Mortality risk associated with heatwave exposure based on daily maximum, minimum, and combined temperature thresholds

Changes in daily extreme temperatures have been identified in many studies conducted at local, regional or global scales. For Romanian territory, only little research on this issue was done. In this article, the extra‐Carpathians regions of Romania located southward and eastward from the Carpathians Chain were considered. This study is focused on analyzing daily extreme temperature trends at a regional scale (eastern, southern and southeastern regions of Romania) across 50 years (1961–2010). Data sets of daily minimum and maximum temperature recorded in 14 weather stations were analyzed. The main goal was to find changes in extreme daily temperatures using a set of 20 indices adopted from the core indices developed by ETCCDMI with appropriate modifications to suit to the analyzed territory. The main results suggest that regional temperature trends at the scale of extra‐Carpathians areas of Romania are similar to those calculated for global and European continental scales; the climate has become warmer during the last decades. It has been identified that both extreme daily maximum and minimum temperatures have increased in the analyzed areas. For all the indices related to hot temperature most trends are significantly positive. The strongest increase was detected for hot related extremes such as summer days and tropical nights as well as for maximum values of maximum and minimum daily temperatures. For indices related to cold there are different sign slopes, but negative slopes prevail, especially for number of days under a defined threshold. This is also an evidence of the important warming in the area. Generally, it was found that the daily maximum temperature is getting more extreme, whereas the minimum is getting less extreme. Copyright © 2012 Royal Meteorological Society

Vapour pressure deficit (VPD) has a significant effect on the amount of water required by the crop to maintain optimal growth. Data required to calculate the mean VPD on a daily basis are rarely available, and most models use approximations to estimate it. In APSIM (Agricultural Production Systems Simulator), VPD is estimated from daily maximum and minimum temperatures with the assumption that the minimum temperature equals dew point, and there is little change in vapour pressure or dew point during any one day. The accuracy of such VPD estimations was assessed using data collected every 15 min near Wagga Wagga in New South Wales, Australia. Actual vapour pressure of the air ranged from 0.5 to 2.5 kPa. For more than 75% of the time its variation was less than 20%, and the maximum variation was up to 50%. Daytime mean VPD ranged from 0 to 5.3 kPa. Daily minimum temperature was found to be a poor estimate of dew point temperature, being higher than dew point in summer and lower in winter. Thus the prediction of vapour pressure was poor. Vapour pressure at 0900 hours was a better estimate of daily mean vapour pressure. Despite the poor estimation of vapour pressure, daytime mean VPD was predicted reasonably well using daily maximum and minimum temperatures. If the vapour pressure at 0900 hours from the SILO Patched Point Dataset was used as the actual daily mean vapour pressure, the accuracy of daytime VPD estimation was further improved. Simulations using historical weather data for 1957–2002 show that such improved accuracy in daytime VPD estimation slightly increased simulated crop yield and deep drainage, while slightly reducing crop water uptake. Comparison of the APSIM RUE/TE and CERES-Wheat approaches for modelling potential transpiration revealed differences in crop water demand estimated by the two approaches. Although the differences had a small effect on the probability distribution of simulated long-term wheat yield, water uptake, and deep drainage, this finding highlights the need for a scientific re-appraisal of the APSIM RUE/TE and energy balance approaches for the estimation of crop demand, which will have implications for modelling crop growth under water-limited conditions and calculation of water required to maintain maximum growth.

This 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.

Urban climate and day of the week

Substrate and soil temperatures were analyzed throughout 14 representative fruit–vegetable crop cycles and treatments grown in low-cost Mediterranean greenhouses, mostly around the cold winter period. The mean daily temperatures of most common substrates (perlite and coconut-coir bags) were lower (between 0.9 and 3.0 °C) and more variable than root zone temperatures of the most common soil (enarenado soil) throughout all crop cycles and treatments studied, particularly during the cold period. The mean daily temperature of the perlite and coconut-coir bags was low (around 15 °C) during most of the cold periods, and the minimum daily temperature was very low (around 12 °C) during some crop periods. These low temperatures are generally considered suboptimal for greenhouse production. The seasonal dynamics of the minimum and mean daily temperature of the substrate bags were similar to those observed for the mean daily greenhouse air temperature. The minimum daily temperature of substrate bags was close to the mean daily greenhouse air temperature for all the studied crop cycles. A simple and practical relationship was found for predicting mean daily temperatures of substrate bags from mean daily greenhouse air temperatures (MAE = 0.87 °C; RMSE = 1.15 °C). A substantial spatial and temporal variation in the hourly temperature was found in the cross-section of the coconut-coir bag, but not for the mean daily temperature. No differences were found for the mean daily temperature along the longitudinal section of the bag. In general, representative measurements of the coconut-coir bag can be taken by installing the sensors horizontally and especially vertically around the middle part of the bag.

