Geology ReportsSearch

USGS · 70251251

Limited role of absolute humidity in intraurban heat variability

Abstract

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Darryn W. Waugh, Benjamin Zaitchik, Anna A. Scott, Peter Christian Ibsen, G. Darrel Jenerette, Jason Schartz, Christopher J. Kucharik. 2023. Limited role of absolute humidity in intraurban heat variability. https://doi.org/10.1175/jamc-d-23-0096.1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Assessment of Alaska rain-on-snow events using dynamical downscaling

The ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

Alaska

Characterizing sources of uncertainty from global climate models and downscaling techniques

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Journal of Applied Meteorology and Climatology

Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks

The Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, Central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ETo) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by the FEWS NET to support food insecurity early warning. The ETo reforecasts span the 1982-2009 period and are calculated following ASCE’s formulation of Penman-Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from NCEP’s CFSv2 and NASA’s GEOS-5 models. The skill evaluation using deterministic and probabilistic scores, focuses on the December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON) seasons. The results indicate that ETo forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation >0.35 at lead-2) include Western U.S., northern parts of South America, parts of Sahel region and Southern Africa. The FEWS NET regions where forecasts are most skillful (correlation >0.35 at lead-3) include Northern Sub-Saharan Africa (DJF, dry season), Central America (DJF, dry season), parts of East Africa (JJA, wet Season), Southern Africa (JJA, dry season), and Central Asia (MAM, wet season). A case study over parts of East Africa for the JJA season shows that ETo forecasts in combination with the precipitation forecasts could have provided early warning of recent severe drought events (e.g., 2002, 2004, 2009) that contributed to substantial food insecurity in the region.

Journal of Applied Meteorology and Climatology