Daily Tmax Research Articles

On snowmelt computations in the climate change context: comparison of single-site and multisite downscaling of extreme daily temperature processes

This study focuses on how multisite downscaling procedures can provide a significant improvement in the accuracy of impact assessment results as compared to single-site downscaling techniques. The study compared the accuracy of snowmelt computations given by the multisite multivariate statistical downscaling (MMSD) method and the single-site downscaling (SDSM) procedure. Results of an illustrative application using daily maximum ( Tmax) and minimum ( Tmin) temperature series at seven stations in southern Quebec–Ontario region in Canada indicated that MMSD method was able to reproduce accurately the observed spatial and temporal variabilities of daily extreme temperature processes over different locations. MMSD approach preserved more accurately the interstation correlations of daily Tmax (0.78 and 0.7) and Tmin (0.94 and 0.84), respectively, for calibration and validation period than SDSM (0.08 and 0.11 for Tmax; and 0.02 for Tmin). MMSD was found capable to produce more accurate snowmelt estimations than those given by the SDSM.

Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third‐party automatic weather stations in Australia

AbstractThird‐party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine‐scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p‐value‐based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high‐quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources.

Influência de um corredor fluvial na variação de elementos climáticos: O caso da Cidade de Neuquén (Argentina)

This paper aimed to compare the behavior of climatic elements and extreme thermal events between two landscape units of the city of Neuquén, Argentina, for the period 2013-2020. The study involved daily data of minimum temperature (Tmin), maximum temperature (Tmax), and relative humidity (RH) obtained from the meteorological stations "Neuquén Aero" (NA) and "Paseo de la Costa" (PC), referring to the valley (peri-urban area) and the fluvial corridor (riparian environment) respectively. The influence of the riverbank on the daily Tmin and Tmax during Heat Waves (HW) and Cold Waves (CW) in the valley was analyzed using the methodology of the National Meteorological Service. The mean Tmax (Tmin), at annual, seasonal, and monthly resolutions, were lower (higher) in PC. The mean RH values were higher in NA. During the HW, a regulatory effect was exhibited regarding the number of days that the threshold values were exceeded consecutively in the fluvial littoral.

Extreme warm and cold waves derived from multiple high-resolution gridded datasets in Egypt

The primary purpose of this study is to interpret the variability of extreme warm (Tmax) and cold (Tmin) events over Egypt from 1981 to 2020 using three gridded datasets that evaluated against the observations at 24 in situ stations using robust statistical procedures. These high-resolution gridded datasets are well-matched with the observed data. Furthermore, the monthly maximum of daily Tmax (TXx) and Tmin (TNx) and the monthly minimum of daily Tmax (TXn) and Tmin (TNn) climate indices are used to investigate the variability of extremes over Egypt. It is found that the TXx increased gradually throughout the study period and the Mann–Kendall rank correlation (Tau) is mostly positive in the first and last decades, with the coefficient of variation (COV) ranges from 1 to 5. Also, the lowest TXn values are detected in the first two decades with decadal anomaly ranging from − 2 to + 2, and it has positive Tau values during all decades with COV ranges from 2 to 20. The TNx increased southward, and its decadal anomaly increased gradually over time. The significant positive Tau of TNx is found in the first and last decades, and its COV ranges from 1 to 8. Moreover, the TNn is increased during the study period, and its anomaly is less than + 0.5 °C in the first decade and exceeds + 2 °C in the fourth decade. The significant positive (negative) Tau for TNn appeared during the third (fourth) decade, while its COV ranged from 1 to 12. Finally, the COV and Tau of all indices are often negatively correlated (> − 0.5).

Predicting maximum temperatures over India 10-days ahead using machine learning models

In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models.

