Climate Change: Impacts and Responses

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VOLUME 8 ISSUE 2

The International Journal of

Climate Change: Impacts and Responses _________________________________________________________________________

A Web Platform for Community-based Adaptation Decision-making under Uncertainty YINGJIU BAI, IKUYO KANEKO, HIROAKI NISHI, HIDETAKA SASAKI, AKIHIKO MURATA, KAZUO KURIHARA, AND IZURU TAKAYABU,

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The International Journal of Climate Change: Impacts and Responses www.on-climate.com ISSN 1835-7156 doi:10.18848/1835-7156/CGP First published in 2016 in Champaign, Illinois, USA by Common Ground Publishing www.commongroundpublishing.com The International Journal of Climate Change: Impacts and Responses is a peer-reviewed, scholarly journal.

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A Web Platform for Community-based Adaptation Decision-making under Uncertainty Yingjiu Bai, Keio University, Japan Ikuyo Kaneko, Keio University, Japan Hiroaki Nishi, Keio University, Japan Hidetaka Sasaki, Meteorological Research Institute, Japan Akihiko Murata, Meteorological Research Institute, Japan Kazuo Kurihara, Meteorological Research Institute, Japan Izuru Takayabu, Meteorological Research Institute, Japan Abstract: Decision-making is framed as the response to scientific findings, where uncertainties are deep rooted in both climate change itself and the implementation of policy. However, responses to the challenge of communicating uncertainty to policy makers clearly and simply are insufficient, and this situation is being exploited to undermine the implementation of policy and crisis preparedness for climate change impacts. Although expert knowledge using high resolution projection is particularly useful for community-based adaptation, these data are fraught with uncertainty, and are not freely available to be used in local decision-making processes. This paper has several aims: 1) to promote a web platform for accessible climate projection services so vital in support of local adaptation decision-making and stakeholder participation, especially in depopulated/undeveloped areas; 2) to propose a visualization approach to articulating a clear understanding of the confidence intervals in high resolution (5-km resolution) projections, for the implementation of valid adaptation strategies and reliable actionable planning; and 3) to provide an appropriate and simple method of adjusting bias and quantifying the uncertainty in future outcomes, so that regional climate projections may be transcribed into useful forms for a wide variety of different users. Our discussion focuses on capitalizing on the 5km resolution projections for application in community-based adaptation planning in Japan, so that communities can take appropriate and effective actions themselves via the web platform. This methodology (climate projection services) could be transferred to developing countries to assist in the creation of plans for the adaptation to and mitigation of climate change. Keywords: Community-based Adaptation, Decision-making, High-resolution Projection, Uncertainty, Web Platform

Introduction

T

he Intergovernmental Panel on Climate Change (IPCC) the Fifth Assessment Report (IPCC 2013) noted that the global mean surface temperature change for the period 2016– 2035, relative to 1986–2005, will likely be in the range of 0.3 to 0.7°C, and the increase of global mean surface temperatures for 2081–2100, again relative to 1986–2005, will be 0.3 to 0.7°C (Representative Concentration Pathway (RCP) 2.6), 1.1 to 2.6°C (RCP 4.5), 1.4 to 3.1°C (RCP 6.0), or 2.6 to 4.8°C (RCP 8.5). These projections highlight the need for immediate action, especially in the development of substantive adaptation policies and measures at the local scale. Moreover, IPCC AR5 also notes that differences in vulnerability and exposure arise from nonclimatic factors, and this heightened vulnerability is rarely due to a single factor (IPCC 2014). Such an understanding is critical when seeking support for the needs of an ever-broadening spectrum of society’s decision-makers as they strive to deal with the influences of climate change at the local scale (Overpeck et al. 2011). However, climate change policy making is confronted by a wide range of significant scientific and socioeconomic uncertainties (Lempert et al. 2004). The latest IPCC uncertainty guidance methodology is simpler and easier to use than the previous version, but it is unlikely to be sufficient for decision-making needs (Jones 2011). Communicating, characterizing and managing uncertainty for decision-makers has emerged as an The International Journal of Climate Change: Impacts and Responses Volume 8, Issue 2, 2016, www.on-climate.com, ISSN 1835-7156 © Common Ground, Yingjiu Bai, Ikuyo Kaneko, Hiroaki Nishi, Hidetaka Sasaki, Akihiko Murata, Kazuo Kurihara, Izuru Takayabu, All Rights Reserved Permissions: [email protected]

