Changes In Diurnal Temperature Range Research Articles

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.

All-cause mortality attributable to long-term changes in mean temperature and diurnal temperature variation in China: a nationwide quasi-experimental study

Previous studies have demonstrated an association between short-term exposure to ambient temperature and mortality. However, the long-term effects of elevated temperature and temperature variability on mortality have remained somewhat elusive in epidemiological studies. We conducted a comprehensive epidemiological study utilizing Chinese population census data from 2000 and 2010. Census-derived demographic and socioeconomic factors were paired with temperature data from the European Re-Analysis Land Dataset across 2823 counties. We employed a difference-in-difference approach to quantitatively examine the relationship between all-cause mortality and annual exposure to mean temperature and diurnal temperature range (DTR). Additionally, we evaluated the potential effects of socioeconomic and environmental covariate modifications on this relationship and calculated the attributable mortality. Lastly, we projected excess deaths attributable to annual temperature exposure under various shared socioeconomic pathways (SSPs, e.g. SSP126, SSP370, and SSP585). For each 1 °C rise in annual mean temperature and DTR, the mortality risk could increase by 6.12% (95% CI: 0.84%, 11.69%) and 7.72% (95% CI: 3.75%, 11.84%), respectively. Counties with high labor-force ratios and high NO2 and O3 concentrations appeared to be sensitive to the annual mean temperature and DTR. Climate warming from 2000 to 2010 may have resulted in 5.85 and 14.46 additional deaths per 10 000 people attributable to changes in annual mean temperature and DTR, respectively. The excess mortality related to changes in annual mean temperature and DTR is expected to increase in the future, with special attention warranted for long-term temperature changes in Southwest China. Our findings indicate that long-term mean temperature and DTR could significantly impact mortality rates. Given the spatial heterogeneity of increased mortality risk, the formulation of region-specific strategies to tackle climate change is crucial.

Temporal variation of the relationships between rice yield and climate variables since 1925.

Long-term time-series datasets of crop yield and climate variables are necessary to study the temporal variation of climate effects on crops. The aim of this study was to broadly assess assessment of the effects of climate on rice, and the associated temporal variations of the effects during the long-term period. We conducted field experiments in Taiwan from 1925 to 2019 to collect and analyze rice yield data and evaluate the impacts of changes in average temperature, diurnal temperature range (DTR), rainfall, and sunshine duration on rice yield during cool and warm cropping seasons. We then estimated the relationships between annual grain yield and the climate variables using the time series of their first difference values. We also computed the total relative and annual actual yield changes using regression coefficients for each climate variable for the intervals 1925-1944, 1945-1983, and 1996-2019 to reveal the impacts of climate change on yields and the associated temporal variations during the overall experimental period. The annual daily average temperature calculated from the trend of the regression lines increased by 0.94-1.03 °C during the 95-year period. The maximum temperature remained steady while the minimum temperature increased, leading to decreased DTR. The total annual rainfall decreased by 237-352 mm and the annual total sunshine duration decreased by 93.9-238.9 h during the experimental period. We observed that during the cool cropping season, yield response to temperature change decreased, while that to DTR and rainfall changes increased. During the warm cropping season, all the yield responses to temperature, DTR, and rainfall changes were negative throughout the experimental period. In recent years (1996-2019) the estimated annual actual rice yield changes during the cool cropping season were negatively affected by climate variables (except for sunshine duration), and slightly positively affected (except for temperature) during the warm cropping season. Compared to the effects of temperature and DTR, those of rainfall and sunshine duration on rice yield changes were weak. This study contributes to provide impacts of climate change on rice yield and associated long-term temporal variations over nearly a century.

