Daily Maximum Temperature Research Articles

Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

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.

Climate Change Projections for Stroke Incidence in Taiwan: Impact of 2°C and 4°C Global Warming Level.

This study aimed to establish the exposure-lag-response effect between daily maximum temperature and stroke-related emergency department visits and to project heat-induced stroke impacts under global warming levels (GWL) of 2°C and 4°C. Stroke-related emergency department visits in Taiwan from 2001 to 2020 were identified using the National Health Insurance Research Database (NHIRD). The study population consisted of 1,100,074 initial stroke cases matched with 2,200,148 non-stroke controls. We employed Distributed Lag Nonlinear Models (DLNM) in a case-crossover study to investigate the association between temperature and stroke. Generalized Estimating Equations (GEE) models with a Poisson function were used to correlate high-temperature exposure with annual stroke incidence rates. Projections were made under two global warming scenarios, GWL 2.0°C and 4.0°C, using Coupled General Circulation Model (GCMs). Baseline data from 1995 to 2014 were transformed for spatial distribution at the township level. Geographic Information System (GIS) spatial analysis was performed using Quantum GIS 3.2.0 software. DLNM exposure-lag-response effect revealed that daily maximum temperature exceeding 34°C significantly increased the risk of stroke-related emergency department visits, particularly for ischemic stroke. Under the 2°C GWL scenario, the frequency of days with temperatures surpassing 34°C is projected to rise substantially by the median year of 2042, with a further increase to 92.6 ± 18.0 days/year by 2065 under the 4°C GWL scenario. Ischemic stroke showed the highest increase in temperature-related incidence rates, notably rising from 7.80% under the GWL 2°C to 36.06% under the GWL 4°C. Specifically, the annual temperature-related incidence rate for ischemic stroke is expected to increase significantly by 2065. Regions such as Taichung, Hsinchu, Yilan, and Taitung demonstrated pronounced changes in heat-related ischemic stroke incidence under the GWL 4°C. The findings emphasize the importance of addressing temperature-related stroke risks, particularly in regions projected to experience significant temperature increases. Effective mitigation strategies are crucial to reduce the impact of rising temperatures on stroke incidence and safeguard public health.

The influence of temperature and genomic variation on intracranial EEG measures in people with epilepsy.

Heatwaves have serious impacts on human health and constitute a key health concern from anthropogenic climate change. People have different individual tolerance for heatwaves or unaccustomed temperatures. Those with epilepsy may be particularly affected by temperature as the electroclinical hallmarks of brain excitability in epilepsy (inter-ictal epileptiform discharges and seizures) are influenced by a range of physiological and non-physiological conditions. Heatwaves are becoming more common and may affect brain excitability. Leveraging spontaneous heatwaves during periods of intracranial EEG recording in participants with epilepsy in a non-air-conditioned telemetry unit at the National Hospital for Neurology and Neurosurgery in London from May to August 2015-22, we examined the impact of heatwaves on brain excitability. In London, a heatwave is defined as three or more consecutive days with daily maximum temperatures ≥28°C. For each participant, we counted inter-ictal epileptiform discharges using four 10-min segments within, and outside of, heatwaves during periods of intracranial EEG recording. Additionally, we counted all clinical and subclinical seizures within, and outside of, heatwaves. We searched for causal rare genetic variants and calculated the epilepsy PRS. Nine participants were included in the study (six men, three women), median age 30 years (range 24-39). During heatwaves, there was a significant increase in the number of inter-ictal epileptiform discharges in three participants. Five participants had more seizures during the heatwave period, and as a group, there were significantly more seizures during the heatwaves. Genetic data, available for eight participants, showed none had known rare, genetically-determined epilepsies, whilst all had high polygenic risk scores for epilepsy. For some people with epilepsy, and not just those with known, rare, temperature-sensitive epilepsies, there is an association between heatwaves and increased brain excitability. These preliminary data require further validation and exploration, as they raise concerns about the impact of heatwaves directly on brain health.

