Future urban climate projection in a tropical megacity based on global climate change and local urbanization scenarios
Introduction
Future climate change is expected to increase the vulnerability of urban populations to associated risks, which are further exacerbated in low- and middle-income countries (Revi et al., 2014). Moreover, urban agglomerations have local effects on the atmosphere (Arnfield, 2003; Masson, 2000; Oke et al., 2017) that could intensify the effects of global climate change. For example, from 1961 to 2010, the mean annual temperatures in 39 major cities increased at rates of 0.12–0.45 °C per decade (Rosenzweig et al., 2015). Meanwhile, urban areas have been projected to be warmer than surrounding rural areas (Kalnay and Cai, 2003) and are projected to experience more tropical nights in the future (McCarthy et al., 2010; Oleson, 2012) under climate change. Such urban effects will have a significant impact on energy consumption for cooling (Martilli, 2014), heat waves (Anderson et al., 2018; Lemonsu et al., 2015; Wouters et al., 2017; Zhao et al., 2018), sea breeze penetration (Varquez et al., 2014), and urban extreme rainfall (Niyogi et al., 2017).
Until recently, general circulation models could not capture urban effects due to their coarse spatial and temporal resolutions (Best, 2006). These models either did not include or roughly assumed urbanization in their future projections. However, recent advances in mesoscale climate modeling have enabled the analysis of urban effects under climate change, although most relevant studies have been conducted at city scales (Doan et al., 2019; Doan et al., 2016; Hamdi et al., 2014; Lemonsu et al., 2015), with very few focused on a global scale (Fischer et al., 2012; Oleson et al., 2011). The results of these studies have revealed that urban areas are expected to experience greater temperature increases than surrounding rural areas under a given future climate change scenario. However, such studies have been limited to the assumption that future urban land use is static to present conditions. In reality, urban expansion in developing countries is expected to accelerate in the near future. Therefore, projecting the future urban climate solely based on climate change effects under the assumption of a static urban area is unrealistic.
Application of urban growth controls (Georgescu et al., 2014; Stone et al., 2010) and urban fabric modifications (Georgescu, 2015; de Munck et al., 2018) to weather models has shown that urban adaptation strategies determine the magnitude of urban effects, indicating that sound urban planning is necessary to achieve highly resilient cities. Recent studies have highlighted the behavior of urban effects under climate change and future urban expansion scenarios in cities of American (Li et al., 2016; Tewari et al., 2017), European (Hamdi et al., 2014; Wouters et al., 2017), Asian (Adachi et al., 2012; Yang et al., 2016), and Australian (Argüeso et al., 2015). The results have shown that due to background temperature increases local urban effects cause greater warming than under global future climate scenarios. However, these studies limited urban expansion to practical urbanization scenarios or two-dimensional urban land-use scenarios with simple parameterizations of urban morphology. By contrast, few advanced studies have coupled future global climate change scenarios and local urbanization based on socio-economic scenarios (Kusaka et al., 2016; Masson et al., 2014). Such studies excel in the projection of future urban morphological parameters and heat emissions based on socio-economic scenarios. Despite these studies representing future urban climates more realistically, they have been restricted to developed countries and their study areas. More recent studies analyzed urban climate by combining global climate change scenario and local urbanization based on issued urban development master plan in developing countries (Doan et al., 2019; Doan et al., 2016; Iizuka, 2018; Lee et al., 2017; Yang et al., 2016). However, it is very challenging to get such urban planning data in global scale.
To address this shortcomings, we developed a future urban climate projection by integrating global climate change projection based on pseudo global warming method and local urbanization scenario based entirely on globally available datasets. Compared with previous similar studies, our goal is to develop a local urbanization scenario considering building volumes and heat emission derived from global socio-economic scenario. We aversely using any data from local governments or stakeholders to optimize global implementation. The framework was designed to provide a generic, repeatable, and realistic approach to futuristic urban climate studies. We applied the method to project the future urban climate in Greater Jakarta, Indonesia, as a large tropical megacity in Southeast Asia, until the 2046–2055. We integrated Representative Concentration Pathways (RCP; van Vuuren et al., 2011) and urbanization scenario derived from Shared Socioeconomic Pathways (SSP; O'Neill et al., 2014) in mesoscale weather simulations. Accordingly, we were able to analyze the global, urban, and combined global–urban effects. The ultimate goal is that this approach can be applied to other megacities in developing countries.
