Analyzing the Historical and Projected Evolution of the Global Diurnal Temperature Range (DTR)

The Diurnal Temperature Range (DTR) serves as a crucial meteorological indicator, reflecting the difference between daily maximum and minimum temperatures and the magnitude of diurnal extremes. The anomalous values of DTR are often linked to the occurrence of various climatic extremes such as droughts, heatwaves, and wet spells, which make it necessary to understand the evolution of DTR both historically and for the future. This study focuses on analyzing the evolution of DTR globally by employing the non-stationary Multidimensional Ensemble Empirical Mode Decomposition (MEEMD) method. To accomplish this, historical temperature data spanning 69 years (1951-2019) and CMIP6 Bias corrected data covering 150 years (1951-2100) were utilized. The non-linear trend characteristics in temperature are computed using CRU 0.50 x 0.50 gridded temperature data for historical trends and five different bias-corrected climate projection datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) for the assessment of trends in future DTR by considering two SSP scenarios, i.e., SSP 245 and SSP 585, each corresponding to intermediate and high emissions scenarios. The CMIP6 models that are considered are CanESM5, GFDL CM4, MIROC6, NorESM2-MM, and MPI-ESM1-2-HR. The results from the analysis reveal the decrease in global DTR, with a faster rate of increase in minimum temperature than in maximum temperature. However, the southern regions of Australia and Africa showed an increase in DTR. The CMIP6 simulations showed that CanESM5 and MPI-ESM1-2-HR showed a decreasing trend in global DTR for both scenarios of ssp, with an increase in DTR for South America and the southern part of Africa for CanESM5, while GFDL CM4, MIROC6, and NorESM2-MM showed a decrease in global DTR. The findings underscore the importance of understanding regional climatic variations when assessing global temperature trends. The observed contrasting regional patterns in DTR highlight the influence of localized hydroclimatic factors, including land-use changes, aerosols, and atmospheric feedback mechanisms. These insights are crucial for refining climate models and improving future climate projections under different emission pathways. Overall, the study emphasizes the necessity of incorporating non-linear approaches like MEEMD to capture complex climatic trends and underscores the role of DTR as a key indicator of climate change and its impacts at both global and regional scales.