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Abstract Increasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modeling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3‐7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual‐mean canopy urban heat island intensity from 0.8°C in 2015 to 0.2°C by 2099. Compared to high‐density and medium‐density urban areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. This scheme offers potential applications like simulating natural albedo variations across urban surfaces and enables the inclusion of other urban parameters, such as surface emissivity. 

Advances in data science and artificial intelligence (AI) offer unprecedented opportunities to provide actionable insights, drive innovative solutions, and create long-term strategies for sustainable development in response to the triple existential crises facing humanity: climate change, pollution, and biodiversity loss. The rapid development of AI models has been the subject of extensive debate and is high on the political agenda, but at present the vast potential for AI to contribute positively to informed decision making, improved environmental risk management, and the development of technological solutions to sustainability challenges remains underdeveloped. In this paper, we consider four inter-dependent areas in which data science and AI can make a substantial contribution to developing sustainable future interactions with the environment: (i) quantification and tracking progress towards the United Nations Sustainable Development Goals; (ii) embedding AI technologies to reduce emissions at source; (iii) developing systems to increase our resilience to natural hazards; (iv) Net Zero and the built environment. We also consider the wider challenges associated with the widespread use of AI, including data access and discoverability, trust and regulation, inference and decision making, and the sustainable use of AI. 

Abstract Urban areas are increasingly vulnerable to the impacts of climate change, necessitating accurate simulations of urban climates in Earth system models (ESMs) in support of large‐scale urban climate adaptation efforts. ESMs underrepresent urban areas due to their small spatial extent and the lack of detailed urban landscape data. To enhance the accuracy of urban representation, this study integrated the local climate zones (LCZs) scheme within the Community Earth System Model (CESM) to better represent urban heterogeneity. We adopted a modular approach to incorporate the 10 built LCZ classes into CESM as a new option in addition to the default urban three‐class scheme (i.e., tall building district, high density, and medium density). CESM simulations using the LCZ‐based urban characteristics were validated globally at 20 flux tower sites, showing site‐averaged improvement in modeling upward longwave radiation () and anthropogenic heat flux (), but increased uncertainties in modeling sensible heat flux (). The root‐mean‐square error between the observed and simulated using the LCZ decreased by 4% compared to using the default. Model sensitivity experiments revealed that and had comparable sensitivity to LCZ urban morphological and thermal parameter subsets. This study assessed and demonstrated the implementation as the starting point for future work on better resolving urban areas in Earth system modeling.