A Machine Learning application towards a better representation of Madrid’s urban climate

Cities are considered local “hotspots” of climate change. Urban areas concentrate a large fraction of global population, wealth, and emissions, exposing their inhabitants to climate change impacts. Therefore, the improvement of urban present climate description and future projections are paramount for designing adaptation and mitigation strategies. The Global Climate Models are state-of-the-art tools for projecting future climate. However, most of the simulations have coarse resolutions and do not have physical urban parametrisations to adequately represent the physical properties and processes at the urban scale.

The advantage of applying a machine learning approach –  Extreme Gradient Boosting (XGBoost) – is explored for better describing Madrid’s urban climate. Namely, the ability to reproduce present and future climates: 2-m air temperature (Tmax, Tmin); surface temperature (LST); urban heat island (UHI) and surface urban heat island (SUHI) effects. The XGBoost is evaluated at monthly and daily scales for local ground temperatures and, also at hourly scale, to represent the spatial structure of land surface temperature w.r.t. remote sensing data. Firstly, for present climate, XGBoost is trained with a set of ERA5 predictors (0.25°), ground stations, and Land Surface Temperature observations. Secondly, a number of sensitivity cases are performed to assess the results dependency to predictors and their resolution. Thirdly, the learned relationships between the set of predictors and predictands, is applied to Earth System Global Climate Models (ESGCM) predictors, providing historical and future climate projections for the 21^st century under four emission scenarios.

Overall, XGBoost results reveal a good performance and significant added value against ERA5 and the ESGCM. XGBoost greatly improves the reproduction of the present climate Tmax, Tmin, LST, and more importantly, the UHI (-0.5°C and +3°C for Tmax and Tmin), and the SUHI (+1°C and +2°C for Tmax and Tmin). For future climate, the XGBoost significantly corrects the ESGCM UHI misrepresentation but seems to underestimate the expected Madrid’s local warming (3.5°C anomaly).

Acknowledgements: This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020), UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). The authors would like also to acknowledge the EEA-Financial Mechanism 2014–2021 and the Portuguese Environment Agency through the Pre-defined Project-2 National Roadmap for Adaptation XXI (PDP-2). Angelina Bushenkova was supported by a grant through the project “Plano de Ação Climática do Município de Barcelos (PMACB)”.