Stochastic generators are useful for estimating climate impacts on various sectors. Projecting climate risk in various sectors, e.g. energy systems, requires generators that are accurate (statistical resemblance to ground-truth), reliable (do not produce erroneous examples), and efficient. Leveraging data from the North American Land Data Assimilation System, we introduce TemperatureGAN, a Generative Adversarial Network conditioned on months, regions, and time periods, to generate 2 m above ground atmospheric temperatures at an hourly resolution. We propose evaluation methods and metrics to measure the quality of generated samples. We show that TemperatureGAN produces high-fidelity examples with good spatial representation and temporal dynamics consistent with known diurnal cycles.

Legionellosis is a respiratory infection caused by Legionella sp. that is found in water and soil. Infection may cause pneumonia (Legionnaires’ Disease) and a milder form (Pontiac Fever). Legionella colonizes water systems and results in exposure by inhalation of aerosolized bacteria. The incubation period ranges from 2 to 14 days. Precipitation and humidity may be associated with increased risk. We used Medicare records from 1999 to 2020 to identify hospitalizations for legionellosis. Precipitation, temperature, and relative humidity were obtained from the PRISM Climate Group for the zip code of residence. We used a time-stratified bi-directional case-crossover design with lags of 20 days. Data were analyzed using conditional logistic regression and distributed lag non-linear models. A total of 37 883 hospitalizations were identified. Precipitation and relative humidity at lags 8 through 13 days were associated with an increased risk of legionellosis. The strongest association was precipitation at day 10 lag (OR = 1.08, 95% CI = 1.05–1.11 per 1 cm). Over 20 days, 3 cm of precipitation increased the odds of legionellosis over four times. The association was strongest in the Northeast and Midwest and during summer and fall. Precipitation and humidity were associated with hospitalization among Medicare recipients for legionellosis at lags consistent with the incubation period for infection.

Recent advances in clinical prediction for diarrhoeal aetiology in low- and middle-income countries have revealed that the addition of weather data to clinical data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare the use of model estimated satellite- and ground-based observational data with weather station directly observed data for the prediction of aetiology of diarrhoea. We used clinical and etiological data from a large multi-centre study of children with moderate to severe diarrhoea cases to compare their predictive performances. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, given its ease of access, directly observed weather station data is likely adequate for the prediction of diarrhoeal aetiology in children in low- and middle-income countries.

In this article, we examine how weather variables affect markets for U.S. high-end wines, both luxury wines and wines from the same region that are still high-end but not in the very limited highest category. Specifically, we compare so-called “cult wines” with “non-cult wines” from the same subregions that are known for their high-quality wines. We investigate associations between weather conditions and prices, price gaps (the difference between the secondary market price and release price), the number of cases produced, and wine scores assigned for both cult and non-cult wines. We further examine whether associations with weather differ across wine regions. Implementing a fixed-effects methodology, cult and non-cult wines from three U.S. regions were studied: both Napa and Sonoma in California, and Walla Walla on the border of Washington State and Oregon. Overall, the analysis suggests that weather is associated with various characteristics of wine markets, including prices, price gaps for cult wines, wine scores, and cases produced. The nature of the associations depends on the type of wine (cult or non-cult, red or white) and timing of weather conditions throughout the year and growing region.

Welfare is being promoted as a reason why ostriches should not be kept on farms in Europe. It is reasoned that the climate, particularly during winter, is unsuitable for these birds despite there being little scientific evidence to support this claim. This study recorded the frequency of behaviours of male and female adult ostriches kept on a farm in Britain during the spring of 1996. ‘Rainy’, ‘dull and dry’, ‘bright and dry’, and ‘sunny’ weather categories were used to assess the influence of climate on behaviour. Six main behaviours (sitting, standing, pacing, walking, foraging and feeding) were observed together with a variety of low frequency ‘other’ behaviours which were combined for analysis. Gender had no significant effect on any of the behaviour frequencies. During ‘rainy’ periods both males and females showed sitting behaviour five times more than during ‘dull’ and ‘bright’ weather and two and a half times more than during ‘sunny’ weather. Increased sitting behaviour during rainy periods was due to a significant reduction in pacing and ‘other’ behaviours with no significant effect on feeding and foraging behaviours. Sitting during sunny weather also occurred more often than during dull and bright weather but not at the expense of any other particular behaviour. Adult ostriches in Britain alter their behaviour in response to prevailing weather conditions, particularly rain.

Typhoid fever is a major cause of illness and mortality in low- and middle-income settings. We investigated the association of typhoid fever and rainfall in Blantyre, Malawi, where multi-drug-resistant typhoid has been transmitting since 2011. Peak rainfall preceded the peak in typhoid fever by approximately 15 weeks [95% confidence interval (CI) 13.3, 17.7], indicating no direct biological link. A quasi-Poisson generalised linear modelling framework was used to explore the relationship between rainfall and typhoid incidence at biologically plausible lags of 1–4 weeks. We found a protective effect of rainfall anomalies on typhoid fever, at a two-week lag (P = 0.006), where a 10 mm lower-than-expected rainfall anomaly was associated with up to a 16% reduction in cases (95% CI 7.6, 26.5). Extreme flooding events may cleanse the environment of S. Typhi, while unusually low rainfall may reduce exposure from sewage overflow. These results add to evidence that rainfall anomalies may play a role in the transmission of enteric pathogens, and can help direct future water and sanitation intervention strategies for the control of typhoid fever.

Given the growing use of Artificial intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help reduce climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.

This article examines the impact of extreme weather on dairy farm productivity in the northeastern U.S., accounting for the effects of extreme temperatures on dairy cow productivity and on feed production—the predominant feeding system in the region. Using a stochastic frontier production model and 2010–20 dairy farm-level data, we find that although heat stress impacts cow productivity negatively, it increases feed production. No discernable impacts of extreme cold temperatures were found. Additional results indicate the presence of significant labor-augmenting productivity and that larger farms experience larger productivity growth thanks to increasing returns to scale and allocative efficiency.

Condensation inside marine containers occurs during voyages owing to weather changes. In this study, we define the condensation probability along one of the major routes for container ships between Asia and Europe. First, the inside and outside air conditions were measured on land in Japan, and a correlation analysis was conducted to derive their relationship. Second, onboard measurements were conducted for 20,000 twenty-foot equivalent unit (TEU) ships to determine the variation in outside air conditions. Complicated patterns of weather change were observed with changes in latitude, sea area, and season. Third, condensation probability was estimated based on a multi-regression analysis with land and onboard measured data. The maximum condensation probability in westbound or eastbound voyages in winter was found to be approximately 50%. The condensation probability estimation method established in this study can contribute to the quantification of cargo damage risks for the planning of marine container transportation voyages.

This article considers two passages in which either the sky (Plin. HN 17.74) or the sun (Manilius 2.941) is described as ‘green’; it argues that in both cases such a colour epithet is out of place and proposes to correct uiridi caelo to nitido caelo in the former case, and uiridis … Phoebus to rutilus … Phoebus in the latter.