Abstract
Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble.
Highlights
Climate modeling is traditionally directed toward research to improve our understanding of the climate system [1] and to develop climate projections that aid climate change decision-making [2].regional and local scales are not well represented by global climate models GCMs, [3].Downscaling techniques can be used to reduce the biases of GCMs, translate the GCM-simulated response to local scales, and provide added information for decision-making [4,5]
We explore the influence of statistical downscaling on the ensemble weighting followed by an examination of the historical accuracy and projected changes resulting from each ensemble
The Skill and Historical Independence Weighting (SI-h) weighted CMIP5 ensemble has both smaller skill weights and greater independence weights than the SI-h weighted Localized Canonical Analogs (LOCA) ensemble (Figure 3)
Summary
Regional and local scales are not well represented by global climate models GCMs, [3]. Downscaling techniques can be used to reduce the biases of GCMs, translate the GCM-simulated response to local scales, and provide added information for decision-making [4,5]. Downscaling techniques include regional climate modeling and statistical downscaling (SD) and bias correction methods. SD methods are both computationally efficient and flexible, which has led to their use for impact assessments, e.g., [6,7,8,9,10], including the National Climate Assessment NCA, [11]. Numerous assessments of GCMs currently exist, e.g., [12,13,14,15,16,17], and downscaling is being investigated in programs such as the Coordinated Regional Downscaling Experiments CORDEX, e.g., [18]. There has been a transition from using an unweighted, multi-model ensemble mean to more advanced methods that account for the skill and independence of models to inform
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