A Percentile Method to Determine Cold Days and Spells in Bangladesh

The 10th percentiles (10P) of the daily minimum (Tmin) and maximum (Tmax) during 1971–2000 were determined to estimate a threshold for cold days. This 10P (a standard of extreme climatic condition suggested by the World Meteorological Organization) threshold was applied with the daily Tmin and Tmax in the winter months (December, January, and February) of 2000 to 2021 to calculate the number of cold days, and consecutively, cold spells, and their trends. A cold day was declared when the daily Tmax and/or Tmin was lower than that of the 10P threshold, and the average temperature was ≤17 °C in a weather station. In this research, the cold days and spells were categorized into five classes, namely extreme (≤13 °C), severe (>13–14 °C), very (>14–15 °C), moderate (>15–16 °C), and Mild (>16–17 °C). Moreover, a cold spell was considered when such cold days persisted for ≥2 consecutive days in at least two nearby stations. The results revealed a higher number of average cold days during winter in the western and northwestern districts of Bangladesh, and it reduced gradually in the south, southeast, and northeast. Dinajpur and Rajshahi districts showed the highest number of extreme and severe categories of cold days, i.e., 4.81 and 3.24 days/year, respectively. Rajshahi division had the highest number of cold spells on average (3.24/year), and Rangpur division had the highest number of extreme-category (the category that carries the lowest temperature range, ≤13 °C) cold spells (1.29/year). January was the coldest month, with the maximum number of cold days and spells. The highest average number of cold days (25.54%) was observed during the second ten days of January (i.e., 11–20 January). Significant increasing trends were found in the cold days of 11–20 December (5 stations), 21–31 December (3 stations), and the month of December (13 stations). In contrast, significant decreasing trends were noticed for the 1–10 January period in three weather stations. Our proposed 10P method could be used to determine the cold days and spells in Bangladesh that might be useful for the policy makers in formulating appropriate strategies in minimizing the impact of cold regimes during the winter season.

Whether the updates of dynamical processes can improve the performance of BCC‐CSM model to reproduce the long‐range correlation of the daily temperature?

AbstractMany studies have shown that the climate system has the long‐range correlation (LRC) characteristics, which provide a good test bed for evaluating the performance of climate models. In this study, the LRC characteristics of the observed surface daily temperature data in China is first analysed by detrended fluctuation analysis. It is found that the daily temperature records, including daily mean temperature (Tmean), daily maximum temperature (Tmax), and daily minimum temperature (Tmin), all exhibit LRC characteristics with scaling exponents more than 0.6 in most of China. Then, the performances of two different versions for the Beijing Climate Center Climate System Model (BCC‐CSM), namely, BCC‐CSM1.1m in CMIP5 and BCC‐CMS2‐MR in CMIP6, are quantitatively evaluated by comparing the differences of the LRC characteristics between the observations and the simulations. The results indicate that BCC‐CSM2‐MR performs better than BCC‐CSM1.1(m) in western Northwest China, most of south of the middle and lower reaches of Yangtze River, North China, central Inner Mongolia for the daily Tmean and Tmax. As far as the daily Tmin is concerned, BCC‐CSM1.1(m) performs better than BCC‐CSM2‐MR in western South China and southern Xinjiang, and the performance of BCC‐CSM2‐MR in CMIP6 has not been significant improved. Compared with BCC‐CSM1.1(m), BCC‐CSM2‐MR cannot significantly improve the simulations for the LRC characteristics of the three daily temperature variables in central and eastern Qinghai‐Tibet plateau, and the areas of between lower reaches of Yellow River and Yangtze River.

Updated analysis of surface warming trends in North China based on in-depth homogenized data (1951-2020)

The reliability of climate change detection and research is significantly impacted by the inhomogeneity of surface climate observation data. However, there is an ongoing debate regarding whether comprehensive homogenization has been performed in large-scale homogenized data sets. In this study, we examined the homogeneity of the original maximum and minimum temperature (Tmax and Tmin) data for 662 meteorological stations in North China by using multiple methods and combining with metadata. The quantile matching method was employed to adjust the daily Tmax and Tmin series. In order to avoid the potential systematic bias resulting from homogenization, no reference series were introduced during the adjustment process. The adjustment results indicate that Tmin in North China is significantly affected by non-climatic factors, particularly station relocations and environmental changes around the stations. The application of homogenization in this study led to a notable increase in the overall temperature trends of the stations, with Tmin exhibiting a larger increase and the diurnal temperature range demonstrating a more significant downward trend. Based on the homogenized data, the annual and seasonal mean temperature trends in North China from 1951 to 2020 were re-evaluated. These temperature trends generally surpass those reported in previous research for the same period from 1961 to 2000. The higher estimate of temperature trends may be attributed to the recovered urbanization effect in the newly homogenized data. Thus, the obtained homogenization data still exhibit a significant urbanization bias that requires further assessment and adjustment.

