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We developed statistical models (called impact functions) in this study that mimicked the responses of summer mean, maximum, and minimum surface temperatures to given land-use changes (LUC) simulated with a regional climate model (RCM). The island of Shikoku in western Japan was used as an example. The impact functions were based on the RCM-simulated sensitiv- ity of temperatures to changes for each of 5 land-surface parameters (surface albedo, evaporative efficiency, roughness length, heat capacity, and thermal conductivity). Thus, the relative impor- tance of a specific land-surface parameter for the local temperature (e.g. greater sensitivity of daily mean temperature to evaporative efficiency) determines the impact functions. We compared the impact functions to the independently evaluated RCM simulations to assess how well our results matched the historical LUC-induced RCM warming simulations in this area between 1987 and 2006. The impact function performance varied with grid size: the 10 km results matched the RCM simulations best, with an error of 1015%. Next, the impact functions were used to estimate the potential warming range for different LUC scenarios. Given the geographical distribution of present-day land use/land cover, the Monte Carlo simulations with impact functions indicate that LUC-induced changes in summer mean temperature potentially range from 0.4 (when forest is converted to paddy) to +1.3 °C (when forest is converted to building lots). This result indicates that, on an area mean basis, the cooling achieved through land-use management is limited in its ability to offset climate change-induced warming in future decades.

Impact functions for land‑use-induced surface warming, and their applications in uncertainty analysis