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Alexander J. Ross, Ryan C. Grow, Lauren D. Hayhurst, Haley A. MacLeod, Graydon I. McKee, Kyle W. Stratton, Marissa E. Wegher, and Michael D. Rennie

Abstract

Groundhog Day is a widespread North American ritual that marks the onset of spring, with festivities centered around animals that humans believe have abilities to make seasonal predictions. Yet, the collective success of groundhog Marmota monax prognosticators has never been rigorously tested. Here, we propose the local climate-predicted phenology of early blooming spring plants (Carolina spring beauty, or Claytonia caroliniana, which overlaps in native range with groundhogs) as a novel and relevant descriptor of spring onset that can be applied comparatively across a broad geographical range. Of 530 unique groundhog-year predictions across 33 different locations, spring onset was correctly predicted by groundhogs exactly 50% of the time. While no singular groundhog predicted the timing of spring with any statistical significance, there were a handful of groundhogs with notable records of both successful and unsuccessful predictions: Essex Ed (Essex, Connecticut), Stonewall Jackson (Wantage, New Jersey), and Chuckles (Manchester, Connecticut) correctly predicted spring onset over 70% of the time. By contrast, Buckeye Chuck (Marion, Ohio), Dunkirk Dave (Dunkirk, New York), and Holland Huckleberry (Holland, Ohio) made incorrect predictions over 70% of the time. The two most widely recognized and long-tenured groundhogs in their respective countries—Wiarton Willie (Canada) and Punxsutawney Phil (United States)—had success rates of 54% and 52%, respectively, despite over 150 collective guesses. Using a novel phenological indicator of spring, this study determined, without a shadow of a doubt, that groundhog prognosticating abilities for the arrival of spring are no better than chance.

Open access
Nathan Beech and Micah J. Hewer

Abstract

Grapevine growth and wine production are both closely connected with weather and climate, making anthropogenic climate change a source of great uncertainty for the grape and wine industries. To assess the impacts of climate change on viticulture and oenology in the Fraser Valley, British Columbia, Canada, where no such assessment has been published to this date, a series of key indicators and critical thresholds were selected on the basis of their relevance to the local climate. Trends among these indicators and thresholds were calculated over a historic period (1970–2019) and projected over the twenty-first century for one intermediate-emissions climate change scenario and one high-emissions climate change scenario. Historic trends were assessed using Environment and Climate Change Canada weather station data from Abbotsford, British Columbia. Two statistical downscaling methods were evaluated with regard to their ability to reproduce observed conditions in the Fraser Valley, and the most effective method was used to create projections of local, daily climate change scenarios. During the historic period, temperatures increased significantly while precipitation and moisture variables displayed insignificant trends, reflecting the trends observed across other wine regions in Canada and the northwestern United States. Throughout the twenty-first century, warming is expected to continue while precipitation decreases modestly. Extreme heat is projected to become far more frequent, whereas extreme cold and potential frost days become rare. In the short term, modifications to vineyard and winery operations may be sufficient adaptation strategies. Over the long term, new grape varieties will most likely need to be planted in existing vineyards and suitability for cool-climate varieties may shift northward in direction or upward in elevation.

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K. Fagiewicz, P. Churski, T. Herodowicz, P. Kaczmarek, P. Lupa, J. Morawska-Jancelewicz, and A. Mizgajski

Abstract

This study determines the conditions and provides a recommendation for fostering cocreation for climate change adaptation and mitigation (CCA&M). In postulating that insufficient cocreation by stakeholders in the quadruple helix model is an important factor contributing to the low effectiveness of climate actions in the regions, we have focused our research on identifying real stakeholder engagement in climate action and identifying the needs, barriers, and drivers for strengthening the cocreation process. We identified the needs for action highlighted by stakeholders as having an impact on reducing barriers and stimulating drivers. We treated the identified needs for action as deep leverage points (intent and design) focused on three realms—knowledge, values, and institutions—in which engagement and cocreation can be strengthened and have the potential to increase the effectiveness of climate action taken by stakeholders within our quadruple helix. We recommend knowledge-based cocreation, which puts the importance of climate action in the value system and leads to paradigm reevaluation. The implementation of the identified needs for action requires the support of institutions, whereby they develop standards of cooperation and mechanisms for their implementation as a sustainable framework for stakeholder cooperation. The research has proved how the quadruple helix operates for climate action in the Poznań Agglomeration. We believe that this case study can be a reference point for regions at a similar level of development, and the methods used and results obtained can be applied in similar real contexts to foster local stakeholders in climate action.

