4:00 PM - 4:15 PM
[AAS02-15] Future Changes in Typhoon Characteristics Based on SST Ensemble Simulations by AGCM
Keywords:Typhoon, Atmospheric Global Climate Model, Slab-Ocean Model, Sea Surface Temperature (SST), Variability
The IPCC AR6 states that the proportion of intense typhoons has increased over the last four decades and that this cannot be explained entirely by natural variability. It is said with high confidence that the average and maximum precipitation associated with typhoons and severe convective storms will increase with future warming. The climate simulations using the Meteorological Research Institute Atmospheric General Circulation Model (MRI-AGCM) are widely employed for evaluating extreme weather events associated with climate change in Japan. The frequency and intensity of typhoons are influenced by regional meteorological conditions and natural variability within the Earth system. Scenario-based warming experiments such as d4PDF enable investigations into the impact of meteorological and climatic fields on typhoon characteristics. However, probabilistic assessments of future typhoon characteristics that account for natural variability, such as SST fluctuations, remain insufficient.
This study probabilistically evaluates the variability and future changes in typhoon intensity and frequency by analyzing the relationship between sea surface temperature (SST) spatial patterns and typhoon characteristics. Previously, we developed the slab-ocean coupled atmospheric global climate model (MRI-AGCM), which improved the model performance of typhoon intensity by the short-term atmosphere-ocean interaction. We conducted large ensemble climate simulations based on the coupled model under several sea surface boundary conditions. Representative monthly mean SST patterns over the western North Pacific were extracted, and ensemble experiments were performed under historical and future +4K conditions (SSP585 scenario) to assess projected changes quantitatively.
To perform climate simulations using GCM, sea surface boundary conditions must be prescribed. We applied empirical orthogonal function (EOF) analysis and cluster analysis to the mean SSTs in September, selecting representative SST patterns. Climate simulations were conducted continuously for 150 months under fixed climate conditions corresponding to the selected September SST patterns. This approach enabled ensemble experiments focused on typhoons while maintaining relatively low computational costs.
This study targeted the 70 years from 1950 to 2019, covering the region 0°–40°N, 100°–180°E. Since typhoon characteristics are expected to depend not only on global warming but also on natural variability regarding SST, we removed long-term trends from the SST data at each grid point and normalized them by subtracting the temporal mean and dividing by the standard deviation.
As a result of the SST ensemble climate experiments, the average number of typhoons decreases with climate change, and the intensity of typhoons occurring at the same probability level increases. Furthermore, approximately 60% of the variance in the maximum wind speed can be explained by the difference in SST patterns and the increase in mean SST. It was also demonstrated that the influence of warming becomes more pronounced for less frequent, extreme typhoons. Specifically, the number of projected typhoons in September decreases from 3.18 to 2.48 in the 60km-model simulation and from 3.81 to 2.78 in the 20km-model. Additionally, typhoons with a 1-in-100 probability in historical simulations potentially occur 4 or 5 times in 100 under +4K conditions.
This study probabilistically evaluates the variability and future changes in typhoon intensity and frequency by analyzing the relationship between sea surface temperature (SST) spatial patterns and typhoon characteristics. Previously, we developed the slab-ocean coupled atmospheric global climate model (MRI-AGCM), which improved the model performance of typhoon intensity by the short-term atmosphere-ocean interaction. We conducted large ensemble climate simulations based on the coupled model under several sea surface boundary conditions. Representative monthly mean SST patterns over the western North Pacific were extracted, and ensemble experiments were performed under historical and future +4K conditions (SSP585 scenario) to assess projected changes quantitatively.
To perform climate simulations using GCM, sea surface boundary conditions must be prescribed. We applied empirical orthogonal function (EOF) analysis and cluster analysis to the mean SSTs in September, selecting representative SST patterns. Climate simulations were conducted continuously for 150 months under fixed climate conditions corresponding to the selected September SST patterns. This approach enabled ensemble experiments focused on typhoons while maintaining relatively low computational costs.
This study targeted the 70 years from 1950 to 2019, covering the region 0°–40°N, 100°–180°E. Since typhoon characteristics are expected to depend not only on global warming but also on natural variability regarding SST, we removed long-term trends from the SST data at each grid point and normalized them by subtracting the temporal mean and dividing by the standard deviation.
As a result of the SST ensemble climate experiments, the average number of typhoons decreases with climate change, and the intensity of typhoons occurring at the same probability level increases. Furthermore, approximately 60% of the variance in the maximum wind speed can be explained by the difference in SST patterns and the increase in mean SST. It was also demonstrated that the influence of warming becomes more pronounced for less frequent, extreme typhoons. Specifically, the number of projected typhoons in September decreases from 3.18 to 2.48 in the 60km-model simulation and from 3.81 to 2.78 in the 20km-model. Additionally, typhoons with a 1-in-100 probability in historical simulations potentially occur 4 or 5 times in 100 under +4K conditions.