Daily Minimum Air Temperature Research Articles

Intra-annual stem radial growth of Qinghai spruce and its environmental drivers in the Qilian Mountains, northwestern China

Tree stem radial growth could be used to estimate forest productivity, which plays a dominant role in the carbon sink of terrestrial ecosystems. However, it is still obscure how intra-annual stem radial growth is regulated by environmental variables. Here, we monitored Qinghai spruce stem radial growth over seven years and analyzed the environmental drivers of the intra-annual stem radial changes in the Qilian Mountains at low (2700 m) and high altitudes (3200 m). We found that stem radial growth initiated when the daily mean minimum air temperature reached 1.6oC, while the cessation of stem growth was unrelated to temperatures and water conditions. Initiations of stem growth at 2700 m were significantly earlier than that at 3200 m. Maximum growth rates were observed before the summer solstice at low altitude, whereas at high altitude, the majority of them occurred after the summer solstice. Most variability in annual stem increment (AI) can be explained by the rate (Rm) than by the duration of stem growth (∆t), and 78.9 % and 69.6 % of the variability in AI were attributable to Rm for the lower and upper site, respectively. Structural equation modeling revealed that precipitation (P) could both directly positively influence stem radial increment (SRI) and indirectly positively influence SRI through influencing relative humidity (RH), but the positive effect of P on SRI was higher at low altitude than at high altitude. Daily minimum air temperature (Tmin) was also the main direct diver of SRI, and the positive effect of Tmin on SRI was higher at high altitude than at low altitude. Considering the trends in climate warming and humidification over the past decades, climate changes would result in earlier initiation of Qinghai spruce stem growth and promote the growth through positive response to increased precipitation in low altitude and through elevated temperature in high altitude, respectively.

Increased diurnal temperature range in global drylands in more recent decades

AbstractThe diurnal temperature range (DTR) has generally decreased at global scale according to the IPCC reports and previous publications. Here, we examined the change in DTR within the past four decades and found that DTR decreased in wet areas, but it increased in dry areas. This is because the daily minimum air temperature (Tmin) increased at a slower rate than the daily maximum air temperature (Tmax) in the dry areas, while it increased more rapidly in the wet areas. The changes in cloud cover, water vapour and moisture flux were observed to have contributed to the observed change in DTR. The change in moisture flux largely contributed to this change through the indirect effect on the change in water vapour across the aridity gradient. The significant impact of moisture flux is linked to the differential change in moisture flux between the dry and wet zones.

Forecasting Daily Maximum and Minimum Air Temperatures in The Cilacap District Using Arima and Exponential Smoothing

This research aims to predict daily maximum and minimum air temperatures in Cilacap Regency using ARIMA and Exponential Smoothing. Data was obtained from recordings carried out by BMKG Cilacap using maximum and minimum thermometers taken from January 1, 2016, to December 31, 2021. The results show that the best forecasting model uses the ARIMA (2,1,2) model for maximum temperature and the ARIMA (1,1,1) model for minimum temperature, with the MAPE value of 2.09% for the maximum temperature and 2.44% for the minimum temperature, while the RMSE value obtained is 0.9177 for the maximum temperature and 0.8001 for the minimum temperature. Based on the ARIMA model, Cilacap's daily maximum temperature in 2022 was predicted to be around 30.6ᵒC, with a 95% confidence interval between 28ᵒC - 35ᵒC, while the minimum temperature was predicted to be around 25.1ᵒC, with a 95% confidence interval between 23ᵒC - 28ᵒC.

1991–2020 climate normal in the European Alps: focus on high-elevation environments