Open access
David C. Eisenhauer

Abstract

This paper presents a case study of how boundary objects were deployed to support a collaborative knowledge production process that resulted in the creation of climate change knowledge usable to municipal governments in the New Jersey shore region. In doing so, a case is made that boundary objects are useful throughout the collaborative process in overcoming ambiguity and disagreement. This points to boundary objects possessing a wider array of capabilities than is frequently theorized in the climate policy literature. Effectively designing and using boundary objects, however, requires carefully considering how they interface and interact with one another.

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Mustafa Hakkı Aydoğdu, Mehmet Reşit Sevinç, and Mehmet Cançelik

Abstract

In Şanlıurfa, Turkey, agriculture is the most important source of income. This study aimed to determine Şanlıurfa farmers’ willingness to pay for drought adaptation policies and the factors affecting their willingness. The data were obtained from face-to-face surveys with farmers, selected using a simple random sampling method. According to the results, 50.26% perceive a risk of drought, and 35.86% are willing to pay for adaptation policies. Among those willing to pay, the average amount was $22.63 ha−1 [$1 (U.S. dollar) = TRY 5.676 (Turkish lira)], and the average for all participants was $13.55 ha−1. This adds up to a total of $14,363,000 yr−1 for Şanlıurfa. This amount is 1.47% of the annual average income of the participants and is thus within their ability to pay. Age, amount of land farmed, education level, experience, and income were factors affecting willingness to pay. Many respondents, however, were unaware of drought adaptation policies. Because there is concern that drought risk is increasing, awareness needs to be increased, for example, through extension services. To the best of our knowledge, this is the first study of its kind, and the results may be useful for creating and applying drought adaptation policies in both Turkey and other regions with similar socioeconomic characteristics.

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Marcos Samuel Matias Ribeiro, Lara de Melo Barbosa Andrade, Maria Helena Constantino Spyrides, Kellen Carla Lima, Pollyane Evangelista da Silva, Douglas Toledo Batista, and Idemauro Antônio Rodrigues de Lara

Abstract

The occurrence of environmental disasters affects different social segments, impacting health, education, housing, economy, and the provision of basic services. Thus, the objective of this study was to estimate the relationship between the occurrence of disasters and extreme climate, sociosanitary, and demographic conditions in the Northeast region of Brazil (NEB) during the period from 1993 to 2013. Initially, we analyzed the spatial pattern of the incidence of events; subsequently, generalized additive models for location, scale, and shape were used to identify and estimate the magnitude of associations between factors. Results showed that droughts are the predominant disasters in NEB representing 81.1% of the cases, followed by events triggered by excessive rainfall such as flash floods (11.1%) and floods (7.8%). Climate conditions presented statistically significant associations with the analyzed disasters, in which indicators of excess rainfall positively contributed to the occurrence of flash floods and floods but negatively contributed to the occurrence of drought. Sociosanitary factors, such as percentage of households with inadequate sewage, waste collection, and water supply, were also positively associated with the model’s estimations, that is, contributing to an increase in the occurrence of events, with the exception of floods, which were not significantly influenced by sociosanitary parameters. A decrease of 19% in the risk of drought occurrence was estimated, on average. On the other hand, events caused by excessive rainfall increased by 40% and 57%, in the cases of flash floods and floods, respectively.

