Means Of Daily Maximum Temperatures Research Articles

Body mass decline in a Mediterranean community of solitary bees supports the size shrinking effect of climatic warming.

The long-known, widely documented inverse relationship between body size and environmental temperature ("temperature-size rule") has recently led to predictions of body size decline following current climatic warming ("size shrinking effect"). For keystone pollinators such as wild bees, body shrinking in response to warming can have significant effects on pollination processes but there is still little direct evidence of the phenomenon because adequate tests require controlling for confounding factors linked to climate change (e.g., habitat change). This paper assesses the shrinking effect in a community of solitary bees from well-preserved habitats in the core of a large nature reserve experiencing climatic warming without disturbances or habitat changes. Long-term variation in mean body mass was evaluated using data from 1704 individual bees (137 species, 27 genera, 6 families) sampled over 1990-2023. Climate warmed at a fast rate during this period, annual mean of daily maximum temperature increasing 0.069°C/year on average during 2000-2020. Changes in bee body mass verified expectations from the size shrinking effect. The mean individual body mass of the community of solitary bees declined significantly, irrespective of whether the analysis referred to the full species sample or only to the subset of species that were sampled in both the old (1990-1997) and recent (2022-2023) periods. On average, body mass declined ~0.7%·year-1 , or an estimated average cumulative reduction of ~20 mg per individual bee from 1990 to 2023. Proportional size reduction was greatest among large-bodied species, ranging from around -0.6%·year-1 for the smallest species to -0.9%·year-1 for the largest ones. Declining rate was steeper for cavity-nesting than ground-nesting species. The pollination and mating systems of bee-pollinated plants in the study region are probably undergoing significant alterations as a consequence of supra-annual decline in bee body mass.

On the Seasonal and Spatial Dependence of Extreme Warm Days in Antarctica

AbstractThe spatial distribution of trends in temperature extremes over Antarctica remains largely unknown. Here we investigate the seasonal and spatial characteristics of extreme warm occurrence across Antarctica. The Antarctic inland areas show significant positive trends in the number of extreme warm days in austral spring and summer. The trends in the seasonal mean of daily maximum temperature show strong coherence with the trends in extreme warm occurrences. In addition, the long‐term longwave radiation, water vapor flux and regional atmospheric circulation changes are closely connected to the trends of extreme warm days in all seasons outside of summer; the summer longwave radiation and interior wind trends show little coherence with warm extreme trends, indicating other processes at play driving extreme warm days in summer.

Historical variability and future changes in seasonal extreme temperature over Iran

The extreme temperature indices (ETI) are an essential indicator of climate change. The detection of their changes over the next years can play an essential role in the climate action plan (CAP). In this study, four temperature indices (mean of daily minimum temperature (TN), mean of daily maximum temperature (TX), cold-spell duration index (CSDI), and warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices,the maximum number of consecutive frost days (CFD) and the maximum number of consecutive summer days (CSU), were used to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperatures of five general circulation models (GCMs), including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M, from the set of CMIP5 bias-correction models. We investigated two representative concentration pathway (RCP) scenarios of RCP4.5 and RCP8.5 during the historical (1965–2005) and future (2021–2060 and 2061–2100) periods. The performance of each model evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES showed the highest, and NorESM1-M and IPSL-CM5A-LR showed the lowest performance in Iran. Then, an ensemble model was generated using independence weighted mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods, and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the twenty-first century, no cold spells are projected in almost every part of Iran. The CSU index showed that summer days are increasing sharply; according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively, at the end of the twenty-first century. It projected that in the future, the spring and autumn seasons will be shorter and summers will be much longer than before.

Evaluation of the CORDEX regional climate models (RCMs) for simulating climate extremes in the Asian cities

