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The Andes is the longest mountain range in the world, stretching from tropical South America to austral Patagonia (12°N-55°S). Along with the climate differences associated with latitude, the Andean region also features contrasting slopes and elevations, reaching altitudes of more than 4,000 m. a.s.l., in a relatively narrow crosswise section, and hosts diverse ecosystems and human settlements. This complex landscape poses a great challenge to weather and climate simulations. The interaction of the topography with the large-scale atmospheric motions controls meteorological phenomena at scales of a few kilometers, often inadequately represented in global (grid spacing ∼200–50 km) and regional (∼50–25 km) climate simulations previously studied for the Andes. These simulations typically exhibit large biases in precipitation, wind and near-surface temperature over the Andes, and they are not suited to represent strong gradients associated with the regional processes. In recent years (∼2010–2024), a number of modeling studies, including convection permitting simulations, have contributed to our understanding of the characteristics and distribution of a variety of systems and processes along the Andes, including orographic precipitation, precipitation hotspots, mountain circulations, gravity waves, among others. This is Part I of a two-part review about atmospheric modeling over the Andes. In Part I we review the current strengths and limitations of numerical modeling in simulating key atmospheric-orographic processes for the weather and climate of the Andean region, including low-level jets, downslope winds, gravity waves, and orographic precipitation, among others. In Part II, we review how climate models simulate surface-atmosphere interactions and hydroclimate processes in the Andes Cordillera to offer information on projections for land-cover/land-use change or climate change. With a focus on the hydroclimate, we also address some of the main challenges in numerical modeling for the region. [ABSTRACT FROM AUTHOR]

In the Andes, the complex topography and unique latitudinal extension of the cordillera are responsible for a wide diversity of climate gradients and contrasts. Part I of this series reviews the current modeling efforts in simulating key atmospheric-orographic processes for the weather and climate of the Andean region. Building on this foundation, Part II focuses on global and regional climate models challenging task of correctly simulating changes in surface-atmosphere interactions and hydroclimate processes to provide reliable future projections of hydroclimatic trajectories in the Andes Cordillera. We provide a review of recent advances in atmospheric modeling to identify and produce reliable hydroclimate information in the Andes. In particular, we summarize the most recent modeling research on projected changes by the end of the 21st century in terms of temperature and precipitation over the Andes, the mountain elevation-dependent warming signal, and land cover changes. Recent improvements made in atmospheric kilometer-scale model configurations (e.g., resolution, parameterizations and surface forcing data) are briefly reviewed, highlighting their impact on modeling results in the Andes for precipitation, atmospheric and surface-atmosphere interaction processes, as mentioned in recent studies. Finally, we discuss the challenges and perspectives of climate modeling, with a focus on the hydroclimate of the Andes. [ABSTRACT FROM AUTHOR]

ABSTRACT The downscaling of global climate models ( GCMs) aims at incorporating finer scale information to their horizontal resolution in order to represent regional and local processes better. There are two main approaches to downscaling: statistical (based on data relationships between synoptic atmospheric variables and observations of local variables) and dynamical (based on the modelling of regional atmospheric processes and land-surface interactions). In this study, some predictive capabilities regarding the generation of station-scale mean monthly temperature and rainfall of both a statistical artificial neural network ( ANN-based) and a dynamical weather research and forecasting ( WRF-based) downscaling approach are assessed. We have devised two versions of the statistical downscaling approach. One of them includes regional orographic variables as predictors to allow for spatial extrapolation; the other is purely local. Historical observational data, from the period 1990 to 1999, of two major watersheds in the Ecuadorian Southern Andes, the Jubones and Paute river basins, were used. Since, to a certain extent, the value added by downscaling techniques can be attributed to terrain information, it is worth noting that some characteristics of the selected catchments (as notorious altitude differences and the presence of qualitatively different precipitation regimes) provide a scientifically interesting location for exploring how finer scale effects are captured. For this reason, we concentrate on the ability of downscaling techniques to reproduce seasonality. A decade of evaluation proved that both approaches were able to qualitatively describe precipitation and temperature seasonal variations for different regimes at representative weather stations. Furthermore, the seasonality of precipitation represented by both downscaling approaches surpassed the seasonality representation of reanalysis data. However, shortcomings on the estimates were found. Specifically, dynamical downscaled precipitation estimates were prone to overestimation. Despite the fact that the considered downscaling approaches are different in nature, their ability to represent the high spatio-temporal variability in this region highlights the importance of evaluating their strengths and limitations. [ABSTRACT FROM AUTHOR]

Investigation was made on the climate change signal for hydrometeorological and hydrological variables for the Paute River basin, in southern Ecuador Andes, making use of an adjusted quantile perturbation approach for climate downscaling, and the impact of climate change on runoff for two nested catchments within the basin. The analysis was done making use of long daily series of seven representative rainfall and temperature sites along the study area and considering climate change signals of global and regional climate models for IPCC SRES scenarios A1B, A2 and B1. The determination of runoff was carried out using a lumped conceptual rainfall-runoff model. The study found that the range of changes in temperature is despicably lower that the range of changes in rainfall. However, changes differ from site to site, showing that more significant changes in temperature are observed at higher elevation sites. For rainfall, high differences in rainfall change are found and strongly related to the rainfall regime. Higher changes are detected for sites located in regions with bimodal rainfall regime. In addition, higher changes are observed on higher temporal resolutions. The runoff changes are strongly related to the changes in rainfall peaks, more than with the changes in temperature; also showing strong spatial differences over the Andean region considered. [ABSTRACT FROM AUTHOR]