THE INTERNATIONAL JOURNAL OF CLIMATE CHANGE: IMPACTS AND RESPONSES

important debating point across many related disciplines (Webster 2003; Lempert et al. 2004; Jones 2000; 2011), and uncertainty has been seen to pose nontrivial challenges for the implementation of valid adaptation strategies in reliable local climate change action planning (Keller et al. 2008). Recently, the Coupled Model Intercomparison Project (CMIP) methodology has made projections possible for anyone wanting to openly access state-of-the-art climate model outputs and climate data to provide the backbone for decisions (Overpeck et al. 2011). Furthermore, the latest high-solution regional climate model (RCM) has been a huge increase in the volumes of data available. For example, the 5-km Non-hydrostatic RCM (Japan), following the SRES-A1B scenario, has archived more than 4.0 terabytes of model data (CMIP3 archived 36 terabytes, CMIP5 archived 2.5 petabytes). These successes have led to new demands (new critical requirements for research) to ensure that the ever-expanding volumes of data are easily and freely available. This stance will enable new research to be undertaken, will increase understanding and the reliability of information, and will be useful to a wide variety of different users. Following the 2003 European heat wave (the exceptional summer of 2003 caused around 70,000 heat-related deaths, mainly in western and central Europe), climatologists, medical specialists, and social scientists expedited efforts to revise and integrate risk governance frameworks for adaptation to climate change (Schröter et al. 2004; Lass et al. 2011). An increasing number of non-experts, not only in developed countries but also in developing countries, are seeking access to the latest climate knowledge to inform their local adaptation decisions. Nevertheless, several barriers to the innovative use of projections—for example, a lack of trained staff, and limited opportunities for data sharing—impede advances in collaborative research and practice (Lang 2011). Meanwhile, the optimal use of the latest climate data, while adequately addressing uncertainty, is a nontrivial task as an essential part of scientific support for decision makers. The key here is that new data-sharing systems have to be readily accessible for interdisciplinary users, especially local policy-makers in depopulated/undeveloped areas, and complex models must produce easy-to-understand (scientific) results. Clearly, that requires providing cost-effective services with web-based access tools. Unfortunately, it is extremely difficult for any individual research group to assess the uncertainty in local climate change with their current knowledge (Webster 2003). This paper is a pilot study on how to advance local adaptation through a combination of open access, user-friendly projected data sharing, and expert knowledge exchange. The principal objectives are: 1) to promote a web platform for accessible climate projection services vital to the support of local adaptation decision-making and stakeholder participation, especially in remote areas; 2) to propose a visualization approach to articulating a clear understanding of the confidence intervals in high resolution (5-km resolution) projections, for the implementation of valid adaptation strategies and reliable actionable planning; and 3) to provide an appropriate and simple method of adjusting bias and quantifying uncertainty in future outcomes, to transcribe the regional climate projections into useful forms for a wide variety of different users. In this paper, three major challenges for an open-accessible and visualized climate datasharing system that revolves around optimal use of projections, characterizing uncertainty, and creating community-based actionable information have been addressed. First, a web platform organizing, visualizing, and mapping the high-resolution projected data under uncertainty is made available to non-expert users. Second, a platform for user-friendly climate projection services is constructed, so that the volumes of complex results are freely and readily accessible in forms useful to both the researcher and the public. Third, an appropriate and simple method of adjusting for bias and quantifying uncertainty in future outcomes is provided. This is done on the one hand to involve users in accessing high-solution climatic data cost-effectively to improve routine decision making, and to clarify regional priorities. These challenges will provide essential climate services, project understanding, articulate model limitations, and ensure the integrity of evidence-based decision-making processes. In particular, local communities and governments in

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remote regions, and in poor and undeveloped areas, will benefit from the availability via the Internet of such information, where they have inadequate or no climate services. This methodology (climate projection services) could be transferred to developing countries to assist in creating plans for their feasible adaptation to climate change and mitigation; especially to the poorest countries/regions that are more vulnerable to climate change because they tend to be in geographically sensitive areas. To test this approach, Tokyo (area 2188.67 km2; population 13.20 million as of 1 January 2014), a Japanese megacity, was chosen for a pilot study. This paper describes the methods focused on capitalizing on 5-km resolution projections in the application of community-based adaptation planning, and their standard deviation and statistical significance visualized via the web platform in the “Data Capture and Sharing System” section. The “Pilot Study of Tokyo” section explains how this process works. As pilot research, the process of tackling 5-km resolution projections following the SRES-A1B scenario only, is explained in this study. This paper examines the challenges of ensuring that this model is harnessed to capture, record, store, combine, and share a diversity of huge projected datasets, and of being freely and readily accessible in forms useful to both the researcher and the public.