A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

Abstract. Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed in the scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡ at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-based Ta measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates ranged from 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship between Ta and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and we expect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data have been published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).

Influence of Meteorological Temperature and Pressure on the Severity of Heart Failure Decompensations.

To investigate the relationship between ambient temperature and atmospheric pressure (AP) and the severity of heart failure (HF) decompensations. We analysed patients coming from the Epidemioloy Acute Heart Failure Emergency (EAHFE) Registry, a multicentre prospective cohort study enrolling patients diagnosed with decompensated HF in 26 emergency departments (EDs) of 16 Spanish cities. We recorded patient and demographic data and maximum temperature (Tmax) and AP (APmax) the day before ED consultation. Associations between temperature and AP and severity endpoints were explored by logistic regression. We used restricted cubic splines to model continuous non-linear associations of temperature and AP with each endpoint. We analysed 16,545 patients. Daily Tmax and APmax (anomaly) of the day before patient ED arrival ranged from 0.8 to 41.6° and from - 61.7 to 69.9 hPa, respectively. A total of 12,352 patients (75.2%) were hospitalised, with in-hospital mortality in 1171 (7.1%). The probability of hospitalisation by HF decompensation showed a U-shaped curve versus Tmax and an increasing trend versus APmax. Regarding temperature, hospitalisation significantly increased from 20 °C (reference) upwards (25 °C: OR = 1.12, 95% CI = 1.04-1.21; 40 °C: 1.65, 1.13-2.40) and below 5.4 °C (5 °C: 1.21, 1.01-1.46). Concerning the mean AP of the city (anomaly = 0 hPa), hospitalisation increased when APmax (anomaly) was above + 7.0 hPa (atmospheric anticyclone; + 10 hPa: 1.14, 1.05-1.24; + 30 hPa: 2.02. 1.35-3.03). The lowest probability of mortality also corresponded to cold-mild temperatures and low AP, with a significant increased risk only found for Tmax above 24.3 °C (25 °C: 1.13, 1.01-1.27; 40 °C: 2.05, 1.15-3.64) and APmax (anomaly) above + 3.4 hPa (+ 10 hPa: 1.21, 1.07-1.36; + 30 hPa: 1.73, 1.06-2.81). Sensitivity analysis confirmed the main analysis results. Temperature and AP are independently associated with the severity of HF decompensations, with possible different effects on the need for hospitalisation and in-hospital mortality.

Energy Balance, Microclimate, and Crop Evapotranspiration of Winter Wheat (Triticum aestivum L.) under Sprinkler Irrigation

Understanding the impact of sprinkler irrigation on field energy balance, microclimate, and crop evapotranspiration is of great importance for optimizing irrigation scheduling and enhancing crop growth. In this study, the microclimate variables, energy, and water flux were measured using an eddy covariance system during four wheat (Triticum aestivum L.) growing seasons in a sprinkler-irrigated field of North China Plain. The variation patterns of microclimate, energy balance (net solar radiation Rn, soil heat flux G, latent heat LE, and sensible heat H) and crop evapotranspiration (ET) were analyzed during and after sprinkler irrigation events. A novel quantitative model using a stepwise regression method was developed to predict the change in microclimate after sprinkler irrigation by considering irrigation, weather, meteorology, and crop traits. The results showed that the reflectance rate of the wheat canopy decreased by 0.01, and the daily LE/Rn increased by 0.19–0.23 in the 1–3 days after sprinkler irrigation with 40–50 mm water, which finally resulted in crop ET increased by 1.8–4.7 mm during irrigation interval, and seasonal total ET could increase by 9–24 mm when five normal sprinkler irrigations were implemented in a wheat season. The mean daily H/Rn decreased by 0.06–0.17, indicating weak energy exchange between canopy and environment. The measured daily minimum (Tmin), maximum temperatures (Tmax) and daily mean vapor pressure deficit (VPD) decreased by approximately 0.8 °C, 0.9 °C, and 0.25 kPa, respectively, and daily mean relative humidity increased by approximately 7.5% on the first 3 days after sprinkler irrigation; and these changes decreased and were negligible on the 5th–7th days. The decreases in daily Tmin, Tmax, and mean VPD after sprinkler irrigation could change more under higher irrigation amounts and sunny days with a larger crop leaf area index based on the fitted models.

