Effects of heatwave features on machine-learning-based heat-related ambulance calls prediction models in Japan.

Researchers agree that there is substantial evidence of an increasing trend in both the frequency and duration of extreme temperature events. Increasing extreme temperature events will place more pressure on public health and emergency medical resources, and societies will need to find effective and reliable solutions to adapt to hotter summers. This study developed an effective method to predict the number of daily heat-related ambulance calls. Both national- and regional-level models were developed to evaluate the performance of machine-learning-based methods on heat-related ambulance call prediction. The national model showed a high prediction accuracy and can be applied over most regions, while the regional model showed extremely high prediction accuracy in each corresponding region and reliable accuracy in special cases. We found that the introduction of heatwave features, including accumulated heat stress, heat acclimatization, and optimal temperature, significantly improved prediction accuracy. The adjusted coefficient of determination (adjusted R2) of the national model improved from 0.9061 to 0.9659 by including these features, and the adjusted R2 of the regional model also improved from 0.9102 to 0.9860. Furthermore, we used five bias-corrected global climate models (GCMs) to forecast the total number of summer heat-related ambulance calls under three different future climate scenarios nationally and regionally. Our analysis demonstrated that, at the end of the 21st century, the total number of heat-related ambulance calls in Japan will reach approximately 250,000 per year (nearly four times the current amount) under SSP-5.85. Our results suggest that disaster management agencies can use this highly accurate model to forecast potential high emergency medical resource burden caused by extreme heat events, allowing them to raise and improve public awareness and prepare countermeasures in advance. The method proposed in Japan in this paper can be applied to other countries that have relevant data and weather information systems.

Linking the long-term variability in global wave energy to swell climate and redefining suitable coasts for energy exploitation.

The sustainability of wave energy linked to the intra- and inter-annual variability in wave climate is crucial in wave resource assessment. In this study, we quantify the dependency of stability of wave energy flux (power) on long-term variability of wind and wave climate to detect a relationship between them. We used six decades of re-analysis wind and simulated wave climate in the entire globe and using two 30-yearly periods, we showed that not only the previously suggested minimum period of 10 years for wave energy assessment appears to be insufficient for detecting the influence of climate variability, but also the selection period for wave energy assessment can lead to an over/underestimation of about 25% for wave power. In addition, we quantified the dependency of rates of change of wave power, wind speed and wave parameters and showed that the change in wave power is mainly a function of change in swell wave climate globally. Finally, we redefined the suitability of global hotspots for wave energy extraction using intra-annual fluctuation, long-term change, and the available wave power for the period of six decades. The results highlight the importance of climate variability in resource assessment, sustainability, and prioritizing the hotspots for future development.

Modeling the combined impact of climate change and sea-level rise on general circulation and residence time in a semi-enclosed sea.

This study provides an assessment of possible changes in the general circulation and residence time in the Persian Gulf under potential future sea-level rise and changes in the wind field due to the climate change. To determine the climate-change-induced impacts, Mike 3 Flow Model FM was used to simulate hydrodynamic and transport processes in the Persian Gulf in both historical (1998-2014) and future periods (2081-2100). Historical simulation was driven by ERA-Interim data. A statistical approach was employed to modify the values and directions of the future wind field obtained from the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively) scenarios derived from CMCC-CM model of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The numerical model was calibrated and validated using measured data. Results indicated that in the historical period, residence time ranged between values of less than a month in the Strait of Hormuz and 10 years in the semi-enclosed area close to the south of Bahrain. The changes in wind field based on RCP 8.5 scenario were found to be the most disadvantageous for the Persian Gulf's capacity to flush dissolved pollutants out. Under this scenario, residence time would be 17% longer than that of historical one. This is mainly because the change in the wind field is large enough to overwhelm general circulation, showing a relationship between the residence time and the residual circulation. Impact of change in the wind field according to RCP 4.5 scenario on the modeled residence time is negligible. The numerical outputs also showed that the sea-level rise would slightly decrease the current velocity, resulting in a negligible increase in residence time. The findings of this study are intended to support establishing climate-adaptation management plans for coastal zones of the studied area in line with sustainable development goals.