Alps are an important geographical area of the European continent and, in this area, temperature increase is most evident. However, the 1991–2020 climate normal in the Alps has still not been thoroughly investigated. Aiming to fill this gap with a focus on high-elevation environments, minimum and maximum daily air temperature acquired by 23 automatic weather station were used. The results show that the mean annual values of minimum and maximum temperature for the 1991–2020 climate normal in the Alps are −2.4°C and 4.4°C, respectively, with a warming rate of 0.5°C/10 years. The mean annual temperature comparison between 1961–1990 and 1971–2000, 1961–1990 and 1981–2010, 1961–1990 and 1991–2020 climate normal show an increase of 0.3°C, 0.5°C and 0.9°C, respectively. The results also confirm that seasonal and annual temperatures are rising through the whole Alpine arc, mainly in summer and autumn. This work highlights that annual minimum and maximum temperature do not seem to be affected by a positive elevation-dependent warming. Instead, a positive elevation-dependent warming in the maximum values of the annual minimum temperature was found. If anthropogenic emissions maintain the trend of the last decades, the expected mean annual temperature of the 2001–2030 climate normal is −0.2°C, with an increase of 0.5°C if compared to the 1991–2020 climate normal and with an increase of 1.5°C if compared to the 1961–1990 climate normal. This study highlights the warming rate that is now present in the European Alps, provides indications on the warming rate that will occur in the coming years and highlights the importance of carrying out investigations that consider not only the last 30-year climate normal, but also the most recent 30-year climate normal by comparing them with each other.

Does choice of outdoor heat metric affect heat-related epidemiologic analyses in the US Medicare population?

We calculated daily maximum, minimum, and mean outdoor air temperature (T), heat index (HI), wet-bulb globe temperature (WBGT), and Universal Thermal Climate Index (UTCI) for populous US counties and linked estimates with daily all-cause mortality and heat-related hospitalizations among Medicare beneficiaries (2006-2016). We fit distributed-lag nonlinear models for each metric and compared relative risks (RRs) at the 99th percentile. Across all heat metrics, extreme heat was statistically significantly associated with elevated risks of morbidity and mortality. Associations were more pronounced for maximum daily values versus the corresponding minimum for the same metric. The starkest example was between HImax (RR = 1.14; 95% confidence interval [CI] = 1.12, 1.15) and HImin (RR = 1.10; 95% CI = 1.09, 1.11) for hospitalizations. When comparing RRs across heat metrics, we found no statistically significant differences within the minimum and maximum heat values (i.e., no significant differences between Tmax/HImax/WBGTmax/UTCImax or between Tmin/HImin/WBGTmin/UTCImin). We found similar relationships across the National Climate Assessment regions. Among Medicare beneficiaries in populous US counties, daily maximum and mean values of outdoor heat are associated with greater RRs of heat-related morbidity and all-cause mortality versus minimum values of the same metric. The choice of heat metric (e.g., temperature versus HI) does not appear to substantively affect risk calculations in this population.

Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Accurate estimation of potential evapotranspiration (ET0) is important for the sound design of irrigation schedules, management of water resources, assessment of hydrological drought, and research on atmospheric variations. The present study proposed a novel deep learning (DL) approach for daily ET0 estimations with limited daily climate data: HS- LSTM. This approach was constructed based on a classic ET0 model and a long short-term memory neural network (LSTM). Specifically, the Hargreaves-Samani (HS) model was employed as the classic model, and the predictors were restricted to the daily maximum and minimum air temperature data. Ground truth data for ET0 were employed to train, validate, and test the models. Traditional machine learning (ML) algorithms comprising adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), multi-gene genetic programming (MGGP), and one-dimensional CNN (1D-CNN), as well as the HS-ML models (HS-ANFIS, HS-GP, HS-MGGP, HS-1D-CNN), were also established and assessed for daily ET0 estimations. Compared to the other tested approaches, the errors of the HS-LSTM technique significantly decreased, demonstrating that the novel HS-LSTM approach significantly outperformed the other techniques beyond the study area (in Songliao Basin, Northeast China, which is a semi-humid zone with temperate continental climate). The developed models can then be used to estimate future ET0 with only air temperature forecasts, which can be readily obtained from public weather forecasts. The present study provides a new and promising strategy that can provide more accurate estimations of daily ET0 with limited meteorological data, along with significant implications for enhancing atmospheric research.