Analysis on Heat Characteristics for Summer Maize Cropping in a Semi-Arid Region

Heat stress during flowering is a critical limitation for summer maize production. However, the incidence of heat varies with years and locations, and it poses a great risk to successful maize reproduction and kernel setting. Therefore, it is essential to provide a sound quantification of heat occurrence in relation to maize growth and development. Here, we analyzed the characteristics of heat occurrence based on climate data for over 60 years on Huaibei Plain, China. The effective accumulated temperature showed a slight interannual variation. The average maximum temperature (T¯max) during flowering was 32 °C–33 °C, which was approximately 2 °C higher than that over the whole growing season. The probability (P) for the daily Tmax > 33 °C during flowering was closer to 50% and this maximum temperature ranged between 33 °C and 37 °C. The five levels from normal to extreme heat for Tmax were defined. Across the six studied sites, the mild level heat stress accounted for most of incidents (P, 25–50%), followed by moderate (P, 13–25%) and severe (P, 0.5–13%), and the minimum for extreme heat stress (P, 0.5%). Four phases bracketing flowering during maize development were given, i.e., 1 week prior to anthesis, 1 week during anthesis, 1 week for anthesis-silking, and 1week post silking. There was a greater probability for heat stress incidents from anthesis to silking compared to the other developmental stages. Additionally, maize grain yield slightly increased with the increase in Tmax to 33 °C, but it declined as Tmax surpassed 33 °C. In conclusion, the pattern and characteristics of heat stress were quantified bracketing maize flowering. These findings assist to advise summer maize cropping strategies on the semi-arid and semi-humid Huaibei Plain, China or similar climate and cropping regions.

Spatiotemporal variations of seasonal temperature at different percentiles in the three provinces of northeast China from 1961 to 2019

AbstractThe three provinces of northeast China (NEPs) were one of the major grain producing areas in China and has undergone significant climate warming during past decades. However, nowadays researches on climate warming mostly focused on variations of mean temperature or temperature extremes, which missed much information in the other parts of temperature. Therefore, taken NEPs as study area, variations of maximum temperature (Tmax), minimum temperature (Tmin) and diurnal temperature range (DTR) at every 10% percentile from 1961 to 2019 in NEPs were analysed at seasonal scale. The abrupt changes and decadal shifts of Tmax, Tmin and DTR at different percentiles were further highlighted. Results show that (a) for either regional average or station cases, fluctuations of daily Tmax and daily Tmin both decreased in spring and winter, but increased in autumn. In summer, fluctuations of daily Tmin decreased while those of daily Tmax increased. (b) Spatially, Tmax heated up more in south of NEPs at lower percentiles, and in northwest of NEPs at higher percentiles in spring, summer and winter. Tmin and DTR overall showed consistent spatial differences that changes in north and northwest of NEPs were with greater magnitudes. (c) In spring and winter, the higher Tmax/Tmin had earlier abrupt changes than lower Tmax/Tmin, while in summer and autumn, the abrupt changes occurred earlier for lower Tmax/Tmin. (d) In winter, overwhelming positive shifts were significant before 2000, but turned negative after 2000. In summer, Tmax heated up quickly since 1990s and Tmin since 1980s. Shifts in spring and autumn varied during different periods and generally presented alternative warming at higher and lower percentiles. The research can help better depict the warming patterns and figure out the detailed warming procedures in NEPs, which is a strong supplement for current climate change research.

