Extreme heat is turning into a primary concern for cities worldwide, given the adverse effects that it has on health, the spread of vector-borne disease and reduced productivity among others. The impacts of heat are worse for low-income residents, the elderly, and individuals with chronic diseases and disabilities, thus promoting inequality. In this sense, cities need to address this issue by adopting measures to mitigate and adapt to climate change, in order to promote livable, inclusive and sustainable environments. One of the aims of the Arbothai project is to provide local authorities in Thailand and the Bangkok area with useful future climate information and projections under relevant climate change scenarios. In the case of the Bangkok metropolitan area, we have gathered temperature, humidity and wind speed at a very high spatial resolution (200 meters) using the UrbClim climate model. Details about the model can be found in De Ridder et al. (2015) and Garcia-Diez et al. (2016). The model has already been validated over many cities globally and has been established as a qualified tool for modeling present and future climatic conditions.
Our data for Bangkok contains present-day simulations for the 2001-2020 period using the ERA-5 data as initial conditions, as well as future simulations based on the projections of the latest generation of climate models (CMIP6). More details regarding the downscaling method and future climate simulations can be found in Olsson et al. (2009), Willems and Vrac, (2011) and Souverijns et al. (2022). The future projections are for a mid-century period between 2030-2050 and an end-century period between 2070-2090 for the SSP370 climate change scenario (a high-emissions scenario in which global warming reaches 4°C by 2100). Available output variables for both the present and future periods are surface air and land temperature, humidity and wind speed. So far we have generated monthly means, including differences between the mid century and present-day air temperature for every month of the year, as well as for the relevant season for dengue fever spread in Bangkok, i.e. the high season between June and September (JJAS), and the season preceding it between March and May (MAM). The MAM season is particularly relevant for enhancing prediction several months in advance of the usual dengue peak.
MAM surface air temperature anomaly (°C) JJAS surface air temperature anomaly (°C) for 2030-2050 relative to 2001-2020 for 2030-2050 relative to 2001-2020
Monthly means of relative humidity over the mid-century period have also been calculated for every month of the year as well as for the relevant seasons.
MAM relative humidity (%) JJAS relative humidity (%) for 2030-2050 for 2030-2050
For the present-day simulations the model outputs are for specific humidity and a next step is to convert relative humidity into specific humidity in order to calculate the difference in monthly mean humidity between the future periods and the present period.
Analysis is also currently being performed to obtain the same results for the end-century (2070-2090) period relative to the present-day period.
It is additionally planned to calculate monthly time series of surface temperature and humidity over every grid point of the Bangkok metropolitan area domain for the present and two future time periods.
Monthly surface air temperature (°C) over Monthly surface air temperature (°C) over central Bangkok (2001-2020) central Bangkok (2030-2050)
Specific humidity (g/kg) over Relative humidity (%) over central Bangkok (2001-2020) central Bangkok (2030-2050)
Comparison monthly mean surface air temperature (°C) over Bangkok mid-century (2030-2050) vs present (2001-2020)
These will also be displayed as comparisons between the probability density function distributions for the present-day and the two future periods as in the example for surface air temperature below.
Mean monthly surface air temperature (°C) over central Bangkok (2001-2020 vs. 2030-2050)
We will further investigate future changes in the mean annual cycle of temperature and humidity in comparison to the present period. Of interest is also if extreme precipitation events and prolonged droughts are correlated with higher or lower dengue incidence during the present period, which would provide insight into the future dengue dynamics in relation to hydrological extremes.
Finally, it is planned to re-plot all the spatial maps including the administrative borders of the Bangkok sub-districts, similar to the example below.
