The heat is on: Observations and trends of heat stress metrics during Florida summers

Extreme heat is annually the deadliest weather hazard in the U.S. and is strongly amplified by climate change. In Florida, summer heat waves have increased in frequency and duration, exacerbating negative human health impacts on a state with a substantial older population and industries (e.g., agriculture) that require frequent outdoor work. However, the combined impacts of temperature and humidity (heat stress) have not been previously investigated. For eight Florida cities, this study constructs summer climatologies and trend analyses (1950–2020) of two heat stress metrics: heat index (HI) and wet bulb globe temperature (WBGT). While both incorporate temperature and humidity, WBGT also includes wind and solar radiation, and is a more comprehensive measure of heat stress on the human body. With minor exceptions, results show increases in average summer daily maximum, mean, and minimum HI and WBGT throughout Florida. Daily minimum HI and WBGT exhibit statistically significant increases at all eight stations, emphasizing a hazardous rise in nighttime heat stress. Corresponding to other recent studies, HI and WBGT increases are largest in coastal subtropical locations in Central and South Florida (i.e., Daytona Beach, Tampa, Miami, Key West), but exhibit no conclusive relationship with urbanization changes. Finally, danger (103–124°F) HI and high (> 88°F) WBGT summer days exhibit significant frequency increases across the state. Especially at coastal locations in the Florida Peninsula and Keys, danger HI and high WBGT days now account for > 20% of total summer days, emphasizing a substantial escalation in heat stress, particularly since 2000.

Lack of vegetation exacerbates exposure to dangerous heat in dense settlements in a tropical African city

Both climate change and rapid urbanization accelerate exposure to heat in the city of Kampala, Uganda. From a network of low-cost temperature and humidity sensors, operational in 2018-2019, we derive the daily mean, minimum and maximum Humidex in order to quantify and explain intra-urban heat stress variation. This temperature-humidity index is shown to be heterogeneously distributed over the city, with a daily mean intra-urban Humidex Index deviation of 1.2°C on average. The largest difference between the coolest and the warmest station occurs between 16:00 and 17:00 local time. Averaged over the whole observation period, this daily maximum difference is 6.4°C between the warmest and coolest stations, and reaches 14.5°C on the most extreme day. This heat stress heterogeneity also translates to the occurrence of extreme heat, shown in other parts of the world to put local populations at risk of great discomfort or health danger. One station in a dense settlement reports a daily maximum Humidex Index of >40°C in 68% of the observation days, a level which was never reached at the nearby campus of the Makerere University, and only a few times at the city outskirts. Large intra-urban heat stress differences are explained by satellite earth observation products. Normalized Difference Vegetation Index (NDVI) has the highest (75%) power to predict the intra-urban variations in daily mean heat stress, but strong collinearity is found with other variables like impervious surface fraction and population density. Our results have implications for urban planning on the one hand, highlighting the importance of urban greening, and risk management on the other hand, recommending the use of a temperature-humidity index and accounting for large intra-urban heat stress variations and heat-prone districts in urban heat action plans for tropical humid cities.

Investigating the Relationship between Human Activity and the Urban Heat Island Effect in Melbourne and Four Other International Cities Impacted by COVID-19

Climate change is one of the biggest challenges of our times, even before the onset of the Coronavirus (COVID-19) pandemic. One of the main contributors to climate change is greenhouse gas (GHG) emissions, which are mostly caused by human activities such as the burning of fossil fuels. As the lockdown due to the pandemic has minimised human activity in major cities, GHG emissions have been reduced. This, in turn, is expected to lead to a reduction in the urban heat island (UHI) effect in the cities. The aim of this paper is to understand the relationship between human activity and the UHI intensity and to provide recommendations towards developing a sustainable approach to minimise the UHI effect and improve urban resilience. In this study, historical records of the monthly mean of daily maximum surface air temperatures collected from official weather stations in Melbourne, New York City, Tokyo, Dublin, and Oslo were used to estimate the UHI intensity in these cities. The results showed that factors such as global climate and geographic features could dominate the overall temperature. However, a direct relationship between COVID-19 lockdown timelines and the UHI intensity was observed, which suggests that a reduction in human activity can diminish the UHI intensity. As lockdowns due to COVID-19 are only temporary events, this study also provides recommendations to urban planners towards long-term measures to mitigate the UHI effect, which can be implemented when human activity returns to normal.

Salient features of maximum temperature distribution over Tamil Nadu

In this paper an attempt is made to study the spatial variations of maximum temperature over Tamil Nadu. From the data of daily maximum temperature of eleven stations in Tamil Nadu for the first six months from 1981 to 1997, it is observed that there are two separate regions namely high maximum temperature region of interior stations and low maximum temperature region of coastal stations from January to May and the distinction disappears in June. Hill station Kodaikanal recorded less maximum temperature that Ootacamund from January to May and it reverses in June. During onset phase of southwest monsoon, maximum temperature decreases over Coimbatore, Pamban and Kanyakumari. In the presence of cyclonic storm over the Bay of Bengal the maximum temperatures are fallen during the period when the storm affects Tamil Nadu or Andhra coast in May and June whereas when the storms moves farther away from the coast maximum temperature over interior places decreases and increases over north coastal stations.

