VALUE: A framework to validate downscaling approaches for climate change studies

Abstract

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

“…Part of the validation procedure must ensure that the observed and modeled distributions in the calibration or historical period are statistically similar (Maraun et al, 2015). However, as shown here, validating bias corrected data in another period outside of the calibration period against observations will not isolate bias correction methodological performance.…”

Providing future climate projections using multiple models and methods: insights from the Philippines

“…Most approaches include evaluating models' ability to represent past climate conditions (e.g. Maraun et al 2015;Park et al 2016) as a measure of reliability for future climate simulations. Model weighting (e.g.…”

Intercomparison of Univariate and Joint Bias Correction Methods in Changing Climate From a Hydrological Perspective

“…The pseudo-reality approach has been used in recent studies [20][21][22][23] and was also considered in the European Concerted Research Action ES1102 VALUE (Validating and Integrating Downscaling Methods for Climate Change Research) framework as an important, although not sufficient step, when evaluating bias adjustment method performance [24].…”

“…However, these tests are not sufficient alone to determine whether a particular method is suitable for climate change assessments. Additional tests such as those implemented in the VALUE framework [24] should be conducted to obtain a complete picture of benefits and limitations of bias correcting inter-variable correlations. As discussed above, the used approach does not easily allow to evaluate the potential benefits/adverse effects of the modification of temporal sequencing to the hydrological model results, which would require temporally synchronized model simulations and reference data.…”