A Comprehensive Evaluation of Mean and Extreme Climate for the Conformal Cubic Atmospheric Model (CCAM)

Abstract This study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) reanalysis data from 1985 to 2014, following a 5-yr spinup period. It focuses on daily maximum and minimum temperatures and daily precipitation, comparing CCAM to ERA5 and the Australian Gridded Climate Data (AGCD). The CCAM effectively reduces warm biases in daily minimum temperatures but struggles with cold biases in daily maximum temperatures, particularly in northern Australia during the wet season, possibly due to high-level cloud overestimation. Precipitation tends to be overestimated, especially in extreme rainfall events, though offset by an underestimation of low rainfall. The study showcases improvements in the annual minimum of daily minimum temperatures across most of Australia, while identifying challenges in forecasting cooler extreme temperatures. It adds value to annual maximum daily maximum temperatures in southern Australia but less so in the north. The analysis of the 5% annual exceedance probability (AEP5%) yields mixed results influenced by location and potential ocean temperature changes. Some coastal areas exhibit lost value, possibly linked to ocean temperature shifts. Furthermore, CCAM’s representation of maximum annual daily and 5-day rainfall reveals lost value, particularly in eastern Australia due to an overestimate of extreme rainfall. Despite the challenges of comparing a dynamical downscaling model like CCAM to ERA5, this study highlights its benefits in reducing biases, especially in temperature representation. Given the larger biases in phase 6 of Coupled Model Intercomparison Project (CMIP6) global climate models, CCAM appears suitable for dynamic downscaling in climate projections, emphasizing the need for ongoing model enhancements, including addressing biases related to ephemeral water bodies and extreme rainfall. Significance Statement This study critically assesses the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling ERA5 reanalysis data from 1985 to 2014, offering valuable insights into climate modeling. Focusing on temperature and precipitation, CCAM proves effective in mitigating warm biases in daily minimum temperatures but encounters challenges with cold biases in daily maximum temperatures, particularly in northern Australia. The analysis reveals the overestimation of precipitation, especially in extreme events, yet identifies improvements in annual minimum daily minimum temperatures across Australia. The study underscores CCAM’s potential in reducing biases compared to CMIP6 global climate models, making it a promising tool for dynamic downscaling in climate projections. It emphasizes the necessity for ongoing model enhancements, particularly addressing biases related to ephemeral water bodies and extreme rainfall.

High-resolution urban climate simulations for heat and health applications in Philadelphia

Stakeholders need high-resolution urban climate information for city planning and adaptation to climate risks. Climate models have too coarse a spatial resolution to properly represent cities at the relevant scale, and downscaled products often fail to account for urban effects. We propose here a methodological framework for producing high-resolution urban databases that are used to drive the SURFEX-TEB land surface and urban canopy models. A historical simulation is carried out over the period 1991–2020, based on a reanalysis of the city of Philadelphia (Pennsylvania, USA). The simulation is compared with observations outside and inside the city, as well as with a field campaign. The results show good agreement between the model and observations, with average summer biases of only −1 °C and + 0.8 °C for daily minimum and maximum temperatures outside the city, and almost none inside. The simulation is used to calculate the maximum daily heat index (HIX) and to study emergency heat alerts. The HIX is slightly overestimated and, consequently, the model simulates too many heat events if not bias corrected. Overall, HIX conditions at Philadelphia International Airport are found to be suitable proxies for city-wide summer conditions, and therefore are appropriate to use for emergency heat declarations.

The trends of non-accidental mortality burden attributed to compound hot-dry events in China and its provinces in a global warming world

BackgroundGlobal warming has provoked more co-occurrence of hot extreme and dry extreme, namely compound hot-dry events (CHDEs). However, their health impacts have seldom been investigated. This study aimed to characterize CHDEs and assess its mortality burden in China from 1990 to 2100. MethodsCHDEs were defined as a day when daily maximum temperature > its 90th percentile and Standardized Precipitation Index < its 50th percentile. A two-stage approach, including a distributed lag nonlinear model (DLNM) and a multivariate meta-analysis, was used to estimate exposure–response associations of CHDEs with mortality in 358 counties/districts during 2006–2017 in China, which was then applied to assess the national mortality burden attributable to CHDEs from 1990 to 2100. FindingsWe observed a significant increasing trend of CHDEs in China until mid-21st century, and then flatted, while the duration and intensity of CHDEs continuously increased across the 21st century. CHDEs were much riskier (ER=17.82 %, 95 %CI: 14.17 %-21.60 %) than independent hot events (ER=5.86 %,95 %CI: -0.04 %,12.45 %) or dry events (ER=0.07 %,95 %CI: -1.22 %, 1.38 %), and there was significantly additive interaction between hot events and dry events (AP=0.10,95 %CI: 0.04, 0.16). Females (ER=24.28 %, 95 %CI: 19.21 %-29.56 %), the elderly (ER=23.28 %, 95 %CI: 18.23 %-28.55 %), and people living in humid area (ER=18.98 %, 95 %CI: 15.08 %–23.02 %) had higher mortality risks than their counterparts. Mortality burden attributed to CHDEs significantly increased during historical observation and became stable since mid-21st century in China. InterpretationCHDEs would significantly increase mortality with higher risk for females, the elderly and people living in humid areas. Mortality burden has significantly increased during historical observation and will keep relatively steady since mid-21st century.

