A document by Laura Torres-Rojas. Click on the document to view its contents.

Earth system models currently struggle to account for the complex effects that land surface heterogeneity can have on land-atmosphere interactions. Subgrid land surface heterogeneity is currently not well accounted for in land-atmosphere interactions in earth system models. There have been attempts to include the impact of this heterogeneity on the atmosphere, but they ignore the development of coherent secondary circulations that can be driven by spatial differential surface heating. A wealth of literature, particularly large-eddy simulation (LES) based studies, shows that these circulations have significant impacts on the development and organization of clouds. In this work, we describe a two-column model with a parameterized circulation driven by atmospheric virtual potential temperature profiles, differences in near surface temperature between the two columns, patterns of surface heterogeneity, and the mean background wind. Key aspects of the proposed model structure are compared with LES output, and the model is then implemented between two otherwise independent single column models. While some avenues for improvement exist, when the circulations are parameterized, we see increased cloud development and realistic changes to the mean profiles of temperature and moisture. The proposed model qualitatively matches expectations from the literature and LES, and points to the potential success of its future implementation in coarse grid models.

Earth system models (ESMs) and mesoscale models have come to employ increasingly complex parameterization schemes for the atmospheric boundary layer (ABL), requiring surface boundary conditions for numerous higher order turbulence statistics. Of particular interest is the potential temperature variance (PTV), which is used not only as a boundary condition itself but also to close boundary conditions of other statistics. The existing schemes in ESMs largely rely on the assumptions of Monin-Obukhov similarity theory (MOST), and are not necessarily applicable over complex and heterogeneous surfaces where large scale circulations and roughness sub-layer effects may cause deviations from MOST. The National Ecological Network (NEON) is used here to evaluate existing parameterizations for the surface boundary of PTV, note key deficiencies, and explore possible remedies. The results indicate that existing schemes are acceptable over a variety of surface conditions provided the analysis of a priori filters out low frequency variability not associated with turbulent time scales. There was, however, significant inter-site variability in observed similarity constants and a significant bias when compared to the textbook values of these parameters. Existing models displayed the poorest performance over heterogeneous sites, and rough landscapes. Attempts to use canopy structure and surface roughness characteristics to improve the results confirmed a relation between these variables and PTV, but failed to significantly improve the predictive power of the models. The results did not find strong evidence indicating that large scale circulations caused substantial deviations from textbook models, although additional analysis is required to assess their full impacts.

Land surface features such as elevation, soils, land use, and vegetation fluctuate on scales ranging from millimeters to hundreds of kilometers. The state of the land surface and many hydrological processes vary accordingly. Land surface temperature (LST) is a crucial factor determining the interactions between the land surface and the atmosphere (i.e., energy, water, and carbon fluxes). Decades of global satellite remote sensed LST fields are now available, constituting an unprecedented opportunity to understand better the factors influencing hydrological variability from regional to global scales. An important under-researched aspect regarding variability, at least over continental extents, is determining the scales for which hydrological variations are spatially and temporally related. These scales would serve as indicators for the required time and spatial resolution for observational systems. This presentation will address this gap in understanding across scales through a comprehensive analysis of spatial and temporal correlation lengths of LST across the contiguous United States (CONUS). Correlation lengths (CLs) are measures of the stationarity of a property distribution both in space and time. They reveal the scales of variability for fields thus, contributing to estimating the stationarity of the property. Temporal correlation lengths (tCLs) express the property changes in time for a fixed location, providing a measure of the persistence or variability of the time series. On the other hand, spatial correlation lengths (sCLs) depict the spatial patterns of the property over a predefined area by representing the distance for which variations are spatially related. As part of our evaluation, we will analyze derived fields of tCLs and sCLs for the ~2x2 km2 GOES-16 LST hourly product over CONUS. A 0.25-degree regular grid over CONUS will be defined, and an hourly time step between 2017 and 2021 will be used for the analysis. The obtained CLs will be assessed in terms of the time of the day and season. Additionally, we propose a comparison of well-known spatiotemporal influencing factors of LST such as land cover, surface thermal properties, topography, incoming solar radiation, and meteorological conditions.

Earth System Models (ESMs) traditionally operate at large horizontal resolutions, on the order of 100 km, which can obscure the effects of smaller scale heterogeneity. When examining land surface states and fluxes in ESMs, one common approach to mitigate this issue is to divide the sub-grid land surface into distinct homogeneous clusters and then resolve the water, energy, and biogeochemical processes on each cluster or tile. The literature, as well as work in the Coupling of Land and Atmospheric Subgrid Parameterizations (CLASP) project, indicates that surface heterogeneity has important implications for atmospheric processes as well. Previous work using large-eddy simulation (LES) shows that spatial variability in surface heating can produce significant secondary circulations closely related to the type and scale of heterogeneity that are not captured by single column models. This presentation aims to address this persistent weakness by using a clustering or tiling approach, similar to that used with land surface processes, for the atmosphere. To accomplish this task, we run the Cloud Layers Unified By Binomials (CLUBB) single column model, a sub-grid turbulence and cloud parameterization scheme, over a 100 km box centered at the Southern Great Plains site in Oklahoma for a variety of surface and atmospheric conditions. The model is run independently over multiple surface clusters defined by surface sensible heat fluxes in the given domain. Results indicate that significant differences exist for some cases between the single column and multicolumn cases for liquid water path (LWP) as well as the turbulent kinetic energy (TKE) budget, and that results converge on consistent results with a fairly low number of clusters (i.e., atmospheric columns). We follow this up with a connected multicolumn setup where each column is dynamically connected with the other columns throughout the run to qualitatively capture the circulations observed in the LES output. The existing results show promise for capturing the effects of subgrid scale surface flux heterogeneity on the lower atmosphere in ESMs with the application of a multicolumn CLUBB setup.