“Late-stage” deforestation enhances storm trends in coastal West Africa

Deforestation affects local and regional hydroclimate through changes in heating and moistening of the atmosphere. In the tropics, deforestation leads to warming, but its impact on rainfall is more complex, as it depends on spatial scale and synoptic forcing. Most studies have focused on Amazonia, highlighting that forest edges locally enhance convective rainfall, whereas rainfall decreases over drier, more extensive, deforested regions. Here, we examine Southern West Africa (SWA), an example of “late-stage” deforestation, ongoing since 1900 within a 300-km coastal belt. From three decades of satellite data, we demonstrate that the upward trend in convective activity is strongly modulated by deforestation patterns. The frequency of afternoon storms is enhanced over and downstream of deforested patches on length scales from 16 to 196 km, with greater increases for larger patches. The results are consistent with the triggering of storms by mesoscale circulations due to landscape heterogeneity. Near the coast, where sea breeze convection dominates the diurnal cycle, storm frequency has doubled in deforested areas, attributable to enhanced land–sea thermal contrast. These areas include fast-growing cities such as Freetown and Monrovia, where enhanced storm frequency coincides with high vulnerability to flash flooding. The proximity of the ocean likely explains why ongoing deforestation across SWA continues to increase storminess, as it favors the impact of mesoscale dynamics over moisture availability. The coastal location of deforestation in SWA is typical of many tropical deforestation hotspots, and the processes highlighted here are likely to be of wider global relevance.

Mesoscale Dynamics and Eddy Heat Transport in the Japan/East Sea from 1990 to 2010: A Model-Based Analysis

The driving mechanisms of mesoscale processes and associated heat transport in the Japan/East Sea (JES) from 1990 to 2010 were examined using eddy-resolving ocean model simulations. The simulated circulation showed correctly reproduced JES major basin-scale currents and mesoscale dynamics features. We show that mesoscale eddies can deepen isotherms/isohalines up to several hundred meters and transport warm and low salinity waters along the western and eastern JES boundaries. The analysis of eddy kinetic energy (EKE) showed that the mesoscale dynamics reaches a maximum intensity in the upper 300 m layer. Throughout the year, the EKE maximum is observed in the southeastern JES, and a pronounced seasonal variability is observed in the southwestern and northwestern JES. The comparison of the EKE budget components confirmed that various mechanisms can be responsible for the generation of mesoscale dynamics during the year. From winter to spring, the baroclinic instability of basin-scale currents is the leading mechanism of the JES mesoscale dynamics’ generation. In summer, the leading role in the generation of the mesoscale dynamics is played by the barotropic instability of basin-scale currents, which are responsible for the emergence of mesoscale eddies, and in autumn, the leading role is played by instabilities and the eddy wind work. We show that the meridional heat transport (MHT) is mainly polewards. Furthermore, we reveal two paths of eddy heat transport across the Subpolar Front: along the western and eastern (along 138∘ E) JES boundaries. Near the Tsugaru Strait, we describe the detected intensive westward eddy heat transport reaching its maximum in the first half of the year and decreasing to the minimum by summer.

Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations

Mesoscale dynamics in the mesosphere and lower thermosphere (MLT) region have been difficult to study from either ground- or satellite-based observations. For understanding of atmospheric coupling processes, important spatial scales at these altitudes range between tens and hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesoscale dynamics on these scales using an increased number of detections and a diversity of viewing angles inherent to multistatic networks. In this work, we introduce a four-dimensional wind field inversion method that makes use of Gaussian process regression (GPR), which is a nonparametric and Bayesian approach. The method takes measured projected wind velocities and prior distributions of the wind velocity as a function of space and time, specified by the user or estimated from the data, and produces posterior distributions for the wind velocity. Computation of the predictive posterior distribution is performed on sampled points of interest and is not necessarily regularly sampled. The main benefits of the GPR method include this non-gridded sampling, the built-in statistical uncertainty estimates, and the ability to horizontally resolve winds on relatively small scales. The performance of the GPR implementation has been evaluated on Monte Carlo simulations with known distributions using the same spatial and temporal sampling as 1 d of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased with statistical variances that depend on the geometry and are proportional to the prior velocity variances. A conservative and fast approach can be straightforwardly implemented by employing overestimated prior variances and distances, while a more robust but computationally intensive approach can be implemented by employing training and fitting of model hyperparameters. The latter GPR approach has been applied to a 24 h dataset and shown to compare well to previously used homogeneous and gradient methods. Small-scale features have reasonably low statistical uncertainties, implying geophysical wind field horizontal structures as low as 20–50 km. We suggest that this GPR approach forms a suitable method for MLT regional and weather studies.

