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The bias correction method (BCM) is useful in reducing the statistically downscaled biases of global climate models’ (GCM) outputs and preserving statistical moments of the hydrological series. However, BCM is less efficient under changed future conditions due to the stationary assumption and performs poorly in removing bias at extremes, thereby producing unreliable bias-corrected data. Thus, the existing BCM with normal distribution is improved by incorporating skewed distributions into the model with linear covariate (BCM-QMskewed). In this study, BCM-QMskewed is developed to reduce biases in the extreme temperature data of peninsular Malaysia. The input is the MIROC5 model output gridded data and observations sourced by the Malaysian Department of Irrigation and Drainage (1976–2005). BCM-QMskewed with lognormal (LGNORM) and Gumbel (GUM) has shown considerable skill in correcting biases, capturing extreme and nonstationarity of current and future extreme temperatures data series corresponding to the representative concentration pathways (RCPs) for 2006–2100 based on model diagnostics and precision analysis. Higher projection of extreme temperatures is more pronounced under RCP8.5 than RCP4.5 with precise estimates ranging from 33 to 42 °C and 30 to 32 °C, respectively. Finally, the projection of extreme temperatures is used to calculate cardiovascular disease (CVD) mortality rate which coincides with high extreme temperatures ranging between 0.002 and 0.014.

The bias correction method (BCM) is useful in reducing the statistically downscaled biases of global climate models’ (GCM) outputs and preserving statistical moments of the hydrological series. However, BCM is less efficient under changed future conditions due to the stationary assumption and performs poorly in removing bias at extremes, thereby producing unreliable bias-corrected data. Thus, the existing BCM with normal distribution is improved by incorporating skewed distributions into the model with linear covariate (BCM-QMskewed). In this study, BCM-QMskewed is developed to reduce biases in the extreme temperature data of peninsular Malaysia. The input is the MIROC5 model output gridded data and observations sourced by the Malaysian Department of Irrigation and Drainage (1976–2005). BCM-QMskewed with lognormal (LGNORM) and Gumbel (GUM) has shown considerable skill in correcting biases, capturing extreme and nonstationarity of current and future extreme temperatures data series corresponding to the representative concentration pathways (RCPs) for 2006–2100 based on model diagnostics and precision analysis. Higher projection of extreme temperatures is more pronounced under RCP8.5 than RCP4.5 with precise estimates ranging from 33 to 42 °C and 30 to 32 °C, respectively. Finally, the projection of extreme temperatures is used to calculate cardiovascular disease (CVD) mortality rate which coincides with high extreme temperatures ranging between 0.002 and 0.014.

This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experi-ment–Southeast Asia (SEACLID/CORDEX–SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E–146.5° E, 14.8° S–27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976–2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December–January–Febru-ary (DJF) and June–July–August (JJA) over future periods of the early (2011–2040), mid (2041–2070) and late twenty-first century (2071–2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10–20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10–30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections.

Climate and weather have significant influences on human health. Climate change together with natural phenomena and human activities have the tendency to impact the environment and debilitates human well-being in various ways. Extreme temperature, which is often associated with climate change, has some negative implications on human health, potentially resulting in diseases such as cardiovascular disease. The aim of this study is to analyze the impacts of temperature projection on the mortality rates of cardiovascular disease based on daily average temperature projection using bias correction method. Downscaling approach can be used to downscale the global climate model outputs that are available at coarse resolution. However, to study the impact of climate change need meteorological data or information at finer resolution. In this study, statistical downscaling is used to downscale the GCM’s temperature to local scale’s temperature. The observed daily mean temperature data in 5 years (1970- 1974), the historical GCM data (1976-1980) and the projection data (2076-2080) under RCP4.5 and RCP8.5 were used. However, the global climate model outputs produce biases when applied due to its coarse estimate, hence lead to erroneous results. Thus, bias correction method was used to correct the biases in global climate model outputs to project the future of extreme temperature, and eventually calculate the mortality rate of the cardiovascular diseases. The mortality rate of the cardiovascular disease is calculated by using attributable daily deaths formula. Results revealed that quantile mapping technique is able to capture the variability in global climate model as well as quantifying the biases. The projected trend of heat-related deaths under RCP4.5 is lower than the deaths under RCP8.5. SYAFRINA BINTI ABDUL HALIM// AINA IZZATI BINTI MOHD ESA/ Jing Xiang Chung,Mohd Syazwan Faisal Mohd

Climate and weather have significant influences on human health. Climate change together with natural phenomena and human activities have the tendency to impact the environment and debilitates human well-being in various ways. Extreme temperature, which is often associated with climate change, has some negative implications on human health, potentially resulting in diseases such as cardiovascular disease. The aim of this study is to analyze the impacts of temperature projection on the mortality rates of cardiovascular disease based on daily average temperature projection using bias correction method. Downscaling approach can be used to downscale the global climate model outputs that are available at coarse resolution. However, to study the impact of climate change need meteorological data or information at finer resolution. In this study, statistical downscaling is used to downscale the GCM’s temperature to local scale’s temperature. The observed daily mean temperature data in 5 years (1970- 1974), the historical GCM data (1976-1980) and the projection data (2076-2080) under RCP4.5 and RCP8.5 were used. However, the global climate model outputs produce biases when applied due to its coarse estimate, hence lead to erroneous results. Thus, bias correction method was used to correct the biases in global climate model outputs to project the future of extreme temperature, and eventually calculate the mortality rate of the cardiovascular diseases. The mortality rate of the cardiovascular disease is calculated by using attributable daily deaths formula. Results revealed that quantile mapping technique is able to capture the variability in global climate model as well as quantifying the biases. The projected trend of heat-related deaths under RCP4.5 is lower than the deaths under RCP8.5. SYAFRINA BINTI ABDUL HALIM// AINA IZZATI BINTI MOHD ESA/ Jing Xiang Chung,Mohd Syazwan Faisal Mohd

