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Abstract Natural decadal climate variability in the Pacific, such as the Pacific decadal oscillation (PDO) or the interdecadal Pacific oscillation (IPO), plays a powerful role in evolving global hydroclimate on decadal time scales. Recent generations of general circulation models (GCMs) have been found to simulate the spatial pattern of the PDO well but struggle to capture temporal variability on decadal time scales. To use GCMs to project future climate, we must understand the degree to which climate models can successfully reproduce historical PDO and IPO spatial patterns, temporal behavior, and influence on hydroclimate. We calculate PDO and IPO spatial patterns and time series using 16 models within the CMIP6 archive, all with large (n≥ 10) ensembles, and compare them to observations in an integrated assessment of models’ ability to represent Pacific decadal variability spatiotemporally. All models underestimate decadal variability in the PDO and IPO and have a westward bias in their PDO and IPO North Pacific SST anomalies. We also evaluate hydroclimate teleconnections of the PDO and IPO in models using PDO- and IPO-associated precipitation, circulation, low-cloud, and vapor pressure deficit anomalies. We show that models’ underpowered decadal variability in the Pacific is consistent with their inability to reproduce large-amplitude decadal swings in precipitation in southwestern North America and that models are virtually unable to produce a 30-yr precipitation trend in the southwest of the magnitude observed from 1982 to 2011. We emphasize the importance of model fidelity in simulating Pacific decadal variability for accurate representation of decadal-scale hydroclimate change in Pacific-teleconnected land regions. 

We apply Lagrangian particle tracking to the two-dimensional single-mode Rayleigh–Taylor (RT) instability to study the dynamical evolution of fluid interface. At the onset of the nonlinear RT stage, we select three ensembles of tracer particles located at the bubble tip, at the spike tip, and inside the spiral of the mushroom structure, which cover most of the interfacial region as the instability develops. Conditional statistics performed on the three sets of particles and over different RT evolution stages, such as the trajectory curvature, velocity, and acceleration, reveals the temporal and spatial flow patterns characterizing the single-mode RT growth. The probability density functions of tracer particle velocity and trajectory curvature exhibit scalings compatible with local flow topology, such as the swirling motion of the spiral particles. Large-scale anisotropy of RT interfacial flows, measured by the ratio of horizontal to vertical kinetic energy, also varies for different particle ensembles arising from the differing evolution patterns of the particle acceleration. In addition, we provide direct evidence to connect the RT bubble re-acceleration to its interaction with the transported fluid from the spike side, due to the shear driven Kelvin–Helmholtz instability. Furthermore, we reveal that the secondary RT instability inside the spiral, which destabilizes the spiraling motion and induces complex flow structures, is generated by the centrifugal acceleration. 

Mechanosensory corpuscles detect transient touch and vibration in the skin of vertebrates, enabling precise sensation of the physical environment. The corpuscle contains a mechanoreceptor afferent surrounded by lamellar cells (LCs), but corpuscular ultrastructure and the role of LCs in touch detection are unknown. We report the three-dimensional architecture of the avian Meissner (Grandry) corpuscle acquired using enhanced focused ion beam scanning electron microscopy and machine learning-based segmentation. The corpuscle comprises a stack of LCs interdigitated with terminal endings from two afferents. Simultaneous electrophysiological recordings from both cell types revealed that mechanosensitive LCs use calcium influx to trigger action potentials in the afferent and thus serve as physiological touch sensors in the skin. The elaborate architecture and bicellular sensory mechanism in the corpuscles, which comprises the afferents and LCs, create the capacity for nuanced encoding of the submodalities of touch.