Climatic Suitability for Outdoor Tourism in Romania’s Big Cities

This research aims to assess the climatic temporal suitability over the year and identify the appropriate season for open-air tourism in ten Romanian cities. It was evaluated using the Enhanced Tourism Climatic Index on a temporal scale of one day and then aggregated to 10 days over 61 years (1961–2021). Daily mean and maximum temperature, mean and minimum relative humidity, wind speed, precipitation, and sunshine hours were employed in the investigation. The Mann–Kendall test and Sen’s slope were used for trend detection in the frequency, season duration, and first/last suitable day during the year for outdoor tourism. Acceptable or better weather conditions usually begin in the last part of April and end in mid-October, with Good or better conditions lasting between 260 and 310 days/year. The trend shows a shift of Good conditions earlier in the year (0.3–9.0 days/decade), resulting in a longer season duration (0.8–13.0 days/decade) for open-air activities. The trend is statistically significant mainly for the extra-Carpathian regions. Big differences in open-air events number during the climatically suitable season have been identified among the cities considered (2–19 events/year). This study is useful for better planning open-air events and activities for tourism and recreation.

Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques

Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques

Accuracy of daily extreme air temperatures under natural variations in thermometer screen ventilation

AbstractAccurate air temperatures underpin environmental research. Most professional meteorological air temperature measurements still expose thermometers within traditional, naturally ventilated screens. Their representation of true air temperature depends on screen airflow, and therefore local winds. Accuracies of daily maximum (Tmax) and minimum (Tmin) air temperatures are assessed by comparison between a naturally ventilated large conventional screen and a co‐located aspirated reference screen. In over 1200 days' data, the naturally ventilated Tmin and Tmax both showed small (median &lt; 0.06°C) cold bias, but, in 1% of cases, warm Tmax bias and cold Tmin bias &gt;|1°C|. The Tmin cold bias is associated with calm clear nights, and the Tmax warm bias events with calm winter days at low sun angles, allowing solar heating of the screen. The prevalence of poor natural ventilation, potentially affecting Tmin and Tmax, is estimated across European sites. Poor ventilation occurred at Tmin for 12% of values, and at Tmax for 4%. Climatological averaging will reduce these effects, but, without corroborating wind data, statistical changes in Tmin or Tmax, including identifying “Tropical Nights” (Tmin &gt; 20°C) or occurrences of winter extremes, may have limited value. Wider adoption of aspirated thermometer screens, with an initial overlap period, will largely eliminate these effects.

Spatiotemporal analysis of compound droughts and heat waves in the Horn of Africa

AbstractClimate change has increased the frequency and severity of weather extremes, including droughts, heat waves and compound drought and heatwave (CDHW) events. CDHW events profoundly impact water availability, agriculture, public health and energy production, particularly in the Horn of Africa (HOA). This study examined the historical spatiotemporal patterns of CDHW periods in the HOA during three periods (1951–1980, 1971–2000 and 1991–2020) using the ERA5 reanalysis dataset. This study utilized monthly Standardized Precipitation Evapotranspiration Index (SPEI) data to detect droughts and daily maximum temperature data to identify heatwaves for characterizing the duration, severity and magnitude of CDHW events. The results show a substantial increase in the duration of CDHW events in recent years, with durations reaching up to 25 days. The average duration of heat waves also increased from 7 days before 1993 to 18 days by 2011, culminating in a record‐breaking 43‐day heat wave in 2019. Most areas experienced a significant increase in heatwave duration by more than 12 days from the early period of 1951–1980 to the late period of 1991–2020. Although around 76% and 69% of the HOA exhibited insignificant heatwave trends in the first two periods, Ethiopia and Kenya experienced substantial increases of more than 18 days during the most recent period, with some durations exceeding 25 days in recent decades. The magnitude of CDHW events generally decreased as drought duration intensified, but specific areas, particularly southwest Kenya and Eritrea, exhibited higher CDHW values in the last period. These findings underscore the urgent need to understand and address CDHW events in the HOA. Targeted interventions for disaster risk reduction and resilience‐building are needed to mitigate the adverse effects of these events in this vulnerable region. This study provides a basis for future research and policy formulation in the HOA.