Assessing proximate intermediates between ambient temperature, hospital admissions, and mortality in hemodialysis patients.

Typical thermoregulatory responses to elevated temperatures among healthy individuals include reduced blood pressure and perspiration. Individuals with end-stage kidney disease (ESKD) are susceptible to systemic fluctuations caused by ambient temperature changes that may increase morbidity and mortality. We investigated whether pre-dialysis systolic blood pressure (preSBP) and interdialytic weight gain (IDWG) can independently mediate the association between ambient temperature, all-cause hospital admissions (ACHA), and all-cause mortality (ACM). The study population consisted of ESKD patients receiving hemodialysis treatments at Fresenius Medical Care facilities in Philadelphia County, PA, from 2011 to 2019 (n=1981). Within a time-to-event framework, we estimated the association between daily maximum dry-bulb temperature (TMAX) and, as separate models, ACHA and ACM during warmer calendar months. Clinically measured preSBP and IDWG responses to temperature increases were estimated using linear mixed effect models. We employed the difference (c-c') method to decompose total effect models for ACHA and ACM using preSBP and IDWG as time-dependent mediators. Covariate adjustments for exposure-mediator and total and direct effect models include age, race, ethnicity, blood pressure medication use, treatment location, preSBP, and IDWG. We considered lags up to two days for exposure and 1-day lag for mediator variables (Lag 2-Lag 1) to assure temporality between exposure-outcome models. Sensitivity analyses for 2-day (Lag 2-only) and 1-day (Lag 1-only) lag structures were also conducted. Based on Lag 2- Lag 1 temporal ordering, 1°C increase in daily TMAX was associated with increased hazard of ACHA by 1.4% (adjusted hazard ratio (HR), 1.014; 95% confidence interval, 1.007-1.021) and ACM 7.5% (adjusted HR, 1.075, 1.050-1.100). Short-term lag exposures to 1°C increase in temperature predicted mean reductions in IDWG and preSBP by 0.013-0.015% and 0.168-0.229mmHg, respectively. Mediation analysis for ACHA identified significant indirect effects for all three studied pathways (preSBP, IDWG, and preSBP+IDWG) and significant indirect effects for IDWG and conjoined preSBP+IDWG pathways for ACM. Of note, only 1.03% of the association between temperature and ACM was mediated through preSBP. The mechanistic path for IDWG, independent of preSBP, demonstrated inconsistent mediation and, consequently, potential suppression effects in ACHA (-15.5%) and ACM (-6.3%) based on combined pathway models. Proportion mediated estimates from preSBP+IDWG pathways achieved 2.2% and 0.3% in combined pathway analysis for ACHA and ACM outcomes, respectively. Lag 2 discrete-time ACM mediation models exhibited consistent mediation for all three pathways suggesting that 2-day lag in IDWG and preSBP responses can explain 2.11% and 4.41% of total effect association between temperature and mortality, respectively. We corroborated the previously reported association between ambient temperature, ACHA and ACM. Our results foster the understanding of potential physiological linkages that may explain or suppress temperature-driven hospital admissions and mortality risks. Of note, concomitant changes in preSBP and IDWG may have little intermediary effect when analyzed in combined pathway models. These findings advance our assessment of candidate interventions to reduce the impact of outdoor temperature change on ESKD patients.

Evaluation of re-analyses over China based on the temporal asymmetry of daily temperature variability

As an intrinsic feature of daily surface air temperature (SAT) variability found in station measurements, temporal asymmetry (TA) can be taken as an evaluation metric to access the quality of SAT re-analysis product. In this study, TA calculated from four SAT variables, i.e., daily mean SAT (Tmean), daily maximum SAT (Tmax), daily minimum SAT (Tmin) and diurnal temperature range (TDTR = Tmax − Tmin), is applied to evaluate synoptic-scale performance of four reanalysis products (NCEP-2, JRA-55, ERA-I, and ERA-5) over China. The results show that four re-analyses overall overestimate the TA of daily Tmax and Tmin variability over China, but with a comparatively consistent estimated TA for Tmean. Moreover, the TA of Tmean variability for these four re-analyses shares high spatial consistency with those from the observation. However, four re-analyses own the similar region-dependent spatial patterns of overestimated TA for Tmax and Tmin variability, especially for Tmax. Since high TA is an indicator for strong nonlinear feature, only Tmean reanalysis is the most suitable to explore synoptic-scale extreme events, such as heat waves and cold waves, which are highly related to the strong nonlinear processes.

