Small unmanned aerial vehicles (UAVs), also known as drones, have recently become promising tools in various fields. We investigated the feasibility of wind vector profile measurement using an ultrasonic anemometer installed on a 1-m-wide hexarotor UAV. Wind vectors measured by the UAV were compared to observations by a 55-m-high meteorological tower, over a wide range of wind speed conditions up to 11 m s⁻¹, which is a higher wind speed range than those used in previous studies. The wind speeds and directions measured by the UAV and the tower were in good agreement, with a root-mean-square error of 0.6 m s⁻¹ and 12° for wind speed and direction, respectively. The developed method was applied to field meteorological observations near a volcano, and the wind vector profiles, along with temperature and humidity, were measured by the UAV for up to an altitude of 1000 m, which is a higher altitude range than those used in previous studies. The wind vector profile measured by the UAV was compared with Doppler lidar measurements (collected several kilometers away from the UAV measurements) and was found to be qualitatively similar to that captured by the Doppler lidar, and it adequately represented the features of the atmospheric boundary layer. The feasibility of wind profile measurement up to 1000 m by a small rotor-based UAV was clarified over a wide range of wind speed conditions.

This study examines long-term ensemble projections for historical and future climate conditions over 5,000 years using an atmospheric global circulation model. The future climate condition is assumed as a constant +4K in the global mean temperature from before the Industrial Revolution (c.a. 1850), and the historical climate condition is perturbed by observed sea surface temperature (SST) error. A set of ensemble experiments assesses the impact of low probability phenomena, such as tropical cyclones and storm surge in comparison with conventional time-slice experiments. Future changes in storm surge will be severe at 15–35°N in the northern hemisphere, especially around the East Asia region. In addition, Future changes in regional storm surges targeting Tokyo and Osaka Bays project 0.3–0.45 m increase of storm surge height with a 100-year return period.

This paper presents a multivariate classification of the global wave climate into types driven by atmospheric circulation patterns. The primary source of the net long‐term variability is evaluated based on historical wave simulations. Results show that the monsoon, extratropical, subtropical, and polar wave climate types of the Pacific and North Atlantic Oceans are dominated by natural variability, whereas the extratropical and subtropical wave climate types in the Indian Ocean, and the tropical wave climate types of the Atlantic and Pacific Oceans exhibit a global warming signal. In the Pacific sector of the Southern Ocean, strong natural variability may mask a global warming signal that is yet to emerge as being statistically significant. In addition, wave climate teleconnections were found across the world that can provide a framework for joint strategies to achieve the goals of climate adaption for resilient coastal communities and environments.