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Stephen M. Strader, Alex M. Haberlie, and Alexandra G. Loitz

Abstract

This study investigates the interrelationships between National Weather Service (NWS) county warning area (CWA) tornado risk, exposure, and societal vulnerability. CWA climatological tornado risk is determined using historical tornado event data, and exposure and vulnerability are assessed by employing present-day population, housing, socioeconomic, and demographic metrics. In addition, tornado watches, warnings, warning lead times, false alarm warnings, and unwarned tornado reports are examined in relation to CWA risk, exposure, and vulnerability. Results indicate that southeastern U.S. CWAs are more susceptible to tornado impacts because of their greater tornado frequencies and larger damage footprints intersecting more vulnerable populations (e.g., poverty and manufactured homes). Midwest CWAs experience fewer tornadoes relative to Southeast and southern plains CWAs but encompass faster tornado translational speeds and greater population densities where higher concentrations of vulnerable individuals often reside. Northern plains CWAs contain longer-tracked tornadoes on average and larger percentages of vulnerable elderly and rural persons. Southern plains CWAs experience the highest tornado frequencies in general and contain larger percentages of minority Latinx populations. Many of the most socially vulnerable CWAs have shorter warning lead times and greater percentages of false alarm warnings and unwarned tornadoes. Study findings provide NWS forecasters with an improved understanding of the relationships between tornado risk, exposure, vulnerability, and warning outcomes within their respective CWAs. Findings may also assist NWS Weather Forecast Offices and the Warning Decision Training Division with developing training materials aimed at increasing NWS forecaster knowledge of how tornado risk, exposure, and vulnerability factors influence local tornado disaster potential.

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Vikram S. Negi, Shinny Thakur, Rupesh Dhyani, Indra D. Bhatt, and Ranbeer S. Rawal

Abstract

Mountains are important global sites for monitoring biological and socioecological responses to climate change, and the Himalaya has some of the world’s most rapid and visible signs of climate change. The increased frequency and severity of climate anomalies in the region are expected to significantly affect livelihoods of indigenous communities in the region. This study documents the perceptions of indigenous communities of climate change in the western Himalaya of India. The study highlights the power of knowledge and understanding available to indigenous people as they observe and respond to climate change impacts. We conducted a field-based study in 14 villages that represent diverse socioecological features along an altitudinal range of 1000–3800 m MSL in the western Himalaya. Among the sampled population, most of the respondents (>95%) agreed that climate is changing. However, people residing at low- and high-altitude villages differ significantly in their perception, with more people at high altitudes believing in an overall warming trend. Instrumental temperature and rainfall from nearby meteorological stations also supported the perception of local inhabitants. The climate change perceptions in the region were largely determined by sociodemographic variables such as age, gender, and income as well as altitude. A logistic regression, which exhibited significant association of sociodemographic characteristics with climate change perceptions, further supported these findings. The study concluded that the climate change observations of local communities can be usefully utilized to develop adaptation strategies and mitigation planning in the Himalayan region.

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Carol R. Ember, Ian Skoggard, Benjamin Felzer, Emily Pitek, and Mingkai Jiang

Abstract

All societies have religious beliefs, but societies vary widely in the number and type of gods in which they believe as well as their ideas about what the gods do. In many societies, a god is thought to be responsible for weather events. In some of those societies, a god is thought to cause harm with weather and/or can choose to help, such as by bringing needed rain. In other societies, gods are not thought to be involved with weather. Using a worldwide, largely nonindustrial sample of 46 societies with high gods, this research explores whether certain climate patterns predict the belief that high gods are involved with weather. Our major expectation, largely supported, was that such beliefs would most likely be found in drier climates. Cold extremes and hot extremes have little or no relationship to the beliefs that gods are associated with weather. Since previous research by Skoggard et al. showed that greater resource stress predicted the association of high gods with weather, we also tested mediation path models to help us evaluate whether resource stress might be the mediator explaining the significant associations between drier climates and high god beliefs. The climate variables, particularly those pertaining to dryness, continue to have robust relationships to god beliefs when controlling on resource stress; at best, resource stress has only a partial mediating effect. We speculate that drought causes humans more anxiety than floods, which may result in the greater need to believe supernatural beings are not only responsible for weather but can help humans in times of need.

