Abstract
Future climate projections provide an opportunity to evaluate cultivar climate classification and preferred styles of wine production for a wine grape growing region. However, ensemble selection must account for downscaled archive model skills and interdependence rather than be arbitrary and subjective. Relatedly, methods for generalizing climate model choice remain uncertain, particularly for identifying optimal ensemble subsets. In this study we consider the complete archive of the thirty-two Coupled Model Intercomparison Project Phase 5 (CMIP5) daily Localized Constructed Analogs (LOCA) downscaled historic datasets and their observational data that were used for downscaling and bias corrections. We apply four model averaging methods to determine optimal ensembles for the computation of six common climate classification indices for the Willamette Valley (WV) American Viticultural Area (AVA). Among the four methods evaluated, elastic-net regularization consistently performed best with identifying optimal ensemble subsets. Variation exists among the optimal ensembles computed for each of the six bioclimatic indices. However, a subset of approximately seven to ten climate models were consistently excluded across all six indices’ ensembles. While specific to the archive and wine region, optimal ensemble sizes were noticeably larger than ensemble sizes commonly employed in published studies. Results are reported such that they can be used by researchers to independently perform analyses involving any one of the six bioclimatic indices throughout the WV AVA while using historic and future LOCA CMIP5 climate projections. The data and methods employed herein are applicable for other wine regions.
Highlights
Results are reported such that they can be used by researchers to independently perform analyses involving any one of the six bioclimatic indices throughout the Willamette Valley (WV) American Viticultural Area (AVA) while using historic and future Localized Constructed Analogs (LOCA) Coupled Model Intercomparison Project phase 5 (CMIP5) climate projections
Various indices exist to classify viticulture climate [10], and several recent studies have evaluated the impacts of climate change to viticulture using bioclimatic indices and downscaled future climate projections for wine regions
The study region consists of 553 model-observation comparison points which cover the 13,913 square kilometer Willamette Valley AVA which lies in the Willamette River Basin in the northwestern part of the State of Oregon (OR) in the U.S (Figure 1)
Summary
Climate is significant in determining cultivar suitability and the resulting wine profile typical for a wine grape growing region [1,2,3,4]. Temperature and water availability are recognized as two of the most important environmental factors influencing grapevine growth and the subsequent quality of berries and wine [5,6,7,8,9]. Various indices exist to classify viticulture climate [10], and several recent studies have evaluated the impacts of climate change to viticulture using bioclimatic indices and downscaled future climate projections for wine regions (see Table 1). Quénol et al [11] highlighted the importance of accounting for model uncertainty when performing studies that involve the application of future climate projections. Model simulations of the future from the Coupled Model Intercomparison Project Phase 5
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