Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

In Austria, the first confirmed COVID-19 death occurred in early March 2020. Since then, the question as to whether and, if so, to what extent the COVID-19 pandemic has increased overall mortality has been raised in the public and academic discourse. In an effort to answer this question, Statistics Vienna (City of Vienna, Department for Economic Affairs, Labour and Statistics) has evaluated the weekly mortality trends in Vienna, and compared them to the trends in other Austrian provinces. For our analysis, we draw on data from Statistics Austria and the Austrian Agency for Health and Food Safety (AGES), which are published along with data on the actual and the expected weekly numbers of deaths via the Vienna Mortality Monitoring website. Based on the definition of excess mortality as the actual number of reported deaths from all causes minus the expected number of deaths, we calculate the weekly prediction intervals of the expected number of deaths for two age groups (0 to 64 years and 65 years and older). The temporal scope of the analysis covers not only the current COVID-19 pandemic, but also previous flu seasons and summer heat waves. The results show the actual weekly numbers of deaths and the corresponding prediction intervals for Vienna and the other Austrian provinces since 2007. Our analysis underlines the importance of comparing time series of COVID-19-related excess deaths at the sub-national level in order to highlight within-country heterogeneities.

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.

The Physics of Heat Waves: What Causes Extremely High Summertime Temperatures?

We analyze observations and develop a hierarchy of models to understand heat waves – long-lived, high temperature anomalies – and extremely high daily temperatures during summertime in the continental extratropics. Throughout the extratropics, the number of extremely hot days found in the three hottest months is much greater than expected from a random, single-process model. Furthermore, in many locations the temperature skewness switches from negative on daily timescales to positive on monthly timescales (or shifts from positive on daily timescales to higher positive values on monthly timescales) in ways that cannot be explained by averaging alone. These observations motivate a hierarchy of models of the surface energy and moisture budgets that we use to illuminate the physics responsible for daily and monthly averaged temperature variability. Shortwave radiation fluctuations drive much of the variance and the negative skewness found in daily temperature observations. On longer timescales, precipitation-induced soil moisture anomalies are important for temperature variability and account for the shift toward positive skewness in monthly averaged temperature. Our results demonstrate that long-lived heat waves are due to (i) the residence time of soil moisture anomalies and (ii) a nonlinear feedback between temperature and evapotranspiration via the impact of temperature on vapor pressure deficit. For most climates, these two processes give rise to infrequent, long-lived heat waves in response to randomly distributed precipitation forcing. Combined with our results concerning high-frequency variability, extremely hot days are seen to be state-independent filigree driven by shortwave variability acting on top of longer-lived, moisture driven heat waves.

Patterned crystal growth and heat wave generation in hydrogels

The crystallization of metastable liquid phase change materials releases stored energy as latent heat upon nucleation and may therefore provide a triggerable means of activating downstream processes that respond to changes in temperature. In this work, we describe a strategy for controlling the fast, exothermic crystallization of sodium acetate from a metastable aqueous solution into trihydrate crystals within a polyacrylamide hydrogel whose polymerization state has been patterned using photomasks. A comprehensive experimental study of crystal shapes, crystal growth front velocities and evolving thermal profiles showed that rapid growth of long needle-like crystals through unpolymerized solutions produced peak temperatures of up to 45˚C, while slower-crystallizing polymerized solutions produced polycrystalline composites and peaked at 30˚C due to lower rates of heat release relative to dissipation in these regions. This temperature difference in the propagating heat waves, which we describe using a proposed analytical model, enables the use of this strategy to selectively activate thermoresponsive processes in predefined areas.

Increasing instability of a rocky intertidal meta-ecosystem

Climate change threatens to destabilize ecological communities, potentially moving them from persistently occupied “basins of attraction” to different states. Increasing variation in key ecological processes can signal impending state shifts in ecosystems. In a rocky intertidal meta-ecosystem consisting of three distinct regions spread across 260 km of the Oregon coast, we show that annually cleared sites are characterized by communities that exhibit signs of increasing destabilization (loss of resilience) over the past decade despite persistent community states. In all cases, recovery rates slowed and became more variable over time. The conditions underlying these shifts appear to be external to the system, with thermal disruptions (e.g., marine heat waves, El Niño–Southern Oscillation) and shifts in ocean currents (e.g., upwelling) being the likely proximate drivers. Although this iconic ecosystem has long appeared resistant to stress, the evidence suggests that subtle destabilization has occurred over at least the last decade.

Biometeorological Conditions during the August 2015 Mega-Heat Wave and the Summer 2010 Mega-Heat Wave in Ukraine

The human-biometeorological conditions in Ukraine during two mega-heat waves were analyzed. The evaluation is based on physiologically equivalent temperature (PET). The calculation of PET is performed utilizing the RayMan model. The results revealed these two mega-heat waves produced strenuous human-biometeorological conditions on the territory of Ukraine. During the summer 2010 mega-heat wave, strong and extreme heat stress prevailed at about midday at the stations where this atmospheric phenomenon was observed. The mega-heat wave of August 2015 was characterized by a lower heat load. The diurnal variation of PET values during the researched mega-HW was similar to that of the diurnal variation of air temperature with minimum values in the early morning and maximum values in the afternoon. On the territory where mega-heat waves were observed, the number of days during which heat stress occurred for 9 h amounted to 97.6% for the period from 31 July to 12 August 2010 and 77.1% for the mega-heat wave of August 2015.

Machine learning models to predict myocardial infarctions from past climatic and environmental conditions

Myocardial infarctions (MI) are a major cause of death worldwide, and temperature extremes, e.g., during heat waves and cold winters, may increase the risk of MI. The relationship between health impacts and climate is complex and is influenced by a multitude of climatic, environmental, socio-demographic, and behavioral factors. Here, we present a Machine Learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany between 1998 and 2015. Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2, and O3), surrounding vegetation, as well as demographic data. We tested the following ML regression algorithms: Decision Tree, Random Forest, Multi-layer Perceptron, Gradient Boosting and Ridge Regression. The models are able to predict the total annual number of MI reasonably well (adjusted R2 = 0.59 − 0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approach provides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes.

Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

The extratropical meridional energy transport in the atmosphere is fundamentally intermittent in nature, having extremes large enough to affect the net seasonal transport. Here, we investigate how these extreme transports are associated with the dynamics of the atmosphere at multiple scales, from planetary to synoptic. We use ERA5 reanalysis data to perform a wavenumber decomposition of meridional energy transport in the Northern Hemisphere mid-latitudes during winter and summer. We then relate extreme transport events to atmospheric circulation anomalies and dominant weather regimes, identified by clustering 500 hPa geopotential height fields. In general, planetary-scale waves determine the strength and meridional position of the synoptic-scale baroclinic activity with their phase and amplitude, but important differences emerge between seasons. During winter, large wavenumbers (k = 2 − 3) are key drivers of the meridional energy transport extremes, and planetary and synoptic-scale transport extremes virtually never co-occur. In summer, extremes are associated with higher wavenumbers (k = 4 − 6), identified as synoptic-scale motions. We link these waves and the transport extremes to recent results on exceptionally strong and persistent co-occurring summertime heat waves across the Northern Hemisphere mid-latitudes. We show that these events are typical, in terms of dominant regime patterns associated with extremely strong meridional energy transports.