The Climate Data Capture and Sharing System International digital datasets including climate observations and climate models have worldwide open access, and they are updated by the IPCC Data Distribution Centre (http://www.ipccdata.org/obs/index.html). In addition, both the quantity and quality of climate models have increased significantly since the IPCC Fourth Assessment Report was issued (WCRP 2013), and a number of new results have been published. This powerful combination of observations and models is the key to providing reliable science-based climate information for decision makers (WCRP 2013). However, local governments have difficulty accessing and understanding the climatic knowledge of experts, so those governments have no corresponding ability to produce local, community-based actionable information. Thus, mismatches are common between national-level policy-making processes and the coordination of local actions for the mitigation of, and adaptation to, climate change (Daniell et al. 2011). New approaches designed to help local robust decision-making and optimal adaptation actions to be facilitated by community members’ ability, are required. The challenges put a new emphasis on providing community-based climate data resource/services for the locally oriented adaptive planning and collective action scenarios available to individuals and communities. Meanwhile, coping with deep uncertainty in our understanding of the RCMs, locations, and impacts of climate thresholds presents another challenge.

The Data Capture and Sharing System In this pilot study, we used high-quality hourly projections (5-km resolution) from the NonHydrostatic Regional Climate Model (NHRCM-5km), following the SRES-A1B scenario developed by the Meteorological Research Institute (MRI) and using Japan Meteorological Agency (JMA) data. The NHRCM-5km is a dynamic downscaling of results from the MRIAGCM3.2S (20-km resolution), an atmospheric general circulation model (AGCM) driven by the ensemble of mean sea surface temperatures derived from the CMIP3 coupled GCMs (CMIP3 Climate Model Documentation, Reference, and Link 2007). The MRI-AGCM3.2S output is successful in simulating the characteristic features of the seasonal cycle of the East Asian summer monsoon and topographically regulated precipitation data, and it has good agreement with ground observations (Mizuta et al. 2012; Sasaki et al. 2011; Sasaki et al. 2012).

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Figure 1: A Web Platform Incorporating Three Different Data Modules

Figure 1 outlines the procedures adopted here, and depicts a web platform with three different modules (databases), and accessible climate projection services consisting of the 5-km resolution projections database, climate changes, and their standard deviation and statistical significance databases. Three incorporated modules were created based on the output from NHRCM-5km, and integrated with geographic-scale and location information, allowing the user to use the Google search function and link Google maps. Meanwhile, climate changes in the near-future and future periods under uncertainties based on the 5-km resolution projections database are made understandable by direct visualization of their standard deviation and statistical significance. The output from NHRCM-5km was compiled as a .csv file in a SQL (Structured Query Language) database. A projections database of climate changes in the near-future and future periods under uncertainty, and direct visualization of standard deviation and statistical significance, were also compiled as a .csv file in a SQL database, for local (community) adaptation identification and analysis. The user can transform these files easily and efficiently into other types of files using appropriate software, such as facilitating re-editing and visualization of the GIS (Geographic Information System) dataset, to transform it into a KML/KMZ file that is available for presentation in Google Earth as required. In particular, three modules were integrated with geographic-scale and location information, and this allows the user to use the Google search function and link Google maps. That makes the resulting maps with geographic scale and location information useful in illustrating vulnerability in terms of potential negative impacts and the limited capacity of communities to adapt.

A Web Platform for Operational Climate Projection Services A web platform was built using three different modules. These modules are the 5-km resolution projections database, climate changes, and their standard deviation and statistical significance databases. The 5-km resolution projections database was designed to identify the long-term and short-term climate changes associated with extreme weather events, such as the number of extremely hot days with maximum temperature exceeding 35°C and 30°C, the number of days 36

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with daily precipitation exceeding 300 mm, and so on (Table 1). Table 1 summarizes the lines of information that cannot be assured by the application of the more traditional expected-utility climate research framework. This information can support local decision-makers in the design of near-term policies that are consistent with “moving long-term targets.” In particular, browsing of large-volume projected data and mapping the climate changes on the same domain allows the user to use the Google search function and link Google maps directly (Figure 2). Table 1: The 5-km Resolution Projections Database of Informed Long-term and Short-term Climate Changes Associated with Extreme Weather Events in the RCCP Web Platform Projections Database Surface Temperature

