The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.

The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.

Selection of appropriate empirical reference evapotranspiration (ET_o) estimation models is very important for the management of agriculture, water resources, and environment. Statistical metrics generally used for performance assessment of empirical ET_o models, on a station level, often give contradictory results, which make the ranking of methods a challenging task. Besides, the ranking of ET_o estimation methods for a given study area based on the rank at different stations is also a difficult task. Compromise programming and group decision-making methods have been proposed in this study for the ranking of 31 empirical ET_o models for Peninsular Malaysia based on four standard statistical metrics. The result revealed the Penman-Monteith as the most suitable method of estimation of ET_o, followed by radiation-based Priestley and Taylor and the mass transfer-based Dalton and Meyer methods. Among the temperature-based methods, Ivanov was found the best. The methodology suggested in this study can be adopted in any other region for an easy but robust evaluation of empirical ET_o models.

Purpose: There is a growing concern in recent years regarding climate change risks to real estate in the developed and developing countries. It is anticipated that the property sector could be affected by variable climate and related extremes as well as by the strategies adopted to combat greenhouse gas (GHG) emissions. This paper aims to analyse the current knowledge regarding future climate changes to understand their possible impacts on the real estate sector of Malaysia with an aim to help stakeholders to adopt necessary responses to reduce negative impacts.Design/methodology/approach: Available literature is reviewed and data related to climatic influences on buildings and structures are analysed to understand the climate change impacts on real estate in Malaysia.Findings: The study reveals that temperature in the Peninsular Malaysia will increase by 1.1 to 3.6°C, rainfall will be more variable and river discharge in some river basins will increase up to 43 per cent during the northeast monsoon season by the end of this century. These changes in turn will pose risks of property damage and increase property lifecycle costs. Furthermore, property prices and the overall growth of the property sector may be affected by the government policy of GHG emission reduction by up to 45 per cent by the year 2030. This study concludes that the property sector of Malaysia will be most affected by the implementation of GHG emission reduction policy in the short term and due to the physical risk posed by variable climate and related extremes in the long term.Originality/value: The study in general will assist in guiding the operational responses of various authorities, especially in terms of those interventions aimed at climate change risk reduction in the property sector of Malaysia.