Validation and Projections of Climate Characteristics in the Saginaw Bay Watershed, MI, for Hydrologic Modeling Applications

Fundamental differences in the nature of climate and hydrologic models make coupling of future climate projections to models of watershed hydrology challenging. This study uses the NCAR Weather Research and Forecast model (WRF) to dynamically downscale climate simulations over the Saginaw Bay Watershed, MI and prepare the results for input into semi-distributed hydrologic models. One realization of the bias-corrected NCAR CESM1 model's RCP 8.5 climate scenario is dynamically downscaled at a spatial resolution of 3 km by 3 km for the end of the twenty-first century and validated based on a downscaled run for the end of the twentieth century in comparison to ASOS and NWS COOP stations. Bias-correction is conducted using Quantile Mapping to correct daily maximum and minimum temperature, precipitation, and relative humidity for use in future hydrologic model experiments. In the Saginaw Bay Watershed the end of the twenty-first century is projected to see maximum and minimum average daily temperatures warming by 5.7 and 6.3°C respectively. Precipitation characteristics over the watershed show an increase in mean annual precipitation (average of +14.3 mm over the watershed), mainly due to increases in precipitation intensity (average of +0.3 mm per precipitation day) despite a decrease in frequency of −10.7 days per year. The projected changes have substantial implications for watershed processes including flood prediction, erosion, mobilization of non-point source and legacy contaminants, and evapotranspirative demand, among others. We present these results in the context of usefulness of the downscaled and bias corrected data for semi-distributed hydrologic modeling.

Synergies between Urban Heat Island and Urban Heat Wave Effects in 9 Global Mega–Regions from 2003 to 2020

Global urbanization significantly impacts the thermal environment in urban areas, yet urban heat island (UHI) and urban heat wave (UHW) studies at the mega–region scale have been rare, and the impact study of urbanization is still lacking. In this study, the MODIS land surface temperature (LST) product was used to depict the UHI and UHW in nine mega–regions globally between 2003 and 2020. The absolute and percentile–based UHW thresholds were adopted for both daily and three–day windows to analyze heat wave frequency, and UHW magnitude as well as frequency were compared with UHI variability. Results showed that a 10% increase in urban built-up density led to a 0.20 °C to 0.95 °C increase in LST, a 0.59% to 7.17% increase in hot day frequency, as well as a 0.08% to 0.95% increase in heat wave number. Meanwhile, a 1 °C increase in UHI intensity (the LST differences between the built-up and Non-built-up areas) led to a 2.04% to 92.15% increase in hot day frequency, where daytime LST exceeds 35 °C and nighttime LST exceeds 25 °C, as well as a 3.30% to 33.67% increase in heat wave number, which is defined as at least three consecutive days when daily maximum temperature exceeds the climatological threshold. In addition, the increasing rates of UHW magnitudes were much faster than the expansion rates of built-up areas. In the mega–regions of Boston, Tokyo, São Paulo, and Mexico City in particular, the increasing rates of UHW hotspot magnitudes were over 2 times larger than those of built-up areas. This indicated that the high temperature extremes, represented by the increase in UHW frequency and magnitudes, were concurrent with an increase in UHI under the context of climate change. This study may be beneficial for future research of the underlying physical mechanisms on urban heat environment at the mega–region scale.

Understanding the transmission dynamics of Escherichia coli O157:H7 super-shedding infections in feedlot cattle