Precipitation

Maximum Snow Depth Relative Humidity Solar Radiation

Present (Sep. 1980–Aug. 2000), Near-future (Sep. 2016–Aug. 2036), and Future (Sep. 2076–Aug. 2096) Monthly mean/maximum/minimum temperature, Number of extremely hot days with maximum temperature exceeding 35C and 30C, Number of extremely hot days with minimum temperature exceeding 25C, Number of extremely cold days with maximum/minimum temperature exceeding 0C Monthly precipitation, Daily maximum precipitation, Number of days with daily precipitation exceeding 300 mm /200 mm/100 mm/50 mm, Hourly maximum precipitation, Number of days with hourly precipitation exceeding 150 mm/100 m/50 mm /0 mm Monthly maximum snow depth, Number of days with monthly maximum snow depth exceeding 200 cm/100 cm/50 cm/20 cm/10 cm/5 cm/1 cm Monthly mean/minimum relative humidity Monthly mean solar radiation

Note: RCCP (http://rccp.sfc.keio.ac.jp) is a free, open-access web platform (only in Japanese) built by the authors’ research group. Table reproduced in English by Yingjiu Bai.

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Figure 2: A Web Platform, RCCP (http://rccp.sfc.keio.ac.jp) Built Using Three Different Modules. (Web Page Image Reproduced in English by Yingjiu Bai.)

New databases relating to climate change in the near-future and future periods under uncertainty were constructed to assist in decision-making capability. These are intended to inform decision-makers by analyzing current and potential impacts of climate change and by recognizing the pressures from other stresses (Figures 1 and 2). Specifically, climate change based on simulation outcomes are saved as a .csv file and the linked google maps are a vehicle for readily depicting specific distribution changes and trends across temporal and geographic scales (Figure 2). This allows the projected information to become available to multiple users, and encourages national and subnational governments to work closely with local authorities. Furthermore, the user can transform the databases relating to climate changes in the nearfuture and future periods into GIS-based databases by geo-software. This has two benefits: first, all data can be re-computed and re-analyzed for regional tasks in the GIS domain, and saved as a new web database; and second, a GIS-based database can be an effective and easy method for analyzing and solving a particular problem; integrating an open database that includes environmental and socioeconomic statistics. Also, multiple local potential impacts on multiple sectors can be projected through the process of decision-making. Details are described in the pilot study. Meanwhile, as an independent module, additional new databases based on characterizing projection uncertainties were constructed. These included the level of the statistical significance tests of changes in future outcomes, quantified by t-test and bootstrap methods. Figure 2 illustrates the projection uncertainties in the near-future and future periods. These become understandable information by direct visualization of standard deviation and statistical significance. The web platform provides information about uncertainty, broadly divided into two steps: the first is to quantify the uncertainty in future outcomes by data processing to ascertain the level of statistical significance of the results; the second is to communicate the quantified 38

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uncertainties to users, in which the web technology can deliver its full potential in advancing improvements in the visualization of standard deviation and statistical significance. Details are given in the pilot study.

Pilot Study of Tokyo: Projective Data to Drive Policy Change and Support Implementation Tokyo has 2.84 million people aged 65 or older, making up 21.5 percent of the Tokyo population and including 8.9 percent of Japan’s total population of older people as of 1 January 2014 (Statistics Bureau, Ministry of Internal Affairs and Communications 2014). Moreover, Tokyo will become an aging-megacity, with the projected percentage of older people exceeding 33.5 percent by 2040 (National Institute of Population and Social Security Research 2013). Similar to megacities in other developed countries, and municipalities with aging populations, these populations are expected to suffer greatly from climate change, and are vulnerable because only a few people have sufficient information on which to base mitigating actions. Thus, the continuing increase in size of the older population is an urgent issue for metropolitan governments. Meanwhile, the population of Tokyo’s 23 inner-city wards has continued to increase; from 2000 to 2014, the population grew by 1.09 million in this area, a 14% growth rate (Tokyo Metropolitan Government HP 2014). The concentration of population within the inner-city area (23 wards) has become a serious problem for the city (Figure 3). Conversely, as population and infrastructure have become concentrated in the inner city of Tokyo, two surrounding localities (Okutama-machi and Hinohara Village; Figure 3) have been depopulated. Okutama-machi is a sparsely populated mountainous region accessible by train or car, and is approximately 2 hours northwest of Tokyo (Figure 3a). Locally, the impounded Lake Okutama feeds the Tama River, and can hold 185 million m3 of water, supplying approximately 20 percent of Tokyo’s water needs. In addition, population, community life and land-use in the Tokyo’s inner-city area and sparsely populated regions vary widely in these attributes across the landscape, so the ability of people to adapt to changing climates in the metropolitan area is unevenly distributed. Lemonsu et al. (2013) suggest that global warming will be more marked at the mega-city scale, because of processes such as the creation of urban heat islands. Figure 4a indicates the number of people hospitalized with heat stroke from 2000 to 2014 in Tokyo. The exceptional summer of 2010 and 2013, heat stroke resulted about 4679 and 4516 hospitalizations for patients, respectively (Figure 4a). Figure 4b indicates that the annual mean temperature in Tokyo has risen about 3°C over the past century, based on long-term observational data from the meteorological station in central Tokyo (in Chiyoda-ku at Figure 3a; the upper straight line in Figure 4b denotes estimated trend of annual mean temperature increase for Tokyo station; slope = 0.0248°C/year, R2 = 0.80). Meanwhile, the climate at Ogouchi station in Okutama-machi (Figure 3a) has been warming up based on observational data (Figure 4b). Nevertheless, the low population level in Okutama-machi makes it a distinctive region within Japan.

