Trends In Diurnal Temperature Range Research Articles

Diurnal Temperature Range Trends Differ Below and Above the Melting Point

AbstractThe globally averaged diurnal temperature range (DTR) has shrunk since the mid‐20th century, and climate models project further shrinking. Observations indicate a slowdown or reversal of this trend in recent decades. Here, we show that DTR has a minimum for average temperatures close to 0°C. Observed DTR shrinks strongly at colder temperature, where warming shifts the average temperature toward the DTR minimum, and expands at warmer temperature, where warming shifts the average temperature away from the DTR minimum. Most, but not all climate models reproduce the minimum DTR close to average temperatures of 0°C and a stronger DTR shrinking at colder temperature. In models that reproduce the DTR minimum, DTR shrinking slows down significantly in recent decades. Models project that the global‐mean DTR will shrink over the 21st century, and models with a DTR minimum close to 0°C project slower shrinking than other models.

Structure, modulation and impacts of southeastern Africa’s diurnal convection

This study explores the structure, modulation, and impacts of the mean diurnal climate across southeast Africa using high-resolution hourly reanalysis and satellite rainfall. This research was motivated by upward trends in diurnal temperature range and vegetation index over the Drakensberg escarpment. Statistical analysis identified a land-sea rainfall dipole pattern associated with diurnal airflows. Point-to-field regressions (Oct–Mar 2000–2023) found that sea breeze-driven convection is underpinned by ‘col’ airflow between adjacent sub-tropical low-pressure cells (30°S, 20°E/22°S, 37°E) and a weak mid-latitude anticyclone (40°S, 10°E). Diurnal residual and case study analysis demonstrate an ~ 6-m/s sea breeze penetrating ~ 200 km inland during the day, as part of an over-turning zonal circulation. Land > sea rainfall during summer is initiated by temperature gradients ∂T/∂x ~ 5 °C at 29°S, 31°E and convergence in the afternoon ∂U/∂x ~ − 4 × 10−5 s−1. Further inland (29°S, 26°E) a 1007-hPa thermal low and counter-clockwise rotating airflows interact with the incoming sea breeze. Together these contribute to evening rainfall ~ 30 mm over the Drakensberg escarpment. The drivers of diurnal rainfall show little response to global climate variability, and help sustain water resources during multi-year dry spells over southeastern Africa.

A Climatological Survey of Corsica for Power System Analyses

Climate and climate change can impact present and future energy production and demand. In light of this issue, this paper conducts climatological analyses focused on the following meteorological quantities: shortwave downward irradiance (SDI), precipitation (Pr), relative humidity (RH), air temperature (Ta), 10 m wind speed (v10), and diurnal temperature range (DTR) for four locations in Corsica. The climate analyses of these atmospheric variables consist of three parts: (1) analysis of the historical trends; (2) correlation analysis; and (3) analysis of climate projections for the decades to come. It is observed that climate change is causing alterations in the trends of Pr, RH, Ta, v10, and DTR. The correlation analysis reveals a positive correlation for the Ta-SDI and v10-Pr pairs (both annually and seasonally), and a negative correlation for Ta-RH (annually and in summer). For the other variable pairs, the sign of the correlations varies depending on the time period and site considered. The trends in the projections from the multi-model ensemble simulations are consistent or inconsistent with each other depending on the time period (annual or seasonal) considered. The observed historical trends suggest that medium-term planning of the Corsican electric power system should already consider ongoing climate change. The correlation analysis provides insights into the combined effect of different atmospheric variables on electrical power systems (EPSs). Climate projections suggest studying long-term planning that is a compromise among the different (but equally likely) outputs of different climate models.

Long-term air temperature trends in North Cyprus

The Eastern Mediterranean region is a major climate change hotspot. The island of Cyprus is likely to face increases in the frequency and intensity of hotter weather conditions and heatwaves in the near future. Studies conducted on the long-term temperature changes in Cyprus are very limited. Here, we present an updated and most detailed assessment of the maximum, minimum, mean and diurnal temperature series in North Cyprus for the period 1975–2021. Data obtained from the meterological stations of North Cyprus have been analysed using Mann–Kendal (MK) test and Theil–Sen slope estimator. Overall the mean minimum temperature trend (Tmin) showed the highest warming rate 0.61 (0.24 ≤ Tmin ≤ 0.99)°C decade−1 followed by the mean temperature trend (Tmean) 0.38 (0.29 ≤ Tmean ≤ 0.50) °C decade−1 and the mean maximum temperature trend (Tmax) 0.28 (0 ≤ Tmax ≤ 0.50) °C decade−1. The magnitude of the warming trend observed in the overall mean minimum temperature of North Cyprus 0.61 °C decade−1, is one of the fastest warming trends reported in the literature. A negative association was detected between the direction of prevailing winds of North Cyprus and the magnitude of increase in the mean temperature trends of the locations with coastal Mediterranean climate, which has pointed out the importance of prevailing winds regarding their cooling effect in coastal areas. The diurnal temperature range trend of North Cyprus indicates an apparent decrease (− 0.33 °C decade−1). The warming impact of urban heat island effect was detected in temperature trends of Nicosia in the Mesaoria plain. The information provided here is invaluable to consider in any climate assessment and adaptation plan in Cyprus. If the current warming trend persists into the future, it will devastatingly impact all sectors and natural systems in the region.