This study evaluates the ability of 21 Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in simulating climate extremes in the fast growing Asian cities which are highly vulnerable to climate change. The three Asian cities have two different climate characteristics, namely Bangkok and its vicinity and Ho Chi Minh City in tropical climate region and Kathmandu in sub-tropical and temperate climate region. The RCMs were evaluated to simulate the six climate indices; Consecutive Dry Days (CDD), Simple Daily Intensity Index (SDII), Number of extremely heavy precipitation days (R50mm), Maximum 1-day precipitation amount (RX1day), Mean of daily maximum temperature (TX mean) and Mean of daily minimum temperature (TN mean). The performance indicators used were correlation coefficient, normalized root mean square deviation, absolute normalized root mean square deviation and average absolute relative deviation. The Entropy method was endorsed to acquire weights of these four indicators and weightage average techniques were used for ranking of 21 RCMs. The result demonstrated that the best model for one climate index is not the same best model for other climate indices. The 3 RCMs; WAS44_SMHI_RCA4_IPSL_CM5A_MR, WAS44_SMHI_RCA4_MIROC5, and WAS44_IITM_REGCM4-4_CSIRO_MK3-6-0 are the best performing RCMs for simulating future climate extremes in Bangkok and its vicinity, Ho Chi Minh city and Kathmandu valley, respectively. Therefore, they are recommended to use for climate change impact and adaptation studies in water resources management in the selected cities.

Spatial Characteristics of Heat Extremes in South Korea from the Climatological Mean Perspective

We investigated the spatial characteristics of heat extremes in South Korea from the climatological mean perspective. A heat extreme was defined as a day when the daily maximum temperature was higher than 33 °C. According to our analyses, the eastern area of the Sobaek and Taebaek mountain ranges (hereafter called the eastern district) is significantly more exposed to heat extremes compared to other areas. The onset date and total number of days of annual heat extremes in the eastern district are approximately 13 days earlier and 3 days higher than those in the western district on average, respectively. Likewise, the annual mean of daily maximum temperatures during heat extreme days are approximately 0.25 °C higher. This larger exposure to heat extremes in the eastern district appears to be attributable to the Föhn phenomenon, which is likely induced by the dominant southwesterly monsoon during the early-to-peak summer. In contrast, differences in the ending dates of annual heat extremes are not noticeable between the eastern and western districts, when the southerly winds are dominant. Our analyses suggest that heat extremes in South Korea cannot be understood by a simple function of latitude, but in conjunction with atmospheric physical processes.

Weekly heat wave death prediction model using zero-inflated regression approach

Driven partly by the shifting climate and growth of vulnerable populations, excess heat-triggered human fatalities are becoming a serious public health risk concern in Korea. This study develops the zero-inflated regression model for predicting the numbers of weekly heat deaths in South Korea. Defining the heat death as the death caused by “exposure to excessive natural heat,” data analyses are performed to examine statistical relationships between the number of heat deaths versus the pertinent temperature-related parameters and the size of the vulnerable population. The weekly mean of daily maximum temperature and the number of consecutive heat wave days with tropical nights are selected as the temperature-related parameters, while the numbers of elderly living alone and those of agricultural workers are selected as the parameters representing the vulnerable population. Using these four regressive parameters, we develop a regression-based model applied for the prediction of the number of heat deaths. Several statistical methods including the Poisson, negative binomial, hurdle, and the zero-inflated models are scrutinized. The results demonstrate that the zero-inflated Poisson regression model is the most appropriate statistical approach, as it addresses the issue of frequent occurrences of zero-valued observations in weekly heat death data. It was evident that a larger number of heat deaths occurred in the period and study region with a correspondingly higher predicted value. In accordance with this statistical performance, we ascertain that the utilized models could be explored by disaster management and public health experts as a scientific contrivance for health risk mitigation and resource allocation more strategically, and for providing the general public with reliable weekly heat wave risk forecasts.

West Africa climate extremes and climate change indices

ABSTRACTObservational analyses of changing climate extremes over the West Africa region have been limited by the availability of long and high‐quality datasets. To help address this gap, a climate extremes indices workshop was held in the Gambia in December 2011 with participants from 14 West African countries. The resulting analysis utilized 15 annual indices derived from observed daily temperatures and 10 annual indices derived from observed daily precipitation. The analysis was conducted for 166 meteorological stations in 13 countries for 2 periods: 1960–2010 and 1981–2010. The analyses of trends in the annual mean temperature indices have identified statistically significant increases of 0.16 °C/decade and 0.28 °C/decade for mean annual maximum and mean annual minimum temperatures, respectively, averaged over all available land stations in the region during the last 50 years. The seasonal‐temperature‐related indices show significant patterns of warming in all seasons. The annual mean of daily minimum temperature has increased more than the annual mean of daily maximum temperature leading to a decreasing trend in the diurnal temperature range. Warm days and warm nights have become more frequent, and cold days and cold nights have become less frequent. The analyses of precipitation‐based indices indicate spatially non‐coherent changes throughout the study area with few statistically significant trends over the longer period. Exceptions to this are the simple daily intensity index and maximum 5‐day precipitation, which show significant increasing regional trends over both the shorter and longer periods. Additionally, over the recent period (1981–2010) most of the precipitation related indices show significant trends towards wetter conditions. However, this period of increased rainfall follows a decade of significantly drier conditions in the region – it is not clear whether the recent upward trends reflect the ‘recovery’ from this long drought period or represents a long‐term response to warming.

