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Abstract An important aspect of energy dissipation in weakly collisional plasmas is that of energy partitioning between different species (e.g., protons and electrons) and between different energy channels. Here we analyse pressure–strain interaction to quantify the fractions of isotropic compressive, gyrotropic, and nongyrotropic heating for each species. An analysis of kinetic turbulence simulations is compared and contrasted with corresponding observational results from Magnetospheric Multiscale Mission data in the magnetosheath. In assessing how protons and electrons respond to different ingredients of the pressure–strain interaction, we find that compressive heating is stronger than incompressive heating in the magnetosheath for both electrons and protons, while incompressive heating is stronger in kinetic plasma turbulence simulations. Concerning incompressive heating, the gyrotropic contribution for electrons is dominant over the nongyrotropic contribution, while for protons nongyrotropic heating is enhanced in both simulations and observations. Variations with plasma β are also discussed, and protons tend to gain more heating with increasing β . 

Abstract Distributed acoustic sensing (DAS) provides dense arrays ideal for seismic tomography. However, DAS only records average axial strain change along the cable, which can complicate the interpretation of surface-wave observations. With a rectangular DAS array located in the City of Oxnard, California, we compare phase velocity dispersion at the same location illuminated by differently oriented virtual sources. The dispersion curves are consistent for colinear and noncolinear virtual sources, suggesting that surface-wave observations in most of the cross-correlations are dominated by Rayleigh waves. Our measurements confirm that colinear channel pairs provide higher Rayleigh-wave signal-to-noise ratio (SNR). For cross-correlations of noncolinear channel pairs, the travel time of each connecting ray path can still be obtained despite the lower SNR of Rayleigh wave signals. The inverted Rayleigh-wave dispersion map reveals an ancient river channel consistent with the local geologic map. Our results demonstrate the potential of DAS-based 2D surface-wave tomography without special treatment of directional sensitivity in areas where one type of wave is dominating or can be identified. 

Abstract Exact laws for evaluating cascade rates, tracing back to the Kolmogorov “4/5” law, have been extended to many systems of interest including magnetohydrodynamics (MHD), and compressible flows of the magnetofluid and ordinary fluid types. It is understood that implementations may be limited by the quantity of available data and by the lack of turbulence symmetry. Assessment of the accuracy and feasibility of such third-order (or Yaglom) relations is most effectively accomplished by examining the von Kármán–Howarth equation in increment form, a framework from which the third-order laws are derived as asymptotic approximations. Using this approach, we examine the context of third-order laws for incompressible MHD in some detail. The simplest versions rely on the assumption of isotropy and the presence of a well-defined inertial range, while related procedures generalize the same idea to arbitrary rotational symmetries. Conditions for obtaining correct and accurate values of the dissipation rate from these laws based on several sampling and fitting strategies are investigated using results from simulations. The questions we address are of particular relevance to sampling of solar wind turbulence by one or more spacecraft. 

Fault zone structures at many scales largely dictate earthquake ruptures and are controlled by the geologic setting and slip history. Characterizations of these structures at diverse scales inform better understandings of earthquake hazards and earthquake phenomenology. However, characterizing fault zones at sub‐kilometer scales has historically been challenging, and these challenges are exacerbated in urban areas, where locating and characterizing faults is critical for hazard assessment. We present a new procedure for characterizing fault zones at sub‐kilometer scales using distributed acoustic sensing (DAS). This technique involves the backprojection of the DAS‐measured scattered wavefield generated by natural earthquakes. This framework provides a measure of the strength of scattering along a DAS array and thus constrains the positions and properties of local scatterers. The high spatial sampling of DAS arrays makes possible the resolution of these scatterers at the scale of tens of meters over distances of kilometers. We test this methodology using a DAS array in Ridgecrest, CA which recorded much of the 2019 M_w7.1 Ridgecrest earthquake aftershock sequence. We show that peaks in scattering along the DAS array are spatially correlated with mapped faults in the region and that the strength of scattering is frequency‐dependent. We present a model of these scatterers as shallow, low‐velocity zones that is consistent with how we may expect faults to perturb the local velocity structure. We show that the fault zone geometry can be constrained by comparing our observations with synthetic tests.

 

Reaction of the complexes [Fe 2 (μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] ( 1-Fe ) and [Co 2 (μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] ( 1-Co ), where [NP(pip) 3 ] 1− is tris(piperidinyl)imidophosphorane, with nitrous oxide, S 8 , or Se 0 results in divergent reactivity. With nitrous oxide, 1-Fe forms [Fe 2 (μ 2 -O)(μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] ( 2-Fe ), with a very short Fe 3+ –Fe 3+ distance. Reactions of 1-Fe with S 8 or Se 0 result in the bridging, side-on coordination (μ-κ 1 :κ 1 -E 2 2− ) of the heavy chalcogens in complexes [Fe 2 (μ-κ 1 :κ 1 -E 2 )(μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] (E = S, 3-Fe , or Se, 4-Fe ). In all cases, the complex 1-Co is inert. 

Fault zone complexities contain important information about factors controlling earthquake dynamic rupture. High‐resolution fault zone imaging requires high‐quality data from dense arrays and new seismic imaging techniques that can utilize large portions of recorded waveforms. Recently, the emerging Distributed Acoustic Sensing (DAS) technique has enabled near‐surface imaging by utilizing existing telecommunication infrastructure and anthropogenic noise sources. With dense sensors at several meters' spacing, the unaliased wavefield can provide unprecedented details for fault zones. In this work, we use a DAS array converted from a 10‐km underground fiber‐optic cable across Ridgecrest City, California. We report clear acausal and coda signals in ambient noise cross‐correlations caused by surface‐to‐surface wave scattering. We use these scattering‐related waves to locate and characterize potential faults. The mapped fault locations are generally consistent with those in the United States Geological Survey Quaternary Fault database of the United States but are more accurate than the extrapolated ones. We also use waveform modeling to infer that a 35 m wide, 90 m deep fault with 30% velocity reduction can best fit the observed scattered coda waves for one of the identified fault zones. These findings demonstrate the potential of DAS for passive imaging of fine‐scale faults in an urban environment.

 

Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.