Temperature Distribution in Asphalt Concrete Layers: Impact of Thickness and Cement-Treated Bases with Different Aggregate Sizes and Crumb Rubber

The temperature estimation within asphalt concrete (AC) overlaid on cement-stabilized bases (CSB) is necessary for pavement analysis and design. However, the impact of different CSB gradations and rubberized CSB on AC temperature has not been thoroughly investigated. This study aims to clarify this effect by examining two types of CSB with nominal particle aggregate sizes of 25 mm and 31.5 mm, as well as the substitution of 5%, 10%, and 20% graded aggregates with rubber aggregates (RA) in CSB Dmax 25 using Ansys-based numerical simulations. The modelling also investigated 11 scenarios with different AC thicknesses (hAC) ranging from 6 to 26 cm. The results indicated that CSB Dmax 31.5 reduced the daily maximum temperature fluctuation at the bottom of the AC (∆TbottomAC) by approximately 8% compared to CSB Dmax 25. The inclusion of 5% RA in CSB Dmax 25 decreased ∆TbottomAC by up to 20%. Additionally, the rubberized CSB increased the maximum temperature gradient between the top and bottom of the AC (ΔTmaxAC) by 9.5% with 5% RA and a 6 cm AC thickness; however, this increase was insignificant when hAC exceeded 12 cm. This study also proposed the use of artificial neural network (ANN) models to predict the AC’s temperature distribution based on depth, the time of day, surface paving temperatures, and hAC. The proposed ANN model demonstrated high accuracy (R2 = 0.996 and MSE = 0.000685),which was confirmed by the numerical simulations, with an acceptable RMSE ranging from 0.28 °C to 0.67 °C.

Complexity of hydrometeorological variables: false nearest neighbour algorithm

ABSTRACT This study examines the complexity of hydrometeorological variables in the Savitri River basin in India. Specifically, the study estimates the dimensionality of daily rainfall, runoff, maximum temperature, minimum temperature, pan evaporation, relative humidity, sunshine duration, and wind speed observed during 2000–2010 at two stations (Kangule and Birwadi). The false nearest neighbour (FNN) algorithm is employed to estimate the dimensionality of each variable. The dimensionality represents the number of variables dominantly governing the system. The FNN dimension values of the eight daily hydrometeorological time series from each station range between 4 and 7, which may be considered as exhibiting a medium level of complexity. Among the eight series, wind speed is found to be the least complex, whereas minimum temperature and sunshine duration are the most complex. An attempt is also made to examine the effect of temporal scale on the complexity of the hydrometeorological variables, by analysing the hourly rainfall and runoff series from the two stations. The results indicate that, for both rainfall and runoff, hourly data (finer scale) exhibits greater complexity, with two to three additional influential variables. The present results have important implications for hydrometeorological modelling, prediction, and disaster management in the small and flood-prone Savitri River basin.

Investigation of Temperature Multifractrality According to Zugspitze Weather Station Data

The main multifractal properties of time series of mean, maximum and minimum daily temperatures are analyzed using the method of multifractal fluctuation analysis. As initial data, we used the results of instrumental temperature observations made at the Zugspitze meteorological station in the period from August 1, 1900 to January 31, 2023. In general, variations in the mean, maximum and minimum daily temperatures demonstrate multifractal behavior, especially for small time scales, up to about 90 days An analysis of the generalized Hurst exponent found that the considered time series have a long-term positive correlation and that the multifractality is weaker with large fluctuations. The singularity spectrum for all time series is truncated to the left, which means that the time series have a multifractal structure that is insensitive to local fluctuations of large values.