Analysis of the Mean of Daily Maximum Temperature within the Romanian Plain (1961-2015)

Analysis of the Mean of Daily Maximum Temperature within the Romanian Plain (1961-2015)

Influence of Weather Variation on Cropping Pattern of Leh District of Ladakh Region

Leh district is situated between 32-36° N latitude and 75-80° E longitude with an altitude ranging from 2500 to 6000 m above mean sea level. Leh is the largest district in the country with an area of 45,100 Km2. Agriculture is the main occupation of the rural people of the district. Initial analysis of the data revealed that the acreage of wheat crop increased from 2003 onward. Notably, a simultaneous decrease in acreage of barley crop was observed. It is noticeable that wheat crop replaced the barley crop from 2003 onwards. Such change in cropping pattern was found related with change in weather condition. It was observed that from 2003 onward monthly mean of daily minimum temperature during May increased from 4 °C to 8 °C up to 2007. Similarly, monthly mean of daily maximum temperature was observed higher (>20 °C) before 2003 but decreased during 2003 to 2009 (range 15 to 20 °C). Snowfall was a common phenomenon during month of May before 2003, but was not observed during 2003 to 2009. Therefore, it is hypothesized that increase in both minimum and maximum temperature during May had enabled the farmers to grow wheat in place of barley. Moreover, during the month of April, snowfall events were significantly reduced in 2003-2009 and in month of May snowfall events was not recorded at all. This may perhaps another reason which enabled farmers to sow the wheat crop during last week of April to mid of May. Interestingly during last two years, the cropping pattern is again reversed. The acreage under wheat crop was decreased and the acreage under barley increased. Such reversal may be due to re-occurrence of snowfall events during first two weeks of May, which was absent during 2003-2009. Therefore, it is quite apparent that weather condition influenced the cropping pattern and crop acreage. However, many other factors like timely availability of seed, quality of seeds as per need of local farmers as they prefer some straw producing varieties, crop rotation pattern, etc., also plays some role in shifting of cropping pattern. Long term data base on crop-weather relationship among with the other factors may help in developing crop weather modeling.

Trends in extreme temperature indices in the Poyang Lake Basin, China

Based on daily maximum and minimum temperature records at 78 meteorological stations in the Basin of China’s largest fresh water lake (Poyang Lake Basin), the temporal and spatial variability of 11 extreme temperature indices are investigated for the period 1959–2010. The analysis indicates that the annual mean of daily minimum temperature (Tmin) has increased significantly, while no significant trends were observed in the annual mean of daily maximum temperature (Tmax), resulting in a significant decrease in the diurnal temperature range. Trends and percentages of stations with significant trends in Tmin-related indices are generally stronger and higher than those in Tmax-related indices; however, no significant trends can be found in Tmax-related indices (TXMean, TX90p, TXx and TX10p) at both seasonal and annual time scale. Low correlations with Global-SST ENSO index are also detected in Tmax-related indices. Significant positive relationships can be found in Tmin-related indices (TNMean, TNx, TNn and TN90p), however, the most significant negative coefficient was also found in cold nights (TN10p) with the Global-SST ENSO index. Singular value decomposition (SVD) correlating extreme temperatures over the Poyang Lake Basin and the North Pacific SST indicates the East China Sea, Western Pacific and Bering Sea to be stronger linked with Tmin than Tmax with the first mode (SVD-1) explaining 90 and 94 % of annual Tmax and Tmin respectively.

