Spatial climate prediction is essential for assessing impacts of climate change and designing and prioritizing adaptation strategies and measures to address the impacts. Climate change projection can also facilitate land use and urban planning through the assessment of impacts of land use change on regional climate change. Currently, climate impact assessment researchers and land use and urban planners need to either request climate prediction researchers to generate necessary data or apply dynamic downscaling to the data by themselves in order to obtain detailed spatial climate prediction data in accordance to their own purposes. However, dynamic downscaling requires advanced specialized knowledge; hence its application is extremely difficult.
Responding to the needs for making local climate change projections and assessments without advanced modeling skills, the University of Tsukuba has developed a Climate Change Downscaler. It aims to keep the workload to a minimum for those non-specialists of climate modeling to acquire information regarding local climate change projections. It also enables assessing impacts of future urbanization and farmland development on regional climate change and mitigating effects of greening and energy-saving policies on regional heat environments.
This training workshop aimed to enhance the understanding on the easy running of climate change downscaler among non-climate modeling specialists, particularly a) climate change impact assessment researchers or government officers at the national or provincial level; b) research institutes engaging in the designing of national or local adaptation strategies; and c) land use and urban planning officers. The workshop provided the participants with hands-on exercises on 1) basic functions of the downscaler, 2) specific functions of the downscaler required for particular purposes for utilization.
The workshop was attended by 26 government officials and researchers from nine countries, namely, Cambodia, Indonesia, Fiji, Japan, Malaysia, Mongolia, the Philippines, Thailand, Viet Nam, and Samoa. The analysis of a pre– and post–training survey revealed an average of 80 percent improvement in the level of understanding on key subject matters including global climate models, the use of the S8 downscaler software, and the relevance and use of the downscaled climate data in the participants’ respective field of work. It also stimulated among the participants a strong interest in getting in-depth training on the downscaler software package so that they could apply it in the formulation of national policies and climate adaptation strategies.