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Figure 3: Locations of Tokyo’s 23 Inner-city Wards, and Okutama-machi and Hinohara Village (Sparsely Populated Mountainous Regions, a) and Population Distribution (as of 1 January 2014) in Tokyo (b)

Figure 4: Number of People Hospitalized because of Heat Stroke from 2000 to 2014 (a) and Annual Mean Temperature Increase (b, Upper Straight Line Denotes Estimated Trend of Annual Mean Temperature Increase for Tokyo Station, Slope = 0.0248°C/year, R2 = 0.80) in the Tokyo Metropolitan Area, Based on Average Records from Tokyo (City Center, located in Chiyoda-ku at Figure 3a) and Ogouchi Stations (Rural Area, located in Okutama-machi at Figure 3a). Data Source (a): Ministry of Health, Labour and Welfare, 2015 Data Source (b): Japan Meteorological Agency, 2015

Regional Climate Change and Adjusting the Bias of Projections The climate informatics module comprises the spatial distribution of projected climatic indices and their changes, using daily, monthly, and annual weather data (focusing on temperature and precipitation) from three study periods. These are the present period (September 1980–August 2000), the near-future period (September 2016–August 2036, and the future period (September 2076–August 2096). Comparing the near-future and future conditions, temperatures in Tokyo

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will increase by more than 0.6–1.6°C and 2.5–3.1°C (summertime), and 1.0–1.6°C and 2.9– 4.1°C (wintertime), respectively. The monthly mean temperature increase at Okutama-machi is the greatest of any area in Tokyo during both periods. As an example, Figure 5 gives the expected monthly mean temperature changes and percentage changes in monthly precipitation in February and August in Tokyo, in both the near-future (2017–2036) and future (2077–2096) periods, based on the NHRCM-5km model. We transformed all databases (csv files) of climate change in the near-future and future periods into GIS-based databases using ArcGIS software. The percentage changes in monthly precipitation in summer and winter are projected to be -6 percent to 15 percent, and -12 percent to 50 percent in the future period, respectively. For the near-future period, July monthly precipitation will decrease by 16 percent to 26 percent, and August monthly precipitation will increase by 41 percent to 50 percent. The end of the rainy season (Baiu front) will shift from late July to early August, and heavy rainfall is projected to have longer duration. For the winter period, the percentage of December monthly precipitation will stay the same, whereas for January and February it will increase by more than 5 percent and 15 percent, to 30 percent, respectively.

a

b

Figure 5: Monthly Mean Temperature Changes and Percentage Changes in Monthly Precipitation in February and August in Tokyo in the Near-future (2017–2036) and Future (2077–2096) Periods based on the NHRCM-5km Model.

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For the future period, the percentage change for June monthly precipitation will increase by 7 percent to 15 percent, but for July and August, it will decrease a small amount. For winter, the percentage change for December monthly precipitation will decrease by 12 percent; however, for January and February it will increase by more than 20 percent to 45 percent, and 34 percent to 50 percent, respectively. In particular, the maximum snow depth in February will decrease by 0.5–6 cm in the near future, and by 0.8–8 cm in the inner-city area, and 9–14 cm in the mountain region during the longer term. However, that depth in March will increase by 0.1–1 cm in the inner-city area, and by 0.3–2 cm in the mountain regions in the near future, and will decrease by 0.3–3 cm in the inner-city area, and by 9–18 cm in the mountain regions during the longer term (Figure 6).