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Yu-Hsuan Lin, Hen-I Lin, Fang-I Wen, and Sheng-Jang Sheu

Abstract

A better understanding of farmers’ investment strategies associated with climate and weather is crucial to protecting farming and other climate-exposed sectors from extreme hydrometeorological events. Accordingly, this study employed a field experiment to investigate the investment decisions under risk and uncertainty by 213 farmers from four regions of Taiwan. Each was asked 30 questions that paired “no investment,” “investment with crop insurance,” “investment with subsidized crop insurance,” and “investment” as possible responses. By providing imperfect information and various probabilities of certain states occurring, the experimental scenarios mimicked various types of weather-forecasting services. As well as their socioeconomic characteristics, the background information we collected about the participants included their experiences of natural disasters and what actions they take to protect their crops from weather damage. The sampled farmers became more conservative in their decision-making as the weather forecasts they received became more precise, except when increases in risk were associated with high returns. The provision of insurance subsidies also had a conservatizing effect. However, considerable variation in investment preferences was observed according to the farmers’ crop types. For those seeking to create comprehensive policies aimed at helping the agricultural sector deal with the costs of damage from extreme events, this study has important implications. This approach could be extended to research on the perceptions of decision-makers in other climate-exposed sectors such as the construction industry.

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Kelly Helm Smith, Mark E. Burbach, Michael J. Hayes, Patrick E. Guinan, Andrew J. Tyre, Brian Fuchs, Tonya Haigh, and Mark D. Svoboda

Abstract

Drought-related decision-making and policy should go beyond numeric hydrometeorological data to incorporate information on how drought affects people, livelihoods, and ecosystems. The effects of drought are nested within environmental and human systems, and relevant data may not exist in readily accessible form. For example, drought may reduce forage growth, compounded by both late-season freezes and management decisions. An effort to gather crowdsourced drought observations in Missouri in 2018 yielded a much higher number of observations than did previous related efforts. Here we examine 1) the interests, circumstances, history, and recruitment messaging that coincided to produce a high number of reports in a short time; 2) whether and how information from volunteer observers was useful to state decision-makers and to U.S. Drought Monitor (USDM) authors; and 3) potential for complementary use of stakeholder and citizen science reports in assessing trustworthiness of volunteer-provided information. State officials and the Cattlemen’s Association made requests for reports, clearly linked to improving the accuracy of the USDM and the related financial benefit. Well-timed requests provided a focus for people’s energy and a reason to invest their time. State officials made use of the dense spatial coverage that observers provided. USDM authors were very cautious about a surge of reports coinciding closely with financial incentives linked to the Livestock Forage Disaster program. An after-the-fact comparison between stakeholder reports and parallel citizen science reports suggests that the two could be complementary, with potential for developing protocols to facilitate real-time use.

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Dasol Kim, Chang-Hoi Ho, Hiroyuki Murakami, and Doo-Sun R. Park

Abstract

Understanding the mechanisms related to the variations in the rainfall structure of tropical cyclones (TCs) is crucial in improving forecasting systems of TC rainfall and its impact. Using satellite precipitation and reanalysis data, we examined the influence of along-track large-scale environmental conditions on inner-core rainfall strength (RS) and total rainfall area (RA) for Atlantic TCs during the TC season (July–November) from 1998 to 2019. Factor analysis revealed three major factors associated with variations in RS and RA: large-scale low and high pressure systems [factor 1 (F1)]; environmental flows, sea surface temperature, and humidity [factor 2 (F2)]; and maximum wind speed of TCs [factor 3 (F3)]. Results from our study indicate that RS increases with an increase in the inherent primary circulation of TCs (i.e., F3) but is less affected by large-scale environmental conditions (i.e., F1 and F2), whereas RA is primarily influenced by large-scale low and high pressure systems (i.e., F1) over the entire North Atlantic and partially influenced by environmental flows, sea surface temperature, humidity, and maximum wind speed (i.e., F2 and F3). A multivariable regression model based on the three factors accounted for the variations of RS and RA across the entire basin. In addition, regional distributions of mean RS and RA from the model significantly resembled those from observations. Therefore, our study suggests that large-scale environmental conditions over the North Atlantic Ocean are important predictors for TC rainfall forecasts, particularly with regard to RA.

