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Urban surface and near-surface air temperatures are known to be often higher than their rural counterparts, a phenomenon now labeled as the urban heat island effect. However, whether the elevated urban temperatures are more persistent than rural temperatures at timescales commensurate to heat waves has not been addressed despite its importance for human health. Combining numerical simulations by a global climate model with a surface energy balance theory, it is demonstrated here that urban surface and near-surface air temperatures are significantly more persistent than their rural counterparts in cities dominated by impervious materials with large thermal inertia. Further use of these materials will result in even stronger urban temperature persistence, especially for tropical cities. The present findings help pinpoint mitigation strategies that can simultaneously ameliorate the larger magnitude and stronger persistence of urban temperatures. 

Abstract The sensitivity of urban canopy air temperature ( $T_a$) to anthropogenic heat flux ( $Q_{AH}$) is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$(where $\mathrm{\Delta }$represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$simulated by the Community Land Model Urban over the contiguous United States (CONUS). In summer, the median $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$is around 0.01 $\text{K\hspace{0.17em}}{\left(\text{W\hspace{0.17em}}{\text{m}}^{-\text{2}}\right)}^{-1}$over the CONUS. Besides the direct effect of $Q_{AH}$on $T_a$, there are important feedbacks through changes in the surface temperature, the atmosphere–canopy air heat conductance ( $c_a$), and the surface–canopy air heat conductance. The positive and negative feedbacks nearly cancel each other out and $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$is mostly controlled by the direct effect in summer. In winter, $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with $c_a$. The spatial and temporal (both seasonal and diurnal) variability of $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$as well as the nonlinear response of $\mathrm{\Delta }{T}_a$to $\mathrm{\Delta }{Q}_{AH}$are strongly related to the variability of $c_a$, highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models. 

Abstract While the significance of quantifying the biophysical effects of deforestation is rarely disputed, the sensitivities of land surface temperature (LST) to deforestation-induced changes in different biophysical factors (e.g., albedo, aerodynamic resistance, and surface resistance) and the relative importance of those biophysical changes remain elusive. Based on the subgrid-scale outputs from two global Earth system models (ESMs, i.e., the Geophysical Fluid Dynamics Laboratory Earth System Model and the Community Earth System Model) and an improved attribution framework, the sensitivities and responses of LST to deforestation are examined. Both models show that changes in aerodynamic resistance are the most important factor responsible for LST changes, with other factors such as albedo and surface resistance playing secondary but important roles. However, the magnitude of the contributions from different biophysical factors to LST changes is quite different for the two ESMs. We find that the differences between the two models in terms of the sensitivities are smaller than those of the corresponding biophysical changes, indicating that the dissimilarity between the two models in terms of LST responses to deforestation is more related to the magnitude of biophysical changes. It is the first time that the attribution of subgrid surface temperature variability is comprehensively compared based on simulations with two commonly used global ESMs. This study yields new insights into the similarity and dissimilarity in terms of how the biophysical processes are represented in different ESMs and further improves our understanding of how deforestation impacts on the local surface climate.