Anthropogenic emissions and land use/cover change contributions to extreme temperature changes over China

Extreme weather and climate events tend to increase and strengthen as global warming intensifies, which severely affect human life and sustainable economic development. Therefore, based on the Detection and Attribution Model Intercomparison Project (DAMIP) and the Land Use Model Intercomparison Project (LUMIP) provided by the Coupled Model Intercomparison Project phase 6 (CMIP6), the optimal fingerprinting method is applied to quantify the greenhouse gases (GHG), aerosols (AA), natural forcing (NAT), and land use/cover change (LUCC) contributions to the intensity, frequency, and duration of extreme temperatures in China during 1960—2020. The results show that GHG is the main driver of extreme temperature changes in China, except for ice days (ID0) and cold spells (CSDI). GHG causes an increase in warm spells (WSDI) by about 10 days and an extension of the growing season length (GSL) by 6 to 8 days. The Tibetan Plateau is the region with the strongest extreme temperature changes influenced by GHG. AA forcing has a cooling effect that partially offsets the warming effect of GHG, especially in southeastern China. It should be pointed out that AA forcing is also the main driver for the changes in diurnal temperature range (DTR) in southeastern China, which exceeds the GHG contribution. Additionally, LUCC has greater impact in nighttime extreme temperature indices changes than the regional AA, and becomes the second dominant factor beside GHG. LUCC leads to an attributable cooling contribution of 0.34 °C for the maximum of daily minimum temperatures (TNx). Spatially, the LUCC effects on extreme temperature changes are stronger in western China than in eastern China. The more robust estimation of the GHG, AA, NAT, and LUCC contributions over distinct regions provides an advanced understanding of anthropogenic impacts on regional extreme temperatures, which is expected to be an important reference for regional climate change adaptation and mitigation.

Evaluation of CMIP6 models for simulations of diurnal temperature range over Africa

The variability in the diurnal temperature range (DTR), an indicator of climate change, remains limited, especially over Africa, due to the scarcity of observed maximum and minimum temperature data. This work investigates the ability of the Coupled Model Intercomparison Project (CMIP6) to simulate DTR over Africa for the period 1980–2014. Datasets from the Climatic Research Unit (CRU TS4.05) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) gridded temperature datasets are utilized as observed data. Similar to the high variability in topography and climate across the continent, the DTR exhibits high heterogeneity over Africa. The Sahara and its environs record the highest DTR, while Central Africa and coastal areas experience the least, given the thermal inertia of water bodies. CMIP6 models overestimate and underestimate DTR over different parts of the continent. Moreover, the multi-model ensemble mean of CMIP6 models shows significant decreasing trends both in seasonal and annual trends. Overall, five CMIP6 models such as EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, EC-Earth3-veg, and IPSL-CM6A-LR show robust skill scores (0.48–0.54). The findings form the basis for investigating the role of temperature extremes on DTR. Further, the variability in DTR across parts of the continent prompts the need for future assessments to investigate future changes in DTR.

Exploring the Dynamic Impact of Extreme Climate Events on Vegetation Productivity under Climate Change

As global warming continues to intensify, the relationship between diurnal temperature range (DTR) and vegetation productivity continues to change over time. However, the impact of DTR changes on vegetation activities remains uncertain. Thus, further study about how DTR changes affect the physiological activities of plants is also urgently needed. In this study, we employed copula function theory to analyze the impact of DTR on Normalized Difference Vegetation Index (NDVI) values during the spring, summer, and autumn seasons from 1982 to 2014 for various land types in the Inner Mongolia Plain (IMP), China. The results showed that the relationship between DTR and NDVI in the IMP was characterized by correlation at the upper tail and asymptotical independence at the lower tail. This demonstrated that the DTR had little effect on NDVI when they reached their minimum value. However, it has a significant impact on NDVI at its maximum values. This study provides valuable insight into the dynamic impact of monthly DTR on different land use types under climate change.

Projection of the diurnal temperature range over Africa based on CMIP6 simulations