Background The presence of Escherichia coli O157:H7 (E. coli O157:H7) super-shedding cattle in feedlots has the potential to increase the overall number (bio-burden) of E. coli O157:H7 in the environment. It is important to identify factors to reduce the bio-burden of E. coli O157 in feedlots by clarifying practices associated with the occurrence of super-shedders in feedlot cattle. Methods The objective of this study is to (1) identify host, pathogen, and management risk factors associated with naturally infected feedlot cattle excreting high concentrations of E. coli O157:H7 in their feces and (2) to determine whether the ingested dose or the specific strain of E. coli O157:H7 influences a super-shedder infection within experimentally inoculated feedlot cattle. To address this, (1) pen floor fecal samples and herd parameters were collected from four feedlots over a 9-month period, then (2) 6 strains of E. coli O157:H7, 3 strains isolated from normal shedder steers and 3 strains isolated from super-shedder steers, were inoculated into 30 one-year-old feedlot steers. Five steers were assigned to each E. coli O157:H7 strain group and inoculated with targeted numbers of 102, 104, 106, 108, and 1010 CFU of bacteria respectively. Results In the feedlots, prevalence of infection with E. coli O157:H7 for the 890 fecal samples collected was 22.4%, with individual pen prevalence ranging from 0% to 90% and individual feedlot prevalence ranging from 8.4% to 30.2%. Three samples had E. coli O157:H7 levels greater than 104 MPN/g feces, thereby meeting the definition of super-shedder. Lower body weight at entry to the feedlot and higher daily maximum ambient temperature were associated with increased odds of a sample testing positive for E. coli O157:H7. In the experimental inoculation trial, the duration and total environmental shedding load of E. coli O157:H7 suggests that the time post-inoculation and the dose of inoculated E. coli O157:H7 are important while the E. coli O157:H7 strain and shedding characteristic (normal or super-shedder) are not. Discussion Under the conditions of this experiment, super-shedding appears to be the result of cattle ingesting a high dose of any strain of E. coli O157:H7. Therefore strategies that minimize exposure to large numbers of E. coli O157:H7 should be beneficial against the super-shedding of E. coli O157:H7 in feedlots.

Simulation-based indices for a climate-resilient agriculture - insights from ADAPTER

<p>Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned to adapt to interannual climate variability and extremes. In the ADAPTER project, we collaborate with stakeholders from these groups and generate practically relevant information, tailored climate change indices and usable information products.</p> <p> </p> <p>The challenges of climate change for agriculture are manifold. The genetic traits of crops need to be adapted to a new climatic average, for instance by breeding new sorts of crops that are specialised for warmer and dryer conditions (i.e. maximising average yields). Agricultural practises need to be adapted to changing seasonal weather patterns under changing climate conditions. It is also vital to ensure the resilience to climate extremes by aiming for a low inter-annual yield variability, in order to prevent price shocks or food shortages.</p> <p> </p> <p>In order to adequately determine the optimal balance between specialisation and risk diversification, the agricultural sector hence requires knowledge not only about changes in the mean climate, but also on the variance around the changing mean. In this contribution, we focus on this second aspect by analysing the potential impact of forced changes in climate variability on the stability of crop yields in central Europe.</p> <p> </p> <p>We analyse the changing climate variability in 85 regional climate model projections from Coordinated Downscaling Experiments over Europe (EURO-CORDEX). We first show how the projections indicate a general increase in climate variability during critical development stages of wheat, rapeseed and maize in Europe. Second, we determine several more specific agronomic climate indices that capture events that have previously been shown to be critical for yields, for instance the occurrence of high daily maximum temperature, the seasonal sum of rainfall, the number of dry days, or the occurrence of compound events with simultaneous drought and increased temperatures. Finally, we illustrate how the results can be made accessible to practitioners in the agriculture sector by co-designing interactive browser applications, thus directly supporting the adaptation of the agricultural system to climate change.</p> <p> </p>

Projection of Hot and Cold Extremes in the Amu River Basin of Central Asia using GCMs CMIP6

The extreme temperature has become more frequent and intense due to global warming, particularly in dry regions, causing devastating impacts on humans and ecosystems. The transboundary Amu river basin (ARB) is the most vulnerable region in Central Asia (CA) to extreme weather linked to climate change. This study aimed to project warm and cold extremes in ARB for three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and two time-horizons, 2020–2059 and 2060-2099, using daily maximum (Tmax) and minimum temperature (Tmin) simulations of global climate models (GCMs) of Coupled Model Inter-comparison Project phase six (CMIP6). Results revealed that the basin's west experiences more hot extremes and the east more cold extremes. Climate change would cause a significant increase in the annual mean of Tmax and Tmin. However, the increase in mean Tmin would be much higher (5.0ºC ) than the mean Tmax (4.6ºC ). It would cause an increase in the hot extremes and a decrease in the cold extremes in the basin. The higher increase in the hot extremes would be in the west, while the higher decrease in the cold extreme in the basin's east. The number of days above 40℃ would increase from 45 to 60 days in the basin's west and northwest compared to the historical period. The number of days below -20℃ would decrease up to 45 days in the basin's east. Overall, the decrease in cold extremes would be much faster than the increase in hot extremes.

Determination of fractal dimension of chaotic at tractor for Maximum temperature over Madras

The dimensions of attractors of daily maximum temperature (during March-May) recorded by the two observatories of Madras, viz., Nungarnbakkarn and Meenambakkarn are estimated from phase space trajectories by the method of deterministic chaos, The dimensions provide the basic information on the minimum number of parameters required to understand the complex dynamical system and also the upper bound (degrees of freedom) of such parameters that are sufficient to model the system, The fractal dimension for the weather event, viz. maximum temperature over Madras is between 3.5 and 3.9 suggesting 4 parameters are necessary to model the system and a maximum of 19 parameters are sufficient.