Extreme heat and precipitation events are turning into primary concerns worldwide, given the adverse effects that they have on health, the spread of vector-borne disease and reduced productivity among others. The impacts of extremes are worse for low-income residents, the elderly, and individuals with chronic diseases and disabilities, thus promoting inequality. In this sense, countries 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 Thailand we have gathered temperature and precipitation at coarse resolution from the latest generation of climate model simulations – CMIP6. These will later be compared to the high-resolution CORDEX simulations that are based on the CMIP6 general runs. However, these high-resolution simulations are still being run and analyzed by the international modeling centers, and the data sets have not yet been archived on the relevant climate data platforms (see here for updates on these CORDEX simulations: https://wcrp-cordex.github.io/simulation status/CORDEX_CMIP6_status.html). 

Our analysis is based on the CMIP6 historical simulations for the period 1995-2014 and two future periods corresponding to the middle of the century (2040-2059) and the end of the century (2080_2099) for two future climate scenarios – “the middle of the road” SSP245 scenario, as well as the “fossil-fueled development” or “business-as-usual” SSP585 scenario. These two climate change pathways project a global temperature increase of 2.4°C and 4.8°C until the end of the 21^st century, respectively.  

So far we have generated mean annual differences between the mid-century/end-century and the historical surface air temperature and precipitation. We have calculated the same anomalies for the relevant season for dengue fever spread in Thailand, 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. 

Annual surface air temperature anomaly (°C) for 2040-2059 relative to 1995-2014 (SSP245)

Annual surface air temperature anomaly (°C) for 2080-2099 relative to 1995-2014 (SSP585)

Annual precipitation anomaly (mm) for 2040-2059 relative to 1995-2014 (SSP245)

Annual precipitation anomaly (mm) for 2080-2099 relative to 1995-2014 (SSP585)

We have additionally generated the anomaly time series of surface temperature and precipitation over Thailand, along with the spread among different models from the CMIP6 generation.

Anomaly correlations between surface temperature and precipitation have also been calculated.

We have investigated model spread of projected temperature and precipitation in Thailand for different future time periods until 2100.

Average mean surface temperature under the SSP245 scenario

Average mean surface temperature under the SSP585 scenario

Average mean surface precipitation under the SSP245 scenario

Average mean surface precipitation under the SSP585 scenario

Finally, we have considered the future projections of surface temperature and precipitation by the different socio-economic pathways based on the CMIP6 conventions.

Analysis of the impacts of ENSO on temperature and precipitation in Thailand has also been performed for the CMIP6 models under the extreme “fossil-fueled development” scenario.