Summary
Global warming is causing Thailand’s sea levels to rise. This study predicts annual increases of 2mm to over 5mm by 2100. Reflecting sunlight through solar radiation modification (SRM) can slow this rise, with aerosol injection being particularly effective at cooling oceans. However, seasonal monsoons will still cause temporary water peaks, worsening coastal flooding. While these technologies help, uncertainty remains due to sinking land and limited data in areas like Bangkok.
Abstract
Sea level rise (SLR) is expected to increase globally with a warming climate. Solar radiation modification (SRM) has the potential to slow SLR by temporarily reducing anthropogenic warming. This study compares observed sea level data along Thailand’s coasts with outputs from 3 global climate models (GCMs) in the Geoengineering Model Intercomparison Project Phase 6: CNRM-ESM2-1, IPSL-CM6A-LR, and UKESM1-0-LL. Observations include tide gauge data from the Permanent Service for Mean Sea Level, vertical land motion from the Global Navigation Satellite Systems, and coastal altimetry from the X-TRACK dataset. SLR trends from observations and models generally agree, including near Bangkok, but uncertainties arise in some areas due to challenges in land motion measurements. GCM outputs for 2 Shared Socioeconomic Pathways (SSP2-4.5, SSP5-8.5) and 2 SRM simulations (G6Solar, G6Sulfur) project end-of-century SLR rates ranging from 2 mm/yr (SSP2-4.5) to over 5 mm/yr (SSP5-8.5). SRM reduces SLR, with G6Sulfur showing slightly greater suppression than G6Solar because of stronger tropical ocean cooling and less ice sheet melting. Seasonal sea-level variabilities are well represented in GCMs and projected to remain unchanged in the future, but will amplify coastal impacts during seasonal highs. The Upper Gulf of Thailand experiences an additional 0.2 m seasonal SLR in December–February because of stronger northeast monsoon, while stronger southwest monsoon causes an additional 0.1 m seasonal SLR along the Andaman coast in June–August. Uncertainties in this study arise from nonlinear land subsidence due to recent groundwater extraction controls, limited observational data coverage, and internal model variability.