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A global map of local climate zones to support earth system modelling and urban-scale environmental science A global map of local climate zones to support earth system modelling and urban-scale... Matthias Demuzere et al.
There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale. These data can support a range of environmental services, since cities are places of intense resource consumption and waste generation and of concentrated infrastructure and human settlement exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data are also required to explore future urbanization pathways and urban design strategies in order to lock in long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability and mitigation role. To serve this purpose, we present a 100 m-resolution global map of local climate zones (LCZs), a universal urban typology that can distinguish urban areas on a holistic basis, accounting for the typical combination of micro-scale land covers and associated physical properties. The global LCZ map, composed of 10 built and 7 natural land cover types, is generated by feeding an unprecedented number of labelled training areas and earth observation images into lightweight random forest models. Its quality is assessed using a bootstrap cross-validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open-source data on surface cover, surface imperviousness, building height, and anthropogenic heat. As each LCZ type is associated with generic numerical descriptions of key urban canopy parameters that regulate atmospheric responses to urbanization, the availability of this globally consistent and climate-relevant urban description is an important prerequisite for supporting model development and creating evidence-based climate-sensitive urban planning policies. This dataset can be downloaded from https://doi.org/10.5281/zenodo.6364594 (Demuzere et al., 2022a).
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B.: A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/-14-3835-2022, 2022.
Preserving The Past, Into The Future
Cities are at the forefront of global climate change science owing to their emissions of greenhouse gases and their exposure to projected hazards, such as sea-level rise and climate warming (IPCC, 2022). As a result, they are the focus of mitigation and adaptation policies and, as they have governance structures in place, are an ideal scale to affect change. The crucial role that cities can play in this arena is recognized at the international level: the new United Nations Agenda and the 11th Sustainable Development Goal focus on urban resilience, climate, and environment sustainability of cities, two of the four challenges identified by the World Meteorological Organisation (WMO) World Weather Research Program are urban-related: high-impact weather, including impacts in cities, and urbanization, the Intergovernmental Panel on Climate Change Cities and Climate Change Scientific Committee identified six research priorities for science to have a stronger role in urban policy and practice, and advocacy groups like C40 (https://www.c40.org/cop26/, last access: 22 August 2022) play an increasingly important role in achieving national emission targets and enhancing resilience (Creutzig et al., 2016; Bai et al., 2018; Masson et al., 2020).
Cities are simultaneously drivers of regional and local climate changes. The conversion of earth's land surface into urban areas is one of the most irreversible human impacts on the global ecosystem (Grimm et al., 2008; Reba and Seto, 2020). In addition to the many modifications to the biosphere, hydrosphere, and lithosphere (Seto et al., 2012; D'Amour et al., 2017; Liu et al., 2019; van Vliet, 2019; Zhang et al., 2019; McDonough et al., 2020), urbanization affects energy demand (Creutzig et al., 2015; Güneralp et al., 2017), releases anthropogenic heat emissions and pollutants (Patella et al., 2018; Takane et al., 2019), and alters the urban climate (Oke et al., 2017). Current and future climate changes represent significant risks to urban populations and to the natural and physical infrastructure systems of cities (Costello et al., 2009; UN, 2019; Wang et al., 2021). In this context, the WMO has advocated the development of integrated urban services (IUS) – using observations (remote and on-site) and models – that addresses the panoply of hazards that cities face and the needs of service providers, including emergency services, public health bodies, energy produces, and urban designers and planners (Baklanov et al., 2018; Grimmond et al., 2020).
