4 December (CLIM) Hay-Chapman PhD Defense

Finley Hay-Chapman, Candidate, Doctor of Philosophy in Climate Dynamics

Title: Analyzing the Role of Land-Atmosphere Coupling Sensitivity and Subgrid Spatial Heterogeneity in Earthsystem Models

December 4 2024, 11am 

Meeting Room E, Johnson Center and via Zoom (for link see AOES outlook calendar or email  pdirmeye@gmu.edu)

Cloud formation, distribution, and other properties may be sensitive to heterogeneous surfaces depending on the strength and location of such heterogeneities and the background atmospheric state. This may drive differences in the cloud population depending on which part of the domain one is located. This may also lead to mesoscale circulations, which may strengthen or weaken this effect. Currently, climate models act on scales (~100 km) that are too large to explicitly represent these processes, which are strongest at smaller scales (around 5-40 km). Therefore, sub-grid scale (SGS) heterogeneity is neglected, and any predictability and model fidelity it may provide is lost. In this dissertation, I analyze these land-atmosphere (L-A) interactions with two new research studies.

We first introduce a novel method for diagnosing land-atmosphere coupling sensitivity on the subdaily timescale. This study defines a new metric, called the coupling sensitivity score (CSS), which uses an ensemble of single-column model runs, each with varying, prescribed surface flux conditions used as a proxy for SGS heterogeneity, and driven by observationally-constrained large-scale forcing data. The CSS can diagnose both positive [increasing cloud with wetter/cooler surface] and negative [increasing cloud with drier/warmer surface] L-A coupling sensitivity. Over the Southern Great Plains (SGP), we show that depending on the large-scale atmospheric state, strong positive or negative L-A feedback behavior may be preferred. Using the CSS this way helps to gain a better first-order understanding of L-A coupling behavior when in the presence of large variations in land surface conditions.

 In the second study, we aim to measure how well the Community Earth System Model (CESM) parameterizes SGS heterogeneity and its effect on a given model grid cell. To do this, we demonstrate a new application of the relative entropy, a metric from information theory. The relative entropy, which measures the similarity between probability density functions (PDFs), is used to measure the fidelity of statistical SGS spatial PDFs of atmospheric properties, which are parameterized within CESM, when they are compared to more realistic spatial distributions simulated by the Weather Research Forecasting – Large-Eddy Simulation (WRF-LES) model. We test the parameterized spatial distributions under four separate parameterization configurations, testing two versions each of the shallow convection/turbulence scheme and the coupling scheme. With this technique, we show that a new, augmented version of the shallow convection/turbulence scheme, CLUBB+MF, marginally outperforms its default version, CLUBB when using the WRF-LES simulations as a target.

The methodologies from these two studies are also applied to two other locations, each with a different hydroclimate than the SGP: one with much higher moisture availability in the Amazon tropical rainforest, and one in the semi-arid north central region of Argentina. Analyzing these new hydroclimates shows that both the CSS metric and our relative entropy method have generable applicability outside of the SGP, and may be used to further understand L-A coupling behavior in the presence of SGS heterogeneity and how we may improve its simulation in today’s state-of-the-art earth system models.