A global attribution analysis of increasing risk of successive runoff-heat extreme events driven by anthropogenic forcing

The successive runoff-heat extreme (SRHE) events, defined as the occurrence of an extreme runoff event followed by a heatwave event, have become more frequent under recent global warming. However, the impact of anthropogenic climate change (ACC) on global changes in SRHE characteristics during the past decades remains unclear. Here, we evaluate the impact of ACC on SRHE frequency and characteristics during 1950–2014, based on the daily maximum and minimum temperature and total runoff data simulated by five global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the “anthropogenic and natural both (ALL)” and “natural only (NAT)” experiments. Attribution of SRHE changes with different durations and intervals between extreme runoff and heatwave (IRHs) is achieved by using the fraction attributable risk (FAR) method. Results indicate that ACC exacerbates SRHE occurrence for most global regions, especially in the Southern Hemisphere (> 60 % increase in probability), but the spatial variability of this impact decreases over time. The globally-average probability of SRHE occurrence due to ACC has increased significantly by 40 % during 1971–2014 (∼10 %/decade), and the probability of SRHE with the short IRHs and long duration tends to be larger than other types of SRHE. ACC also prolongs the duration of SRHE in most global regions, with an increase of 1–4.5 folds in northern South America, central Africa, and southeastern Asia during 1950–2014. Partial correlation analysis indicates that the intensification of heatwave due to ACC contributes more than that of extreme runoff to SRHE occurrence in nearly all global regions, particularly in the Southern Hemisphere.

Urban Heat Island Trends in the Middle East and North Africa: A statistical approach

AbstractThe urban heat island (UHI) effect denotes that temperatures in urban areas are often higher compared to the rural background. This human‐induced phenomenon can exacerbate heat stress and adverse health impacts, adding to ongoing global warming. To examine the intensity and variability of the UHI effect, we used 40 years () of observational data (daily maximum and minimum temperature) from the Global Summary of the Day (GSOD), comprising about 1000 stations of varying temporal extent, spanning the Middle East and North Africa (MENA). The MENA is characterised by hot and dry summers and warms faster than other inhabited regions worldwide. The challenge in using data with diverse spatial and temporal extents is accounting for heterogeneities between each station comprising an urban–rural pair. One has to allow for differences in the distance between the pairs and, elevation, spatial–temporal changes in urbanisation as well as the distance from the coast. A new method is proposed, based on flexible statistical methods (Generalized Additive Models or GAMs), to quantify the temporal trend in the UHI effect while allowing for the aforementioned characteristics using regression splines of appropriately defined variables. A composition of high‐resolution satellite geospatial information, related to urbanisation properties and population data was utilised from the Global Human Settlement Layer database (GHSL‐SMOD), to characterise the stations in terms of their urbanisation type. These data were also used to quantify temporal changes in the extent of urbanisation relative to the surrounding areas. Results indicate consistent upward trends of the UHI effect in the MENA (up to °C), particularly at night (daily minimum temperature) during all seasons.

Solid humic substance enhanced spinach abiotic stress tolerance under combined drought, salinity, and daily heat stress

