Very little is known about the nature of turbulence in the transition zone of a synoptic-scale cold front, especially at the dissipative scales. Lacking this knowledge, accurate models of surface frontogenesis are compromised. To address this problem, high-frequency measurements from sonic and hot-wire anemometers are used to analyze the finescale turbulence in the atmospheric surface layer (ASL) within a cold front observed in the MICROFRONTS field experiment. To quantify the turbulence in the front, velocity spectra and dissipation rates are calculated as functions of time and stability in the ASL. The normalized first and second moments of the one-dimensional velocity spectrum conform to the scaling suggested by Kolmogorov's equilibrium hypotheses, even during the intense turbulence associated with the frontal passage. The spectra compare well with other data collected at high Re in the ASL, but not as well with a recent model of the dissipative range of turbulence. Dissipation rate ⑀ is calculated with one direct and two indirect techniques. The calculations from the different techniques compare well with one another and, when nondimensionalized, with a historical expression for dissipation rate as a function of ASL stability. The magnitude of the dissipation rate increases by an order of magnitude to a maximum value of ϳ1.2 m 2 s Ϫ3 during the frontal passage compared to prefrontal values of ϳ0.05 m 2 s Ϫ3 ; the latter is typical for a slightly stable nighttime boundary layer over land. These results can be used in assessing the effects of turbulence in traditional semigeostrophic models of frontal collapse. The dissipation rate calculations may be of particular use to modelers.

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Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values-initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.

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