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

Association of heat exposure and emergency ambulance calls: A multi-city study

Evidence of the impact of ambient temperatures on emergency ambulance calls (EACs) in developing countries contributes to the improvement and complete understanding of the acute health effects of temperatures. This study aimed to examine the impacts and burden of heat on EACs in China, quantify the contributions of regional modifiers, and identify the vulnerable populations. A semi-parametric generalized additive model with a Poisson distribution was used to analyze the city-specific impacts of the daily maximum temperature (Tmax) on EACs in June–August in 2014–2017. Stratified analyses by sex and age were performed to identify the vulnerable sub-populations. Meta-analysis was undertaken to illustrate the pooled associations. Further subgroup analysis, stratified by climate, latitude, and per capita disposable income (PCDI), and meta-regression analysis were conducted to explore the regional heterogeneity and quantify the contributions of possible modifiers. The city- and region-specific attributable fractions of EACs attributable to heat were calculated. Strong associations were observed between the daily Tmax and total EACs in all cities. A total of 11.7% (95% confidence interval (CI): 11.2%–12.3%) of EACs were attributed to high temperatures in ten Chinese cities, and the central region with a low level of PCDI had the highest attributable fraction of 17.8% (95% CI: 17.2%–18.4%). People living in the central region with lower PCDI, and those aged 18–44 and 0–6 years were more vulnerable to heat than the others. The combined effects of PCDI, temperature, and latitude contributed 88.6% of the regional heterogeneity. The results complemented the understanding of the burden of EACs attributable to heat in developing countries and the quantitative contribution of regional modifiers.

Disparities of weather type and geographical location in the impacts of temperature variability on cancer mortality: A multicity case-crossover study in Jiangsu Province, China

BackgroundConsidering the serious health burden caused by adverse weather events, increasing researches focused on the relationship between temperature variability (TV) and cause-specific mortality, but its association with cancer was not well explored. We aimed to investigate the impacts of TV on cancer mortality and examine the modifying effects of weather type and geographical location as well as other characteristics. Materials and methodsDaily city-specific data of cancer deaths, mean temperature (Tmean), maximum and minimum temperatures (Tmax and Tmin), relative humidity (RH), rainfall, and air pollutants were collected during 2016–2017 in 13 cities in Jiangsu Province, China. TV0-t was defined as the standard deviation of the daily Tmax and Tmin on the exposure 0-t days. A two-stage analysis was applied. First, a time-stratified case-crossover design was used to examine the odds ratio (OR) and attributable fraction of cancer mortality per 1 °C increase in TV by adjusting for potential confounders. Random effect meta-analysis was used to summarize the pooled ORs. Second, stratified analysis was performed for weather type, geographical location, demographics, and other city-level characteristics. The weather was defined as four types according to days during warm or cold season combined with high or low RH. ResultsA total of 303670 cases were included in our study. Meta-analysis showed that the ORs of cancer mortality per 1 °C increase in TV0-t significantly increased and peaked in TV0-2 (OR=1.0098, 95% CI: 1.0039–1.0157). The attributable fraction of TV0-2 on cancer mortality was 4.74%, accounting for 14395 deaths in the study period. Significant ORs of TV-related cancer mortality were found during the warm season combined with high RH and in the northern region of Jiangsu. Susceptible groups of TV-related cancer mortality were identified as female patients, patients aged 45–65 years, and those living in cities with lower per capita green area. ConclusionsTV can significantly increase the risk of cancer mortality, especially during warm and humid days and in the northern region of Jiangsu. Findings are of great significance to formulate urban planning, resource allocation, and health intervention to prolong the life of cancer patients.

