Clouds are the primary source of uncertainty in the prediction of climate change. To reduce the uncertainty of cloud simulations and overcome this difficulty in prediction, many climate modeling centers are now developing a new type of climate model, the global nonhydrostatic atmospheric model, which reduces the uncertainty arising from a cumulus parameterization by computing clouds explicitly using a cloud microphysics scheme. Among the global nonhydrostatic atmospheric models used in recent intercomparison studies, NICAM aims to project climate change by improving our understanding of cloud changes due to warming and related physical processes. NICAM is the first global nonhydrostatic model and was developed by our research team. This review summarizes the outcomes of a recent major five-year research program in Japan for studying climate using NICAM, as well as providing an overview of current issues regarding the use of global kilometer-scale simulations in high-resolution climate modeling.

AbstractMachine learning (ML) techniques have been introduced into various domains in recent years. Thus, it is important to construct reusable knowledge on projects that develop ML-based service systems to implement such projects effectively. In this study, the collection of insights and as well as the development of architecture and design patterns for ML-based service systems are considered. We propose a method for collecting insights and developing patterns for ML projects by referring a development model based on project practices.KeywordsMachine learningML service systemPattern

Recently, machine learning (ML) techniques have been introduced into various domains. This study focuses on projects for the development of ML-based service systems in which ML techniques are applied to enterprise functions. In these projects, constructing reusable knowledge on projects that develop ML-based service systems is important to effectively implement such projects. Here, the collection of insights and development of architecture and design patterns for ML-based service systems are considered. We propose a method for collecting insights by referring to a development model based on project practices and developing patterns for ML projects as an enterprise architecture model. Through a practice, we attempt to collect insights as best practices and construct design patterns for ML projects using the proposed method.

The introduction or strengthening of a carbon tax is being considered in many countries as an economic policy instrument to reduce greenhouse gas (GHG) emissions. However, there is no study analyzing the impact of a carbon tax increase in a uniform method for various products, reflecting the energy taxes and exemptions. Therefore, this study analyzes the price changes of products associated with the introduction of a stronger carbon tax, using Japan as an example. A process-based life cycle assessment database was used to enable a detailed product-level analysis. Five scenarios with different taxation amounts and methods were analyzed. The results show that price changes vary greatly by industry sector and product, even within the same industry sector. For example, seasonal vegetables and recycled plastics are less affected by carbon tax increases. Imported products, such as primary aluminum, are not affected by the Japanese carbon tax change, indicating a risk of carbon leakage. If GHGs other than CO2 are also taxed, the price of CH4 and N2O emitting products, such as rice and beef, would rise significantly. The method presented in this paper enables companies to assume price changes in procured products due to carbon taxes and policymakers to analyze the impact of such taxes on products.