Meteorological conditions and snow-avalanche occurrence over three snow seasons (2017–2020) in Tasiapik Valley, Umiujaq, Nunavik

ABSTRACT In this article, we study snow avalanche activity during the snow seasons of 2017–2020 using four automatic time-lapse cameras strategically positioned along the southwestern slope of Tasiapik Valley, near the village of Umiujaq, in Nunavik (northern Québec, Canada). Over the three snow seasons, cameras helped to detect evidence of 130 avalanche events, scattered over seventy-eight distinct avalanche days. The evolution of weather conditions prior to each avalanche release was detailed according to data from a nearby weather station. Moreover, the time of release, the release type, the surface texture, and whether rocky material was present in the deposits were documented from the photographs. To explore relationships between weather data and avalanche releases, conditional inference tree (CIT) analysis was conducted. Results of the CIT analysis showed that there are different weather patterns associated with avalanche releases depending on the season, and significant thresholds values were defined. In winter, the avalanche probability was greater when three-day snowfall total exceeded 10 cm. In spring, the avalanche probability was greater when cumulative melting degree-days were less than forty-six and when daily minimum air temperature was greater than 2°C. Moreover, cornice failures were found to be a major component of the avalanche dynamic in Tasiapik Valley, mainly because of the slope’s morphology. They have also been the cause of the three largest volume and longest runout avalanches observed by cameras in this study, highlighting potential risks for local communities. The probability of observing cornice failures is enhanced on days when maximum air temperature is greater than −8°C in winter conditions, whereas in spring conditions it is enhanced by daily maximum air temperature greater than 2.5°C. This study represents a necessary first step toward avalanche forecasting based on weather data in Nunavik. Efforts should be continued given the expected higher frequency of natural hazards in northern regions as a consequence of recent climate changes.

Asymmetries of the lag between air temperature and insolation in gauge observations and reanalyses over China

The annual cycle of near-surface air temperature should receive more attention in climate studies because it is especially important to crop growth and to human comfort. The phase of the annual temperature cycle generally lags behind that of insolation due to the time required for the Earth's surface to absorb the energy from solar radiation and then warm the near-surface air. This study decomposes the daily maximum, minimum, and mean air temperatures using Fourier transforms and examines the lag in number of days between the phases of temperature and insolation. It was found that the lags between air temperature and insolation were asymmetrical during the summer and winter seasons. The mean summer lags were 30.8, 32.4, and 30.6 days from gauge observations over China, which were longer than the 21.0, 21.5, and 16.7 days of winter lags for daily maximum, minimum, and mean temperature respectively. Spatially, these lags showed wider climatological and interannual variability in southern than in northern areas because higher atmospheric water vapor content and greater cloud cover caused a longer response time of air temperature to solar radiation. Decreasing variations were derived for the winter lag, at about −1.11 days/decade, −0.68 days/decade, and − 0.78 days/decade for the maximum, minimum, and mean temperature respectively. Currently, reanalysis data can accurately simulate the climatological, long-term, and interannual variability of the summer and winter lags over most of the study area, except for southwestern China, although they have negative biases in characterizing the annual cycle.

EDNA-based detection reveals invasion risks of a biofouling bivalve in the world's largest water diversion project.

Environmental DNA (eDNA) has increasingly been used to detect rare species (e.g., newly introduced nonindigenous species) in both terrestrial and aquatic ecosystems, often with distinct advantages over traditional methods. However, whether water eDNA signals can be used to inform invasion risks remains debatable owing to inherent uncertainties associated with the methods used and the varying conditions among study systems. Here, we sampled eDNA from canals of the central route of the South-to-North Water Diversion Project (hereafter SNWDP) in China to investigate eDNA distribution and efficacy to inform invasion risks in a unique lotic system. We first conducted a total of 16 monthly surveys in this system (two sites in the source reservoir and four sites in the main canal) to test if eDNA could be applied to detect an invasive, biofouling bivalve, the golden mussel Limnoperna fortunei. Second, we initiated a one-time survey in a sub-canal of the SNWDP using refined sampling (12 sites in ~22 km canal) and considered a few environmental predictors. We found that detection of target eDNA in the main canal was achieved up to 1100 km from the putative source population but was restricted to the warmer months (May-November). Detection probability exhibited a significant positive relationship with average daily minimum air temperature and with water temperature, consistent with the expected spawning season. eDNA concentration in the main canal generally fluctuated across months and sites and was generally higher in warmer months. Golden mussel eDNA concentration in the sub-canal decreased significantly with distance from the source and with increasing water temperature and became almost undetectable at ~22 km distance. Given the enormity of the SNWDP, golden mussels may eventually expand their distribution in the main canal, with established "bridgehead" populations facilitating further spread. Our findings suggest an elevated invasion risk of golden mussels in the SNWDP in warmmonths, highlighting the critical period for spread and, possibly, management.