Temperature Trend on Makkah, Saudi Arabia

The main objective of this study is to investigate temperature trend and distribution over 29 years period (1985 to 2013) in Makkah, Saudi Arabia, the holiest city for all Muslims. Monthly mean, maximum, and minimum temperature levels and their trends are investigated using Regression analysis and Theil-Sen nonparametric test. Also, trends in deviations from the reference period (1985-2013) are analyzed. The results showed that the number of hot days and nights increased annually by 1.5966 and 1.832, respectively, while the number of cold nights decreased annually by 0.4054 nights. Both Regression analysis and Theil-Sen test demonstrated positive trends in mean, minimum and maximum temperature levels. Trends are determined for various seasons and months of the year. The annual mean of daily mean, maximum and minimum temperature increased by 0.0398°C, 0.0552°C, 0.0398°C per year, respectively. The minimum value of monthly mean temperature (Tmmean = 23.98°C) was found in January, whereas the maximum value of the mean temperature (Tmmean= 35.95°C) was found in July. Maximum value of monthly mean of daily maximum temperature (Tmmax = 43.88°C) was found in June and minimum (30.54°C) in January. The monthly mean of the daily minimum temperature (Tmmin) varied between a minimum of 18.82°C in January and a maximum of 29.59°C in August. From the above analysis it can be concluded that Makkah is suffering from a considerable warming temperature trend which is confirmed by the Theil-Sen non-parametric test and there is potentially an increasing medical risk from heat waves that will be more intense. This requires specific attention toward: the energy demands for extra cooling, water resources, draughts, and medical preparedness by the decision makers in order to minimize these risks to residents, pilgrims who gather annually to perform hajj rituals and other visitors.

Climate Influences on Meningitis Incidence in Northwest Nigeria

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

Teleconnection between ENSO and climate in South China

This study investigates the features of the teleconnection between El Nino–Southern Oscillation (ENSO) and the climate over the region with the latitude from 21°N to 25°N and longitude from 111°E to 116°E in South China for the period from 1960 to 2005. The climate variables analyzed are the monthly means of daily maximum temperature (Tmax), minimum temperature (Tmin), precipitation and relative humidity (RH), which are recorded at 20 weather stations over the region. The cross correlation coefficients between the ENSO index and those climate variable anomalies are calculated to evaluate the strength of the teleconnection. The analysis results reveal that ENSO has positive influence on most of the climate variables in the study region. Specifically, ENSO has significant effects on Tmin (but not Tmax) with the corresponding time delay of about 4 months. In addition, ENSO has considerable influence on precipitation and RH over the study region with teleconnection lag time of around 2 and 1 month, respectively.

Bias in Minimum Temperature Introduced by a Redefinition of the Climatological Day at the Canadian Synoptic Stations

Abstract On 1 July 1961, the climatological day was redefined to end at 0600 UTC (coordinated universal time) at all synoptic (airport) stations in Canada. Prior to that, the climatological day ended at 1200 UTC for maximum temperature and 0000 UTC for minimum temperature. This study shows that the redefinition of the climatological day in 1961 has created a cold bias in the annual and seasonal means of daily minimum temperatures across the country while the means of daily maximum temperatures were not affected. Hourly temperatures taken at 121 stations for 1953–2007 are used to determine the magnitude of the bias and its spatial variation. It was found that the bias is more pronounced in the eastern regions; its annual mean varies from −0.2° in the west to −0.8°C in the east. Not all days are affected by this change in observing time, and the annual percentage of affected days ranges from 15% for locations in the west to 38% for locations in the east. An approach based on hourly values is proposed for adjusting the affected daily minimum temperatures over 1961–2007. The adjustment on any individual day varies from 0.5° to 12.5°C. The impact of the adjustment is assessed by examining the trends in the annual mean of the daily minimum temperatures for 1950–2007. Overall, with the adjustment, the trends are becoming either more positive or are reversing from negative to positive, and they have changed by as much as 1°C in numerous locations in the eastern regions.

Tropical Malaria Does Not Mean Hot Environments

If global warming progresses, many consider that malaria in presently malaria-endemic areas will become more serious, with increasing development rates of the vector mosquito and malaria parasites. However, the correlation coefficients between the monthly malaria cases and the monthly mean of daily maximum temperature were negative, showing that the number of malaria cases in tropical areas of Africa decreases during the season when temperature was higher than normal. Moreover, an analysis of temperature and development rate using a thermodynamic model showed that the estimated intrinsic optimum temperatures for the development of the malaria parasites, Plasmodium falciparum and P. vivax, in the adult mosquito stage and that of the vector mosquito Anopheles gambiae s.s. were all approximately 23-24 degrees C. Here, the intrinsic optimum temperature is defined in the thermodynamic model as the temperature at which it is assumed that there are no or negligible adverse effects for development. Therefore, this study indicates that the development of malaria parasites in their mosquito hosts and the development of their vector mosquitoes are inhibited at temperatures higher than 23-24 degrees C. If global warming progresses further, the present center of malarial endemicity in sub-Saharan Africa will move to an area with an optimum temperature for both the vector and the parasite, migrating to avoid the hot environment.

