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.

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This paper examines runoff dynamics and heat transfer during rainfall over urban surfaces, in particular pavements. A kinematic wave approach is combined with heat storage and transfer schemes to develop a model for pervious and impervious pavements. The resulting framework is a numerical prognostic model that can simulate the temperature fields in the subsurface and runoff layers to capture the rapid cooling of the surface, as well as the thermal pollution advected in the runoff. Extensive field measurements are conducted over several types of experimental pavements in Arizona to probe the physics and then to validate the model. The experimental data and the model results are in good agreement, and their joint analysis elucidates the physics of the rapid heat transfer from the subsurface to the runoff. A demonstrative application of the model over a (hypothetical) parking lot, with impervious or pervious asphalt, is then presented. It illustrates that the rate of ground surface temperature cooling for the impervious pavement is lower than the pervious one (where infiltration is very effective at removing heat). Finally, the analysis of the energy budgets unravels the relative importance of the various physical mechanisms in transferring heat from the subsurface to the runoff and the atmosphere. This transfer is dominated by terms associated with water flux and subsurface heat extraction, while latent, sensible, and radiative heat fluxes are minor contributors. The findings underline the importance of including rainfall‐induced cooling in geophysical models that seek to study urban heat islands or urban precipitation modification.

Abstract. To compare the impact of surface–atmosphere exchanges from rural and urban areas, fully vegetated areas (e.g. deciduous trees, evergreen trees and grass) commonly found adjacent to cities need to be modelled. Here we provide a general workflow to derive parameters for SUEWS (Surface Urban Energy and Water Balance Scheme), including those associated with vegetation phenology (via leaf area index, LAI), heat storage and surface conductance. As expected, attribution analysis of bias in SUEWS-modelled QE finds that surface conductance (gs) plays the dominant role; hence there is a need for more estimates of surface conductance parameters. The workflow is applied at 38 FLUXNET sites. The derived parameters vary between sites with the same plant functional type (PFT), demonstrating the challenge of using a single set of parameters for a PFT. SUEWS skill at simulating monthly and hourly latent heat flux (QE) is examined using the site-specific derived parameters, with the default NOAH surface conductance parameters (Chen et al., 1996). Overall evaluation for 2 years has similar metrics for both configurations: median hit rate between 0.6 and 0.7, median mean absolute error less than 25 W m−2, and median mean bias error ∼ 5 W m−2. Performance differences are more evident at monthly and hourly scales, with larger mean bias error (monthly: ∼ 40 W m−2; hourly ∼ 30 W m−2) results using the NOAH-surface conductance parameters, suggesting that they should be used with caution. Assessment of sites with contrasting QE performance demonstrates how critical capturing the LAI dynamics is to the SUEWS prediction skills of gs and QE. Generally gs is poorest in cooler periods (more pronounced at night, when underestimated by ∼ 3 mm s−1). Given the global LAI data availability and the workflow provided in this study, any site to be simulated should benefit.