Extending Applicability of Monin-Obkuhov Similarity Theory (MOST) Across Heterogeneous Ecosystems for Improved Land-Atmosphere Exchange

Fundamental for modeling both weather and climate is how models exchange energy and moisture between the land (and ocean) surface and the atmosphere above it. Surface fluxes in particular are one of the primary desired outputs from land models within the earth system model (ESM) context. For the past five decades, modelers have relied on Monin-Obukhov Similarity Theory (MOST) to model surface exchanges. MOST, however, is known for significant errors in its ability to predict how much heat and moisture is exchanged, which can have considerable impacts on the model’s ability to accurately predict weather events, and long term climate. There is broad agreement that the uncertainties and potential errors of MOST based exchange schemes are a significant hurdle to broader improvement in the representation of land-atmosphere interactions within our modeling frameworks. Recent investigations over groups of meteorological towers from Stiperski and Calaf 2023 have provided a promising method to account for these deviations from the ideal, traditional MOST using the anisotropy of turbulence to create new surface exchange relations. These modified relations may be able to capture the deviations from MOST specifically around non-homogeneous surfaces, and non-stationarity. To further assess the validity of the Stiperski relations, we examine them over 7 years of turbulence data from the 45, ecologically diverse eddy-covariance tower sites in the National Ecological Observation Network (NEON).

The relations from Stiperski and Calaf 2023 show significant improvement over traditional MOST based schemes for predicting the velocity variances as well as the variances of heat, moisture and carbon in the NEON network under both stable and unstable stratification. The improvement is consistent across the varied ecosystems present in NEON, including tropical, arctic, and mountainous sites. For the vertical velocity variances, for example, skill scores show a consistent 50% or greater improvement over traditional MOST across sites. Characteristics of anisotropy are also examined across the sites, with an eye towards developing model relations for turbulence anisotropy applicable in large scale schemes (i.e. numerical weather prediction and earth system models. The performance across the varied ecosystems in NEON is also explored. Initial results for the gradients of heat and momentum, which can be used to parameterize surface fluxes in the modeling context, are also shown, with promising improvement over traditional MOST despite significant scatter. The route for application of these schemes in surface layer parameterizations in ESMs is also explored, with an eye towards the potential for significant improvements in modeling of surface fluxes.