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The spatial extent of the Sahara (the largest nonpolar desert) has significant impacts on the livelihood of people residing in its surrounding areas. Despite the fact that climate change would foreseeably impact the location and size of the desert, its future responses (i.e., advance or retreat) are rarely explored in previous studies. Here, through the development of an ensemble Bayesian discriminant analysis approach, we use 10 of the latest high-resolution GCM (global climate model) simulations to document robust annual and seasonal responses of the Sahara Desert to twenty-first-century climate change, with the consideration of modeling uncertainties. We find northward shifts of the Sahara/Sahel and eastern expansion of the nondesert zone under both SSP2–4.5 and SSP5–8.5 scenarios, the former more pronounced in the wet season and the latter in the dry season. Countries located near the Mediterranean may thus experience higher risks of drought, while the projected retreat of the Saharan southern boundary will be beneficial to the local water availability of proximal countries. Significance Statement Given that sub-Saharan Africa is one of the most vulnerable regions to climate change, the Sahara’s expansion would bring unexpected health risks to billions of people. It is thus vital to understand its robust response to global warming. However, previous studies are merely focused on using a simple precipitation threshold as the definition criterion to estimate the varying size of the Sahara Desert. In addition, significant uncertainty in precipitation projections also limits relevant investigations of the Sahara’s future responses. Here, by developing an ensemble Bayesian discriminant analysis approach, we could provide an objective basis for desert identification under large intermodel uncertainty. Further, we find significant northward shifts of both the Sahara and the Sahel, which may induce higher risks of drought over the northwest of North Africa.

The choices of physical schemes coupled in the Regional Climate Model version 4 (RegCM4), the input general circulation model (GCM) results, and the emission scenarios may cause considerable uncertainties in future temperature projections. Therefore, the ensemble approach, which can be used to reflect these uncertainties, is highly desired. In this study, the probabilistic projections for future temperature are generated at 88 Canadian climate stations based on the developed RegCM4 ensemble and obtained Bayesian model averaging (BMA) weights. The BMA weights indicate that the RegCM4 coupled with the holtslag PBL scheme driven by the HadGEM can provide relatively reliable temperature projections at most climate stations. It is also suggested that the BMA approach is effective in simulating temperature over middle and eastern Canada through taking advantage of each ensemble member. However, the effectiveness of the BMA method is limited when all the models in the ensemble cannot simulate the temperature robustly. The projected results demonstrate that the temperature will increase continuously in the future, while the temperature increase under RCP8.5 will be significantly larger than that under RCP4.5.

As an emerging power generation technology, small module reactors (SMRs) have the potential for development with its contribution to reducing greenhouse gas (GHG) emissions. In this study, an SMR-induced environmental input-output model (SEIOM) is proposed to simulate the environmental consequences of SMRs development and provide suggested schemes for SMRs deployment. A case study of Saskatchewan, Canada is conducted to demonstrate the proposed model. Specifically, key industries with high reduction potentials are first identified in the study; then, the power supply for three energy-intensive industries is assumed to be replaced by power generated from SMRs at various penetration degrees. The corresponding changes in direct and indirect GHG emissions and the interrelationships among multiple economic sectors associated with GHG flows are analyzed. The results indicate that there are close interdependences between various sectors and a small group of sectors could play a big role in GHG emission mitigation. In Saskatchewan, “Electricity power generation, transmission and distribution”, “Oil and gas extraction”, “Potash mining” and “Petroleum refineries” are key sectors for realizing GHG emission reduction targets. Meanwhile, it is estimated that replacing the power supply for “Oil and gas extraction” sector with SMRs would contribute the most to the reduction in GHG emission, which is much more than those for “Potash mining” and “Petroleum refineries” sectors. This study is expected to provide a basis for supporting the initiative and application of SMRs.

In this study, an inexact two-stage fractional energy systems planning model (ITF-ESP) is developed through an integration of interval-parameter programming (IPP), two-stage stochastic programming (TSP), fractional programming (FP), and mixed integer linear programming (MILP) methods. Since the proposed model could not be solved directly, it is converted into two interactive sub-models. In order to obtain more precise interval solutions, the sub-model corresponding to f − is solved first. The developed ITF-ESP model is considered as an efficient approach to address dual-objective optimization problems involving capacity expansion issues and policy scenario analysis, as well as uncertainties described as intervals and probability distributions. Effectiveness of the ITF-ESP model is demonstrated through a case study within a Chinese context. The results indicate that although the non-renewable technologies would still play a major role in electricity generation, the renewable technologies are becoming increasingly significant. Comparisons of the ITF-ESP model and the least-cost model are conducted to illustrate the advantages of the proposed ITF-ESP model in reflecting trade-offs between economic development and environmental protection. In addition, compared with the chance-constrained two-stage fractional optimization model (CTFO), interval solutions obtained from the ITF-ESP model can provide multiple alternative management plans in terms of electricity generation and capacity expansion.

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.