Diurnal temperature range (DTR) is one of the key indicators of global climate change. In this study, a multi-model ensemble (MME) of five best-performing models over Africa from Coupled Model Intercomparison Project Phase 6 (CMIP6) under two socioeconomic scenarios (SSP2-4.5 and SSP5-8.5) were employed to compute the spatial variability of the future (2015–2100) DTR relative to the baseline period (1980–2014). The Modified Mann-Kendall Test (mMK) was used to analyze DTR trends, while Theil-Sen's slope estimator was used to assess the magnitude and significance of changes in future DTR. Boxplot plots are used to estimate the uncertainty of future trends relative to the baseline period. Results reveal that the annual DTR over most regions in Africa will decline under SSP5-8.5 scenarios except for the SWAF domain, where a slight increase is projected to occur. Likewise, the seasonal anomalies for DJF present a consistent decline in DTR over SAH (−0.2 °C), WAF (−0.8 °C), CAF (−0.4 °C), NEAF (−0.6 °C), and SEAF (−0.2 °C) under SSP5-8.5. Moreover, the JJA season showed a clear decline under SSP5-8.5 of up to −0.8 °C over CAF after 2050. Most of the continent is likely to experience a significant trend of −0.1 to −0.4/year. Furthermore, the CAF and NEAF regions showed a significant decline (−2.4/year) in DTR under both scenarios of SSP2-4.5 and SSP5-8.5 across the months and years. Large uncertainty is recorded during the DJF season and more predominately over the NEAF, SEAF, WAF, and CAF regions, characterized by negative skews (−0.018 °C/year) and large interquartile ranges (−0.007 to −0.024) in both timescales. Future studies on the projected DTR may focus on the impacts of the variability of the DTR on sectors such as health and morbidity, crop yields, impact assessments, etc.

Changes in Diurnal Temperature Range Over Nigeria from 1960 to 2019

Diurnal temperature range (DTR) is an important derived variable used in detecting signature of observed climate changes. Understanding its changes in recent time is important for managing and coping with climate change induced risks. This study analysed the long term trend and abrupt changes in diurnal temperature range over Nigeria from 1960 to 2019. Descriptive statistics, Mann-Kendall trend test, Sen’s slope and Pettit’s tests were used to assess the characteristics, trend, abrupt change and significance in annual DTR time series. Pearson correlation was used to examine the spatial relationship between DTR and minimum temperature, maximum temperature, precipitation and cloud cover. Results showed that mean DTR amount varied across spatio-temporal scales with steady increase from the coastal region, coupled with a weak inter-annual variability (CV < 3%). The trend analysis showed a significant decreasing DTR in most grid points (GP) and regions of Nigeria, with the exception of Enugu, Ado-Ekiti and Warri. Also a significant negative trend was observed at the Guinea, Sudan and Sahel savanna regions. Abrupt changes occurred in the 1970s in the entire regions and most of the GPs with Ado-Ekiti, Calabar, and Enugu occurred in 1991 while Maiduguri and Jos experienced such changes in 2011. Furthermore, significant abrupt changes were observed at the following GPs and regions; Ikeja, Kaduna, Kano, Katsina, Sokoto, Yelwa, Yola, Guinea, Sudan and Sahel Savannas. This decrease in DTR over most of the GP and the entire region indicate that the climate of Nigeria is becoming warmer possibly due to environmental changes with precipitation and cloud cover being the major drivers of DTR variability in Nigeria. Thus, proper climate actions should be taken through adaptation and resilient planning for the sustainable development of socio-economic and natural systems.

LSTM model for predicting the daily number of asthma patients in Seoul, South Korea, using meteorological and air pollution data.

Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.

Long-Term Impacts of Diurnal Temperature Range on Mortality and Cardiovascular Disease: A Nationwide Prospective Cohort Study.