Temperature extremes have been extensively observed on the Tibetan Plateau (TP), of which the dynamics were mostly monitored by traditional trend analysis based on linear regression. This method however, is often impacted by a quasi‐periodic heterogeneity disturbance in variance, leading to results susceptible to extreme outliers. In this study, we conducted the comprehensive detection of extreme temperature changes using quantile regression on the TP from 100 weather stations from 1979 to 2019. The use of quantile regression enabled us to characterize the entire density function of the extreme temperature time‐series (the daily minimum, mean and maximum temperature) at each location. Our results revealed that the daily minimum temperature was generally warming faster than the daily maximum and mean temperatures with the respective trend interval of [−0.45, 1.29], [−0.1, 0.98], [−0.01, 1.01] °C/decade among the conditional quantile levels. Furthermore, the warming rate of extremely cold (0.05 quantile level) and hot days (0.95 quantile level) was higher than that of the mean condition (0.5 quantile level) in each extreme temperature time‐series. We also found that there was a significant elevation‐dependent warming only in extremely cold days. In addition, results from the teleconnection analysis showed that the Arctic Oscillation had a strong influence on the extreme cold and hot extremes over the northern and southwestern TP region, while the El Niño‐Southern Oscillation mainly modulated temperatures over the southeastern TP area. This study can potentially offer improved understanding of the temperature extremes over TP in the face of global warming.

Freeze-thaw frequencies calculated by eight different counting methods were compared using daily maximum and minimum temperatures from eight north-central United States National Weather Service (NWS) stations. These frequencies were also compared to those obtained using hourly air temperature data from six of the same NWS stations. In addition, the calculated frequencies were compared to measured freeze-thaw frequencies at several depths in a bare soil and a sod-covered soil at the University of Minnesota St. Paul campus climatological observatory. The necessary acceptance of the idealized daily heating cycle when using daily maximum and minimum air temperature data resulted in a higher occurrence of calculated freeze-thaw events than those obtained with hourly data; one method gave 23% more freeze-thaw events with the daily maximum and minimum temperatures. With the freeze-thaw phenomenon centered upon those months in which the mean temperature hovers near O°C, a bimodal frequency occurs at the northern stations (October and April, as at International Falls, Minnesota, and November and March at Fargo, North Dakota), while in warmer climates the bimodal characteristic is replaced by a single-peak frequency in January as at Sedalia and West Plains, Missouri. In the comparison between the calculated freeze-thaw frequencies based on daily maximum and minimum values and the hourly temperature measurements at several heights between the surface and the temperature shelter at the climatological observatory, it was found that the annual total frequencies increased as the height above the surface decreased. For the shallowest height above the surface there was an approximate 13% increase over those measured in the shelter with hourly temperature data. The annual total frequencies of the calculated freeze-thaw events obtained with the daily maximum and minimum temperature measurements in the shelter approximated those actually occurring at the 1-cm depth in a bare soil at the climatological observatory. Results also indicated that on the same day as a thaw at 1 cm the average shelter maximum temperature was 6.3°C, while for a freeze at the same depth the average shelter minimum was −3.5°C. These values are explained, in part at least, by the frequent lack of direct relationship between the timing of a soil freeze-thaw event in a day and the occurrence of the daily maximum or minimum air temperature. The freeze-thaw phenomenon extended below 10 cm in a soil bare of vegetation at St. Paul but averaged only about eight events per year at both 10 and 20 cm, compared to about 62 occurrences at 1 cm; even at 5 cm the average was less than 17 per year. Once frozen to a depth of about 20 cm, the soil remained frozen throughout the winter.

Developing a growing degree day model for North Dakota and Northern Minnesota soybean

Forecasting daily extreme temperatures in Chinese representative cities using artificial intelligence models

