The urban energy balance of a lightweight low-rise neighborhood in Andacollo, Chile

Worldwide, the majority of rapidly growing neighborhoods are found in the Global South. They often exhibit different building construction and development patterns than the Global North, and urban climate research in many such neighborhoods has to date been sparse. This study presents local-scale observations of net radiation (Q*) and sensible heat flux (QH) from a lightweight low-rise neighborhood in the desert climate of Andacollo, Chile, and compares observations with results from a process-based urban energy-balance model (TUF3D) and a local-scale empirical model (LUMPS) for a 14-day period in autumn 2009. This is a unique neighborhood-climate combination in the urban energy-balance literature, and results show good agreement between observations and models for Q* and QH. The unmeasured latent heat flux (QE) is modeled with an updated version of TUF3D and two versions of LUMPS (a forward and inverse application). Both LUMPS implementations predict slightly higher QE than TUF3D, which may indicate a bias in LUMPS parameters towards mid-latitude, non-desert climates. Overall, the energy balance is dominated by sensible and storage heat fluxes with mean daytime Bowen ratios of 2.57 (observed QH/LUMPS QE)–3.46 (TUF3D). Storage heat flux (ΔQS) is modeled with TUF3D, the empirical objective hysteresis model (OHM), and the inverse LUMPS implementation. Agreement between models is generally good; the OHM-predicted diurnal cycle deviates somewhat relative to the other two models, likely because OHM coefficients are not specified for the roof and wall construction materials found in this neighborhood. New facet-scale and local-scale OHM coefficients are developed based on modeled ΔQS and observed Q*. Coefficients in the empirical models OHM and LUMPS are derived from observations in primarily non-desert climates in European/North American neighborhoods and must be updated as measurements in lightweight low-rise (and other) neighborhoods in various climates become available

Scaling And Machine Learning Analysis Of Turbulent Fluxes Of Momentum And Heat In The Microclimate Of An Urban Canyon

Turbulent flow inside the urban roughness sublayer, despite its complexities, plays a crucial role in the microclimate of the built environment. The parameterization of flow in the urban roughness sublayer provides a better understanding of turbulent exchange process leading to accurate weather forecasting. This study focused on developing relationships between turbulent quantities, including momentum and heat fluxes, and mean quantities such as mean wind speeds. Field data, including wind directions, wind speeds, and thermal stability conditions, were collected from an urban canopy in Guelph, Ontario, Canada during the summer 2017. Comparative data was obtained from a nearby rural station. A systematic scaling analysis was performed to identify a range of quantities highly related to turbulent fluxes. All combinations of quantities leading to dimensionless groups were evaluated. Linear and nonlinear correlation coefficients between different groups of variables identified when mean and turbulent quantities were related. Significant improvement in correlation coefficients was observed using high order polynomial regression, revealing the challenge of developing a robust model for predicting nonlinear behavior of turbulence. This study also used artificial neural networks (ANNs) to find nonlinear relationships between turbulent and mean quantities. As used here, an ANN is a multivariable function which attempts to approach the exact value of turbulent flux based on independent variables, properly chosen from dimensionless groups. Results showed that these approaches can successfully relate most, but not all, turbulent quantities to mean quantities

The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET v1.0) : an efficient and user-friendly model of city cooling

The adverse impacts of urban heat and global climate change are leading policymakers to consider green and blue infrastructure (GBI) for heat mitigation benefits. Though many models exist to evaluate the cooling impacts of GBI, their complexity and computational demand leaves most of them largely inaccessible to those without specialist expertise and computing facilities. Here a new model called The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET) is presented. TARGET is designed to be efficient and easy to use, with fewer user-defined parameters and less model input data required than other urban climate models. TARGET can be used to model average street-level air temperature at canyon-to-block scales (e.g. 100 m resolution), meaning it can be used to assess temperature impacts of suburb-to-city-scale GBI proposals. The model aims to balance realistic representation of physical processes and computation efficiency. An evaluation against two different datasets shows that TARGET can reproduce the magnitude and patterns of both air temperature and surface temperature within suburban environments. To demonstrate the utility of the model for planners and policymakers, the results from two precinct-scale heat mitigation scenarios are presented. TARGET is available to the public, and ongoing development, including a graphical user interface, is planned for future work

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models’ evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and southeast England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP–BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model’s cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models’ biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies. Significance Statement Urban climate simulations are subject to spatially heterogeneous biases in urban air temperatures. Common validation methods using official weather stations do not suffice for detecting these biases. Using a dense set of personal weather sensors in London, we detect these biases before proposing an innovative way to correct them with machine learning techniques. We argue that any urban climate impact study should use such a technique if possible and that urban climate scientists should continue investigating paths to improve our methods

Evaluating the association between extreme heat and mortality in urban Southwestern Ontario using different temperature data sources

Urban areas have complex thermal distribution. We examined the association between extreme temperature and mortality in urban Ontario, using two temperature data sources: high-resolution and weather station data. We used distributed lag non-linear Poisson models to examine census division-specific temperature–mortality associations between May and September 2005–2012. We used random-effect multivariate meta-analysis to pool results, adjusted for air pollution and temporal trends, and presented risks at the 99th percentile compared to minimum mortality temperature. As additional analyses, we varied knots, examined associations using different temperature metrics (humidex and minimum temperature), and explored relationships using different referent values (most frequent temperature, 75th percentile of temperature distribution). Weather stations yielded lower temperatures across study months. U-shaped associations between temperature and mortality were observed using both high-resolution and weather station data. Temperature–mortality relationships were not statistically significant; however, weather stations yielded estimates with wider confidence intervals. Similar findings were noted in additional analyses. In urban environmental health studies, high-resolution temperature data is ideal where station observations do not fully capture population exposure or where the magnitude of exposure at a local level is important. If focused upon temperature–mortality associations using time series, either source produces similar temperature–mortality relationships

