Climate change is a major environmental concern globally, particularly in dry areas (Rao et al., 2019; Lyimo & Kangalawe, 2011; Ahmed, Shahid, et al., 2019; Rahman et al., 2018; Aich et al., 2017). With an arid to semi-arid-dominated climate, Afghanistan has experienced a large shift in the climate in recent decades (Omerkhil et al., 2020; Mohanty et al., 2012). The global climate risk index (2017) ranked Afghanistan the 12th vulnerable country to climate risk. The average temperature of Afghanistan showed a rise by 0.13 to 0.29°C/decade in the last fifty years (Savage et al., 2009). The country has also experienced the impacts of climate change, particularly prolonged droughts and severe water stress in recent years (Muhammad et al., 2017). In Afghanistan, 98% of water resource is used for agriculture, and more than 80% of the population get their income from agricultural practices (Sediqi et al., 2019). Therefore, a sharp reduction of available water resources has severely affected the agriculture and agriculture-dependent population and economy. Understanding the possible climate changes is crucial to antedate forthcoming water strain and resulting consequences in agro-economy. Climate change assessment in Afghanistan has several specific challenges (1) lack of sufficiently dense, long term and reliable historical meteorological records due to the four last decades of insecurity and civil war, and (2) geographical characteristics, complex topography, and different climatic regions (Savage et al., 2009). Therefore, these make it challenging to assess climate change implications in the country.
Several tools are available to reproduce historical climate or project future climate changes. Global Climate Models (GCMs) of Coupled Model Intercomparison Project (CMIP) is one of the fundamental tools to reproduce the climate pattern (Shiru et al., 2020; Salman et al., 2018). The GCMs simulate climate considering several scenarios of carbon emissions, and land use and socioeconomic alterations (Dessai & Hulme, 2007). The global carbon releases and socioeconomic changes are related to the national, regional, and global political strategies. Afghanistan has two distinct regions, mountains and deserts, with completely different climates on annual and seasonal scales. The present study attempted to analyze the historical and future climate simulations for different climatic regions in Afghanistan.
A few studies have evaluated Afghanistan's historical and future climate change (Aich et al., 2017; Qutbudin et al., 2019; Sidiqi et al., 2018; Hassanyar & Tsutsumi, 2017) and its impact on water resources and agriculture (Ghulami, 2018). However, those were based on GCMs of CMIP5 and representative concentration pathway (RCP). No studies have been conducted yet to assess historical and future climate over Afghanistan using CMIP6, the latest CMIP dataset phase with around 55 GCMs (Iqbal et al., 2021). Compared to CMIP5 and CMIP3 datasets based on radiative forcing and carbon releases, the CMIP6 employs a set of Shared Socioeconomic Pathways (SSP) to the updated version of coupled global climate models. CMIP6 GCMs consider different mitigation and climate change adaptation based on projected population changes, economic development, ecosystem, and social aspects. It also would reduce the bias in GCMs of the earlier phases of CMIP. Several recent studies have reported the improvement of CMIP6 compared to its earlier version (Hamed, 2021; Kamruzzaman et al., 2021; Song et al., 2021). For instance, (Zamani et al., 2020) reported that CMIP6 GCMs' ensemble outperformed CMIP5 and the relative bias of winter rainfall in all stations of northeastern Iran was much lower than CMIP5. Other studies also revealed improvement of CMIP6 for simulation of summer precipitation over East Asian monsoon and China (Xin et al., 2020). For this reason, it's significant to assess the historical and future climate pattern over Afghanistan using CMIP6.
The use of many GCMs for climate projection is often not preferred due to the large uncertainty associated with each GCMs and human and computational constraints (Shiru, Shahid, Dewan, et al., 2020). Therefore, an ensemble set of GCM with a minimum range of uncertainty is chosen. There are usually two basic ways to select suitable GCM: past-performance where skilled GCMs are identified according to their capacity to simulate the present climate and envelopes where GCMs are chosen according to their agreement in projections (Salman et al., 2018). This study used the past-performance method, as it is most widely used in literature, for the GCM selection.
The GCMs skills are evaluated based on their ability to simulate precipitation and temperature climatology over Afghanistan at annual and seasonal scales. The selected GCMs were used for future climate projections over different climate regions of Afghanistan. The paper is organized as follows: Section 2 describes the study area and datasets. Section 3 outlines the method adopted. Section 4 presents the outcomes, Section 5 discusses the results, and Section 6 provides conclusions.