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Large-scale circulations around a city are co-modulated by the urban heat island and by regional wind patterns. Depending on these variables, the circulations fall into different regimes ranging from advection-dominated (plume regime) to convection-driven (bubble regime). Using dimensional analysis and large-eddy simulations, this study investigates how these different circulations scale with urban and rural heat fluxes, as well as upstream wind speed. Two dimensionless parameters are shown to control the dynamics of the flow: (1) the ratio of rural to urban thermal convective velocities that contrasts their respective buoyancy fluxes and (2) the ratio of bulk inflow velocity to the convection velocity in the rural area. Finally, the vertical flow velocities transecting the rural to urban transitions are used to develop a criterion for categorizing different large-scale circulations into plume, bubble or transitional regimes. The findings have implications for city ventilation since bubble regimes are expected to trap pollutants, as well as for scaling analysis in canonical mixed-convection flows. 

Using a detailed model, sensitivity analyses are conducted to identify the leading physical determinants and heat fluxes that control energy exchange between surface runoff and urban pavements during rainfall. These analyses confirm that pavement characteristics, such as albedo and thermal effusivity, strongly influence the initial temperature of the pavement before rain starts. Moreover, this sensitivity propagates to the runoff and pavement temperatures as well as to sensible heat and evaporation fluxes during and after rainfall. Heat transfer inside the runoff and pavement during rainfall is also very sensitive to the rain temperature and is the leading process in surface cooling (the classically important sensible and latent heat fluxes to the atmosphere are minor contributors). Finally, based on the findings from the sensitivity analyses, using a bulk energy approach, a reduced version of the full model is proposed. This simple model uses the spatially averaged temperatures of the runoff and pavement and can predict their temperatures and the associated energy fluxes almost as accurately as the full model. The reduced model has the added advantages of computational efficiency and simplicity of implementation in coarse earth system models.

 

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.