Projection of temperature extremes of Egypt using CMIP6 GCMs under multiple shared socioeconomic pathways.

Global warming has amplified the frequency of temperature extremes, especially in hot dry countries, which could have serious consequences for the natural and built environments. Egypt is one of the hot desert climate regions that are more susceptible to climate change and associated hazards. This study attempted to project the changes in temperature extremes for three Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, and SSP5-8.5 and two future periods (early future: 2020-2059 and late future: 2060-2099) by using daily maximum (Tmax) and minimum temperature (Tmin) of general circulation model (GCMs) of Coupled Model Inter-comparison Project phase 6 (CMIP6). The findings showed that most temperature extreme indices would increase especially by the end of the century. In the late future, the change in the mean Tmin (4.3 °C) was projected to be higher than the mean Tmax (3.7 °C). Annual maximum Tmax, temperature above 95th percentile of Tmax, and the number of hot days above 40 °C and 45 °C were projected to increase in the range 3.0‒5.4 °C, 1.5‒4.8 °C, 20‒95 days, and 10‒52 days, respectively. In contrast, the annual minimum of Tmin, temperature below the 5th percentile, and the annual percentage of cold nights were projected to change in the range of 2.95‒5.0 °C, 1.4‒3.6 °C, and - 0.1‒0.1%, respectively. In all the cases, the lowest changes would be for SSP1-2.6 in the early period and the greatest changes for SSP5-8.5 in the late period. The study indicates that the country is likely to experience a rise in hot extremes and a decline in cold extremes. Therefore, Egypt should take long-term adaptation plans to build social resiliency to rising hot extremes.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin.

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02172-8.