A high-resolution record of coastal clouds and fog and their role in plant distributions over San Clemente Island, California. (11th October 2021) - Clemesha, Rachel E S; Gershunov, Alexander; Lawson, Dawn M; Longcore, Travis; MacDonald, Beau; Booker, Melissa; Munson, Bryan; O'Connor, Kimberly
Abstract: San Clemente Island (SCI), located in the Southern California Bight, is owned and operated by the U.S. Navy and is home to endemic species, including federally threatened or endangered plants and birds. The SCI ecosystem is influenced by the presence of warm season low-level clouds that shade, cool and, especially when in the form of fog, moisten the environment. We created a new cloud and fog satellite-derived albedo product for SCI at a higher resolution than previous datasets. The record spans 23 summers (1996–2018, May - Sep). The spatial resolution is ∼1 km and the temporal resolution is half hourly (nominally 0600 to 1800 PST). Using Geostationary Operational Environmental Satellite (GOES)-WEST visible band measurements, it was discovered that small (typically on the order of less than 5 km) geographical misalignment of the satellite images was common. The biological ramifications of such a shift could be significant. Thus, to provide a useful 1 km product for a narrow island such as SCI it was necessary to correct misalignments. Misalignment of albedo was easily apparent in clear sky images at the interface of land and water. This concept was used in the automated correction. The northwest coast of SCI is the cloudiest/foggiest area. June, on average, is the cloudiest month on SCI. The intra-day variability reaches ∼20% cloud albedo while interannual and monthly variability are ∼10%. To demonstrate the records' utility in understanding ecological phenomena and patterns, we used the dataset to model plant distributions. We found monthly mean cloud albedo values, a proxy for cloud frequency and persistence, were among the most important environmental variables in understanding plant distributions. The vegetation models indicate locations with appropriate conditions, including clouds and fog during critical periods of the year, for particular vegetation types and thus, can inform restoration and management activities.