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Yoko Kusunose, Lala Ma, and David Van Sanford

Abstract

Weather and climate forecasts can help agricultural producers improve management choices in anticipation of uncertain growing conditions. Current literature conjectures that the extent to which forecasts are useful depends on their accuracy, that is, the probability with which a forecasted event, such as precipitation, is projected to occur. Too little accuracy can potentially render forecasts effectively useless, even if they convey some form of information. In this study, we collect farmer-based data through a questionnaire and a framed field experiment to test for the existence of an accuracy threshold for forecasts, below which forecasts do not induce any behavioral changes. We do this in the context of a very specific management choice—the timing and amount of nitrogen that Kentucky farmers apply to their wheat in early spring—in response to randomly generated 6-to-10-day forecasts of rainfall conditions. We find that forecasts provide economically significant value to decision-makers only when they depart dramatically from what is normally expected. These results have implications that extend beyond the nitrogen-application decision for winter wheat: if this type of behavior is widespread, at current accuracy levels, other types of forecasts may be of little value to decision-makers and therefore go unheeded.

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Maya K. Buchanan, Michael Oppenheimer, and Adam Parris

Abstract

Sea level rise amplifies flooding from tides and storms for coastal communities around the globe. Although the characterization of these physical hazards has improved, it is people’s behavior that will ultimately determine the impact on communities. This study adds to our understanding of how people may respond to various adaptation options and policies, using a household survey in New York City, New York, neighborhoods affected by Hurricane Sandy. We investigate previously overlooked factors that may influence intended household adaptive behavior, such as single-action bias, a cognitive trade-off that households make between adaptation options, whereby taking a small (and often less effective measure) may strongly discourage uptake of a more protective measure. Through a novel application of discrete choice experiments in the coastal adaptation context, we simulate plausible future conditions to assess potential adaptation under climatic and nonclimatic stressors. Our findings suggest that single-action bias plays a substantial role in intended coastal adaptation, whereby the odds of homeowners who have already implemented a modest-cost measure to insure and relocate in the future are 66% and 80% lower, respectively. The odds of homeowners to relocate are also ~1.9, ~2.2, and ~3.1 times as great if their peers relocate, nuisance flooding becomes a frequent occurrence, and property values fall substantially, respectively. We find that renters’ motivation to relocate is largely driven more by external issues such as crime, gentrification, and economic security than by flood hazard.

Open access
Kieran M. Findlater, Milind Kandlikar, Terre Satterfield, and Simon D. Donner

Abstract

Despite long-standing assertions that climate change creates new risk management challenges, the climate change adaptation literature persists in assuming, both implicitly and explicitly, that weather and climate variability are suitable proxies for climate change in evaluating farmers’ risk perceptions and predicting their adaptive responses. This assumption persists in part because there is surprisingly little empirical evidence either way, although case studies suggest that there may be important differences. Here, we use a national survey of South Africa’s commercial grain farmers (n = 389)—similar to their peers in higher-income countries (e.g., North America, Europe, Australia), but without subsidies—to show that they treat weather and climate change risks quite differently. We find that their perceptions of climate change risks are distinct from and, in many regards, oppositional to their perceptions of weather risks. While there seems to be a temporal element to this distinction (i.e., differing concern for short-term vs long-term risks), there are other differences that are better understood in terms of normalcy (i.e., normal vs abnormal relative to historical climate) and permanency (i.e., temporary vs permanent changes). We also find an interaction effect of education and political identity on concern for climate change that is at odds with the well-publicized cultural cognition thesis based on surveys of the American public. Overall, studies that use weather and climate variability as unqualified proxies for climate change are likely to mislead researchers and policymakers about how farmers perceive, interpret, and respond to climate change stimuli.

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Kerstin K. Zander, Simon Moss, and Stephen T. Garnett

Abstract

There is mounting evidence that climate change impacts compromise people’s well-being. Many regions of Australia have experienced record hot temperatures and more frequent and longer heat waves with substantial consequences for people, economies, and ecosystems. Using data from an Australia-wide online survey with 1101 respondents, we investigated the relationship between self-reported measures of heat stress and different dimensions of subjective well-being. After controlling for socioeconomic factors known to affect well-being, we found that heat stress was linked to people’s certainty about and planning for their future but not to their life satisfaction, happiness, social state, capabilities, or purpose in life. This result indicates that, while heat is not associated with present well-being, many people worry about the effect that increased heat will have on their future well-being. People who were uncertain about their future were also more likely than those who did not feel uncertain to think that heat compromised their productivity. People who agreed that they were competent and capable in their activities rated their heat stress–related productivity loss lower than those who disagreed. The findings are relevant for future studies using life-satisfaction approaches to assess consequences of climate change impacts and to studies in “happiness economics.” We recommend that future research on the impact of climate change on well-being go beyond simply life satisfaction and happiness and test multiple dimensions of well-being.

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Erik Løhre, Marie Juanchich, Miroslav Sirota, Karl Halvor Teigen, and Theodore G. Shepherd

Abstract

The use of interval forecasts allows climate scientists to issue predictions with high levels of certainty even for areas fraught with uncertainty, since wide intervals are objectively more likely to capture the truth than narrow intervals. However, wide intervals are also less informative about what the outcome will be than narrow intervals, implying a lack of knowledge or subjective uncertainty in the forecaster. In six experiments, we investigate how laypeople perceive the (un)certainty associated with wide and narrow interval forecasts, and find that the preference for accuracy (seeing wide intervals as “objectively” certain) versus informativeness (seeing wide intervals as indicating “subjective” uncertainty) is influenced by contextual cues (e.g., question formulation). Most important, we find that people more commonly and intuitively associate wide intervals with uncertainty than with certainty. Our research thus challenges the wisdom of using wide intervals to construct statements of high certainty in climate change reports.