The Co-Production of Sustainable Future Scenarios

Scenarios are a tool to develop plausible, coherent visions about the future and to foster anticipatory knowledge. We present the Sustainable Future Scenarios (SFS) framework and demonstrate its application through the Central Arizona-Phoenix Long-term Ecological Research (CAP LTER) urban site. The SFS approach emphasizes the co-development of positive and long-term alternative future visions. Through a collaboration of practitioner and academic stakeholders, this research integrates participatory scenario development, modeling, and qualitative scenario assessments. The SFS engagement process creates space to question the limits of what is normally considered possible, desirable, or inevitable in the face of future challenges. Comparative analyses among the future scenarios demonstrate trade-offs among regional and microscale temperature, water use, land-use change, and co-developed resilience and sustainability indices. SFS incorporate diverse perspectives in co-producing positive future visions, thereby expanding traditional future projections. The iterative, interactive process also creates opportunities to bridge science and policy by building anticipatory and systems-based decision-making and research capacity for long-term sustainability planning

A Microscale Three-dimensional Urban Energy Balance Model for Studying Surface Temperatures

A microscale three-dimensional (3-D) urban energy balance model, Temperatures of Urban Facets in 3-D (TUF-3D), is developed to predict urban surface temperatures for a variety of surface geometries and properties, weather conditions, and solar angles. The surface is composed of plane-parallel facets: roofs, walls, and streets, which are further sub-divided into identical square patches, resulting in a 3-D raster-type model geometry. The model code is structured into radiation, conduction and convection sub-models. The radiation sub-model uses the radiosity approach and accounts for multiple reflections and shading of direct solar radiation. Conduction is solved by finite differencing of the heat conduction equation, and convection is modelled by empirically relating patch heat transfer coefficients to the momentum forcing and the building morphology. The radiation and conduction sub-models are tested individually against measurements, and the complete model is tested against full-scale urban surface temperature and energy balance observations. Modelled surface temperatures perform well at both the facet-average and the sub-facet scales given the precision of the observations and the uncertainties in the model inputs. The model has several potential applications, such as the calculation of radiative loads, and the investigation of effective thermal anisotropy (when combined with a sensor-view model)

Impacts of Urban Albedo Increase on Local Air Temperature at Daily–Annual Time Scales: Model Results and Synthesis of Previous Work

The authors combine urban and soil–vegetation surface parameterization schemes with one-dimensional (1D) boundary layer mixing and radiation parameterizations to estimate the maximum impact of increased surface albedo on urban air temperatures. The combined model is evaluated with measurements from an urban neighborhood in Basel, Switzerland, and the importance of surface–atmosphere model coupling is demonstrated. Impacts of extensive albedo increases in two Chicago, Illinois, neighborhoods are modeled. Clear-sky summertime reductions of diurnal maximum air temperature for the residential neighborhood (λp = 0.33) are −1.1°, −1.5°, and −3.6°C for uniform roof albedo increases of 0.19, 0.26, and 0.59, respectively; reductions are about 40% larger for the downtown core (λp = 0.53). Realistic impacts will be smaller because the 1D modeling approach ignores advection; a lake-breeze scenario is modeled and temperature reductions decline by 80%. Assuming no advection, the analysis is extended to seasonal and annual time scales in the residential neighborhood. Yearly average temperature decreases for a 0.59 roof albedo increase are about −1°C, with summer (winter) reductions about 60% larger (smaller). Annual cooling degree-day decreases are approximately offset by heating degree-day increases and the frequency of very hot days is reduced. Despite the variability of modeling approaches and scenarios in the literature, a consistent range of air temperature sensitivity to albedo is emerging; a 0.10 average increase in neighborhood albedo (a 0.40 roof albedo increase for λp = 0.25) generates a diurnal maximum air temperature reduction of approximately 0.5°C for “ideal” conditions, that is, a typical clear-sky midlatitude summer day

Prioritizing equity in urban heat reduction: Beyond cooling as a metric

Archived simulations for five heat wave events happened over Houston from 2017 - 2019 with different urban overheating mitigation strategies

Passive survivability of buildings under changing urban climates across eight US cities

In the US, more than 80% of fatal cases of heat exposure are reported in urban areas. Notably, indoor exposure is implicated in nearly half of such cases, and lack of functioning air conditioning (AC) is the predominant cause of overheating. For residents with limited capacity to purchase, maintain, and operate an AC system, or during summertime power outages, the ability of buildings to maintain safe thermal conditions without mechanical cooling is the primary protective factor against heat. In this paper, we use whole-building energy simulations to compare indoor air temperature inside archetypical single-family residential buildings without AC at the start and middle of the century in eight US cities. We ran the models using hourly output from 10 year regional climate simulations that explicitly include heating from mid-century projections of urban development and climate change under a ‘business-as-usual’ emissions scenario. Moreover, to identify the impacts from evolving construction practices, we compare different versions of building energy standards. Our analysis shows that summertime overheat time may increase by up to 25% by the middle of century. Moreover, we find that, while newer building energy codes reduce thermal comfort under moderate outdoor weather, they perform better under extreme heat