Dense Network Observations of the Twin Cities Canopy-Layer Urban Heat Island

Abstract

Data from a dense urban meteorological network (UMN) are analyzed, revealing the spatial heterogeneity and temporal variability of the Twin Cities (Minneapolis–St. Paul, Minnesota) canopy-layer urban heat island (UHI). Data from individual sensors represent surface air temperature (SAT) across a variety of local climate zones within a 5000-km2 area and span the 3-yr period from 1 August 2011 to 1 August 2014. Irregularly spaced data are interpolated to a uniform 1 km × 1 km grid using two statistical methods: 1) kriging and 2) cokriging with impervious surface area data. The cokriged SAT field exhibits lower bias and lower RMSE than does the kriged SAT field when evaluated against an independent set of observations. Maps, time series, and statistics that are based on the cokriged field are presented to describe the spatial structure and magnitude of the Twin Cities metropolitan area (TCMA) UHI on hourly, daily, and seasonal time scales. The average diurnal variation of the TCMA UHI exhibits distinct seasonal modulation wherein the daily maximum occurs by night during summer and by day during winter. Daily variations in the UHI magnitude are linked to changes in weather patterns. Seasonal variations in the UHI magnitude are discussed in terms of land–atmosphere interactions. To the extent that they more fully resolve the spatial structure of the UHI, dense UMNs are advantageous relative to limited collections of existing urban meteorological observations. Dense UMNs are thus capable of providing valuable information for UHI monitoring and for implementing and evaluating UHI mitigation efforts.

Urban heat island impacts on plant phenology: intra-urban variability and response to land cover

“…This study represents the first comparison between temperature-based phenological estimates from an urban sensor network and remotely sensed estimates; as urban meteorological networks become more common (e.g. Smoliak et al 2015), future work should focus on understanding the mechanisms by which land cover influences the vegetative response to urban warming and implications of UHI-induced variability in phenology for water, energy, and nutrient cycling.…”

“…These studies primarily select one or several species to monitor phenology at discrete points and are not designed to capture variability within urban areas (Mimet et al 2009, Fotiou et al 2011, Comber and Brunsdon 2015. While sensor networks are widely used to describe UHIs (see Schatz and Kucharik 2014 for a summary of previous work), very few studies have investigated the spatial variability of UHI effects on GSL (Todhunter 1996, Smoliak et al 2015.…”

Urban climate effects on extreme temperatures in Madison, Wisconsin, USA

“…The first model controlled for weather effects, with daily ΔT or ΔAT as the response variable. Covariates were daily average wind speed, percent sun, and either soil moisture (warm weather model) or snow depth (cold weather model), which are the primary meteorological determinants of UHI intensity in our study area (Schatz and Kucharik 2014) and elsewhere (Oke 1982, Runnalls and Oke 2000, Malevich and Klink 2011, Smoliak et al 2015. The second model used the residuals from the first model as the response variable, with daily T MAX or T MIN as the explanatory covariate.…”

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

“…The data from a dense urban meteorological network were analyzed to show the spatial heterogeneity and temporal variability of atmospheric UHI in the Twin Cities (Minneapolis‐St. Paul, MN, USA) [ Smoliak et al , ]. The data from the Oklahoma City urban meteorological network (36 stations) in 2009 and 2010 were used to investigate the spatial variability of UHIs and their results do not support the roughness warming theory to explain atmospheric UHI [ Hu et al , ].…”