Climate trends and maize production nexus in Mississippi: empirical evidence from ARDL modelling

Climate change poses a significant threat to agriculture. However, climatic trends and their impact on Mississippi (MS) maize (Zea mays L.) are unknown. The objectives were to: (i) analyze trends in climatic variables (1970 to 2020) using Mann–Kendall and Sen slope method, (ii) quantify the impact of climate change on maize yield in short and long run using the auto-regressive distributive lag (ARDL) model, and (iii) categorize the critical months for maize-climate link using Pearson’s correlation matrix. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), relative humidity (RH), and carbon emissions (CO2). The pre-analysis, post-analysis, and model robustness statistical tests were verified, and all conditions were met. A significant upward trend in Tmax (0.13 °C/decade), Tmin (0.27 °C/decade), and CO2 (5.1 units/decade), and a downward trend in DTR ( − 0.15 °C/decade) were noted. The PT and RH insignificantly increased by 4.32 mm and 0.11% per decade, respectively. The ARDL model explained 76.6% of the total variations in maize yield. Notably, the maize yield had a negative correlation with Tmax for June, and July, with PT in August, and with DTR for June, July, and August, whereas a positive correlation was noted with Tmin in June, July, and August. Overall, a unit change in Tmax reduced the maize yield by 7.39% and 26.33%, and a unit change in PT reduced it by 0.65% and 2.69% in the short and long run, respectively. However, a unit change in Tmin, and CO2 emissions increased maize yield by 20.68% and 0.63% in the long run with no short run effect. Overall, it is imperative to reassess the agronomic management strategies, developing and testing cultivars adaptable to the revealed climatic trend, with ability to withstand severe weather conditions in ensuring sustainable maize production.

Climate trends and soybean production since 1970 in Mississippi: Empirical evidence from ARDL model

Studying historical response of crops to weather conditions at a finer scale is essential for devising agricultural strategies tailored to expected climate changes. However, determining the relationship between crop and climate in Mississippi (MS) remains elusive. Therefore, this research attempted to i) estimate climate trends between 1970 and 2020 in MS during the soybean growing season (SGS) using the Mann-Kendall and Sen slope method, ii) calculate the impact of climate change on soybean yield using an auto-regressive distributive lag (ARDL) econometric model, and iii) identify the most critical months from a crop-climate perspective by generating a correlation between the detrended yield and the monthly average for each climatic variable. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). All required diagnostic-tests i.e., pre-analysis, post-analysis, model-sensitivity, and assessing the models' goodness-of-fit were performed and statistical standards were met. A positive trend in Tmin (+0.25 °C/decade), and a negative trend in DTR (−0.18 °C/decade) was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted as +0.11 °C/decade, +3.03 mm/decade, and −0.06 %/decade, respectively. Furthermore, soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18.11 % over the long-term and by 5.51 % over the short-term for every 1 °C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7.76 % and 3.04 %, respectively. Altogether, soybeans in MS exhibited variable sensitivity to short- and long-terms climatic changes. The results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.

Past and future trends of diurnal temperature range and their correlation with vegetation assessed by MODIS and CMIP6

