Enhanced mesoscale climate projections in TAR and AR5 IPCC scenarios: a case study in a Mediterranean climate (Araucanía Region, south central Chile)
Future climate changes will affect agriculture, hydrology, and other socio-economic fields (Caldeira and Rau 2000; Mearns 2001; IPCC 2007a, b). Atmosphere Ocean Global Climate Model (AOGCM) scenarios enable policy makers to develop new environmental strategies and mitigation methods (IPCC 2007a, b). Several different prospective scenarios are projected based on assumptions of population growth, environmental policies, technological growth, social inequality, and globalization (SRES 2000). Two scenarios for representing high CO2 emissions (A2) and moderate CO2 emissions (B2) (IPCC 2007a, b) are used as technical research in order to support public policies (e.g. Räisänen et al. 2004; RupaKumar et al. 2006; Fuenzalida et al. 2006; Conde et al. 2011; Krüger et al. 2012). These are included in the IPCC Third Assessment Report (TAR) and the Assessment Report 4 (AR4) and updated in the Assessment Report 5 (AR5), released between September 2013 and November 2014 (IPCC 2015).
Although physical laws driving the atmospheric–oceanic circulation are well-identified and the global-scale boundary conditions for modeling are highly precise and well established (Collins 2007; Räisänen 2007), climate models have different error sources (Räisänen 2007; Baigorria et al. 2008; Challinor et al. 2009). Furthermore, AOGCM were developed for global conditions (Zorita 2000), and they produce low-scale resolution climate projections (about 200–300 km). Statistical and dynamical downscaling techniques (Zorita 2000; Wilby et al. 2004) are used to improve these projections at a higher-resolution (20–50 km) over specific zones (domains) Some mesoscale projections performed are: CREAS (Regional Climate Change Scenarios for South America) in Argentina, Uruguay, and Brazil (Marengo and Ambrizzi 2006), “Variabilidad Climática para el Siglo XXI” is performed in Chile (Fuenzalida et al. 2006), and PRUDENCE over Europe (Déqué et al. 2005). In the last time was developed the CORDEX as an international effert for developing high resolution grids (Giorgi et al. 2009). Downscaled datasets inherit AOGCM uncertainties, and we should include them in order to design climate-change adaptation strategies.
On the other hand, we also should consider climate variability. For example, El Niño Southern Oscillation (ENSO) (Aceituno 1998; Vuille and Garreaud 2011) is one of the main phenomena affecting climate variability. This phenomenon affects the Pacific Anticyclone, which is the main barrier to fronts producing rain in Chile (Garreaud et al. 2009). Southern oscillation is a temporal pattern and is the difference between the measured pressure in two places: Darwin (Australia, 12°27?S, 130°50?W) and Papetee (Tahiti, 17°32?S, 140°34?W). In normal conditions, Papetee shows higher pressures than Darwin; however, this relationship is reversed under El Niño conditions (Kiladis and van Loon 1988; Guevara-Díaz 2008). Moreover, La Niña is the increase in pressure difference between Papetee and Darwin, matching with a decrease in sea temperature in coastal Chile (Kiladis and van Loon 1988). Thus, three phases of ENSO are defined: La Niña, Neutral, and El Niño phases. Nonetheless, ENSO is not the only phenomenon related to climate variability. Mantua et al. (1997) described a Pacific Decadal Oscillation (PDO) consisting of coherent interdecadal covariability in the dominant pattern of North Pacific pressure patterns and sea surface temperature. PDO can modulate the interannual ENSO-related global teleconnections (Krishnan and Sugi 2003; Wang et al. 2008) and their combined effect modulates a large part of hydrological variability within continents (Andreoli and Kayano 2005; da Silva et al. 2011; Vuille and Garreaud 2011; Wang et al. 2014). However, although ENSO is not explicitly represented in long-term projections from AOGCM (Räisänen 2007; Tebaldi and Knutti 2010; Van Haren et al. 2013), La Niña and El Niño synoptic conditions are observed. Assessing the rainfall pattern under neutral-ENSO phases allow us to understand climate variability under normal conditions, which is the basis for designing climate change mitigation countermeasures. Notwithstanding, since La Niña and El Niño conditions are not a typical pattern, it is necessary to study whether climate models represent climate variability during these phases. Since climate projections include ENSO-equivalent synoptic conditions, we can compare projections with current synoptic conditions, thus helping us to understand future climate conditions.
