Influence of teleconnections on night-time minimum temperature variability in the Southwestern U.S.
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ORIGINAL PAPER
Influence of teleconnections on night-time minimum temperature variability in the Southwestern U.S. Robert J. Erhardt1 • Yiwei Wang1 Accepted: 2 October 2020 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Night-time minimum temperatures are increasing, faster than day-time maximum temperatures. They are also strongly connected to key outcomes such as health, vegetation, and crop productivity, and therefore changes in the distributions of night-time minimum temperatures have strong impacts on living systems. Night-time minimum temperatures are also related to teleconnections, which are indices that capture climate phenomenon occurring on a large spatial and temporal scale. As autocorrelated sequences, teleconnections are predictable and therefore inform distributions of weather in the near future. In this paper, we link four well-known teleconnections to night-time minimum temperatures in the American Southwest. These teleconnections are covariates for the mean process, but also for the variance, which is modelled as an exponential generalized autoregressive conditional heteroskedastic process (AR-EGARCH). The resulting models are used to estimate the probability of a low temperature event across the phases of each teleconnection, demonstrating the impact each teleconnection has on variability of night-time minimum temperatures. Keywords Arctic oscillation AR-EGARCH El Nin˜o North atlantic oscillation Pacific decadal oscillation Southern oscillation index
1 Introduction Global temperatures are on the rise (Pachauri et. al. 2014). While there is much attention paid to average temperatures and maximum temperatures, night-time minimum temperatures have been increasing roughly twice as fast as daytime maximum temperatures since 1950 (Vose et. al. 2005). Night-time minimum temperatures show a strong relationship with health (Murage et al. 2017), vegetation (Shiflett et. al. 2017), carbon capture (Turnbull, Murthy and Griffin 2002), and crop productivity (Peng et. al. 2004). In the United States, night-time minimum temperatures are increasing the most in the southwest region (Rowe and Derry 2012). Recent papers have explored the behavior of daily temperature sequences to better understand long-term consequences of warming. Dupuis (2012) quantified the & Robert J. Erhardt [email protected] 1
Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, USA
tail-behavior for daily temperatures in four North American cities by fitting extreme value models, and showed how those extremes were shifting over time in response to climate change. Dupuis (2014) modeled night-time minimum daily temperatures at 12 locations in the American Southwest, to capture the dynamics using an extension of the Generalized Autoregressive Conditional Heteroskedasticy (GARCH) model. GARCH models first entered the literature in the econometrics community (Nelson 1991). These models are time series models which allow for the variance of the process to vary over time in a heteroske
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