On the role of land-surface hydrology schemes in simulating the daily maximum and minimum air temperatures of Australia using a regional climate model (RegCM4)

AbstractThe present study addresses the possible effects of soil moisture changes on the simulated daily maximum and minimum air temperatures of Australia for a duration of 13 years. Therefore, the community land model version 4.5 (CLM4.5; coupled to the RegCM4) was used to represent the soil moisture and processes associated with it. The CLM4.5 has two land-surface hydrology schemes: TOPMODEL (TOP) and Variable Infiltration Capacity (VIC) and two simulations were conducted, namely: TOP and VIC. The results showed that VIC has lower soil moisture than TOP, leading to a decrease in vegetation transpiration, evaporation, and an increase in soil evaporation relative to TOP. However, there is no considerable difference between the two simulations compared with reanalysis products. In comparison to in-situ measurements, the RegCM4 can reasonably model the climatological annual cycle of mean air temperature (TMP) and its performance varies with the study site (e.g., RegCM4 overestimates TMP by 2.76 and 5.46 °C at Yanco and Tumbarumba, respectively). In summary, the simulated maximum and minimum air temperatures are sensitive to the physical parameterization of RegCM4 rather than variations in soil moisture. Likewise, improvements to the land-surface hydrology schemes TOP/VIC are required to better model Australia's daily maximum and minimum air temperatures.

Climate Change Effects on Irrigated Corn Growth in the Lower Mississippi Delta

Abstract In this study, the possible climate change impacts on irrigated corn production in the lower Mississippi delta (LMD) region were analyzed. The observed daily maximum and minimum air temperature, wind speed, relative humidity (RH), and precipitation from 1960 to 2018 were used in the analysis. The length of the growing season, evapotranspiration (ET), and crop yield estimates from the precalibrated Root Zone Water Quality Model (RZWQM) were also used. Trend analyses were performed on growing-season averages for temperature, RH, and wind speed; growing-season totals for precipitation and ET; daily values of minimum and maximum temperature; and averages of RH and wind speed at critical corn growth stages. The last day of spring freezing (LDSF) and days with an average daily air temperature Ta of more than 35°C during corn silking were also included in the analysis. The trend analysis was performed using the modified Mann–Kendall test, Pettitt test, and Sen’s slope at a significance level of p ≤ 0.05. The results from our study pointed out increases in minimum Ta, increases in the number of days with Ta exceeding 35°C during the corn silking stage, increases in RH and decreases in ET, advancement of the LDSF by 2 weeks, and 8% reductions in corn yield that could be attributed to changes in climate. If the observed trends in climate (climate variability and change) and yield reductions continue in the region, it could be challenging to grow the corn crop in the LMD profitably. Significance Statement In recent years, growing corn and soybeans instead of cotton has been gaining popularity in the lower Mississippi delta (LMD) region. Our study is aimed to understand how climate change in the recent past (1960–2018) has affected irrigated corn production in LMD. This will help us to better understand the expected consequences of the future climate. We used observations of daily weather data from 1960 to 2018 and corn yield, water balance, and crop yield results from a computer model designed to simulate corn production. Tools were used to analyze trends and patterns in the data and results. Our analysis pointed out significant changes in weather, water balance, and yield decline for irrigated corn in the LMD.

A cold wave of winter 2021 in central South America: characteristics and impacts.

The online version contains supplementary material available at 10.1007/s00382-023-06701-1.

