| 研究課題/領域番号 |
21K14249
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| 研究機関 | 東京工業大学 |
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研究代表者 |
バルケズ アルビンCG 東京工業大学, 環境・社会理工学院, 准教授 (30754783)
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| 研究期間 (年度) |
2021-04-01 – 2025-03-31
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| キーワード | Global urban climatology / Climate change / urbanization / GIS Database |
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| 研究実績の概要 |
The first phase (year 2021) of the project has passed. The purpose of the first year was to improve and incorporate the geospatial database into the global and regional climate models, and to ensure that the global (GCM; Community Earth Systems Model) and regional climate models (RCM; updated Weather Research and Forecasting Model) can be smoothly operated in the supercomputer (Tsubame Supercomputer of Tokyo Tech). Furthermore, GRUCCAM (the project) requires that the outputs of the GCM models should be used as inputs to the RCM models. All these have been completed until the end of March 2022. Last March 2022, we confirmed that we can run the GCMs and RCMs smoothly in the supercomputer. A student has also requested to the main developers of WRF to incorporate our revisions (to allow distributed urban parameters) via their Github repository (to allow more users use our modifications/updates).
A paper (under second-stage review process) was submitted by a doctoral student to the "Urban Climate" journal that shows an RCM simulation of multiple cities that uses present and future scenarios of worst-case climate future and worst-case urbanization future. In the work, the effects of urbanization depends on the period and locations within the megacity, where in some locations' urbanization may enduce warming as significant as the background climate warming. A paper (under review) on improving the urbanization growth model by considering railroad networks was also submitted for publication to "Sustainable Cities and Society".
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We have succeeded to simulate 2020-2050 global climates under 4 scenarios (scenarios are defined according to a combination of according to the latest CMIP6, IPCC specifications) in the supercomputer with the GCM (Community Earth System Model). There were two cases for each simulation. The first case is where we switched on Airconditioning usage globally. The second cases was where we switched off Airconditioning usage globally. Airconditioning usage is directly proportional with the anthropogenic heat emissions. These emissions influence the surface energy balance, which in turn influences the atmospheric circulations and life on earth. We have also run some tests on how to use the model outputs as inputs into the RCM. The testing requires the construction of a program that will automatically convert the outputs of the GCM into the required inputs for RCM (direct downscaling of futuristic GCM). Currently, the GCM outputs are being stored and inputted into the RCM for higher-resolution studies.
The first year plan was achieved, but I was hoping to have at least one published work in the first year. The publication was not achieved due in part to the COVID-19, which limited coordination and performance with my students (when we were all working remotely). Nevertheless, we have already submitted three articles to separate prestigious journals, "Urban Climate" and "Sustainable Cities and Society" and "Scientific Data". The topics include the analyses of the multiple megacities, an updated urbanization growth model, and a database of distributed urban parameters.
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| 今後の研究の推進方策 |
According to the project timeline, the focus of this year is to successfully downscale the scenarios modelled with the GCM into the RCM. In the RCM, we will investigate deeply the effect of urban areas relative with the combined global effects of climate change and urbanization (taken from the GCMs). The analyses will focus on answering the key question on the combined and separate local and global effects of urbanization and climate change to all and individual cities. Within the first half of the year, the initial findings will be sent for publication. While conducting simulations with RCMs, we will also conduct simulations with the GCM under various urban-related settings (e.g. switching AC off only on certain regions, incorporating distribution of buildings). If time permits, we can also begin formulating adaptation and mitigation strategies into our model.
We will also double our efforts to share the repository of models (i.e. updated RCM; distributed urban parameter datasets) so other collaborators and users could use the same approach. I believe this is an essential method to promote our works to the scientific community.
In the time of writing, the university has loosen its restrictions to laboratory entry by students. Thus, I hope there will be more fruitful interactions and collaborations with external researchers and students.
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| 次年度使用額が生じた理由 |
The main reason for not being able to use the available funds for last year was due to the travel restrictions due to COVID-19. Also due to COVID-19, working was also limited to online. Hence, it was difficult to order and received the needed equipment for the project. A big part of the fund was intended for purchasing storage (NAS) and building a workstation. We only partially built a workstation that was enough to test the models and do partial analyses of the model outputs.
Despite the conditions caused by COVID-19, we were able to meet the targets of the first year. We hope to fully optimize the shifted funds from last year to this year for purchasing local storage / computation units, travel for meetings/conferences, and continuous simulations with the Tsubame supercomputer.
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| 備考 |
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