Influence of temperature and rainfall on the evolution of cholera epidemics in Lusaka, Zambia, 2003–2006: analysis of a time series

In this study, we aimed to describe the evolution of three cholera epidemics that occurred in Lusaka, Zambia, between 2003 and 2006 and to analyse the association between the increase in number of cases and climatic factors. A Poisson autoregressive model controlling for seasonality and trend was built to estimate the association between the increase in the weekly number of cases and weekly means of daily maximum temperature and rainfall. All epidemics showed a seasonal trend coinciding with the rainy season (November to March). A 1 degrees C rise in temperature 6 weeks before the onset of the outbreak explained 5.2% [relative risk (RR) 1.05, 95% CI 1.04-1.06] of the increase in the number of cholera cases (2003-2006). In addition, a 50 mm increase in rainfall 3 weeks before explained an increase of 2.5% (RR 1.02, 95% CI 1.01-1.04). The attributable risks were 4.9% for temperature and 2.4% for rainfall. If 6 weeks prior to the beginning of the rainy season an increase in temperature is observed followed by an increase in rainfall 3 weeks later, both exceeding expected levels, an increase in the number of cases of cholera within the following 3 weeks could be expected. Our explicative model could contribute to developing a warning signal to reduce the impact of a presumed cholera epidemic.

The January Thaw at New Brunswick, NJ

Abstract An investigation of the January thaw phenomenon, a period of unseasonable warmth, was conducted using daily maximum temperatures recorded at New Brunswick, New Jersey, from 1858–1981. Student's t-tests, comparing long-term means of daily maximum temperature to values from a fitted seasonal trend curve, indicate temperatures higher on 22–23 January and lower on the 29th than seasonally expected. It was found that the January thaw does not have a fixed time of occurrence but occurs most frequently from the 19th to the 28th. During this time the interannual variability of daily maximum temperature is significantly higher than during the remainder of the month. Evidence of a tendency for a secondary thaw maximum to occur, centered on the 26th, is evident in several different analyses. Examination of daily temperature curves for 10-, 20- and 40-year periods reveals a shift in the mean thaw date from 22–23 January to the 26th. This change has evolved over the last 30–40 years. It was concluded that the...

Seasonal influence on vitamin A alcohol concentration in chick liver.

Synopsis A seasonal influence has been found on the percentage of vitamin A found in liver in the alcohol form. This percentage is much higher in summer than in winter. A direct correlation can be shown between the means of daily maximum temperatures during the life of the chicks and the percentage of vitamin A in liver in the alcohol form.

SINGULARITIES IN DAILY TEMPERATURES

The means of daily maximum temperatures using twenty years of record are obtained. The average of these means over n consecutive calendar days is used as a predictor of the daily maximum temperature one and more years in advance. Data from ten stations in the United States for the period 1905 to 1957 are analyzed in this fashion and a series of predictions made for several values of n. If singularities are sufficiently large and persistent, then the averages over only a few days (small n) should be better predictors than averages over a larger number of days. It is found that the prediction errors decrease with increasing n and it is concluded that, whenever averages are used to estimate daily temperature values far in advance, any singularities, even if they exist at all, are much too small to be useful.

Methods Used in Evaluating the Productivity of Some Illinois Soils1

Multiple curvilinear regression analyses of crop yields on major Illinois soil types and associations under different weather conditions and systems of management were made with the aid of an electronic digital computer. The independent variables studied in relation to corn yield were: rainfall and the mean of daily maximum temperatures for the period July 1 to August 31; nitrogen applied in the current year and in the previous year; pounds of P₂O₅ and K₂O applied in the current year plus an estimate of carry-over; an index of the kind and frequency with which legume and legume-grass mixtures were grown preceding the crop; and year. Yield estimates for a 10-year period ending in 1955 on 7 representative soil types and associations are presented, together with measures of the reliability of the yield estimates. The amount of yield variation explained by the regression equations varied from about 18 to 56% in the examples presented. The primary objective of this study is to determine the relative productivity of major Illinois soils. The results presented are more satisfactory for this purpose than for predicting the net effect of individual management factors on crop yields. For the kind of sampling and analysis employed in this study, data are needed for approximately 100 to 200 fields in order to make reasonably accurate estimates of crop yields for a soil type or an association of two closely related soils under various climatic conditions and management practices.