Figure 6: Changes in Maximum Snow Depth in February and March during the Near-future (2017–2036) and Future (2077–2096) Periods Derived from the NHRCM-5km Model

Recent RCM improvements have enhanced that model’s ability to simulate many aspects of climate variability. However, the RCMs and computer technology are not sufficient to provide credible probabilistic projections of changes in daily climate, such as extremely hot days or spells of sustained heavy rainfall. The web platform provides a projected changes database associated with monthly, seasonal and annual climate for twenty-year periods (for example, change in August temperature or in wintertime). So the climatic changes are provided as probabilistic projections (for example, changes to seasonal extremes such as the warmest day of summer or wettest day of winter). Downscaling uncertainty and systematic limitations in accurately simulating regional climate conditions persist. To characterize projection uncertainties, the level of statistical significance test of changes in future outcomes were quantified using t-tests (test of changes in monthly mean/maximum/minimum temperature), and the bootstrap method (tests of percentage changes in monthly precipitation and changes in maximum snow depth), through web technology. As an example, Figure 7a is a web image that shows the direct visualization of standard deviations and the statistical significance of percentage changes in monthly precipitation at Tokyo Station (Chiyoda-ku), during the near-future period (2017–2036). In particular, Figure 7b illustrates the statistical significance of percentage changes in monthly precipitation in February and August in Tokyo during the near-future period (2017–2036) using the bootstrap method. To estimate the systematic errors of simulated precipitation, root mean square (RMS) errors and the bias of monthly mean temperature and monthly precipitation were calculated. In this study, we used a high-quality daily dataset based on observations obtained from the JMA (JMA 2014). Observed data were available from forty-nine observation stations covering Tokyo

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(Figure 8a). Twenty years of data (September 1980 through August 2000) were analyzed. Table 2 shows the RMS errors and bias of the monthly mean temperature and monthly precipitation data using model simulations and actual observations. Both monthly mean temperature and monthly precipitation indicate a significant correlation between the model simulations and actual observations (for each month; there were 980 samples, R = 0.78 to 0.95). As an example, Figure 8b-8e indicates a good correlation between the observations and the NHRCM-5km modeling of monthly mean temperature (February R2 = 0.95; August R2 = 0.94), and monthly precipitation (February R2 = 0.85; August R2 = 0.48), in February and August.

a

b

Figure 7: A Web Image of the Visualization of Standard Deviation and Statistical Significance of Percentage Changes in Monthly Precipitation at Tokyo Station (a), and the Level of Statistical Significance of the Percentage Changes in Monthly Precipitation in February and August in Tokyo (b) during the Near-future Period (2017–2036) Using the Bootstrap Method

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Figure 8: Locations of the 49 Observing Stations (Red Points in a) and Examples of Good Correlation between Observations and NHRCM-5km Modeling of Monthly Mean Temperature (b and d; February R2 = 0.95, August R2 = 0.94), and Monthly Precipitation (c and e; February R 2 = 0.85, August R2 = 0.48), in February and August (980 samples).

Table 2: RMS Errors and Bias in Monthly Mean Temperatures and Monthly Precipitation, between the Model Simulations and Observations during the Present Period (September 1980–August 2000)

Bias (°C) RMS (°C) Bias (%) RMS (%)

Jan.

Feb.

Mar.

Apr.

May Jun. Jul. Mean Temperature

-2.4

-2.0

-1.2

-0.9

-0.1

0.5

2.5

2.1

1.4

1.0

0.5

0.7

Aug.

Sep.

Oct.

Nov.

Dec.

-0.2

-0.3

-0.5

-0.5

-1.8

-1.8

0.6

0.6

0.8

1.0

2.0

2.3

Precipitation 58.8

11.7

2.4

8.2

-0.9

-4.5

32.0

-5.4

9.6

9.1

-3.0

129.0

66.8

20.9

18.0

20.3

15.7

13.6

49.8

25.5

20.0

18.9

13.7

135.1

Note: Forty-nine observation stations (red points in Figure 8a); for each month there are 980 samples.