Streamflow Composition and the Contradicting Impacts of Anthropogenic Activities and Climatic Change on Streamflow in the Amu Darya Basin, Central Asia

Abstract Transboundary rivers are often the cause of water-related international disputes. One example is the Amu Darya River, with a catchment area of 470 000 km2, which passes through five countries and provides water resources for 89 million people. Intensified human activities and climate change in this region have altered hydrological processes and led to water-related conflicts and ecosystem degradation. Understanding streamflow composition and quantifying the change impacts on streamflow in the Amu Darya basin (ADB) are imperative to water resources management. Here, a degree-day glacier-melt scheme coupled offline with the Variable Infiltration Capacity hydrological model (VIC-glacier), forced by daily precipitation, maximum and minimum air temperature, and wind speed, is used to examine streamflow composition and changes during 1953–2019. Results show large differences in streamflow composition among the tributaries. There is a decrease in the snowmelt component (−260.8 m3 s−1) and rainfall component (−30.1 m3 s−1) at Kerki but an increase in the glacier melt component (160.0 m3 s−1) during drought years. In contrast, there is an increase in the snowmelt component (378.6 m3 s−1) and rainfall component (12.0 m3 s−1) but a decrease in the glacier melt component (−201.8 m3 s−1) during wet years. Using the VIC-glacier and climate elasticity approach, impacts of human activities and climate change on streamflow at Kerki and Kiziljar during 1956–2015 are quantified. Both methods agree and show a dominant role played by human activities in streamflow reduction, with contributions ranging 103.2%–122.1%; however, the contribution of climate change ranges from −22.1% to −3.2%.

Disaster event-based spring frost damage identification indicator for tea plants and its applications over the region north of the Yangtze River, China

Spring frost damage (SFD) is the main meteorological disaster limiting the tea industry in the region of the north of the Yangtze River (NYR) in China. Research regarding an SFD indicator for tea plants is of great significance for prevention and control of spring frosts, as well as for timely monitoring and early warning. Based on daily minimum air temperatures (Tmin) and spring frost disaster records in NYR from 1961 to 2020, the Receiver Operating Characteristic curve (ROC) was used to evaluate the accuracy of frost identification, and the critical temperature threshold for SFD to tea plants was determined by jointly comparing Overall Accuracy and the Youden Index. The results showed that the area under the ROC curve was 0.977, indicating that Tmin performed well in identifying frost events. The critical temperature threshold for spring frost in NYR was determined as Tmin of 3.7 °C, and 93 % of frost occurrences were correctly identified. Based on the determined critical threshold of SFD, the number of spring frost days, first frost date (FFD), and last frost date (LFD) in NYR from 1961 to 2020 were analyzed. The results showed that the number of spring frost days in NYR decreased from north to south. FFD appeared earlier and LFD ended later in northern NYR than in southern NYR. These results provide theoretical and technical support for the monitoring and evaluation of spring frosts in NYR, and also for implementing SFD prevention and control measurements for tea plants by use of a critical temperature threshold.

Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios

ABSTRACT Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems. In this study, we focus on the major Belt and Road Initiative (BRI) regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C, 2°C, and 3°C above pre-industrial levels under the Representative Concentration Pathway (RCP) 8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848° (~5 km) globally from EnviDat. The temperature variables include the daily maximum (Tmax), minimum (Tmin) and average (Tmp) surface air temperatures, and the diurnal temperature range (DTR). We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986–2005. The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986–2005. Furthermore, we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C, 2°C, and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean. Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions, and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions. The dataset is available at https://doi.org/10.57760/sciencedb.01850.

Tropical nights (1976–2019) as an indicator of climate change in Ukraine

The article presents the results of an analysis of the number, frequency and intensity of tropical nights in Ukraine. We have selected such weather stations that are located at different latitudes and correspond with the north, west, east, and south as well as the central part of Ukraine. This was done in order to analyse the differences in tropical nights across latitudes. Also we have weather stations located at the approximately one latitude to analyse how tropical nights vary in longitude. Data from weather stations for 1976-2019 were under analysis. We can say that the increase in the minimum air temperature during summer is on the whole territory of Ukraine. This leads to the fact that since the mid-1990s tropical nights begin to be observed in the east of Ukraine, as well as in the south. After 2000, tropical nights also have begun to be observed in the centre and even in the west of Ukraine. Between two half-periods (1976-1997 and 1998-2019) the increase of number of tropical night events in the south of Ukraine (up to 300 cases) is much more significant than the increase in their number in the northwest (up to 20 cases). As well as the frequency, the intensity of tropical nights also increases. There are cases with minimum daily air temperatures above 25°C. The analysis of the North Atlantic Oscillation (NAC) and the Mediterranean (Cairo-Algeria) indices in the context of explaining the tropical night’s occurrence in Ukraine turned out to be quite effective.

