12.3 Assessing the Impact of Marine Clouds on Offshore Wind offshore the US West Coast in a Changing Climate

Wednesday, 31 January 2024: 5:00 PM
347/348 (The Baltimore Convention Center)
Sue Ellen Haupt, PhD, Atmospheric Science, National Center for Atmospheric Research, Boulder, CO; and M. S. Bukovsky, PhD, T. W. Juliano, S. McGinnis, A. Mitra, and R. Krishnamurthy

To meet the U.S. goals of 15 GW of floating offshore wind by 2035, wind energy will need to be generated off the U.S. West coast where the deeper water demands that floating offshore wind is developed. Thus, we wish to better understand the wind resource in the region and its variability across scales ranging from very short through the climate scales. One primary feature of the West coast marine boundary layer is the predominance of low-level stratocumulus, which has the potential to alter the dynamics of the boundary layer. As long-wave radiation is emitted from the tops of these stratocumulus decks, there is potential to cool the atmosphere at the level of the cloud tops. Such cooling can destabilize the marine boundary layer and provide complex turbulence conditions.

We examine the connection between low-level cloud cover in the marine boundary layer off the U.S. West coast and its relationship to wind speeds and temperature gradients. We consider first current climate conditions and whether global climate models (GCMs) downscaled with the Weather Research and Forecasting (WRF) model can capture the cloud patterns observed from satellite, which we observe to be a good match. For the most successful downscaled GCM, we look at projected changes in these variables for the near future climate (2025-2055), a period that may represent the lifetime of wind plants first developed in this region. We generally see relatively small changes in projected cloud cover and wind speeds and those observed are consistent with the dynamic response to temperature gradient changes. We additionally assess the variability across a representative set of downscaled climate models to assess uncertainty in our conclusions.

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