Data that Quantify and Describe Adaptive Management Issues in Tokyo Figures 7 and 8 give examples of the understanding and use of the high resolution projections service by web platform to support local decision-making. GIS datasets based on the projections database can furnish a mapping approach that can be used to produce a regional developmental snapshot based on governmental statistical information, which also uses the social uncertainty surrounding local reactions to policy decisions and local stakeholders’ interests. For example, Figure 9 shows the older population distribution, and location of medical institutions (hospitals for disaster medical care and medical emergency centers) and fire department/firehouse in

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Tokyo. There is no emergency hospital and in Okutama-machi and its surrounding areas (sparsely populated mountainous regions). However, the number of people hospitalized per one million population because of heat stroke in Okutama-machi was highest in the districts outside the inner city from 2009 to 2013 (Table 3), and the number of people hospitalized per one million population from heat stroke in Hinohara Village was highest in the districts outside the inner city in 2014, even though these regions have the lowest population density of 0–10 persons/km2 covering more than half of their land areas. In contrast, Chiyoda-ku is a very famous business and government agency area, with the highest population density in Japan. The number of people hospitalized per 1 million population because of heat stroke in Chiyoda-ku was highest in Tokyo from 2009 to 2014 (Table 3).

Figure 9: Older Population Distribution (as of 1 January 2014), and Location of Hospitals for Disaster Medical Care, Medical Emergency Centers and Fire Department/Firehouse in Tokyo

Table 3: Number of People Hospitalized per One Million Population because of Heat Stroke in Tokyo from 2009 to 2014

Number of people hospitalized per one million population from heat stroke (Average value in 23 inner-city wards or districts outside the inner city) 2009 2010 2011 2012 2013 2014

Chiyoda-ku (city center) Okutamamachi (rural area)

347.8 (61.0) 453.4 (58.7)

1391.3 (393.3) 604.6 (325.4)

1739.2 (316.5) 755.7 (293.8)

1345.0 (254.7) 604.5 (256.8)

1443.3 (356.5) 992.6 (323.6)

1422.1 (263.4) 330.9 (245.1)

Data Source: Fire and Disaster Management, Agency of Ministry of International Affairs and Communications 2014

The summer of 2010 was exceptionally warm in Japan: the monthly maximum temperature in August in Tokyo reached 29.6°C. Extremely high monthly maximum temperatures exceeding 30°C were experienced on more than seventy days during the entire summer. The heat stroke resulted in 272 deaths and about 4,700 hospitalizations for patients (Figure 4a). In Figure 10 we

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can see that there are clear correlations between the number of people hospitalized because of heat stroke and the daily maximum/mean temperatures, based on 122 daily records (from June 1 to September 30) in 2010 obtained from 927 cities throughout Japan (daily maximum temperatures R2 = 0.77; daily mean temperatures R2 = 0.75). The number of older people hospitalized because of heat stroke was more than the number of other adults (Figures 10). Barriopedro et al. (2011) pointed out that some RCMs project several events similar to 2010 over the period 2020–2049. Figures 5a and 11 indicate the heat-wave issues in Tokyo in the nearfuture period. Figure 11 shows that the monthly mean/maximum temperatures in July will increase by more than 1.6°C in Okutama-machi (Level of Statistical Significance: p≦0.01), and the maximum temperature in August is expected to increase more quickly (will increase by more than 0.6°C) in Okutama-machi (Level of Statistical Significance: p≦0.1), than in any other area of Tokyo during the near-future period. No significant change in the number of extremely hot days with maximum temperatures exceeding 35°C has been forecast. However, the aging population ratios exceed 50 percent and 60 percent in some areas of Okutama-machi. These results suggest that promoting both remote medical services and a telemedicine center in Okutama-machi is absolutely essential for an adequate response to local climate change issues. Because Okutama-machi has poor transportation facilities, this creates a wide gap between the services available in this small town and in the inner-city area. As seen from the above, Okutama-machi has different adaptation options to the other areas of Tokyo. Communities or individuals should initially make their concerns clear, and then be allowed to select community-specific adaptation options, and to develop a locally appropriate implementation plan. A web platform, and the analysis and results of a vulnerability assessment based on high resolution projections with descriptions of uncertainty will support community action, and will help avoid misunderstandings among citizens.

Figure 10: A Sample of the Correlations between Number of People Hospitalized because of Heat Stroke and the Daily Maximum (a)/Mean (b) Temperatures (122 Daily Records from June 1 to September 30, 2010 Obtained form 927 Cities throughout Japan) Data Sources: Japan Meteorological Agency, 2010; Fire and Disaster Management Agency of the Ministry of International Affairs and Communications, 2014.

