Because of a projected surge of several billion urban inhabitants by mid-century, a rising urgency exists to advance local and strategically deployed measures intended to ameliorate negative consequences on urban climate (e.g., heat stress, poor air quality, energy/water availability). Here we highlight the importance of incorporating scale-dependent built environment induced solutions within the broader umbrella of urban sustainability outcomes, thereby accounting for fundamental physical principles. Contemporary and future design of settlements demands cooperative participation between planners, architects, and relevant stakeholders, with the urban and global climate community, which recognizes the complexity of the physical systems involved and is ideally fit to quantitatively examine the viability of proposed solutions. Such participatory efforts can aid the development of locally sensible approaches by integrating across the socioeconomic and climatic continuum, therefore providing opportunities facilitating comprehensive solutions that maximize benefits and limit unintended consequences.

We conducted microclimate simulations in ENVI-Met 3.1 to evaluate the impact of vegetation in lowering temperatures during an extreme heat event in an urban core neighborhood park in Phoenix, Arizona. We predicted air and surface temperatures under two different vegetation regimes: existing conditions representative of Phoenix urban core neighborhoods, and a proposed scenario informed by principles of landscape design and architecture and Urban Heat Island mitigation strategies. We found significant potential air and surface temperature reductions between representative and proposed vegetation scenarios:

1. A Park Cool Island effect that extended to non-vegetated surfaces.
2. A net cooling of air underneath or around canopied vegetation ranging from 0.9 °C to 1.9 °C during the warmest time of the day.
3. Potential reductions in surface temperatures from 0.8 °C to 8.4 °C in areas underneath or around vegetation.

We examined the horizontal and vertical nocturnal cooling influence of a small park with irrigated lawn and xeric surfaces (∼3 ha) within a university campus of a hot arid city. Temperature data from 0.01- to 3-m heights observed during a bicycle traverse of the campus were combined with modeled spatial temperature data simulated from a three-dimensional microclimate model (ENVI-met 3.1). A distinct park cool island, with mean observed magnitudes of 0.7–3.6°C, was documented for both traverse and model data with larger cooling intensities measured closer to surface level. Modeled results possessed varying but generally reasonable accuracy in simulating both spatial and temporal temperature data, although some systematic errors exist. A combination of several factors, such as variations in surface thermal properties, urban geometry, building orientation, and soil moisture, was likely responsible for influencing differential urban and non-urban near-surface temperatures. A strong inversion layer up to 1 m over non-urban surfaces was detected, contrasting with near-neutral lapse rates over urban surfaces. A key factor in the spatial expansion of the park cool island was the advection of cooler park air to adjacent urban surfaces, although this effect was mostly concentrated from 0- to 1-m heights over urban surfaces that were more exposed to the atmosphere.

Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.