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Zoe Schroder and James B. Elsner

Abstract

Environmental variables are routinely used in estimating when and where tornadoes are likely to occur, but more work is needed to understand how tornado and casualty counts of severe weather outbreak vary with the larger-scale environmental factors. Here the authors demonstrate a method to quantify “outbreak”-level tornado and casualty counts with respect to variations in large-scale environmental factors. They do this by fitting negative binomial regression models to cluster-level environmental data to estimate the number of tornadoes and the number of casualties on days with at least 10 tornadoes. Results show that a 1000 J kg−1 increase in CAPE corresponds to a 5% increase in the number of tornadoes and a 28% increase in the number of casualties, conditional on at least 10 tornadoes and holding the other variables constant. Further, results show that a 10 m s−1 increase in deep-layer bulk shear corresponds to a 13% increase in tornadoes and a 98% increase in casualties, conditional on at least 10 tornadoes and holding the other variables constant. The casualty-count model quantifies the decline in the number of casualties per year and indicates that outbreaks have a larger impact in the Southeast than elsewhere after controlling for population and geographic area.

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Brooke Fisher Liu and Anita Atwell Seate

Abstract

Since the tragic tornado outbreaks in central Alabama and Joplin, Missouri, in 2011, the National Weather Service (NWS) has increasingly emphasized the importance of supporting community partners who help to protect public safety. Through impact-based decision support services (IDSS), NWS forecasters develop relationships with their core partners to meet their partners’ decision-making needs. IDSS presents a fundamental shift in NWS forecasting through highlighting the importance of connecting with partners instead of simply providing partners with forecasts. A critical challenge to the effective implementation of IDSS is a lack of social science research evaluating the success of IDSS. This paper addresses this gap through a cross-sectional survey with 119 NWS forecasters and managers in the central and southern regions of the United States. Findings uncover how NWS forecasters and management team members evaluate the importance of IDSS. Findings also provide a new instrument for NWS field offices to assess and improve their relationships with core partners.

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Adrian Brügger, Christina Demski, and Stuart Capstick

Abstract

The proportion of the world’s population exposed to above-average monthly temperatures has been rising consistently in recent decades and will continue to grow. This and similar trends make it more likely that people will personally experience extreme weather events and seasonal changes related to climate change. A question that follows from this is to what extent experiences may influence climate-related beliefs, attitudes, and the willingness to act. Although research is being done to examine the effects of such experiences, many of these studies have two important shortcomings. First, they propose effects of experiences but remain unclear on the psychological processes that underlie those effects. Second, if they do make assumptions about psychological processes, they do not typically corroborate them with empirical evidence. In other words, a considerable body of research in this field rests on relatively unfounded intuitions. To advance the theoretical understanding of how experiences of climate change could affect the motivation to act on climate change, we introduce a conceptual framework that organizes insights from psychology along three clusters of processes: 1) noticing and remembering, 2) mental representations, and 3) risk processing and decision-making. Within each of these steps, we identify and explicate psychological processes that could occur when people personally experience climate change, and we formulate theory-based, testable hypotheses. By making assumptions explicit and tying them to findings from basic and applied research from psychology, this paper provides a solid basis for future research and for advancing theory.

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Hua He, Guorong Chai, Yana Su, Yongzhong Sha, Shengliang Zong, and Hairong Bao

Abstract

This study assessing the lag and interactive effects between the daily average temperature and relative humidity on respiratory disease (RD) morbidity in Lanzhou, China, using data from daily outpatient visits for RD between 2014 and 2017 and meteorological and pollutant data during the same period analyzed with Poisson generalized linear model and distributed lag nonlinear models; the effects are further explored by classifying the RD by gender, age, and disease type. The results showed that the effect of temperature and relative humidity on outpatient visits of different populations and types of RD is nonlinear, with a significant lag effect. Relative to 11°C, every 1°C decrease in temperature is associated with 10.98% [95% confidence interval (CI): 9.87%–12.11%] increase for total RD. Chronic obstructive pulmonary disease is affected only by low temperature, upper respiratory tract infection is affected by both low and high temperatures, and asthma is influenced by high temperature. When the relative humidity is less than 32%, every 1% decrease in relative humidity is associated with 6.00% (95% CI: 3.00%–9.11%) increase for total RD; relative humidity has different effects on the outpatient risk of different types of RD. Temperature and relative humidity have an obvious interactive effect on different types and populations of RD: when both temperature and humidity are at low levels, the number of outpatient visits for RD is higher. When the relative humidity is ≤50% and the temperature is ≤11°C, total RD outpatient visits increase by 4.502% for every 1°C drop in temperature; that is, a dry environment with low temperature has the most significant impact on RD.

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