Despite their importance as a spatial nexus of climate drivers and of governance, cities are largely excluded from global climate science owing to their relatively small extent and our limited knowledge of their spatial structures. Global-scale climate models have only recently evolved to accommodate urban-scale landscapes, even though the urban parameters that are used by these models are limited in scope (Zhao et al., 2021). At regional and urban scales, model developments use far more detailed parameters that include descriptions of the net impacts of buildings in creating distinct urban canopy and boundary layers. While some theoretical challenges remain, it is now possible to simulate urban effects on climate between and above buildings at sub-urban scales (Barlow, 2014). Scientific advances will soon allow variable-resolution modelling that will incorporate the hierarchy of climate processes and impacts. However, the absence of suitable and universal global urban landscape data to inform these models represents a serious impediment to progress (Zhao et al., 2021; Hertwig et al., 2021). Hence, a comprehensive database is needed on cities globally that supports multi-scale modelling, provides a spatial framework for interpreting on-site and remote measurements, and allows the meaningful transfer of knowledge among and within cities (Rosenzweig et al., 2010; Hidalgo et al., 2018).
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The critical data needed to support urban climate science include information on urban form and functions. Measures of form include e.g. building density, street widths, building heights, construction materials, and fraction of vegetated areas. These attributes largely influence the local climate and the “adaptation” capacity of a city (e.g. to ensure a comfortable thermal environment for its inhabitants). Urban functions describe the emissions of waste heat, materials, and gases into the overlying atmosphere. Appropriate measures would include the anthropogenic heat flux (AHF) and CO2 emissions. Form and function are correlated. For example, population density regulates energy consumption and therefore the potential to mitigate global warming by reducing the greenhouse gas emissions; variations in building layout and heights moderate surface roughness and contribute to the atmospheric dispersive conditions and thus the air quality (Martilli, 2014). Models are needed to assess the net benefits of climate-based interventions that may have unintended outcomes. For example, densely built and occupied cities (so-called compact 15 min cities) will reduce traffic, energy demand, and CO2 emissions and in some cases improve air quality (Stone et al., 2007; McDonough et al., 2020; Williams et al., 2010) but will enhance warming and heat stress by reducing vegetative cover and the sky view factor in the street canyons and increase the spatial density of the anthropogenic heat (Demuzere et al., 2014; Lai et al., 2019). Understanding how different urban forms interact with the atmosphere is key to redesigning cities, and, more importantly, planning future urbanization. It is therefore essential to have information that differentiates between urban forms that can be used by atmospheric models to simulate the future climatic conditions and different urban form scenarios. Our objective here is to generate these data to support model evolution and stimulate research on multi-scale climate projections to manage urban risks.
Acquiring urban data at a global scale is not a trivial exercise owing to the operational definition of “urban”, the scattered extents of cities globally, and their complex intra-urban geographies; for example, the Global Human Settlement Layer Urban Centres Database identifies over 13 000 settlements (Florczyk et al., 2019), while X. Li et al. (2020) generated over 60 000 global urban boundaries. At the global scale, there are several datasets that identify the extent of contiguous urban areas based on built-up or impervious surface cover (Zhou et al., 2015; Corbane et al., 2017; Esch et al., 2017; Marconcini et al., 2020; Gong et al., 2020; X. Zhang et al., 2020; Zhao et al., 2022) but none that provide intra-urban morphological details (green cover, built density, building heights, etc.) that are needed by scientists to generate the urban canopy parameters (UCPs) to run models and by urban policy-makers to make informed decisions based on analyses of risk. For many cities, relevant information may be gleaned from local sources that maintain municipal geographic databases (e.g. Biljecki et al., 2021), but these data vary in terms of their quality, consistency, and accessibility, which limits their wider applicability (Zhu et al., 2019). The 100 m-resolution global local climate zone (LCZ) map presented here addresses this need for more detailed intra-urban data. This product is the outcome of more than a decade of research on how best to acquire, evaluate, and deploy urban data in support of climate science (Stewart and Oke, 2012; Bechtel and Daneke, 2012; Ching et al., 2018).
The LCZ typology is currently the only universal classification that categorizes urban landscapes using a scheme that identifies readily recognizable neighbourhood types based on their form and function, which modify the surface energy and water budgets. Critically, each LCZ type is linked to meaningful UCP value ranges that can be used for physically based modelling (Stewart and Oke, 2012; Ching et al., 2019; Demuzere et al., 2020a). This goes beyond the urban
