An asynchronous regional regression model for statistical downscaling of daily climate variables

Abstract

ABSTRACT:The asynchronous regional regression model (ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere-ocean general circulation models (AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions of the model in downscaling daily minimum and maximum temperature and precipitation for 20 stations in North America from diverse climate zones. Using cross-validation to maximize the independent comparison period, historical downscaled simulations are evaluated relative to observations in terms of three different quantities: the probability distributions, giving a visual image of the skill of each model; root-mean-square errors; and bias in nine quantiles that represent both means and extremes. Successive versions of the model show improved accuracy in simulating extremes, where AOGCMs are often most biased and which are frequently the focus of impact studies. Overall, the quantile regression-based technique is shown to be efficient, robust, and highly generalizable across multiple variables, regions, and climate model inputs.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

“…For example, Abatzoglou and Brown (2012), Stoner et al (2013), Ahmed et al (2013) each applied quantile mapping to daily climate model data that had been interpolated to a high-resolution observational grid. However, Maraun (2013) and Gutmann et al (2014) demonstrated that this approach-applying a univariate bias correction algorithm to interpolated climate data at individual grid points-can lead to fields with unrealistic spatial structure, especially if the variable being downscaled operates on spatial scales that are substantially finer than the climate model grid.…”

Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?

“…The ESD method examined is a variant of the asynchronous regional regression model (ARRM version 1) of Stoner et al (2013) and the variable of interest is daily maximum near-surface air temperature (tasmax). The ARRM is one of a widely-used class of ESD methods that operate on the distributional characteristics of data samples.…”

Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves

“…For extreme values, output from the ARRM statistical downscaling method, which includes bias-correction and cross-validation, showed improved accuracy and ability to be efficient and generalizable across regions (Stoner et al 2013). For precipitation (and other variables), the MACA method has been found to outperform the BiasCorrected Spatial Disaggregation method (BCSD), used to create the Bureau of Reclamation dataset, due to the ability to jointly downscale certain variables (Abatzoglou and Brown 2012).…”