Date of Award




Document Type

Master's Thesis

Degree Name

Master of Science (MS)


Department of Atmospheric and Environmental Sciences

Content Description

1 online resource (xix, 137 pages) : illustrations (some color), color map.

Dissertation/Thesis Chair

Mathias Vuille

Committee Members

Paul Roundy


ENSO, regional climate models, South America, Tropical Andes, Climatology, Weather

Subject Categories

Atmospheric Sciences | Climate


High-resolution regional climate models (RCM) run over a limited domain are increasingly used to simulate seasonal to interannual climate variability over South America and to assess the spatiotemporal impact of future climate change under a variety of emission scenarios. Global climate models (GCM) are often too coarse to resolve local circulations and the topography of the Andes, leading to problems with simulation of temperature and precipitation patterns throughout the domain. A RCM model can also better represent the climate at a regional scale; however, they are subject to errors introduced by the driving global models. For this study, the Hadley Centre Regional Climate Modeling System version 3 (HadRM3), PRECIS (Providing REgional Climate for Impact Studies), was run from 10 deg-N to 27 deg-S and 86 deg-W to 44 deg-W under two different simulations. The first was simulated with lateral boundary conditions from the Hadley Centre Atmospheric Model version 3 (HadAM3; BL) and the second with European Center for Medium-Range Weather Forecasting (ECMWF) reanalysis (ERA) data. Our results indicate that the ERA-forced simulation is able to more accurately portray seasonal temperature and precipitation than BL when compared to two observational datasets. EOF analyses are performed on both temperature and precipitation for both the wet (DJF) and the dry season (JJA) to extract the primary mode of climate variability (ENSO) and are spatially regressed against different climate indices. A backwards-regression is then performed in which different climate indices are regressed onto the models seasonal temperature and precipitation fields to isolate the climate signal. Again for both of these analyses, ERA portrays a more accurate fingerprint than BL. Our study, consistent with others, indicates that lateral boundary conditions driven by reanalysis data increase the skill of the PRECIS regional climate model.