Global-to-Regional Urban Climate Change Assessment and Modeling (GRUCCAM)
Project/Area Number |
21K14249
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Research Category |
Grant-in-Aid for Early-Career Scientists
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Allocation Type | Multi-year Fund |
Review Section |
Basic Section 22040:Hydroengineering-related
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Research Institution | Tokyo Institute of Technology |
Principal Investigator |
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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Project Period (FY) |
2021-04-01 – 2025-03-31
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Project Status |
Granted (Fiscal Year 2022)
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Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2024: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2023: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2022: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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Keywords | Global urban climatology / Urban Climate / GIS Database / GIS dataset construction / Climate change / urbanization / Weather modeling / Geospatial dataset / Hydrology |
Outline of Research at the Start |
The Global-to-Regional Urban Climate Change Assessment and Modeling (GRUCCAM) project advances a subfield called "Global Urban Climatology" (GUC). GUC aims to acquire a universal understanding of city-climate interactions in multiple scales. Global-covering urban parameter datasets from publicly available sources (e.g. satellite imagery) are to be constructed. Present and future pathways of cities (i.e. urbanization), in line with climate change pathways, will then be inputted to global and regional climate models to elucidate the aforesaid interactions and its implications to society.
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Outline of Annual Research Achievements |
In the end of fiscal year 2022, we were able to reach acceptance stage of two international publications of high impact (please refer to T2R2) (officially accepted in fiscal year 2023). The papers focused on the improvement of urbanization modelling to consider railway-induced urban growth and investigation of combined urbanization and global climate-change influence to warming in large megacities (23). A domestic paper was also accepted where we looked at trends of thermal comfort (UTCI) across the Japan, as evaluated from historical meteorological observations. We have also presented these and an additional finding related to anthropogenic-heat-induced warming in a regional climate in international conferences such as the Asia Oceania Geosciences Society (AOGS) 19th annual meeting, the American Geophysical Union Fall Meeting 2022, and the 2nd International Conference on Tropical Meteorology and Atmospheric Science. The conferences served as an avenue to promote our works and provide better foresight in continual research. The PI was also invited to give a keynote talk on the topic of "Global Urban Climatology" during the 2nd International Seminar on Earth Sciences and Technology, Bandung, Indonesia.
We have also succeeded in incorporating our global anthropogenic heat flux (AH4GUC), that is publicly available, into the widely used CESM2 (NCAR). With an undergraduate student, we found that the effect of AH4GUC on the modelled air temperature is significant globally. We verified that the intensity of effect matches that of the default set-up that utilizes air-conditioning.
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Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We have reported last year that we expected a delay in publishing our result due to COVID-19. Considering that, the project is going smoothly as expected with one domestic paper published and two international papers receiving acceptance status towards the end of March 2022. We are advancing the research steadily with no critical issues in the methodology. Due to the highly advanced modelling approach (considering detailed urbanization in present and future climate), it was initially difficult to conceptualize a feasible strategy to meet the objective. Fortunately, with the help of the enthusiastic lab students (and in their education) and collaborators, the project was able to keep up with the required development and implementation of the models. Collaborations have also been established post-pandemic, which helped promote to keep the project back on schedule.
The global climate models scenarios and outputs were also increased. This will enable the project to advance with the downscaling model. Downscaling will provide finer detail of climate within and surrounding the cities. Given our previous experience in downscaling, we expect that at this timing, we can produce more climate projections centering in cities at a faster rate. The knowledge advanced from the downscaling models, if published, will mark the successful implementation of the project (which we hope to achieve by this year).
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Strategy for Future Research Activity |
We will continue with downscaling the climate model utilizing outputs of three groups of global climate models: the earth system model we ran with Tsubame (CESM2), the reanalyses models for present-case modeling, and future climate model with CMIP6. The first model (CESM2) will be used to assess a background climate that is influenced (or not influenced) by the global release of anthropogenic heat. The second model will be used to assess past vs present urban climate conditions. The third model is necessary to update our previous assessment that utilized the CMIP5 climate scenarios. Last year, CMIP6 global models have become widely used and may be downscaled. We are also presenting various topics in the upcoming International Conference on Urban Climate.
Furthermore, It is necessary to assess various adaptation/mitigation strategies to suggest simple and efficient solutions. With a doctoral student, the project will look into adaptation strategies to future climate warming in multiple cities that are expected to experience heat wave conditions. The student will assess solutions that will not require much energy to produce. We have also started collaborations with a lab (Dr. Ihara, University of Tokyo) who will use our model outputs to evaluate the energy that will be used for cooling under heat-wave conditions.
We have also began involving students of computer science backgrounds to apply machine-learning/deep-learning models to more recent satellite data. The purpose is to improve urban parameterization in weather models and detection of vulnerable areas (e.g. slums).
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Report
(2 results)
Research Products
(10 results)