Previous studies have documented the associations between short-term diurnal temperature range (DTR) exposure and cardiovascular disease (CVD) via time-series analyses. However, the long-term impacts of DTR through a population-based prospective cohort have not been elucidated thoroughly. This study aimed to quantify the longitudinal association of DTR exposure with all-cause mortality and CVD in a nationwide prospective cohort and, by extension, project future DTR changes across China under climate change. We included 22,702 adults (median age 56.1 years, 53.7% women) free of CVD at baseline from a nationwide cross-sectional study in China during 2012-2015, and examined three health outcomes during a follow-up survey in 2018-2019. We estimated the chronic DTR exposure as baseline annual mean daily maximum minus minimum temperature. The Cox proportional hazards regression was adopted to assess the multivariable-adjusted hazard ratio and its corresponding 95% confidence interval (95% CI). We employed 31 downscaled global climate models under two shared socioeconomic pathways for future projection. During the median follow-up period of ~5 years, 1096 subjects died due to all causes while 993 and 597 individuals developed fatal or nonfatal CVD and fatal or nonfatal stroke, respectively. The cumulative incidence rates of all-cause mortality, CVD, and stroke were 10.49, 9.45, and 5.64 per 1000 person-years, respectively. In the fully adjusted models, the risks for all-cause mortality, CVD, and stroke would increase by 13% (95% CI: 8-18%), 12% (95% CI: 7-18%), and 9% (95% CI: 2-16%) per 1 °C increment in DTR, respectively. Moreover, linear positive associations for the concentration-response curves between DTR and mortality and CVD were observed. We also found significantly greater DTR-related mortality risks among rural residents than their urban counterparts. The DTR changes featured a dipole pattern across China under a warming climate. The southern (northern) China would experience increased (decreased) DTR exposure by the end of 21st century. The present study indicates that chronic DTR exposure can exert long-term impacts on mortality and CVD risks, which may inform future public health policies on DTR-related susceptible population and regions.

Impact of the COVID-19 Pandemic on the 2020 Diurnal Temperature Range (DTR) in the Contiguous USA

Following the emergence of COVID-19, nations around the world implemented effective restrictions that limited people’s movements and economic activity, which reportedly led to environmental improvements. The lowering of air emissions is one environmental indicator that has been connected to the pandemic. The diurnal temperature range (DTR) is one environmental indicator that has been linked to air pollution. In this study, it was hypothesized that because of the pandemic restrictions and slowdowns, the DTR in 2020 for a country that implemented major restrictive measures in reaction to the pandemic would be higher than in previous years, despite or in addition to background climatic forcings. Based on information from weather stations in the contiguous United States of America (USA), the DTR for the year 2020 was compared to the five years before it as a test of this hypothesis. It was verified that the annual mean DTR of 2020 was higher than the three years prior (2017–2019), but lower than the DTR of 2015 and 2016. Compared to historical trends (since 1911), the DTR change in 2020 is within past mean DTR variations that occurred over approx. 12-year cycles, linked to sunspot activity (Schwabe solar cycle). Moreover, climatic effects such as El Niño, La Niña and the prolonged trend of global warming reduce the confidence in the perceived effect of the pandemic. To determine if or how anthropogenic and environmental factors can magnify the impact of the COVID-19 restrictions on the regional mean DTR, five other parameters (annual snowfall quantities, gross domestic product per capita, population density, latitude (northern/southern), and longitude (coastal/inner)) were also examined against changes in DTR from 2015 to 2020. This analysis pointed to the environmental and industrial factors being more strongly correlated with short-term climate changes than societal factors and geographical location.

Anthropogenic Influence on the Diurnal Temperature Range since 1901

Abstract The diurnal temperature range (DTR) as measured by the difference between daily maximum (Tmax) and minimum (Tmin) temperatures is of great importance to human health, ecology, and agriculture. The link of its long-term change to anthropogenic forcing is still unclear. This study shows evidence of human influence on long-term changes in DTR over the globe, five continents, and China during the past century (1901–2014). Using multiple observational datasets, we find a general decrease in the DTR over most of the global land since 1901, especially after the mid-1950s. Changes in DTR are due to different warming rates of Tmax and Tmin in response to external forcings. The climate models that participated in phase 6 of the Coupled Model Intercomparison Project Phase 6 (CMIP6) generally reproduce most of the changes in DTR, along with those in Tmax and Tmin. The models have underestimated the observed changes in DTR, however. A formal detection and attribution analysis shows that the anthropogenic forcing signal, including both greenhouse gas and aerosol emissions but dominated by the greenhouse gas emissions, is the main driver for these changes. The anthropogenic aerosol signal can be detected in Tmax and Tmin but not in DTR during the period of 1901–2014 over the globe and most continents. These indicate the observed decrease in DTR is not a simple response to anthropogenic aerosol emission. The natural signal is negligible in almost all the cases. Globally, anthropogenic influence is estimated to explain more than 90% of the observed changes in the three variables. In China, human influence is also clearly detected, although model simulated results on the regional scale have larger deviation. Significance Statement The diurnal temperature range (DTR) is of great importance in many areas. We compare multiple observational datasets with the simulations by climate models that participated in the latest phase (phase 6) of the Coupled Model Intercomparison Project (CMIP6), finding evidence of human influence on long-term changes in DTR over the past century (1901–2014) and robust evidence for the period since the early 1950s. The decrease in DTR as seen in the observational dataset is caused by different warming rates of daily maximum and daily minimum temperature in response to anthropogenic forcing, including both greenhouse gases and aerosols.

