Heatwaves in Peninsular Malaysia: a spatiotemporal analysis

Abstract

One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950-2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km²/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.

Keywords: Hot extremes; LandScan population; Peninsular Malaysia; Reanalysis data; Tropical region.

Figure 1

Elevation and geographical location of PM.

Figure 2

Variations in monthly Tmax and Tmin in PM for the period 1948 to 2016 based on ERA5 datasets with the 95th percentile confidence interval.

Figure 3

Spatial distribution of (a) Tmax (b) Tmin across PM based on ERA5 datasets for the period 1950 to 2022.

Figure 4

The affected area of heatwaves defined based on consecutive 3 or more days for (a) Tmax more than 95, 97.5 and 99-th percentiles and (b) Tmax more than 95, 97.5 and 99-th percentile and Tmin more than 95, 97.5 and 99-th percentile.

Figure 5

Duration of heatwaves defined for (a) Tmax more than 95, 97.5 and 99-th percentiles and (b) Tmax more than 95, 97.5 and 99-th percentile and Tmin more than 95, 97.5 and 99-th percentile.

Figure 6

Tmax during heatwaves defined for (a) Tmax more than 95, 97.5 and 99-th percentiles and (b) Tmax more than 95, 97.5 and 99-th percentile and Tmin more than 95, 97.5 and 99-th percentile.

Figure 7

Time series of heatwave index for Tmax > 95th percentile (black line); Tmax > 97.5th percentile (blue line); Tmax > 99th percentile (red line).

Figure 8

Spatial distribution of heatwaves when defined based on 95th percentile (left panel), 97.5th percentile (middle panel) and 99th percentile (right panel) threshold for the decades 1950, 1960, 1970 and 1980.

Figure 9

Spatial distribution of heatwaves when defined based on the 95th percentile (left panel), 97.5th percentile (middle panel) and 99th percentile (right panel) threshold for the decades 1990, 2000, 2010 and 2020.

Figure 10

Spatial distribution of affected population to heatwaves when defined based on 95th percentile (left panel), 97.5th percentile (middle panel) and 99th percentile (right panel) threshold for the years 2000, 2010 and 2020.

Figure 11

Time series of the affected population to heatwaves for Tmax > 95th percentile (black line); Tmax > 97.5th percentile (blue line); Tmax > 99th percentile (red line) from 2000 to 2022.