Section snippets
Methodology
Two global emission scenarios of RCP, RCP2.6 and RCP8.5 were selected considering the best and worst emission scenario respectively. We paired RCP with SSP to include local urbanization adaptation scenario thus creating an integrated global-urban climate projection analysis. We used SSP1 and SSP3 scenario generated from Integrated Assessment Model of Asia Pacific Integrated Model (AIM) globally available at regional and country scale (Fujimori et al., 2012, Fujimori et al., 2017). RCP scenario
Present climate validation
Validation was performed using 2006–2015 observation data from three World Meteorological Organization-standardized urban stations located within the target domain: Kemayoran station (KMO), Tanjung Priok station (TPR), and Cengkareng station (CGK), with 3-hourly data taken from ogimet.com (see Appendix A1). The model predicted the nighttime temperature well, with root mean square error (RMSE) values of 1.03, 1.14, and 1.45 for TPR, KMO, and CGK, respectively. However, the model generally
Discussion
The findings of this study imply that the effect of combined climate change and local urbanization is highly varied at a 1-km resolution, despite the overall small average values. It is apparent that the future urban climate is approximately a linear sum of future global climate change and local urbanization effect, which agrees with similar previous studies (Doan and Kusaka, 2018; Lee et al., 2017). The urbanization effect itself strongly related to how urban parameters and AHE change in the
Conclusion
Projections of the future climate of tropical, rapidly urbanizing megacities cannot rely solely on the effects of emission scenarios. The results of this study imply that urban effects can reach the same magnitude as global effects. Even though urban signals dominated the climate signal by an average factor of 12.2 (13.0) under the RCP2.6&Compact (RCP8.5&BaU) scenario, the findings showed that the urban signal distribution values were highly and significantly spatially diverse. In order to
Acknowledgement
This research was supported by the Environment Research and Technology Development Fund (S-14) of the Ministry of the Environment, Japan. We are grateful to Shinichiro Fujimori and AIM/CGE team, Center for Social and Environmental Systems Research, National Institute for Environmental Studies, Tsukuba, Japan for providing the AIM/CGE model for Indonesia (http://www.nies.go.jp/social/dp/pdf/2012-01.pdf). Global AHE data with a 1-h temporal resolution and 1-km spatial resolution can be downloaded
References (77)
- et al.
Urban roughness parameters estimation from globally available datasets for mesoscale modeling in megacities
Urban Clim.
(2017) - et al.
Evaluating the impacts of greening scenarios on thermal comfort and energy and water consumptions for adapting Paris city to climate change
Urban Clim.
(2018) - et al.
Global anthropogenic heat flux database with high spatial resolution
Atmos. Environ.
(2017) Future environmental assessment and urban planning by downscaling simulations
J. Wind Eng. Ind. Aerodyn.
(2018)- et al.
Impacts of disaster mitigation/prevention urban structure models on future urban thermal environment
Sustain. Cities Soc.
(2015) - et al.
Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model
Comput. Environ. Urban. Syst.
(2010) - et al.
Impacts of land use changes from the Hanoi master plan 2030 on urban heat islands: part 2. Influence of global warming
Sustain. Cities Soc.
(2017) - et al.
Vulnerability to heat waves: impact of urban expansion scenarios on urban heat island and heat stress in Paris (France)
Urban Clim.
(2015) An idealized study of city structure, urban climate, energy consumption, and air quality
Urban Clim.
(2014)- et al.
Adapting cities to climate change: A systemic modelling approach
Urban Clim.
(2014)