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Yoshiro Tsutsui

disturbance term. I also estimate the equation that includes seasonal dummies, where winter is defined as December to February, spring as March to May, summer as June to August, and fall as September to November. However, the dummies are not significant at all, and the estimates and their significance of other variables are almost unchanged, so that I do not mention these results in later sections. 11 I estimate each equation with three models: ordinary least squares (OLS), a fixed effects model, and a

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Adam J. Kalkstein, Miloslav Belorid, P. Grady Dixon, Kyu Rang Kim, and Keith A. Bremer

nonlinear and lagged effects of an exposure variable such as temperature on a specific human health outcome. Further, DLNM is effective at controlling for cyclical patterns such as day-of-week and seasonal effects along with long-term trends such as population change and the implementation of suicide prevention efforts, all of which have been highlighted in previous environment–suicide research as having important impacts on suicide rates ( Dixon and Kalkstein 2009 ; Mok et al. 2012 ; Hiltunen et al

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Micah J. Hewer and William A. Gough

the past. These impacts corresponded to a temporal climate analog for the 2020s, 2050s, or 2080s, depending on a range of potential climate change impacts (RCP4.5–RCP8.5). Furthermore, the statistical significance of these historical effects and therefore the meaningfulness of potential future impacts were also presented by means of the P values associated with the tests of significant differences. 3. Results a. The effect of seasonal climatic anomalies 1) Winter Figure 2 presents the time

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J. Nalau, S. Becken, S. Noakes, and B. Mackey

(e.g., informed stocking of food and water), and lower costs for staff (optimized staffing schedules). Slightly longer-term forecasts (e.g., seasonal) assist in developing weather-adjusted product portfolios, diversification activities, and marketing. Issues, however, remain about the quality and type of weather and climate information and how it is communicated and distributed to and between different user groups ( de Freitas; 2003 ; Rutty and Andrey 2014 ; Wilson 2011 ; Zabini et al. 2015

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J. D. Tamerius, M. S. Perzanowski, L. M. Acosta, J. S. Jacobson, I. F. Goldstein, J. W. Quinn, A. G. Rundle, and J. Shaman

models for indoor relative humidity (%), while outdoor specific humidity is greater than 10 g kg −1 . The coefficients for univariate models for each of the variables present in the multivariate models are included in the first column. 4. Discussion The preceding analysis shows a number of interesting seasonal effects, especially with regard to indoor temperature. During the warm season (outdoor temperature greater than 15°C) outdoor temperature is the strongest predictor of indoor temperature. The

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Henry J. F. Penn, S. Craig Gerlach, and Philip A. Loring

general have strong seasonal dimensions. We propose a framework for capturing these nuanced aspects of how communities are impacted by change, one based on the concepts of cumulative effects and community capacity ( CEQ 1997 ; Beckley et al. 2009 ). We pair these concepts with a visual decision calendar framework (see also Corringham et al. 2008 ; Kim and Jain 2010 ; Ray and Webb 2016 ), which we then operationalize with the data on climate and weather impacts gleaned through interviews with

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Dorian Speakman

Scottish Borders ( Archer et al. 2013 ). Trees and hedgerows have reduced runoff and erosion in arable farmland that was suffering increased problems of soil erosion in Norfolk in eastern England ( Evans 2006 ). By utilizing tree cover and soil conservation the above evidence points toward the benefits of agroecology in buffering the effects of adverse weather and harnessing those benefits for adaptation. c. Marginal lands in Britain: Adverse climates for agriculture The uplands and the north and west

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Melanie Brown and Dominique Bachelet

in climate impacts ( Table 11 ). We define climate impacts here as the effects of climate on both natural resources and society. Land managers were specifically interested in vegetation shifts, including the spread of juniper and invasive annual grasses, plant migration, and wildlife habitat changes. One manager emphasized, “A main concern or question I have is about the final impacts. What are they going to be? What will be the seasonality and the amounts of moisture? What’s going to happen and

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Scott R. Templeton, Alan A. Hooper, Heather D. Aldridge, and Norman Breuer

as potential users of seasonal climate forecasts and their roles as educators and advisors of farmers. The study of Prokopy et al. (2013) was similarly focused on agricultural advisors as potential users of historical weather and climate information. Moreover, the sample proportions in the studies from Florida ( Cabrera et al. 2006 ), North Carolina ( Breuer et al. 2011 ), and the upper Midwest ( Prokopy et al. 2013 ) were not used to statistically test whether majorities of extensionists in

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Jessica Bolson and Kenneth Broad

of seasonal prediction: Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004) . Climate Dyn. , 33 , 93 – 117 . Zebiak, S. , and Cane M. A. , 1987 : A model El Niño–Southern Oscillation . Mon. Wea. Rev. , 115 , 2262 – 2278 . Zhang, E. , and Trimble P. J. , 1996 : Predicting effects of climate fluctuations for water management by applying neural networks . World Resour. Rev. , 8 , 334 – 348 . Zhao, L. M. , and Reisman A. , 1992

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