Impact of Heterogeneity on Scalar Flux Variance Relations Across Diverse Ecosystems

Abstract

Monin-Obukhov Similarity Theory (MOST), the traditional surface layer theory used to understand the behavior, scaling and exchange of heat, water vapor and carbon dioxide between the land surface and atmosphere relies on a number of commonly broken assumptions. In particular, traditional theory breaks down under three different forms of heterogeneity highlighted in this work: spatial heterogeneity in the sources of the scalars, heterogeneity in the Reynolds stress tensor (turbulence anisotropy), and temporal heterogeneity (non-stationarity). The work explores the relationship between the idealized flux-variance relations and these three forms of heterogeneity across a diverse network of 47 flux towers representing a broad range of ecosystems including forests, agricultural land, grasslands, tundra, tropical and arid: the National Ecological Observation Network (NEON). Results use high resolution spatial data (1 meter resolution) to show a direct relationship between spatial heterogeneity and deviation from traditional scaling relations. The study indicates an interplay between stationarity and anisotropy, with the non-dimensionalized scalar variance scaling more strongly with anisotropy under more non-stationary turbulence conditions. Updated flux-variance relations that leverage turbulence anisotropy for the scaling of heat are introduced, as are novel anisotropy-generalized scalings for water vapor and carbon dioxide. The work also explores in detail how the scaling relations, and their relationship with heterogeneity, vary across the diverse sites in the NEON network. Deviations from traditional theory in carbon dioxide scaling in particular are well correlated with the bioactivity of the site. Results have important implications for development of improved surface layer parameterizations in large scale atmospheric models and flux-variance based flux measurements.