Global warming-induced climate change caused a change in temperature extremes' recurrence, intensity, duration, timing, and geographical variability ( Hadi Pour et al., 2019; Pérez et al., 2021). The changes in temperature extremes significantly affected human health, agricultural yield, economic activities, social conflicts, biophysical environment (Shahid et al., 2017; Li et al., 2019; Pour et al., 2020a; Pérez et al., 2021; Zhao et al., 2021a). Natural hazards like the frequency and severity of heat waves, cold spells, aridity and forest fires have also been exacerbated by increasing temperature extremes (Ahmed et al., 2018; Ajjur and Al-Ghamdi, 2021; Khan et al., 2019). The recurrence, duration and severity of temperature extremes have also increased in CA like many other regions (Feng et al., 2018; Peng et al., 2020; Pour et al., 2020b; Zhang et al., 2019b). Global warming would be further intensified in the forthcoming years even if the carbon emission reduction is possible according to the Paris agreement (Du et al., 2021; Hamed et al., 2022c; Zhao et al., 2021a). Dry regions like CA are more vulnerable to temperature extremes for their friable ecosystem (Khan et al., 2019a; Salman et al., 2017). Evaluation of possible changes in temperature extremes is vital for such regions for adaptation and mitigation planning.
Global climate models (GCMs) of CMIP5 and earlier versions have been widely used for the projection of climatic extremes in many regions of the globe (Ajjur and Al-Ghamdi, 2021; Li et al., 2019; Seong et al., 2021; Sharafati et al., 2020; Ying et al., 2020; Zhao et al., 2021b). CMIP6 is the latest generation of CMIPs that integrated radiative concentration pathways with shared socioeconomic pathways for climate change projections. The CMIP6 GCMs have some advantages, including enhancement in model structure, spatial resolution, uncertainty, and representing synoptic progressions (Eyring et al., 2016; Hamed et al., 2022c; Kamal et al., 2021; Su et al., 2021a). This emphasizes reassessing the changes in temperature extremes for new scenarios using CMIP6 to update the knowledge of climate change implications on temperature extremes and rationalize the adaptation strategies planned based on CMIP5 projections.
Uncertainties associated with GCMs hinder the reliable projection of extreme temperature (Pour et al., 2018; Shiru et al., 2020; Shiru et al., 2019). Generally, projection uncertainties are minimized using GCMs ensemble, selected according to their ability to replicate the observed climate (Ahmed et al., 2020; Hamed et al., 2022a; Hassan et al., 2020; Lutz et al., 2016; Salman et al., 2018). Different methods, including statistical metrics, multi-objective linear programming and machine learning methods, have been used to assess GCMs' past performance and create an ensemble using best performing GCMs (Hamed et al., 2022b; Nashwan and Shahid, 2020; Noor et al., 2019; Shiru et al., 2020; Srivastava et al., 2020).
The extreme indices are commonly used to assess the changes in climatic extremes. The Expert Team on Climate Change Detection and Indices (ETCCDI) proposed 27 extreme climate indices, including 16 temperature extremes (Ying et al., 2020). These extremes are based on days with temperatures above or below specific physically-based thresholds or quantiles (Zhang et al., 2011; Khan et al., 2019b). Therefore, they can provide different characteristics of both hot and cold extremes related to social impacts. This has made them widely used for assessing temperature extremes across the globe.
Amu river, the largest transboundary river and the primary source of CA's freshwater, flows within five riparian's countries (Dilshod et al., 2021). The livelihoods and economy of approximately 80 million people from these countries depend on water supplies by ADR (Hu et al., 2021; Saidmamatov et al., 2020). About 6 million hectares (ha) of lands in Uzbekistan in Turkmenistan, Afghanistan, Tajikistan and Kirgizstan are irrigated by the Amu river (Ahmad and Wasiq, 2004). The basin's hydrology has undergone tremendous change in the recent past due to human intervention in river flow (Khaydar et al., 2021). The increased temperature in the basin has worsened this situation and imposed further stresses on water resources and public health (Hoell et al., 2020; Hu et al., 2021; Su et al., 2021b). Xu et al. (2021) projected that increased temperature in the Amu river basin (ARB) would decrease river flow by 78.8–98.7% during 2021–2050. The faster glacier melting under a warmer climate would shift the peak flow from summer to spring. Su et al. (2021b) projected to decline in the glacier extent in ARB by 71.9% for RCP8.5 over 2021–2100. This decline of the glacier would reduce future runoff and water availability especially for irrigation in summer in crop season the downstream of the river. Increased temperature extremes would worsened the situation. Studies related to changes in temperature extremes in CA is very limited (Table 1). Focus of none of the previous studies was ARB.
The present study aimed to assess the change in temperature extremes in ARB. Daily Tmax and Tmin simulations of four CMIP6 GCMs for four SSPs were used to project the changes in twelve temperature extremes over ARB. Maps were prepared to show the geographical distribution for the changes for different scenarios and future horizons, which could be used for growing public awareness and devising adaptations.
Table 1
Existing temperature extreme studies in CA's regions
Author | Major Findings |
Feng et al. (2018) | Rise in both Tmax and Tmin, but it was faster for Tmax. Warm indices showed a substantial increase from Turkmenistan to China. |
Hu et al. (2016) | Increasing trend in Tmin and Tmax at annual and seasonal scales, except in winter Tmax. Increase in warm nights and days and decrease in the cold nights and days |
Peng et al. (2020) | Increase in annual maximum of Tmax and minimum of Tmin for all RCPs |
Zhang et al. (2019a) | Increase in Tmax and Tmin and extremes related to Tmin. |
Zhu et al. (2020) | Increase in Tmax and Tmin and days having Tmin > 25°C, while decrease in days having Tmin < 0°C for all RCPs. |
Liu et al. (2020) | Increase in mean temperature in CA by 1.48°C for 1.5°C warming scenario. Increase in hot days and growing season length. |
RCP= Representative concentration pathways. |