Using climate change models to assess the probability of weather extremes events: a local scale study based on the generalized extreme value distribution

https://doi.org/10.1590/1678-4499.2018144 M. Fontolan et al. AGROMETEOROLOGY - Article Using climate change models to assess the probability of weather extremes events: a local scale study based on the generalized extreme value distribution Mariana Fontolan, Ana Carolina Freitas Xavier, Heloisa Ramos Pereira, Gabriel Constantino Blain* Instituto Agronômico - Centro de Ecofisiologia e Biofísica - Campinas (SP), Brazil. ABSTRACT: Regional climate models (e.g. Eta) nested to global climate Although both models project changes to warmer conditions, models (e.g. HadGEM2-ES and MIROC5) have been used to assess the responses of Eta-Hadgem2-ES to both RCPs are significantly potential impacts of climate change at regional scales. This study larger than that of Eta-Miroc5. While Eta-Hadgem2-ES suggests used the generalized extreme value distribution (GEV) to evaluate the location of Campinas will be free from agronomic frost events, the ability of two nested models (Eta-HadGEM2-ES and Eta-MIROC5) Eta-Miroc5 indicates that air temperature values equal to or lower to assess the probability of daily extremes of air temperature and than 5 and 2 °C are expected to present a cumulative probability precipitation in the location of Campinas, state of São Paulo, Brazil. of ~0.20 and ~0.05, respectively (RCP 8.5). Moreover, while the Eta- Within a control run (1961-2005), correction factors based on the Miroc5 projected a reduction in the extreme-precipitation amounts, GEV parameters have been proposed to approach the distributions the Eta-Hadgem2-ES projected implausible large daily precipitation generated from the models to those built from the weather station of amounts. The Eta-Miroc5 performed better than the Eta-Hadgem2-ES Campinas. Both models were also used to estimate the probability for assessing the probability of air temperature and precipitation of daily extremes of air temperature (maximum and minimum) and in Campinas. This latter statement holds particularly true for daily- precipitation for the 2041-2070 period. Two concentration paths extreme precipitation data. of greenhouse gases (RCP 4.5 and 8.5) have been considered. Key words: climate change, local impacts, Eta model, nested models. *Corresponding author: Received: Apr. 25, 2018 – Accepted: Jun. 30, 2018 146 Bragantia, Campinas, v. 78, n. 1, p.146-157, 2019 Climate change models and weather extremes INTRODUCTION There are increasing evidences that climate change has affected the spatial and temporal distribution of extreme meteorological events at global and regional scales (Richards 1993; Karl et al. 1999; Manton et al. 2001; Kharin and Zwiers 2005; Wang et al. 2004; Vincent et al. 2005; Haylock et al. 2006; Alexander et al. 2006; Nadarajah and Choi 2007; Pujol et al. 2007; Felici et al. 2007; El Adlouni et al. 2007; Furió and Meneu 2011; Sugahara et al. 2009). This change is of particular concern because such events constitute 80% of the annually US$100 billion damages in global economy, with thousands of deaths each year (IFRC/RCS 2011). Naturally, these major social disruptions trigged by extreme weather conditions justify scientific efforts addressing their geophysical dynamics as well as their changing statistical properties (New et al. 2007). Global climate models (GCM; also called atmosphereocean general circulation models) may be regarded as invaluable tools for assessing Earth’s potential response to altered atmospheric conditions (Kharin et al. 2007; 2013; Cooley and Sain 2010; Foley 2010; Chou et al. 2014a). Although the GCM have gained in complexity in recent years (e.g. IPCC 2007; 2013), they typically have a spatial resolution ranging from 100 to 300 km. This feature limits their ability to assess impacts of climate change at a local scale (Cooley and Sain 2010; Chou et al. 2014a). On such background, regional climate models (RCM) have been nested to GCM in order to provide suitable spatial resolution for local impact studies (Cooley and Sain 2010; Chou et al. 2014a). Accordingly, these nested Regional Global models, with grid size of tens of kilometers, provide a unique opportunity to understand potential impacts of climate change at fine scales (Cooley and Sain 2010). Different from weather forecast models, the abovementioned models are not intended to accurately represent observational weather data. Instead, their runs over a time span – usually decades – are only intended to simulate plausible climate projections for a particular set of boundary conditions that have been provided by a specific GCM (Cooley and Sain 2010). From a mathematical standpoint, this latter statement implies that datasets generated from RCM runs are expected to present feasible statistical distributions of the meteorological variables. In control runs – where these nested models are used to represent the current/observed climate (e.g. 1961-1990; Cooley and Sain 2010) – the parameters shaping the distribution of generated/simulated data should approach those of their corresponding observational data (Kharin et al. 2007; Kharin et al. 2013). The generalized extreme value distribution (GEV) has been used to model the distribution of extreme weather events, including air temperature and precipitation (Frei et al. 2006; Kharin et al. 2007; 2013; Cooley and Sain 2010). Therefore, this distribution can be used to quantify how well parameters shaping the distribution of simulated data represent the regional climatology of a particular area. More specifically, this 3-parameter function can be used to assess model’s bias affecting the location, the scale and the tail behavior of distributions built from simulated data in regard to those built from observational data. In addition, because it is impossible to collect observations for future climate conditions (Foley et al. 2010), using the GEV in control runs is a preliminary step to verify if a particular climate model can be used to assess potential effects of climate change on future extreme weather events (Kharin et al. 2007; 2013). Regarding regional climate models, the Eta model – developed by the Brazilian National Institute for Space Research (INPE) – has been used in the elaboration of a National Communication to the United Nations Framework Convention of Climate Change (Chou et al. 2014a). Although the Eta model, nested to GCM such as HadGEM2-ES or MIROC5, has already been used to address climate change over South America under distinct downscaling scenarios (Chou et al. 2014a; 2014b), there is no study assessing the performance of such nested models on the basis of the Extreme Value Theory. This theory, which is based on distributions such as the GEV, was used in this study to evaluate the following hypothesis: both Eta-HadGEM2-ES and Eta-MIROC5 models can be used in studies addressing local impacts of climate change. In order to provide statistical information supporting this hypothesis, the goal of this study was to evaluate the abilit (...truncated)