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Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical component of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin–Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have recently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations' applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January–31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity ( u * ), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib u * and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations' performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., u * ) and turbulence (i.e., TKE) quantities, although caution is needed when applying the RibH parameterizations to drylands. These findings represent an important milestone for the applicability of Rib parameterizations, given the large fraction of Earth's surface covered by drylands. Significance Statement: Weather forecasting models rely upon complex mathematical relationships to predict temperature, wind, and moisture. Monin–Obukhov similarity theory (MOST) has long been used to forecast these quantities near the land surface, even though MOST's limitations are well known in the scientific community. Researchers have suggested an alternative to MOST called the bulk Richardson (Rib) approach. To allow for the Rib approach to be used in weather forecasting models, the approach needs to be tested over different land-cover and climate types. In this study, we applied the Rib approach to dry areas of the United States and found that the approach better represented turbulence variables than MOST relationships. These findings are an important step toward using Rib relationships in weather forecasting models. [ABSTRACT FROM AUTHOR]

Airborne turbulence measurement gives a spatial distribution of air-surface fluxes that networks of fixed surface sites typically cannot capture. Much work has improved the accuracy of such measurements and the estimation of the uncertainty peculiar to streams of turbulence data measured from the air. A particularly significant challenge and opportunity is to distinguish fluxes from different surface types, especially those occurring in patches smaller than the necessary averaging length. The flux fragment method (FFM), a conditional-sampling variant of eddy covariance in the space-time domain, was presented in 2008. It was shown capable of segregating the mean flux density (CO2, H2O, sensible heat) in maize from that in soybeans over the patchwork farmlands of Illinois. This was, however, an ideal surface for the method, and the random-error estimate used a relatively rudimentary bootstrap resampling. The present paper describes an upgraded random-error estimate that accounts for the serial correlation of the time/space series and the heterogeneity of the signal. Results are presented from the Alaskan tundra. Though recognized as important, systematic error estimates are not covered in this paper. Some discussion is offered on the relation of the FFM to other approaches similarly motivated, particularly those using wavelets. Successful measurement of the variation of air-surface exchange over heterogeneous surfaces has value for developing and improving process models relating surface flux to remotely sensible quantities, such as the vegetative land-cover type and its condition. [ABSTRACT FROM AUTHOR]