Spinach is a crop of economic importance in the Wintergarden region of Texas, where increasing soil salinity, irrigation water restrictions, and heat stress challenge its production. Improving abiotic stress tolerance and resource use efficiency are important goals for sustainable agricultural production. Although the effect of humic substances (HS) on abiotic stress tolerance in spinach has been evaluated under single stress, such as heat, drought, or salinity, their impact under combined abiotic stresses is unknown. We hypothesized that solid humic substances would be effective in alleviating multifactorial abiotic stress in spinach, which we examined in our first experiment, and further hypothesized that salinity stress in spinach would occur after a threshold intensity but that humic substance applications would ameliorate stress intensity past this threshold, which we examined in our second experiment. Three-week-old spinach seedlings (Spinacia oleracea L. cv. ‘Seaside’) were grown using an automatic gravimetric watering system in a climate-controlled high tunnel where daily maximum temperatures exceeded 32 °C. In a factorial experiment, we applied HS (control and 1 % v/v HS media amendment), drought (100 % and 50 % field capacity), and salinity (25 mL of RO water or 100 mM NaCl every other day) treatments. HS and HS×Salinity treatments more than doubled shoot and root FW and DW, leaf area, root length, and root surface area compared to other stresses, indicating HS improved growth and stress tolerance. Improved growth was matched with improved resource use efficiency, as HS and HS×Salinity treated plants exhibited the greatest WUE. Drought stress also increased WUE relative to the control but lowered transpiration rate responses to VPD in contrasts to HS and HS×Salinity treated plants, which increased their transpiration rates in response to increasing VPD. Salinity treatments increased sodium content (Na+) in both shoots and roots. No HS effect was observed in shoot Na+ accumulation, but HS reduced Na+ in roots under the Drought×Salinity treatment. Strong interactions between drought and salinity were observed for shoot and root Na+, Na+/K+, and shoot K+, showing that spinach accumulates Na+ and reduces K+ when drought and salinity stress are combined. HS had no apparent effect on leaf metabolites. Drought and salinity differentially impacted MDA, proline, and antioxidant content and activities, while HS showed no effect. Drought stress significantly increased MDA and proline while reducing TPC, TFC, ABTS, and FRAP. Salinity stress significantly decreased MDA, TPC, ABTS, FICA, and FRAP. In conclusion, HS improved overall spinach growth and WUE while decreasing root Na accumulation, and subsequently enhancing spinach stress tolerance.

High-resolution surface temperature changes for Portugal under CMIP6 future climate scenarios

Future changes in the mean, maximum and minimum temperature in continental Portugal were investigated using high-resolution future climate projections based on the latest IPCC AR6 CMIP6 climate scenarios. The results show that the mean, maximum and minimum temperatures are projected to substantially increase in all continental Portugal, particularly at the south-central inland regions. For the near-term future (2046–2065 period), SSP3-7.0 is the future climate scenario that projects higher increases, of around 1 °C, 1.5 °C and 2 °C for the daily mean, maximum and minimum temperatures, respectively. For the long-term future (2081–2100 period), the projected warming is higher, particularly under the SSP5-8.5 future climate scenario with projected warmings of 3 °C, 3.5 °C and 2.5 °C for the daily mean, maximum and minimum temperatures, respectively. Occurrences of hot days (maximum temperature above 30 °C), very hot days (maximum temperature above 40 °C) and tropical nights (minimum temperature above 20 °C) are all projected to increase up to 35–40, 12–15 and 50 more days per year, respectively, mainly in the interior areas of Portugal. Oppositely, the occurrence of frost days is projected to decrease in practically all mountainous areas in Portugal. These results show a clear tendency of a significant increase in the surface temperatures and frequency of occurrence of extreme temperature episodes in continental Portugal, which can have severe impacts on the population, environment, economy and vital human activities such as agriculture.

EXPOSURE TO AMBIENT HEAT AND RISK OF SPONTANEOUS ABORTION: A CASE-CROSSOVER STUDY.

Few epidemiologic studies have examined the association of ambient heat with spontaneous abortion, a common and devastating pregnancy outcome. We conducted a case-crossover study nested within Pregnancy Study Online, a preconception cohort study (2013-2022). We included all participants reporting spontaneous abortion (n=1,524). We defined the case window as the 7 days preceding the event and used time-stratified referent selection to select control windows matched on calendar month and day of week. Within each 7-day case and control window, we measured mean, maximum, and minimum of daily maximum outdoor air temperatures. We fit splines to examine non-linear relationships across the entire year and conditional logistic regression to estimate odds ratios (OR) and 95% CI of spontaneous abortion with increases in temperature during the warm season (May-September) and decreases during the cool season (November-March). We found evidence of a U-shaped association between outdoor air temperature and spontaneous abortion risk based on year-round data. When restricting to warm season events (n=657), the OR for a 10-percentile increase in the mean of lag 0-6 daily maximum temperatures was 1.1 (95% CI: 0.96, 1.2) and, for the maximum, 1.1 (95% CI: 0.99, 1.2). The OR associated with any extreme heat days (>95th county-specific percentile) in the preceding week was 1.2 (95% CI: 0.95, 1.5). Among cool season events (n=615), there was no appreciable association between lower temperatures and spontaneous abortion risk. Our study provides evidence of an association between high outdoor temperatures and incidence of spontaneous abortion.