Our case study is focused on a Mediterranean climate, and Chilean data were used. We investigate precipitation variability within the Araucanía Region (Chile; 37° to 40°S and 71° to 74°W), which presents a very homogeneous climate associated with the Pacific anticyclone. The Pacific anticyclone produces weather conditions characterized by an important decrease in rainfall during the summer months, coinciding with higher annual temperatures (Armesto et al. 2008). The first Chilean mesoscale downscaling was computed by the Universidad de Chile’s Department of Geophysics (hereinafter DGF), with a dynamical downscaling of Hadley Centre Coupled Model (HadCM3) output (2.5° × 3.75° latitude by longitude, Pope et al. 2000; Gordon et al. 2000), using the PRECIS model (providing regional climates for impacts studies, see http://www.metoffice.gov.uk/precis/). This consisted of downscaling both the baseline data (between 1961 and 1991), together with B2 and A2 scenarios (between 2070 and 2100) at 0.25° × 0.25° resolution throughout Chile (see Fuenzalida et al. 2006 for main details and results of the experiment). HadCM3 projections are included in the IPCC Third Assessment Report (TAR, Fuenzalida et al. 2006). We refer to these downscaled fields as DGF-PRECIS.
Our goal is to first define a methodology to construct a precise, high-resolution climatology of the rainfall variability within a region under different ENSO phases and to assess its spatial variability. This initial analysis allows us to construct a database with which we can correct the data from projections, subsequently allowing us to measure the severity of future changes. We detail the steps to evaluate and correct both climate projections (TAR and AR5). In addition, several authors reported that ENSO changes extreme event frequency (Jaksic 1998; Grimm and Tedeschi 2009). Within the study zone, we construct rainfall histograms to measure frequency of rainy/dryer months, and we evaluate the statistical significance of ENSO event impacts on rainfall (one-way ANOVA test with a 95 % significance level through a Monte Carlo analysis). Although DGF-PRECIS is an important progress for assessing the effects of climate change, there are at least three issues left to be solved: (a) DGF-PRECIS dataset have been not validated with in situ data, (b) the effect of ENSO on the projected variability has not been quantified, and (c) the last IPCC report (AR5) offers new scenarios (Moss et al. 2010), while the differences between these new projections (RCP 25, RCP 45, RCP60 and RCP85) and the old A2 and B2 projections (from TAR) within our regions are still unknown. To be consistent with the original HadCM3 model, we use the Hadley model outputs included in the IPCC AR5 simulations, called HadGEM (Data distribution Center of IPCC, DDC 2015; http://www.ipcc-data.org/sim/gcm_monthly/AR5/WG1-Archive.htm). Next, we compare the DGF-PRECIS baseline database (between 1961 and 1991) with in situ data, specifically focusing on different ENSO conditions, validating the downscaled fields, and identifying possible limitations of the projected fields over the twenty-first century. Based on this comparison, we generate a corrected projection for the A2 and B2 climate change scenarios. Subsequently, we also validate and correct the new AR5 projected fields scenarios using the HadGEM simulations (Jones et al. 2011; Baek et al. 2013). Finally, we compare the TAR B2 scenario with the RCP 45 scenario and the TAR A2 with the RCP85. Additionally, we present results of the RCP 25 scenario currently used as the ideal scenario (see Table 3).
This paper is structured as follows. In “Methods” section, we present the in situ data used to construct the climatology, and we describe the model dataset used to project the rainfall condition up to the twenty-first century. Next, we discuss the methodology to study the effect of ENSO cycles on both in situ data and the DGF-PRECIS baseline. Moreover, we present statistics to evaluate the statistical significance of ENSO changes and effects in in situ fields. Correction methods and the comparison with AR5 scenarios are also presented. We present the results in “Results and discussion” section: the climatology, ENSO impact evaluation, the TAR high-resolution dynamical downscaled projection, validation and correction, and the comparison with AR5 scenarios. Finally, we discuss the results and draw our main conclusions in last section.