Temperature anomalies and changes in the diurnal temperature range (DTR) are expected to pose physiological challenges to biota; hence, both spatial and temporal variations in DTR provide important insights into temperature-induced stress in humans, animals, and vegetation. Furthermore, vegetation could dampen temperature variability. Here, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data of Land Surface Temperature (LST) to evaluate the global variation in DTR and its rate of change in spatial and temporal scales for the two decades spanning from 2001 to 2020. We show that North America, Africa, and Antarctica, as well as the global mean, experienced statistically significant DTR rates of change over the last 20 years in either summer, winter, or the annual mean. The rates were all negative, indicating the day-night temperature differences are decreasing in those regions because night temperatures are increasing at a faster rate than day temperatures. MODIS data of the Normalized Difference Vegetation Index (NDVI) revealed a strongly negative correlation with DTR, with a spatial correlation coefficient of −0.61. This correlation demonstrates a prominent dampening effect of vegetation on diurnal temperature oscillations. For future DTR projections, we used 19 models in the Coupled Model Intercomparison Project 6 (CMIP6) to predict global DTR trends from 2021 to 2050 with low and high CO2 concentration scenarios. The high CO2 emission scenario projects significant decreases in DTR in circumpolar regions, central Africa, and India compared to the low CO2 scenario. This difference in the two scenarios underscores the substantial influence of increased global temperatures and elevated CO2 concentration on DTR and, consequently, on the ecosystems in certain regions.

Diurnal Temperature Range and Its Response to Heat Waves in 16 European Cities—Current and Future Trends

An important indicator of climate change is the diurnal temperature range (DTR), defined as the difference between the daily maximum and daily minimum air temperature. This study aims to investigate the DTR distribution in European cities of different background climates in relation to the season of the year, climate class and latitude, as well as its response to exceptionally hot weather. The analysis is based on long-term observational records (1961–2019) coupled with Regional Climate Model (RCM) data in order to detect any projected DTR trends by the end of the 21st century under intermediate and high emission greenhouse gases (GHGs) scenarios. The analysis reveals marked variations in the magnitude of DTR values between the cities, on the one hand, and distinct patterns of the DTR distribution according to the climate class of each city, on the other. The results also indicate strong seasonal variability in most of the cities, except for the Mediterranean coastal ones. DTR is found to increase during hot days and heat wave (HW) days compared to summer normal days. High latitude cities experience higher increases (3.7 °C to 5.7 °C for hot days, 3.1 °C to 5.7 °C for HW days) compared to low latitude cities (1.3 °C to 3.6 °C for hot days, 0.5 °C to 3.4 °C for HW days). The DTR is projected to significantly decrease in northernmost cities (Helsinki, Stockholm, Oslo), while it is expected to significantly increase in Madrid by the end of the 21st century under both the intermediate- and high-emission scenarios, due to the asymmetric temperature change. The asymmetrical response of global warming is more pronounced under the high-emission scenario where more cities at higher latitudes (Warsaw, Berlin, Rotterdam) are added to those with a statistically significant decrease in DTR, while others (Bucharest, Nicosia, Zurich) are added to those with an increase in DTR.

Regional features of future warming using high‐resolution downscaled multimodel climate scenarios in mainland China

AbstractThe frequency and magnitude of global warming events varies greatly across different regions and countries. The climatic diversity for China and future warming features are projected across 12 climatic zones based on the ensemble of the five well‐performing high‐resolution downscaled climate models for each zone. There are warming patterns for the mean near‐surface air temperature (Tm), maximum near‐surface air temperature (Tmax), minimum near‐surface air temperature (Tmin) as well as heatwave and frost events. Under RCP4.5 and RCP8.5 scenarios, the three indices (i.e., Tm, Tmax and Tmin) countrywide are likely to increase at respective rates of 0.30–0.31 and 0.64–0.67°C·decade−1. The extent of frost event (FE) are projected to decrease at a rate of −1912 and −4442 day·km−2·decade−1 while the extent of heatwave event (HE) increase at a rate of 1116 and 3557 day·km−2·decade−1 under RCP4.5 and RCP8.5 scenarios, respectively. A higher increment in temperatures, a decreasing trend in diurnal temperature range (DTR), frost days and FE are highlighted in the western and northern zones, while these trends are opposite in southern zones. The warming is likely to get faster in the current colder zones (western and northern zones) while heatwave is more intense and severe in Jianghuai, Jianghan, the south Yangzi River, South China and Xinjiang. These potential changes indicate that adaption and mitigation strategies are necessary in response to future warming.

Trends in climate change observed under tropical wet and tropical montane climates; A case study from Sri Lanka