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Maria Carmen Lemos, Kimberly S. Wolske, Laura V. Rasmussen, James C. Arnott, Margaret Kalcic, and Christine J. Kirchhoff

Abstract

Scholarship on climate information use has focused significantly on engagement with practitioners as a means to enhance knowledge use. In principle, working with practitioners to incorporate their knowledge and priorities into the research process should improve information uptake by enhancing accessibility and improving users’ perceptions of how well information meets their decision needs, including knowledge credibility, understandability, and fit. Such interactive approaches, however, can entail high costs for participants, especially in terms of financial, human, and time resources. Given the likely need to scale up engagement as demand for climate information increases, it is important to examine whether and to what extent personal interaction is always a necessary condition for increasing information use. In this article, we report the results from two experimental studies using students as subjects to assess how three types of interaction (in-person meeting, live webinar, and self-guided instruction) affect different aspects of climate information usability. Our findings show that while in-person interaction is effective in enhancing understanding of climate knowledge, in-person interaction may not always be necessary, depending on the kinds of information involved and outcomes desired.

Open access
Susan Joslyn and Raoni Demnitz

Abstract

Despite near unanimous agreement among climate scientists about global warming, a substantial proportion of Americans remain skeptical or unconcerned. The two experiments reported here tested communication strategies designed to increase trust in and concern about climate change. They also measured attitudes toward climate scientists. Climate predictions were systematically manipulated to include either probabilistic (90% predictive interval) or deterministic (mean value) projections that described either concrete (i.e., heat waves and floods) or abstract events (i.e., temperature and precipitation). The results revealed that projections that included the 90% predictive interval were considered more trustworthy than deterministic projections. In addition, in a nationally representative sample, Republicans who were informed of concrete events with predictive intervals reported greater concern and more favorable attitudes toward climate scientists than when deterministic projections were used. Overall, these findings suggest that while climate change beliefs may be rooted in partisan identity, they remain malleable, especially when targeted communication strategies are used.

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JungKyu Rhys Lim, Brooke Fisher Liu, and Michael Egnoto

Abstract

On average, 75% of tornado warnings in the United States are false alarms. Although forecasters have been concerned that false alarms may generate a complacent public, only a few research studies have examined how the public responds to tornado false alarms. Through four surveys (N = 4162), this study examines how residents in the southeastern United States understand, process, and respond to tornado false alarms. The study then compares social science research findings on perceptions of false alarms to actual county false alarm ratios and the number of tornado warnings issued by counties. Contrary to prior research, findings indicate that concerns about false alarm ratios generating a complacent public may be overblown. Results show that southeastern U.S. residents estimate tornado warnings to be more accurate than they are. Participants’ perceived false alarm ratios are not correlated with actual county false alarm ratios. Counterintuitively, the higher individuals perceive false alarm ratios and tornado alert accuracy to be, the more likely they are to take protective behavior such as sheltering in place in response to tornado warnings. Actual country false alarm ratios and the number of tornado warnings issued did not predict taking protective action.

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Thomas W. Corringham and Daniel R. Cayan

Abstract

This paper quantifies insured flood losses across the western United States from 1978 to 2017, presenting a spatiotemporal analysis of National Flood Insurance Program (NFIP) daily claims and losses over this period. While considerably lower (only 3.3%) than broader measures of direct damages measured by a National Weather Service (NWS) dataset, NFIP insured losses are highly correlated to the annual damages in the NWS dataset, and the NFIP data provide flood impacts at a fine degree of spatial resolution. The NFIP data reveal that 1% of extreme events, covering wide spatial areas, caused over 66% of total insured losses. Connections between extreme events and El Niño–Southern Oscillation (ENSO) that have been documented in past research are borne out in the insurance data. In coastal Southern California and across the Southwest, El Niño conditions have had a strong effect in producing more frequent and higher magnitudes of insured losses, while La Niña conditions significantly reduce both the frequency and magnitude of losses. In the Pacific Northwest, the opposite pattern appears, although the effect is weaker and less spatially coherent. The persistent evolution of ENSO offers the possibility for property owners, policy makers, and emergency planners and responders that unusually high or low flood damages could be predicted in advance of the primary winter storm period along the West Coast. Within the 40-yr NFIP history, it is found that the multivariate ENSO index would have provided an 8-month look-ahead for heightened damages in Southern California.

Open access
Travis M. Williams and William R. Travis

Abstract

Weather index insurance is a popular means of mitigating agricultural risks. Drought is a significant cause of lost agricultural production, and so precipitation index–based plans are common. Simple “percent of normal” indices are often used because they are easy to calculate and communicate to policyholders. However, the ability of such indices to reflect production losses is limited, reducing the ability of insurance to efficiently mitigate risk. This is especially true in rangeland livestock production given the cumulative effects of rainfall and other factors on range production and the complex relationships between range and livestock weight gain, which is the rancher’s main product and source of income. More sophisticated drought indices incorporate the complexities of drought into their design and would, in theory, serve as more appropriate payment triggers. This study uses a suite of drought indices to test correlation with production and the behavior of insurance based on those indices. Payout patterns based on each index were simulated within the actuarial framework of a precipitation-based insurance program aimed at livestock producers. Results were compared with the program’s precipitation index, showing that drought indices have higher correlations with range production, a tendency to incentivize growing-season protection, more even geographic distributions of risk, reduced policyholder ability to seek higher payments through strategic coverage choices, and increased provider ability to adjust payment patterns to reduce the risk of nonpayment given loss.

Open access