Morphodynamics facilitate cancer cells to navigate 3D extracellular matrix

Cell shape is linked to cell function. The significance of cell morphodynamics, namely the temporal fluctuation of cell shape, is much less understood. Here we study the morphodynamics of MDA-MB-231 cells in type I collagen extracellular matrix (ECM). We systematically vary ECM physical properties by tuning collagen concentrations, alignment, and gelation temperatures. We find that morphodynamics of 3D migrating cells are externally controlled by ECM mechanics and internally modulated by Rho/ROCK-signaling. We employ machine learning to classify cell shape into four different morphological phenotypes, each corresponding to a distinct migration mode. As a result, we map cell morphodynamics at mesoscale into the temporal evolution of morphological phenotypes. We characterize the mesoscale dynamics including occurrence probability, dwell time and transition matrix at varying ECM conditions, which demonstrate the complex phenotype landscape and optimal pathways for phenotype transitions. In light of the mesoscale dynamics, we show that 3D cancer cell motility is a hidden Markov process whereby the step size distributions of cell migration are coupled with simultaneous cell morphodynamics. Morphological phenotype transitions also facilitate cancer cells to navigate non-uniform ECM such as traversing the interface between matrices of two distinct microstructures. In conclusion, we demonstrate that 3D migrating cancer cells exhibit rich morphodynamics that is controlled by ECM mechanics, Rho/ROCK-signaling, and regulate cell motility. Our results pave the way to the functional understanding and mechanical programming of cell morphodynamics as a route to predict and control 3D cell motility.

Competition between baroclinic instability and Ekman transport under varying buoyancy forcings in upwelling systems: An idealized analog to the Southern Ocean.

Coastal upwelling rates are classically determined by the intensity of the upper-ocean offshore Ekman transport. But (sub-)mesoscale turbulence modulates offshore transport, hence the net upwelling rate. Eddy effects generally oppose the Ekman circulation, resulting in so-called “eddy cancellation”, a process well studied in the Southern Ocean. Here we investigate how air-sea heat/buoyancy fluxes modulate eddy cancellation in an idealized upwelling model. We run CROCO simulations with constant winds but varying heat fluxes with and without submesoscale-rich turbulence. Eddy cancellation is consistently evaluated with three different methods that all account for the quasi-isopycnal nature of ocean circulation away from the surface. For zero heat fluxes the release of available potential energy by baroclinic instabilities is strongest and leads, near the coast, to nearly full cancellation of the Ekman cross-shore circulation by eddy effects, i.e., zero net mean upwelling flow. With increasing heat fluxes eddy cancellation is reduced and the transverse flow progressively approaches the classical Ekman circulation. Sensitivity of the eddy circulation to synoptic changes in air-sea heat fluxes is felt down to 125 m depth despite short experiments of tens of days. Mesoscale dynamics dominate the cancellation effect in our simulations which might also hold for the real ocean as the relevant processes act below the surface boundary layer. Although the idealized setting overemphasis the role of eddies and thus studies with more realistic settings should follow, our findings have important implications for the overall understanding of upwelling system dynamics.

Observed Deep Cyclonic Eddies around Southern Greenland

Recent mooring measurements from the Overturning in the Subpolar North Atlantic Program have revealed abundant cyclonic eddies at both sides of Cape Farewell, the southern tip of Greenland. In this study, we present further observational evidence, from both Eulerian and Lagrangian perspectives, of deep cyclonic eddies with intense rotation (𝜁/f > 1) around southern Greenland and into the Labrador Sea. Most of the observed cyclones exhibit strongest rotation below the surface (700-1000 dbar), where maximum azimuthal velocities are ~30 cm/s at radii of ~10 km, with rotational periods of 2-3 days. The cyclonic rotation can extend to the deep overflow water layer (below 1800 dbar), albeit with weaker azimuthal velocities (~10 cm/s) and longer rotational periods of about one week. Within the mid-depth rotation cores, the cyclones are in near solid-body rotation and have the potential to trap and transport water. The first high-resolution hydrographic transect across such a cyclone indicates that it is characterized by a local (both vertically and horizontally) potential vorticity maximum in its core and cold, fresh anomalies in the overflow water layer, suggesting its source as the Denmark Strait outflow. Additionally, the propagation and evolution of the cyclonic eddies are illustrated with deep Lagrangian floats, including their detachments from the boundary currents to the basin interior. Taken together, the combined Eulerian and Lagrangian observations have provided new insights on the boundary current variability and boundary-interior exchange over a geographically large scale near southern Greenland, calling for further investigations on the (sub)mesoscale dynamics in the region.

Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability

Mesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.