Elizabeth Berg

In the summer of 2022, our team of climatologists and researchers piloted the first heat warning system that ranks heat events based upon human health outcomes. This system uses local all-cause mortality data in addition to meteorological factors including air mass identification and the Excess Heat Factor to identify heat events that are likely to endanger human health in the region and applies three categories representing increasing severity. In this study, we evaluated this system by comparing the heat events it identified in Seville, Spain between 1995 and 2022 with those identified by the Spanish national meteorology agency (AEMET). We also compared the outputs from both systems against all-cause mortality data over the same time period. We found that our system captured more dangerous heat events than the meteorology-only criteria of AEMET and that the majority of identified heat events occurred during periods of elevated mortality. Additionally, the agreement between systems differed by event severity; roughly half of the high mortality "Category 3" events identified by our system were also identified by AEMET, but AEMET only issued alerts during one of the 24 more minor "Category 1" events that our system identified, even though these events were also correlated with increased mortality. Overall, these results show the potential of health-based warning systems in identifying dangerous events that might be missed by traditional meteorology-based systems.

Maintaining and increasing canopy cover is frequently promoted as a strategy for mitigating excess heat in cities. However, the impact of changing tree cover on surrounding air temperatures is often unpredictable and can depend on tree species, size, shape, and location. In this study, we explore whether the removal of ash trees in one downtown neighborhood in Madison, Wisconsin affected surrounding air temperatures at small spatial scales (15 to 35 m). We used a bicycle-mounted temperature and radiation sensor to repeatedly record high-frequency observations along four transects. We observed no discernible difference between daytime air temperatures near locations surrounded by tree removals and temperatures at locations without any nearby tree removals. Overall, across the four clusters of streets that were monitored, proximity to mature ash trees did not correlate to reduced air temperatures. Attempts to model temperature as a function of surrounding land cover and street tree characteristics all had poor predictive power (R² ranged from 0.01 to 0.54), and explanatory variables related to tree cover were never statistically significant. The finding that the removal of ash trees did not impact daytime air temperatures patterns at the neighborhood scale suggests that the most densely developed streets in Madison may not easily support sufficient canopy cover to experience tree-induced cooling, underscoring the importance of pursuing a variety of strategies to mitigate urban warming.

The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (T_air) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both T_air measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, T_air and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R² = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between T_air and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between T_air and LST measurements of the UHI.