Climate change-related changes in temperature and precipitation trends must be investigated at local, regional and global levels. Temperature and precipitation trends in two selected regions having tropical wet and tropical montane climates (i.e., Colombo and Nuwara Eliya respectively) in Sri Lanka were studied for a 30 year period from 1989 to 2019, to evaluate the temporal dynamics of climate change. Precipitation trends were analyzed on annual, monthly, and seasonal scales, while the trends in mean, minimum, and maximum temperatures were examined on annual and monthly scales. Decadal time series plots were used to study decadal variations in average temperature and precipitation. The trends in extreme temperature and precipitation events were also evaluated. In addition, the trends in diurnal temperature range (DTR), cool and warm nights, and heat index (HI) were studied. The significance of trends was evaluated using the Mann-Kendall test, while the magnitude of the slope was assessed by Sen’s slope estimator. Clear statistically significant increasing trends were observed for the mean annual temperatures under the tropical wet and tropical montane climates, and no clear trends were observed in annual precipitation in both districts. There were decreasing trends in south-west monsoon rainfall, with a significant decrease in Nuwara Eliya under the tropical montane climate. Increasing trends were observed for the average monthly precipitation in November (i.e., during the inter-monsoonal rains) and average monthly temperature in April (i.e., the hottest month) over the last decade (i.e., 2010-2019) in Colombo. The DTR has significantly decreased over the last three decades in Colombo. A significant upward trend was observed for HI values during the last decade in Colombo. Colombo also showed a statistically significant decreasing trend in the number of cool nights and a statistically significant decreasing trend in the number of warm nights over the last decade.

Increasing Global Terrestrial Diurnal Temperature Range for 1980–2021

AbstractThe 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment.

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.

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities

In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

Trend analysis and prediction of temperature change in the continental, Thailand

Diurnal Temperature Range (DTR) is one of global warming indicator which using only daily minimum temperature (TMin) and daily maximum temperature (TMax) for calculation. The study aims 1) to analyse trend of TMax, TMin and DTR over the early period of 21st century (1987 - 2020) and 2) to forecast and analyse trend of TMax, TMin and DTR over the mid and late period of 21st century (2021 - 2040 and 2041-2100) using MIROC5 (rcp85) model, cooperated with EQM Statistical downscaling method. TMax, TMin, and and DTR trends of all periods were investigated by Mann Kendall Trend Test and Sen?s slope, then presented in the form of spatial maps. The most of TMax and TMin trends for all season and all regions tended to be increasing from year by year with the large increasing of changing in Northern and Northeast region especially in Winter, except for the last period which had a large increasing in Summer. However, DTR trends were quietly fluctuated with the large decreasing of changing in Eastern and Southern part, but the trends become to increase in the last period. A large DTR decreasing of changing usually occurred in Winter and Rainy season. The TMax and TMin of all periods were expanding year by year and leading the DTR decreased in the first and second period, however, a changing of TMax and TMin rate leaded DTR increasing for the last period. It is a good signal for heat transfer performance which can help the earth cooling in night time.

Climate Change Trends in a European Coastal Metropolitan Area: Rainfall, Temperature, and Extreme Events (1864–2021)

This paper summarises an updated climate change trends analysis—developed for the period from 1 October 1864 to 30 September 2021 within the scope of a Horizon 2020-funded project to increase climate resilience in European coastal cities—for a representative site of the Lisbon Metropolitan Area (Portugal). By using long ground-based daily records of rainfall and surface temperature at the Lisboa-Geofísico climatological station, the analysis aimed to identify (i) long-term and recent climate trends in rainfall and temperature, (ii) changes in extreme rainfalls, heatwaves, and droughts, and (iii) possible effects of the coupled changes of minimum and maximum daily temperatures (Tmin and Tmax, respectively) on drought development based on the diurnal temperature range (DTR) indicator. To detect these trends and quantify their magnitude, the Mann−Kendall and Sen’s slope estimator tests were implemented. The analysis of the mean annual temperatures indicated that the study area has warmed ∼1.91 °C through the 157 analysed years. Results evidenced statistically significant upward trends in both Tmin and Tmax, and in the number of Tmax heatwave days. In what concerns the extreme hydrological events, the analysis of annual maximum rainfall series and peaks-over-threshold (POT) techniques showed more frequent and intense events in recent years, reaching up to ∼120.0 mm in a single day. With regard to drought, the study proved that the characterisation based on the commonly used standardised precipitation index (SPI) might differ from that based on the standardised precipitation evapotranspiration index (SPEI), as the latter can take into account not only rainfall but also temperature, an important trigger for the development of drought. According to the SPEI index, severe and extreme drought conditions have been more frequent in the last 60 years than in any other recorded period. Finally, a decreasing DTR trend towards the present was found to influence evapotranspiration rates and thus drought characteristics.

