the data management and partial analysis. The funder had no role in designing the question, the analysis or the interpretation of the results.

Local temperature impact of urban heat mitigation strategy based on WRF integrating urban canopy parameters and local climate zones

Urbanization has led to significant alterations in surface properties, contributing to surging urban heatwave events. The detrimental impact of urban heatwaves on public health highlights the urgency for proactive evaluations of diverse heat mitigation strategies. This study assesses the impacts of cool roof (CR), green roof (GR) and rooftop photovoltaic panel (PV) strategies on urban air temperatures within Guangzhou and Foshan agglomeration, utilizing the Weather Research and Forecasting with Urban Canopy Model (WRF-UCM). A comparative analysis of heat exchanges is conducted between two types of urban morphology datasets: local climate zone maps with categorized urban canopy parameters (LCZ-UCPs) and gridded urban canopy parameters from real building databases (gridded-UCPs). The results indicate that the discrepancies in the average temperature between LCZ-UCP and gridded-UCP is almost negligible, whereas increase to 0.39 °C and 0.54 °C for the daily maximum and minimum temperature, respectively. CR provides the most substantial cooling at an average of 0.44 °C, followed by GR and PV. Scenarios with the two types of morphology datasets reveal varying mitigation efficiencies. CRs with LCZ-UCPs show more pronounced temperature reductions, whereas GRs and PVs with gridded-UCPs in central urban regions demonstrate stronger cooling effects. CRs and PVs cool temperature by decreasing sensible heat flux, whereas GR is largely influenced by enhanced evapotranspiration especially with grass plantation. These findings elucidate the differing efficiencies of the three mitigation strategies and highlight the representation discrepancies in climatic simulations brought by the two urban canopy datasets. This emphasizes the importance of accurate urban morphological datasets in evaluating mitigation strategies in WRF-UCM, thus providing practicable insights for urban planning and climate policy-making.

Projecting future minimum mortality temperature in China

Minimum mortality temperature (MMT) increases with global warming due to climate adaptation, which is crucial for the precise assessment of mortality burden attributed to climate change. Nevertheless, forecasting future MMT poses a challenge given the unavailability of future mortality data. Here, we attempted to develop a novel approach to project future MMT. First, we estimated the MMT of 334 locations in China using a distributed lag nonlinear model. Then, meta regression models were applied to investigate the associations between MMT and several temperature variables(Most Frequent Temperature(MFT), average daily mean temperature, average daily minimum temperature, average daily maximum temperature and percentiles of temperature from 1st to 100th). A generalized linear regression model was employed to investigate whether significant differences existed in the relationships between MMT and temperature from the 1st to the 100th percentile. Finally, an optional indicator of MMT for projecting future values was identified. Our results indicated that temperatures in the 85th to 89th percentiles were closely associated with MMT, with the 88th percentile temperature serving as the most effective indicator, as confirmed by meta-regression models. Using the 88th percentile of temperature as alternative indicator of MMT, compared with the period of 2006-2015, the projected MMT in most districts and counties in China tended to rise under three representative concentration pathways (RCPs) in the 2030s (2030-2039), 2060s (2060-2069), and 2090s (2090-2099). Our findings provide some insight to project future MMT for assessing mortality burden related to temperature change driven by global warming.

Linking regional economic impacts of temperature-related disasters to underlying climatic hazards

Abstract Temperature-induced disasters lead to major human and economic damage, but the relationship between their climatic drivers and impacts is difficult to quantify. In part, this is due to a lack of data with suitable resolution, scale and coverage on impacts and disaster occurrence. Here, we address this gap using new datasets on subnational sector-disaggregated economic productivity and geo-coded disaster locations to quantify the role of climatic hazards on economic impacts of temperature-induced disasters at a subnational scale. Using a regression-based approach, we find that the regional economic impacts of heat-related disasters are most strongly linked to the daily maximum temperature (TXx) index. This effect is largest in the agricultural sector (6.37% regional growth rate reduction per standard deviation increase in TXx anomaly), being almost twice as strong as in the manufacturing sector (3.98%), service sector (3.64%), and whole economy (3.64%). We also highlight the role of compound climatic hazards in worsening impacts, showing that in the agriculture sector, compound hot-and-dry conditions amplify the impacts of heat-related disasters on growth rates by a factor of two. In contrast, in the service and manufacturing sectors, stronger impacts are found to be associated with compound hot and wet conditions. These findings present a first step in understanding the relationship between temperature-related hazards and regional economic impacts using a multi-event database, and highlight the need for further research to better understand the complex mechanisms including compound effects underlying these impacts across sectors.

Trend and teleconnection analysis of temperature extremes in New South Wales, Australia

AbstractThis study investigates possible trends and teleconnections in temperature extremes in New South Wales (NSW), Australia. Daily maximum and minimum temperature data covering the period 1971–2021 at 26 stations located in NSW were used. Three indices, which focus on daily maximum temperature, daily minimum temperature, and average daily temperature in terms of Excessive Heat Factor (EHF) were investigated to identify the occurrence of heatwaves (HWs). The study considered HWs of different durations (1-, 5-, and 10-days) in relation to intensity, frequency, duration, and their first occurrence parameters. Finally, the influences of three global climate drivers, namely – the El Niño/Southern Oscillation (ENSO), the Southern Annular Mode (SAM), and the Indian Ocean Dipole (IOD) were investigated with associated heatwave attributes for extended Austral summers. In this study, an increasing trend in both hot days and nights was observed for most of the selected stations within the study area. The increase was more pronounced for the last decade (2011–2021) of the investigated time period. The number, duration and frequency of the heatwaves increased over time considering the EHF criterion, whereas no particular trend was detected in cases of TX90 and TN90. It was also evident that the first occurrence of all the HWs shifted towards the onset of the extended summer while considering the EHF criterion of HWs. The correlations between heatwave attributes and climate drivers depicted that heatwave over NSW was positively influenced by both the IOD and ENSO and negatively correlated with SAM. The findings of this study will be useful in formulating strategies for managing the impacts of extreme temperature events such as bushfires, floods, droughts to the most at-risk regions within NSW.

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.