A comparison between two empirical downscaling strategies

Abstract

A new approach involving the use of common empirical orthogonal functions (EOFs) in statistical downscaling of future global climate scenarios is proposed. The advantage of this method is that it minimizes the errors associated with the downscaling of future climate scenarios. The time series from the common EOF analysis are used both for the calibration of the statistical models and the prediction of future climate scenarios. This paper presents a systematic comparison between the common EOF approach and downscaling with a more conventional method based on the 'Perfect Prog' concept.Three different sets of experiments are carried out where the two downscaling methods are compared, and the robustness of the methods is explored taking the predictor field from different regions. The analysis indicates that climate scenarios derived using the common EOF approach are associated with smaller errors. Another important finding is that the choice of predictor domain may influence the downscaled results, and that the merit of downscaling may depend on this area as well as location and season.

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

“…However, this is generally ignored (Benestad, 2001). A study by Brinkmann (2002) suggests that, for studies where only one grid point is used, the optimum grid point location for downscaling may be a function of the timescale under consideration and is not necessarily related solely to location.…”

Modeling the temperature evolution of Svalbard permafrost during the 20th and 21st century

“…The predictors were represented in form of common empirical orthogonal functions (Sengupta and Boyle, 1998;Benestad, 2001), which use principal component analysis (Strang, 1988) to describe how different spatial structures vary in time. The calibration and projection were carried out for each of the calendar months separately, and subsequently assembled for the whole year.…”

Intercomparison and analyses of the climatology of the West African Monsoon in the West African Monsoon Modeling and Evaluation project (WAMME) first model intercomparison experiment

“…The model intercomparison results have not only been used to identify the discrepancies, consensuses, and models' common weakness; they have also been used to identify the climate modes (e.g., in Barnett 1999;Stouffer et al 2000;Benestad 2001). Further brief information regarding CEOF is summarized in the appendix, and a comprehensive explanation about CEOF for atmospheric model intercomparisons can be found in Sengupta and Boyle (1998).…”

“…CEOF and common principal component analysis, which share a similar approach with different algorithms, describe modes with identical structures in the observations and the GCM results and are associated with time series that describe the temporal variations for observations and GCM data (Sengupta and Boyle 1998;Barnett 1999;Benestad 2001Benestad , 2004. CEOF isolates patterns of variability that are present in all models and makes it possible for the variability associated with these patterns to be compared quantitatively between the models and observation.…”