Means and Extremes: Evaluation of a CMIP6 Multi-Model Ensemble in Reproducing Historical Climate Characteristics across Alberta, Canada

This study evaluates General Circulation Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for their ability in simulating historical means and extremes of daily precipitation (P), and daily maximum (Tmax), and minimum temperature (Tmin). Models are evaluated against hybrid observations at 2255 sub-basins across Alberta, Canada using established statistical metrics for the 1983–2014 period. Three extreme indices including consecutive wet days (CWD), summer days (SD), and warm nights (WN) are defined based on the peak over the threshold approach and characterized by duration and frequency. The tail behaviour of extremes is evaluated using the Generalized Pareto Distribution. Regional evaluations are also conducted for four climate sub-regions across the study area. For both mean annual precipitation and mean annual daily temperature, most GCMs more accurately reproduce the observations in northern Alberta and follow a gradient toward the south having the poorest representation in the western mountainous area. Model simulations show statistically better performance in reproducing mean annual daily Tmax than Tmin, and in reproducing annual mean duration compared to the frequency of extreme indices across the province. The Kernel density curves of duration and frequency as simulated by GCMs show closer agreement to that of observations in the case of CWD. However, it is slightly (completely) overestimated (underestimated) by GCMs for warm nights (summer days). The tail behaviour of extremes indicates that GCMs may not incorporate some local processes such as the convective parameterization scheme in the simulation of daily precipitation. Model performances in each of the four sub-regions are quite similar to their performances at the provincial scale. Bias-corrected and downscaled GCM simulations using a hybrid approach show that the downscaled GCM simulations better represent the means and extremes of P characteristics compared to Tmax and Tmin. There is no clear indication of an improved tail behaviour of GPD based on downscaled simulations.

Models for predicting pseudothecium maturity and ascospore release of Phyllosticta spp. in South African citrus orchards

Ascosporic infection plays a major role in the epidemiology of citrus black spot (CBS) in South Africa, a disease caused by Phyllosticta citricarpa. Phyllosticta pseudothecium maturation and ascospore release models have been integrated in infection models to predict the availability of the primary inoculum source. However, these models have not been validated on a broader data set and this study aimed to validate and improve these epidemiological models. New pseudothecium maturation and ascospore release models for P. citricarpa were developed, based on weather and ascospore trap data from 13 locations and up to five seasons. From the 29 data sets analysed, 3775 3-hourly periods with ascospore events were recorded on 1798 days; 90% of these events occurred between 16.0 °C and 32.1 °C (daily Tmin and Tmax of 15.4 °C and 33.5 °C, respectively) and 75% occurred above a relative humidity (RH) of 55.9% (daily RH > 47.9%). Rain was recorded during 13.8% of these ascospore events and 20.0% of ascospore days. Using logistic regression, a Gompertz model that best predicted pseudothecium maturation, or the probability of onset of ascospore release, was developed and was markedly more accurate than the previously described models. The model consisted of DDtemp [cumulative degree-days from midwinter (1 July) calculated as (minimum + maximum daily temperature) / 2 – 10 °C] and DDwet (DDtemp accumulated only on days with >0.1 mm rain or vapour pressure deficit <5 hPa) as variables in the formula: probability of first ascospore event = exp(-exp(-(-3.131 + 0.007 × DDtemp - 0.007 × DDwet))). A Gompertz model [PAT = exp(-2.452 × exp(-0.004 × DDwet2))] was also developed for ascospore release; DDwet2 = DDtemp accumulated, from first seasonal ascospore trap day, only on days with >0.1 mm rain or vapour pressure deficit <5 hPa. Similar to the DDwet2 model described in a previous study, this model adequately predicted the general trend in ascospore release but poorly predicted periods of daily, 3-day and 7-day ascospore peaks.
 Significance:
 
 We developed a new pseudothecium maturation model from 29 data sets, comprising different climatic regions in South Africa, and validated previously published models. The new model was markedly more accurate in predicting the onset of ascospore release and can be used to improve existing CBS epidemiological models and improve risk assessment and management of CBS in South African citrus orchards.