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.

Detection and Attribution of Human Influence on the Global Diurnal Temperature Range Decline

AbstractA decline in the global diurnal temperature range (DTR) and its implications for human and natural systems have been widely reported, yet it remains unclear whether humans have a detectable influence on the DTR and to what extent anthropogenic greenhouse gases (GHG) may be driving such changes. Results indicate that the effect of anthropogenic forcing on the DTR is detectable separately from natural forcing across the globe and in many regions. GHG is the dominant contributor to DTR changes and caused the global DTR to decrease by −0.32°C during 1951–2018, close to the observed change of −0.41°C. Decreased anthropogenic aerosols (AER) increased the DTR in Europe, while increased AER decreased the DTR in Asia. If greenhouse gas emissions continue to rise, further decreases in the DTR are likely to be observed in the future.

Diurnal temperature range variability driven by cloud cover and precipitation in Mongolian Plateau under global warming

AbstractThe diurnal temperature range (DTR) has significantly decreased in many land areas as a consequence of global warming. The DTR spatial distribution and future projections of spatiotemporal variations in the case of global warming levels at 1.5 and 2.0°C under the RCP4.5 emission scenario was investigated using datasets from the Climatic Research Unit (CRU‐TS v.3.24.01) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the Mongolian Plateau. And the main factors influencing DTR spatiotemporal variations were also evaluated using the Geo‐detector model at different time scales. The results showed that variations of the DTR significant decrease in snow‐winter dry‐warm summer climate region are higher than in other climate regions in spatial scale. As a result of the increasing rates of Tmin, which was more than double that of Tmax, the DTR decreased significantly on a temporal scale. In climate projections, the DTR was lower at global warming of 2.0°C (2037–2056) than 1.5°C (2017–2036). Precipitation was determined to be the predominant factor underlying the annual and seasonal (spring, summer, and autumn) DTR changes. Total cloud cover was the main factor in winter. Soil moisture was the key factor for the spatial variations in DTR in the warm and dry seasons. This study enhances the understanding of climate change in the Mongolian Plateau and provides a strong reference for other DTR variation studies in similar climates.

Evidence of decreasing diurnal temperature range in eastern Northern Hemisphere

As a key variable in the climate system, the diurnal temperature range (DTR) has received a lot of attention in the atmospheric science community. The majority of published papers interpret DTR change in terms of variables like water vapor, cloud cover, and enhanced vegetation index. In this study, we found that the DTR has an obvious decreasing trend in the semi-arid Northeastern Hemisphere, and the decreasing trend is mainly caused by the continuous release of CO2 closely associated with a drying process in the Northeastern Hemisphere. As a result of the continued high emission of CO2, such accelerated decline changes in DTR in drylands may become much more pronounced in the future, posing a series of environmental problems.

Quantile based modeling of diurnal temperature range with the five‐parameter lambda distribution

AbstractDiurnal temperature range is an important variable in climate science that can provide information regarding climate variability and climate change. Changes in diurnal temperature range can have implications for hydrology, human health and ecology, among others. Yet, the statistical literature on modeling diurnal temperature range is lacking. In this article we propose to model the distribution of diurnal temperature range using the five‐parameter lambda (FPL) distribution. Additionally, in order to model diurnal temperature range with explanatory variables, we propose a distributional quantile regression model that combines quantile regression with marginal modeling using the FPL distribution. Inference is performed using the method of quantiles. The models are fitted to 30 years of daily observations of diurnal temperature range from 112 weather stations in the southern part of Norway. The flexible FPL distribution shows great promise as a model for diurnal temperature range, and performs well against competing models. The distributional quantile regression model is fitted to diurnal temperature range data using geographic, orographic, and climatological explanatory variables. It performs well and captures much of the spatial variation in the distribution of diurnal temperature range in Norway.

