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G. Roder, G. Sofia, Z. Wu, and P. Tarolli

not a unique temporal set of variables for the assessment of vulnerability. And similarly not all the variables explain the vulnerability for all hazard settings. As commented by Weichselgartner (2001) , concentrating attention on a single hazard may produce accurate and in-depth analysis surrounding it because of the unique physical process characteristics. This means that there are factors related to vulnerability at a broad spectrum (e.g., Zhou et al. 2014 ; Guillard-Gonçalves et al. 2015

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Lawrence C. Hamilton

—evidence for biased assimilation. Even without knowing the alternative claims, respondents’ beliefs provide them with cues about which answers to choose. If one believes that climate is not changing, then probably the ice is not melting. Or, if climate is changing but humans are not the cause, then possibly volcanoes are to blame. Thus, although scientific knowledge about these facts no doubt affects climate beliefs, the questions’ internal logic and survey data analysis both argue that in these cases

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Thomas W. Corringham and Daniel R. Cayan

flooding due to hurricanes and tropical storms ( NOAA/National Weather Service 2018 ); dollar figures are inflation-adjusted to 2017 dollars throughout ( U.S. Bureau of Economic Analysis 2018 ). Several factors are responsible for the increasing trend in flood damages, including population growth, income growth, and increased migration toward coastal areas ( Villarini and Slater 2017 ; Glantz 2001 ; Changnon et al. 2000 ). There is evidence that global climate change is partly responsible, through

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Peter N. Peregrine

archaeological record (and, again, what is visible in the archaeological and geological record is often a time period of repeated climate-related disasters rather than a single catastrophic event); and that 3) is spatially and culturally distinct from other cases in the sample in order to minimize the likelihood of autocorrelation (a formal analysis of autocorrelation effects could not be conducted here because the linguistic data normally used as a control is lacking for most archaeological cases). To

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L. Bruce Railsback

: 10.1002/qj.49708436106 . 10.1002/qj.49708436106 IMF , 2015 : International Monetary Fund World Economic Outlook Database. Accessed 13 March 2016. [Available online at www.imf.org/external/pubs/ft/weo/2015/02/weodata/weoselgr.aspx .] Jackson , D. B. , and K. M. Beaver , 2015 : The role of adolescent nutrition and physical activity in the prediction of verbal intelligence during early adulthood: A genetically informed analysis of twin pairs . Int. J. Environ. Res. Public Health , 12 , 385

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Mathew Alexander Stiller-Reeve, David B. Stephenson, and Thomas Spengler

different monsoon definitions to the four datasets. Even though these data cover different periods, they all span the period 1978–2007. Hence, we choose this period to compile a climatology of onsets that is consistent for all the datasets. We keep the data resolution consistent for analysis by bilinearly interpolating all the results to 0.25°, which is identical to the highest-resolution dataset. We realize that the datasets may not be ideal at these higher resolutions, especially in regions of rapid

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