Global warming causes a temperature rise and alteration of other meteorological variables that directly or indirectly affect human comfort. The wet bulb globe temperature (WBGT) incorporates the effects of multiple meteorological variables to provide a reliable measure of human thermal stress. This study assessed the characteristics and changes in hourly, daily, monthly, seasonal and annual outdoor WBGT over peninsular Malaysia (PM) for the period 1959-2021 using the Liljegren method. The WBGT values were classified into five categories to assess the human thermal stress levels. The mean daily WBGT in PM varies from 21.5°C in the central south elevated region to 30.5°C in the western coastal region. It always reaches a heat-related illness risk level (31.20 °C) in the afternoon during monsoon and extreme stress conditions during inter-monsoonal periods. The trend analysis revealed an increase in WBGT for all the time scales. The higher increase in the mean and maximum WBGT was estimated in the coastal and south regions, nearly by 0.10 to 0.25 °C/decade. The increase in mean nighttime WBGT was 0.24 °C/decade, while in mean daytime WBGT was 0.11 °C/decade. The increase in WBGT caused a gradual expansion of areas experiencing daily WBGT exceeding a highrisk level for 5 hours (11 am to 3 pm). The information and maps generated in this study can be used for mitigation planning of heat-related stress risk in PM, where temperature extremes have grown rapidly in recent years.
A study has been conducted for projection of monthly rainfall in Baghdad of Iraq using a General Circulation Models (GCM) of Coupled Model Intercomparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCP) scenarios namely RCP2.6, RCP4.5, RCP6.0 and RCP8.5. For this purpose, monthly gridded precipitation datasets produced by the centre for climatic research, University of Delaware (UDel) and GCM BCC-CSM1-1 simulated precipitation data at 46 grid points surrounding Baghdad were used. The statistical downscaling models were developed using Support Vector Machine (SVM) and Random Forest (RF). The performance of downscaling model assessed using different statistical measures showed that SVM could simulate historical rainfall in the region very well. Projection of rainfall using SVM revealed that rainfall at Baghdad will change in the range of 3.5% to -6.2% in the end of this century.
Reliable projection of climate is essential for climate change impact assessment and mitigation planning. General Circulation Models (GCMs) simulations are generally downscaled into much finer spatial resolution for climate change impact studies at local and regional scales. The objective of the present study is to use a two-stage bias correction approach for downscale and project future changes of daily average temperature. The approach was applied for downscaling and projection of daily average temperature of Senai meteorological station located in Johor Bahru, Malaysia using a GCM of Coupled Model Intercomparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCP) scenarios. The two-stage bias correction method was based on correction in mean factor and variability inflation factor. The model performances were assessed using different statistical measures including mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), index of agreement (MD), Nash–Sutcliffe model efficiency (NSE) and coefficient of determination (R2). Results showed that the downscaling method could simulate historical daily average temperature at the station very well. The GCM projected an increase in daily average temperature by 1.4ºC, 2.2ºC, 2.5ºC, and 3.4ºC under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively in the end of this century. It is expected that the finding of the study would help in climate change impact assessment and adopting necessary adaptation measures.
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