Trends in the Diurnal Temperature Range over the Southern Slope of Central Himalaya: Retrospective and Prospective Evaluation

The Diurnal Temperature Range (DTR) profoundly affects human health, agriculture, eco-system, and socioeconomic systems. In this study, we analyzed past and future changes in DTR using gridded Climate Research Unit (CRU) datasets for the years 1950–2020 and an ensemble means of thirteen bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios for the rest of the 21st century over the southern slope of Central Himalaya, Nepal. Furthermore, the potential drivers (precipitation and cloud cover) of seasonal and annual DTR were studied using correlation analysis. This study found that the DTR trends generally declined; the highest decrease was observed in the pre-monsoon and winter at a rate of 0.09 °C/decade (p ≤ 0.01). As expected, DTR demonstrated a significant negative correlation with cloudiness and precipitation in all four seasons. Further, the decreased DTR was weakly related to the Sea Surface Temperature variation (SST) in the tropical Pacific and Indian Oceans. We found that the projected DTR changes in the future varied from a marginal increase under the SSP1-2.6 (only pre-monsoon) scenario to continued significant decreases under SSP2-4.5 and SSP5-8.5. Insights based on retrospective and prospective evaluation help to understand the long-term evolution of diurnal temperature variations.

Changes in the diurnal temperature range over East Asia from 1901 to 2018 and its relationship with precipitation

Since the 1950s, the East Asian diurnal temperature range (DTR), defined as the difference between the daily maximum (Tmax) and minimum temperatures (Tmin), has gradually decreased. Precipitation changes have often been cited as a primary cause of the change. However, the East Asian DTR change before 1950 and its relationship with precipitation remain unclear. Here, we used a newly developed China Meteorological Administration-Land Surface Air Temperature dataset v1.1 to examine the climatological patterns and long-term trends of the DTR in East Asia from 1901 to 2018 and its relationship with precipitation. The mean annual DTR averaged over East Asia for 1951–2018 was approximately 10.0 °C. East Asian DTR changes during 1901–2018 show two distinct characteristics. First, the DTR decreased significantly by approximately 0.60 °C during 1901–2018, and the decrease rate in the second half of the twentieth century (by ~0.53 °C) was significantly larger than that over the rest of the Northern Hemisphere and the global land due to rapid urbanization over East Asia. Second, before the 1950s, the DTR in East Asia showed a significant non-linear increase especially in middle latitude areas, mainly due to the warming rate of Tmax is higher than that of Tmin. Additionally, we found that the spatial pattern of long-term DTR change shows a significant negative correlation with mean precipitation patterns except in arid and semi-arid areas during 1901–2018. The decreasing trend of DTR gradually became smaller from arid regions to humid regions during 1901–2018, mainly because the difference between Tmax and Tmin warming rate gradually became smaller.

Large-scale pattern of the wintertime diurnal temperature range variations over North America during 1979–2018

The atmospheric circulation and remote forcing of SST anomalies play an imporant role in the formation of large-scale cloud cover and precipitation anomalies as well as the patterns of diurnal temperature range (DTR) variability. In this study, the dominant patterns of the wintertime DTR variations over North America (NA) and the underlying mechnisms are investigated based on observational and reanalysis datasets during 1979–2018. The first leading mode is characterized by a widespread same-sign loading in the majority of NA. The corresponding principal component (PC) shows an obvious interannual time-scale variations. The second mode displays a north-south dipole-like pattern in the NA and the corresponding PC exhibits a decline trend during 1979–2018. Both patterns of DTR variations are closely associated with the changes in local cloud cover and precipitation. Furthermore, clear effects are identified of El Niño-Southern Oscillation (ENSO) and Tropical/Northern Hemisphere (TNH) teleconnection on the formation of the two dominant patterns of wintertime DTR variabilty over NA through changing the atmospheric circulation and associated transportation of temperature and moisture. Our findings provide a new insight into understanding the DTR changes during recent decades.