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Aubrey R. Jones and Nathaniel A. Brunsell


A series of model runs using the University of Oklahoma’s Advanced Regional Prediction System (ARPS) were conducted to investigate the relative impacts of energy balance partitioning and net radiation on soil moisture–precipitation feedbacks in the U.S. central plains and to examine how the dominant physical processes are affected by changes in mean soil moisture and spatial resolution. Soil temperature and Bowen ratio are influenced nonlinearly by soil moisture, and by varying the mean soil moisture in the model it was possible to examine the relationship between soil moisture and the scaling characteristics of these fields using the statistical moments. Information theory metrics were used to provide an indication of the uncertainty associated with varying model resolutions. It was determined that energy balance partitioning plays a dominant role in the occurrence of soil moisture–precipitation feedback, while net radiation was not impacted by mean soil moisture. A strong relationship was seen between soil moisture and the scaling properties of Bowen ratio, while soil moisture did not appear to influence the scaling characteristics of soil temperature. Spatial resolution had a large effect on the representation of boundary layer turbulence, with coarser resolutions unable to capture turbulent motions, which are necessary for convective processes. The ability of the model to capture boundary layer turbulence will alter the dynamics of soil moisture–precipitation feedback as the horizontal transport of moisture by turbulent motions will affect the spatial and temporal scales over which feedback occurs. Higher-resolution runs are generally associated with a higher information content. This may provide a methodology for monitoring land–atmosphere feedbacks via remotely sensed soil moisture and vegetation fields through statistical knowledge of the dependency of the resulting precipitation signal on soil moisture and vegetation fields at the resolution they were observed.

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Bharat Rastogi, A. Park Williams, Douglas T. Fischer, Sam F. Iacobellis, Kathryn McEachern, Leila Carvalho, Charles Jones, Sara A. Baguskas, and Christopher J. Still


The presence of low-lying stratocumulus clouds and fog has been known to modify biophysical and ecological properties in coastal California where forests are frequently shaded by low-lying clouds or immersed in fog during otherwise warm and dry summer months. Summer fog and stratus can ameliorate summer drought stress and enhance soil water budgets and often have different spatial and temporal patterns. Here, this study uses remote sensing datasets to characterize the spatial and temporal patterns of cloud cover over California’s northern Channel Islands. The authors found marine stratus to be persistent from May to September across the years 2001–12. Stratus clouds were both most frequent and had the greatest spatial extent in July. Clouds typically formed in the evening and dissipated by the following early afternoon. This study presents a novel method to downscale satellite imagery using atmospheric observations and discriminate patterns of fog from those of stratus and help explain patterns of fog deposition previously studied on the islands. The outcomes of this study contribute significantly to the ability to quantify the occurrence of coastal fog at biologically meaningful spatial and temporal scales that can improve the understanding of cloud–ecosystem interactions, species distributions, and coastal ecohydrology.

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