Background/Aim: Under a warming climate, increasing concerns are focused on impacts of high temperatures on health. This project aims to assess relationships between temperature and humidity and renal disease morbidity in Vietnam.Methods: We obtained daily hospital admission data (2000-2015) from 14 provincial and city hospitals across Vietnam, along with province-level weather data. Using a case-crossover design, we assessed relationships between temperature (daily mean, maximum, and minimum temperature, and heat index), relative humidity, and heatwave events and urolithiasis, acute renal disease, and other renal disease. We evaluated lagged and cumulative effects. To evaluate effects of prolonged extreme heat, we analyzed heatwave days defined as three or more consecutive days with daily maximum temperature ≥ 95th percentile, starting with the third day.Results: This study included 107,266 cases of renal disease during warm seasons. In same-day models, higher temperatures were significantly associated with increased urolithiasis risk, but not with increased risk for acute or other renal diseases. The odds ratios (OR) for urolithiasis generally decreased with increasing lag days. Among cumulative measures, 4-day average temperature showed the strongest association with urolithiasis. Our optimal model included 4-day average temperature and same-day heatwave day. For urolithiasis, the OR per 5ºC increase in 4-day average of mean temperature was 1.18 (95% confidence interval: 1.07,1.30), and the OR for being a heatwave day was 1.13 (1.04, 1.23). Results for maximum temperature, minimum temperature and heat index models were similar.Conclusions: Exposure to high temperature is a risk factor for urolithiasis. This effect is not limited to temperature on the same day, but extends to previous days. The greatest risk is conferred by cumulative exposure in the past four days, with a same-day heatwave day adding to the risk.

Phenological models are valuable tools for predicting vegetation phenology and investigating the relationships between vegetation dynamics and climate. However, compared to temperate and boreal ecosystems, phenological modeling in alpine regions has received limited attention. In this study, we developed a semi-mechanistic phenological model, the Alpine Growing Season Index (AGSI), which incorporates the differential impacts of daily maximum and minimum air temperatures, as well as the constraints of precipitation and photoperiod, to predict foliar phenology in alpine grasslands on the Qinghai–Tibetan Plateau (QTP). The AGSI model is driven by daily minimum temperature (Tmin), daily maximum temperature (Tmax), precipitation averaged over the previous month (PA), and daily photoperiod (Photo). Based on the AGSI model, we further assessed the impacts of Tmin, Tmax, PA, and Photo on modeling accuracy, and identified the predominant climatic controls over foliar phenology across the entire QTP. Results showed that the AGSI model had higher accuracy than other GSI models. The total root mean square error (RMSE) of predicted leaf onset and offset dates, when evaluated using ground observations, was 12.9 ± 5.7 days, representing a reduction of 10.9%–54.1% compared to other models. The inclusion of Tmax and PA in the AGSI model improved the total modeling accuracy of leaf onset and offset dates by 20.2%. Overall, PA and Tmin showed more critical and extensive constraints on foliar phenology in alpine grasslands. The limiting effect of Tmax was also considerable, particularly during July–November. This study provides a simple and effective tool for predicting foliar phenology in alpine grasslands and evaluating the climatic effects on vegetation phenological development in alpine regions.

Season and outdoor ambient temperature: effects on birth weight

Contribution of climatic variables in predicting rice yield

The temporal variability of daily mean maximum and minimum temperatures and seasonal rainfall totals in summer (June to September) measured at 25 stations from 1950 to 2010 was analyzed in light of five climate indices. Long-term trend analysis using the Mann-Kendall method revealed a significant increase in maximum temperatures at 44% of stations and in minimum temperatures at 68% of stations. In contrast, a significant decrease in rainfall totals is observed for 16% of stations. The Lombard test revealed that shifts in mean values of maximum and minimum temperatures occurred after the 1970s decade whereas shifts in mean values of rainfall occurred before that decade. Daily mean maximum and minimum temperatures primarily show a positive correlation with summer (June to September) Atlantic Multidecadal Oscillation (AMO) and Arctic Oscillation (AO) indices, whereas rainfall totals are negatively correlated with the Southern Oscillation Index (SOI). The study shows that, contrary to the generally accepted view, summer warming is more widespread than winter warming in southern Quebec. Climate models used to predict future temperatures must take this into account.

Trends in urban fraction around meteorological station were used to quantify the relationship between urban growth and local urban warming rate in temperature records in China. Urban warming rates were estimated by comparing observed temperature trends with those derived from ERA‐Interim reanalysis data. With urban expansion surrounding observing stations, daily minimum temperatures were enhanced, and daily maximum temperatures were slightly reduced. On average, a change in urban fraction from 0% to 100% induces additional warming in daily minimum temperature of +1.7 ± 0.3°C; daily maximum temperature changes due to urbanization are −0.4 ± 0.2°C. Based on this, the regional area‐weighted average trend of urban‐related warming in daily minimum (mean) temperature in eastern China was estimated to be +0.042 ± 0.007 (+0.017 ± 0.003)°C decade−1, representing about 9% (4%) of overall warming trend and reducing the diurnal temperature range by −0.05°C decade−1. No significant relationship was found between background temperature anomalies and the strength of urban warming.