THIS ARTICLE PROVIDES detailed information on projected changes in annual precipitation extremes over Southeast Asia under global warming of 2°C based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment Southeast Asia (SEACLID/CORDEX-SEA). Four indices of extreme precipitation are considered: annual total precipitation (PRCPTOT), consecutive dry days (CDD), frequency of rainfall exceeding 50 mm/day (R50mm), and intensity of extreme precipitation (RX1day). The ensemble mean of 10 simulations showed reasonable performance in simulating observed characteristics of extreme precipitation during the historical period of 1986–2005. The year 2041 was taken as the year when global mean temperature reaches 2°C above pre-industrial levels under unmitigated climate change scenario based on Karmalkar and Bradley (2017). Results indicate that the most prominent changes during the period of 2031–2051 were largely significant. Robust increases in CDD imply impending drier conditions over Indonesia, while increases in RX1day suggest more intense rainfall events over most of Indochina under 2°C global warming scenario. Furthermore, northern Myanmar is projected to experience increases in CDD, R50mm and RX1day, suggesting that the area may face more serious repercussions than other areas in Southeast Asia.

THIS ARTICLE PROVIDES detailed information on projected changes in annual precipitation extremes over Southeast Asia under global warming of 2°C based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment Southeast Asia (SEACLID/CORDEX-SEA). Four indices of extreme precipitation are considered: annual total precipitation (PRCPTOT), consecutive dry days (CDD), frequency of rainfall exceeding 50 mm/day (R50mm), and intensity of extreme precipitation (RX1day). The ensemble mean of 10 simulations showed reasonable performance in simulating observed characteristics of extreme precipitation during the historical period of 1986–2005. The year 2041 was taken as the year when global mean temperature reaches 2°C above pre-industrial levels under unmitigated climate change scenario based on Karmalkar and Bradley (2017). Results indicate that the most prominent changes during the period of 2031–2051 were largely significant. Robust increases in CDD imply impending drier conditions over Indonesia, while increases in RX1day suggest more intense rainfall events over most of Indochina under 2°C global warming scenario. Furthermore, northern Myanmar is projected to experience increases in CDD, R50mm and RX1day, suggesting that the area may face more serious repercussions than other areas in Southeast Asia.

Haze is a common phenomenon afflicting Southeast Asia (SEA), including Malaysia, and has occurred almost every year within the last few decades. Haze is associated with high level of air pollutants; it reduces visibility and affects human health in the affected SEA countries. This manuscript aims to review the potential origin, chemical compositions, impacts and mitigation strategies of haze in Malaysia. “Slash and burn” agricultural activities, deforestation and oil palm plantations on peat areas, particularly in Sumatra and Kalimantan, Indonesia were identified as the contributing factors to high intensity combustions that results in transboundary haze in Malaysia. During the southwest monsoon (June to September), the equatorial SEA region experiences a dry season and thus an elevated number of fire events. The prevailing southerly and south-westerly winds allow the cross-boundary transportation of pollutants from the burning areas in Sumatra and Kalimantan in Indonesia, to Peninsular Malaysia and Malaysian Borneo, respectively. The dry periods caused by the El Niño - Southern Oscillation (ENSO) prolong the duration of poor air quality. The size range of particulate matter (PM) in haze samples indicates that haze is dominated by fine particles. Secondary inorganic aerosols (SIA, such as SO42- and NH4+) and organic substances (such as levoglucosan, LG) were the main composition of PM during haze episodes. Local vehicular emissions and industrial activities also contribute to the amount of pollutants and can introduce toxic material such as polyaromatic hydrocarbons (PAHs). Haze episodes have contributed to increasing hospital visits for treatments related to chronic obstructive pulmonary diseases, upper respiratory infections, asthma and rhinitis. Respiratory mortality increased 19% due to haze episodes. Children and senior citizens are more likely to suffer the health impacts of haze. The inpatient cost alone from haze episodes was estimated at around USD 91,000 p

Haze is a common phenomenon afflicting Southeast Asia (SEA), including Malaysia, and has occurred almost every year within the last few decades. Haze is associated with high level of air pollutants; it reduces visibility and affects human health in the affected SEA countries. This manuscript aims to review the potential origin, chemical compositions, impacts and mitigation strategies of haze in Malaysia. “Slash and burn” agricultural activities, deforestation and oil palm plantations on peat areas, particularly in Sumatra and Kalimantan, Indonesia were identified as the contributing factors to high intensity combustions that results in transboundary haze in Malaysia. During the southwest monsoon (June to September), the equatorial SEA region experiences a dry season and thus an elevated number of fire events. The prevailing southerly and south-westerly winds allow the cross-boundary transportation of pollutants from the burning areas in Sumatra and Kalimantan in Indonesia, to Peninsular Malaysia and Malaysian Borneo, respectively. The dry periods caused by the El Niño - Southern Oscillation (ENSO) prolong the duration of poor air quality. The size range of particulate matter (PM) in haze samples indicates that haze is dominated by fine particles. Secondary inorganic aerosols (SIA, such as SO42- and NH4+) and organic substances (such as levoglucosan, LG) were the main composition of PM during haze episodes. Local vehicular emissions and industrial activities also contribute to the amount of pollutants and can introduce toxic material such as polyaromatic hydrocarbons (PAHs). Haze episodes have contributed to increasing hospital visits for treatments related to chronic obstructive pulmonary diseases, upper respiratory infections, asthma and rhinitis. Respiratory mortality increased 19% due to haze episodes. Children and senior citizens are more likely to suffer the health impacts of haze. The inpatient cost alone from haze episodes was estimated at around USD 91,000 p