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Chuanhao Wu, Pat J.-F. Yeh, Jiali Ju, Yi-Ying Chen, Kai Xu, Heng Dai, Jie Niu, Bill X. Hu, and Guoru Huang

Abstract

Drought projections are accompanied with large uncertainties due to varying estimates of future warming scenarios from different modeling and forcing data. Using the standardized precipitation index (SPI), this study presents a global assessment of uncertainties in drought characteristics (severity S and frequency Df) projections based on the simulations of 28 general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). A hierarchical framework incorporating a variance-based global sensitivity analysis was developed to quantify the uncertainties in drought characteristics projections at various spatial (global and regional) and temporal (decadal and 30-yr) scales due to 28 GCMs, three representative concentration pathway scenarios (RCP2.6, RCP4.5, RCP8.5), and two bias-correction (BC) methods. The results indicated that the largest uncertainty contribution in the globally projected S and Df is from the GCM uncertainty (>60%), followed by BC (<35%) and RCP (<16%) uncertainty. Spatially, BC reduces the spreads among GCMs particularly in Northern Hemisphere (NH), leading to smaller GCM uncertainty in the NH than the Southern Hemisphere (SH). In contrast, the BC and RCP uncertainties are larger in the NH than the SH, and the BC uncertainty can be larger than GCM uncertainty for some regions (e.g., southwest Asia). At the decadal and 30-yr time scales, the contributions for three uncertainty sources show larger variability in S than Df projections, especially in the SH. The GCM and BC uncertainties show overall decreasing trends with time, while the RCP uncertainty is expected to increase over time and even can be larger than BC uncertainty for some regions (e.g., northern Asia) by the end of this century.

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Yong-Fei Zhang, Mitchell Bushuk, Michael Winton, Bill Hurlin, Xiaosong Yang, Tom Delworth, and Liwei Jia

Abstract

The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice–ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (~10%) from up to 3 times larger than this (~30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.

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Hongjie Li and Yongsheng Xu

Abstract

Stratified geostrophic turbulence theory predicts an inverse energy cascade for the barotropic (BT) mode. Satellite altimetry has revealed a net inverse cascade in the baroclinic (BC) mode. Here the spatial variabilities of BT and BC kinetic energy fluxes in the Antarctic Circumpolar Current (ACC) were investigated using ECCO2 data, which synthesize satellite data and in situ measurements with an eddy-permitting general circulation model containing realistic bathymetry and wind forcing. The BT and BC inverse kinetic energy cascades both reveal complex spatial variations that could not be explained fully by classical arguments. For example, the BC injection scales match better with most unstable scales than with the first-mode deformation scales, but the opposite is true for the BT mode. In addition, the BT and BC arrest scales do not follow the Rhines scale well in terms of spatial variation, but show better consistency with their own energy-containing scales. The reverse cascade of the BT and BC modes was found related to their EKE, and better correlation was found between the BT inverse cascade and barotropization. Speculations of the findings were proposed; however, further observations and modeling experiments are needed to test these interpretations. Spectral flux anisotropy exhibits a feature associated with oceanic jets that is consistent with classical expectations. Specifically, the spectral flux along the along-stream direction remains negative at scales up to that of the studied domain (~2000 km), while that in the perpendicular direction becomes positive close to the scale of the width of a typical jet.

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E. Rousi, F. Selten, S. Rahmstorf, and D. Coumou

Abstract

Changes in atmospheric circulation under increasing greenhouse gas concentrations are important because of their implications for weather extremes and associated societal risks. However, uncertainties in models and future projections are still large and drivers behind circulation changes are not well understood. Particularly for Europe, a potential weakening of the Atlantic meridional overturning circulation (AMOC) is considered important as it affects SST patterns and ocean–atmosphere heat fluxes and, subsequently, European climate. Here we detect and characterize changes in atmospheric circulation patterns over the North Atlantic under increasing CO2 concentrations in simulations of a very high-resolution, fully coupled climate model (CM2.6) with a realistic representation of the AMOC. We use an objective clustering technique (self-organizing maps) and validate the model’s clusters against reanalysis data. We compare the frequency of those patterns in a CO2 doubling experiment, characterized by an AMOC decline, with those in a preindustrial run, and find statistically significant changes. The most robust findings are 1) a ~30% increase in zonal flow regimes in February, relevant for flood risk in northwestern Europe, and 2) a ~60% increase in anticyclonic (high pressure) circulation directly west of the United Kingdom in August, relevant for western and central European drought. A robust decrease in the frequency of Scandinavian blocking is also seen across most months and seasons. Despite the uncertainties regarding atmospheric circulation response to climate change, our findings contribute to the increasing evidence for the emergence of robust high-impact changes over Europe.

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