Climatology and trends of wintertime diurnal temperature range over East Asia in CMIP6 models: Evaluation and attribution

Based on the 18 realizations of three state-of-the-art climate models participated in the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper examines the changes in climatology and trends of winter diurnal temperature range (DTR) over East Asia in response to natural and anthropogenic forcings. In the observation, the climatology of DTR during 1979–2014 displays a horizontal tile from southwest to northeast over East Asia. In addition, the observed DTR also presents an obviously declining trend in recent decades, with the magnitude of roughly −0.10 °C decade−1. The historical all-forcing simulations well reproduce the geographical distributions and magnitude of climatology and trends of winter DTR over East Asia in 1979–2014. Furthermore, the winter DTR exhibits a continuous declining trend from the historical scenario to the future SSP245 scenario over East Asia, with the most significant declining during the first half of twenty-first century (i.e., 2021–2060). Based on the individual forcing experiments, it suggests that the greenhouse gases forcing plays a primary role in driving the climatology and trends of DTR over East Asia, especially for the SSP245 scenario. Further analysis indicates that the increasing precipitation under greenhouse gases forcing largely explain the declining DTR trends over East Asia, especially over the northern East Asia both in historical and SSP245 scenarios.

Linkage of extreme temperature change with atmospheric and locally anthropogenic factors in China mainland

Much attention has been given to the large-scale anthropogenic influence on extreme temperature changes. However, the possible effects of atmospheric factors and the role of the locally anthropogenic driver on the observed regional change in extreme temperatures have not been well understood. Datasets of the China Meteorological Administration (CMA) were used to examine the possible influence. The data include the homogenized surface air maximum/minimum temperature, surface sunshine duration (SSD), water vapor pressure (VAP), and total cloud cover (CLO) during 1961–2020. We calculated the seasonal means of maximum temperature (Tx(mean)), minimum temperature (Tn(mean)) and diurnal temperature range (DTR) to analyze the characteristics of extreme surface air temperature (ESAT) changes in winter and summer, and their relationship to SSD, VAP, and CLO, as well as the urbanization effect in the extreme temperature indices series. The changes in the trends for SSD and VAP were more closely correlated with ESAT in winter and summer, respectively. The decreasing of SSD led to a slowing of the ESAT warming, while an increase of the VAP led to the intensification of ESAT warming. In addition, CLO also had an important role in the change of ESAT. The seasonal mean correlation coefficient over China mainland between Tn(mean) and VAP were statistically significant and higher than those with SSD and CLO. The seasonal mean correlation between Tx(mean)/DTR and SSD were also statistically significant. The SSD and VAP strongly influenced the Tx(mean) in winter. The urbanization contribution to the DTR trends reached >25% in both winter and summer. SSD strongly influenced the trend of ESAT in urban area whereas CLO/VAP had larger influence on the trend of ESAT in rural area, which were all related to the urbanization effect as observed in the data series.

Diurnal Temperature Range as Index of Climate Change in Lafia, Nasarawa State, Nigeria

The study focused on determining the Diurnal temperature range trend as an index of climate change in Lafia, Nasarawa State, Nigeria. The study relied on remote sensing data- the ERA5 – Temperature, which is the fifth generation reanalysis from ECMWF. The data for the period of forty (40) years was extracted and used to analyze the annual range, diurnal temperature, trend analysis and daily maximum and minimum temperatures. The results shows a decrease in the trend analysis for the decadal and pentad which means that the difference between the daily maximum temperature and daily minimum temperature is no longer wide. However, the trend analysis carried out for the annual DTR shows a positive trend which shows that the Maximum daily temperature is higher than the daily minimum range in the study area. The study recommended for more intense study of DTR in order to stop over generalizing the index of climate change over a macro area.

Deep learning models comparable assessment and uncertainty analysis for diurnal temperature range (DTR) predictions over Indian urban cities

For monthly time spans of Indian cities (Ahmedabad, Bengaluru, Guwahati, Kolkata, and New Delhi), this article used daily (max and min) temperature data from 1951 to 2020 and approximated diurnal temperature range (DTR). RClimDex (a user interface for extreme computing indices) was used to do statistical analysis and comparisons of climatological characteristics such time series, means, extremes, and trends. During these years, the DTR trend in the researched area was more variable. This research examines the appropriateness of three deep neural networks [recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)] for one-step-ahead DTR time series (DTRTS) prediction. To test the efficiency of models in the testing set, six statistical error metrics were used [Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of correlation (R), Percent Bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd). The forecasting inaccuracy in predicting the outcome The Wilson score approach was used to do a quantitative uncertainty analysis on DTR. The results show that the LSTM outperforms the other two models in terms of forgetting, remembering, and updating information. It is more accurate on datasets with longer sequences and exhibits substantially higher volatility throughout its gradient descent. It got concluded with LSTM being well adapted to learning from experience to categorize, analyze, and predict time series, and it may be employed as a new dependable artificial intelligence technique for DTRTS forecasting.

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.