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).

Interannual variability of ice cover in the Caspian Sea

Satellite remote sensing data (SAR and Ocean Color), MERRA-2 reanalysis and records at Astrakhan meteorological station were used to investigate interannual variability of ice cover characteristics in the North Caspian Sea for 23 winter seasons (November 1 – April 15) from 1999/2000 to 2021/2022. The maximum annual ice cover area, ice freeze onset and melt dates and ice cover duration were determined from satellite remote sensing data, mostly SAR instruments on board the European Space Agency’s satellites, ranging from ERS-2 to the Sentinel-1A, -1B tandem. We propose a new band combination for Sentinel-2 MSI and Landsat-8 OLI that allows better distinguishing ice cover from clouds or land than the standard RGB composites. In the absence of SAR data, this method was used to estimate the above mentioned parameters with high spatial and temporal resolution. To assess the severity of winters, the criterion on the basis of the sum of freezing degree-days (SFDD) was applied. For this purpose, we used values of daily minimum air temperature over the North Caspian (44.46°–47.14°N, 46.70–52.90°E), daily mean and daily minimum ones over its coldest eastern part (with the western border at 50°E), obtained from the MERRA-2 reanalysis, as well as data from the meteorological station in Astrakhan (46.35°N, 48.07°E). The resulting SFDD sequences show that until the winter of 2011/2012, there was a cooling trend on average (with noticeable interannual variability), whereas after that winter it changed to warming for Astrakhan and virtually disappeared for the North Caspian and its eastern part. A noticeable interannual variability is also shown by the maximum ice area and the duration of the ice period, both parameters with maximums in the winter of 2011/2012. We discuss in detail the correspondence between the SFDD and ice cover characteristics variations, as well as previously published results. In agreement with the other authors, we find that in the 21st century, compared to the 20th century, the number of very severe and severe winters has decreased, while the number of mild winters has increased.

Effect of climate change and urban heat islands on the deterioration of concrete roads

Concrete pavements constitute a relatively small portion of US roadways but carry a disproportionately high volume of traffic. The potential deterioration in the long-term performance of these pavements due to climate change and the Urban Heat Island (UHI), however, has received little attention. To evaluate pavement performance, the present study considered thick and thin concrete pavements in the desert climate of Phoenix, AZ and the continental climate of Boston, MA using the Mechanistic-Empirical Pavement Design Guide (PavementME). To simulate potential climate change and UHI, the daily minimum and maximum air temperatures were uniformly and independently increased and the change in pavement damage as compared to the existing climate was evaluated. It was found that the change in damage had a strong, positive correlation with the change in Diurnal Temperature Range (DTR). This can be explained by the increase in concrete stresses due to combined applications of highly non-uniform temperature distributions through the slab and heavy truck loads. These stresses in turn affect the development of cracks in the pavement and hence its long-term performance. Additional variability in the DTR during each day of the service life further enhances this effect. It was found that increasing the thickness of concrete pavements actually leads to a greater sensitivity to change in DTR.

Causes for Asymmetric Warming of Sub‐Diurnal Temperature Responding to Global Warming

AbstractDefined as the difference between the daily maximum (Tmax) and minimum surface air temperature (Tmin), the diurnal temperature range (DTR) is critical to local climate, ecosystem, and socio‐economic functioning. This study investigates how DTR responds to the future global warming, using a novel, fine‐resolution convection‐permitting regional climate model. Results demonstrate the asymmetrical impact of global warming, that is, the more substantial warming in Tmin, and the lower in Tmax. This asymmetry results in the DTR reduction of 0.5°K in the mid‐latitude and 0.25°K in the low‐altitude area (under 2°K warming scenario). The increase in daytime cloudiness under warmer climates is likely responsible for the reduction of incoming short‐wave radiation reduction, consequently causing Tmax “underwarming.” Study results also suggest that atmospheric instability could play a vital role in the difference in DTR response between the tropical and mid‐latitude areas.