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Future projections of wind patterns in California with the variable-resolution CESM: a clustering analysis approach

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Abstract

Wind energy production is expected to be affected by shifts in wind patterns that will accompany climate change. However, many questions remain on the magnitude and character of this impact, especially on regional scales. In this study, clustering is used to group and analyze large-scale wind patterns in California using model simulations from the variable-resolution Community Earth System Model (VR-CESM). Specifically, simulations have been produced that cover historical (1980–2000), mid-century (2030–2050), and end-of-century (2080–2100) time periods. Once clustered, observed changes to wind patterns can be analyzed in terms of both the change in frequency of those clusters and changes to winds within-clusters. Statistically significant capacity factors changes have been found at all five wind plant sites. Decomposition of the capacity factor changes into frequency changes and within-cluster changes enables a better understanding of their drivers. A further examination of the synoptic-scale fields associated with each cluster then provides a better understanding of how changes to large-scale meteorological fields are important for driving changes in localized wind speeds.

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Fig. 1

This figure is a reproduction of Figure 1 from Millstein et al. (2018)

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This figure is a reproduction of Figure 2 from Wang et al. (2018)

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Notes

  1. These wind plants names are representatives of an agglomeration of plants in close proximity to each other. Based on the calssification from California Energy Commission (CEC) (https://ww2.energy.ca.gov/maps/renewable/wind.html), Shiloh represents “Solano Wind Resource Area”, Altamont represents “Altamont Wind Resource Area”, Tehachapi represents “Tehachapi Wind Resource Area”, San Gorgonio represents “San Gorgonio Wind Resource Area”, Ocotillo represents “East San Diego Wind Resource Area”.

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Acknowledgements

The authors want to thank the Craig Collier, Daran Rife, and Christopher Hayes for the helpful conversations throughout this project. We would further like to thank the two anonymous reviewers for their thorough evaluation of the manuscript and valuable suggestions. This work was funded by the California Energy Commission under the Electric Program Investment Charge Grant, “EPC-15-068: Understanding and Mitigating Barriers to Wind Energy Expansion in California.” This work was supported by the U.S. Department of Energy (DOE) under Lawrence Berkeley National Laboratory Contract No. DE-AC02-05CH11231. This project is further supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, hatch project under California Agricultural Experiment Station project CA-D-LAW-2203-H. Author Ullrich is supported by Department of Energy Office of Science award number DE-SC0016605,“An Integrated Evaluation of the Simulated Hydroclimate System of the Continental US”.

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  1. University of California, Davis, Davis, CA, 95616, USA

    Meina Wang & Paul Ullrich

  2. Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA

    Dev Millstein

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  1. Meina Wang
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Correspondence to Meina Wang.

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Wang, M., Ullrich, P. & Millstein, D. Future projections of wind patterns in California with the variable-resolution CESM: a clustering analysis approach. Clim Dyn 54, 2511–2531 (2020). https://doi.org/10.1007/s00382-020-05125-5

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