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a

b

Figure 11: Changes in Monthly Mean/Maximum Temperatures in July and August (a) and their Level of Statistical Significance (t-test) in Tokyo (b) during the Near-future Period (2017–2036)

Conclusion The NHRCM-5km model (applicable only to Japan) is the most complete and current evaluation of the projected impact of climate change on Japan. We validated the integration of the openaccessible projections database, and the visualized climate data-sharing system, into appropriate decision-making processes at the community level; and then provided a simple method of adjusting the bias and quantifying the uncertainty in future outcomes for a local adaptation plan. Our results reveal that non-expert administrators will benefit from understanding the projections graphically using the web platform. This pilot study tested the bias adjustment of such simulations, articulated the limitations of the model, and provided a visualization of the data using standard deviations and tests for statistical significance. These methods should help prevent decision-makers from acting capriciously. As a result, communities will be able to take appropriate and effective climate change-related actions by understanding the risks they face. For example, the optimal adaptation projects in Okutama-machi should be to link adaptation action to current community development (e.g., social-infrastructure improvements), investment (remote

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medical services) and planning decisions (building an emergency hospital). Moreover, our findings clearly illustrate the importance of measuring the bias of projections, and in particular, of creating a valid and feasible means for characterizing and adjusting the projections of uncertainty that can facilitate good decision making. These challenges will assist communities to provide essential climate services, understanding of projections, articulation of model limitations, and ensure the integrity of evidence-based decision-making processes. Optimal adaptive decision depends on not only understanding expert information under uncertainty carefully, but also our expectation of how this will change, and how we will need to respond these changes in the future. Caution is however necessary when examining links between national and local climate policy responses. This is because regions vulnerable to climate change are also under pressure from forces such as changing population demographics, which reduce local fiscal efficiency and result in poor service from the society’s infrastructure. Thus, reliable scientific assessment of the risks of future climate change, and its likely impact, is a critical component of the support for local decision makers in developing and implementing effective responses in this area. We have constructed a new webGIS platform to provide the open-accessible climate projection services with AR5 4 scenarios that are vital components of climate change models for local adaptation decision-making and stakeholder participation; especially in remote areas and developing countries. Sufficient information is now available for effective policy making in relation to the consequences of potential climate change, and this information indicates an explicit need for initiatives by local government in respect of the design of adaptation strategies, and of adaptive capacities in general. Increasingly, climate scientists and other scientists who are at the interface between research and applications are working closely together, sharing online projected data systems with services (datasets that are available for re-editing and visualization in web browsers) that are central to adaptive capacity, because these make real-time information available to multiple users, even in developing countries.

Acknowledgment This study was conducted under the Green Society ICT (Information and Communication Technology) Life Infrastructure Project conducted by Keio University, (Project Chief Leader: Professor Ikuyo Kaneko, Graduate School of Media and Governance, Keio University), and was supported by Funds for Integrated Promotion of Social System Reform and Research and Development, sponsored by the Ministry of Education, Culture, Sports, Science and Technology of Japan. The authors would like to thank the Meteorological Research Institute of the Japan Meteorological Agency and the SOUSEI Program of the MEXT of Japan for research cooperation and resources supporting this research. We would also like to thank Keio SFC Academic Society (Research Grant 2015), Keio University for providing the funding of this publication.

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ABOUT THE AUTHORS Dr. Yingjiu Bai: Project Associate Professor, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa-ken, Japan Dr. Ikuyo Kaneko: Professor, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa-ken, Japan Dr. Hiroaki Nishi: Professor, Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa-ken, Japan Dr. Hidetaka Sasaki: Head, Third Laboratory in the Atmospheric Environment and Applied Meteorology Research Department, Meteorological Research Institute, Tsukuba, Ibaraki-ken, Japan Dr. Akihiko Murata: Senior Researcher, Third Laboratory in the Atmospheric Environment and Applied Meteorology Research Department, Meteorological Research Institute, Tsukuba, Ibaraki-ken, Japan Kazuo Kurihara, MSc: Researcher, Atmospheric Environment and Applied Meteorology Research Department, Meteorological Research Institute, Tsukuba, Ibaraki-ken, Japan Dr. Izuru Takayabu: Director, Atmospheric Environment and Applied Meteorology Research Department, Meteorological Research Institute, Tsukuba, Ibaraki-ken, Japan

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The International Journal of Climate Change: Impacts and Responses seeks to create an interdisciplinary forum for discussion of evidence of climate change, its causes, its ecosystemic impacts, and its human impacts. The journal also explores technological, policy, strategic, and social responses to climate change. The International Journal of Climate Change: Impacts and Responses is a peer-reviewed, scholarly journal.

ISSN 1835-7156