Range Of Spatial Scales Research Articles

Study of PVI-based diagnostics for 1D time-series in space plasma

Context. In the last few decades, increasing evidence has been found in both numerical studies and high-resolution in situ data that magnetic turbulence spontaneously generates coherent structures over a broad range of scales. Those structures play a key role in energy conversion because they are sites where magnetic energy is locally dissipated in plasma heating and particle energization. How much turbulent energy is dissipated via processes such as magnetic reconnection of thin coherent structures, namely current sheets, remains an open question. Aims. We aim to develop semi-automated methods for detecting reconnection sites over multiple spatial scales. This is indeed pivotal in advancing our knowledge of plasma dissipation mechanisms and for future applications to space data. Methods. By means of hybrid–Vlasov–Maxwell 2D–3V simulations, we combine three methods based on the partial variance of increments measured at a broad range of spatial scales and on the current density, which together, and in a synergistic way, provide indications as to the presence of sites of magnetic reconnection. We adopt the virtual satellite method, which in upcoming works will allow us to easily extend this analysis to in situ time-series. Results. We show how combining standard threshold analysis to a 2D scalogram based on magnetic field increments represents an efficient diagnostic for recognizing reconnecting structure in 1D spatial- and time-series. This analysis can serve as input to automated machine-learning algorithms.

Markov Chain Abstractions of Electrochemical Reaction-Diffusion in Synaptic Transmission for Neuromorphic Computing.

Progress in computational neuroscience toward understanding brain function is challenged both by the complexity of molecular-scale electrochemical interactions at the level of individual neurons and synapses and the dimensionality of network dynamics across the brain covering a vast range of spatial and temporal scales. Our work abstracts an existing highly detailed, biophysically realistic 3D reaction-diffusion model of a chemical synapse to a compact internal state space representation that maps onto parallel neuromorphic hardware for efficient emulation at a very large scale and offers near-equivalence in input-output dynamics while preserving biologically interpretable tunable parameters.

Transport changes and COVID-19: From present impacts to future possibilities.

Changes in people's movement and travel behaviour have been apparent in many places during the COVID‐19 pandemic, with differences seen at a range of spatial scales. These changes, occurring as a result of the COVID‐19 ‘natural experiment’, have afforded us an opportunity to reimagine how we might move in our day‐to‐day travels, offering a hopeful glimpse of possibilities for future policy and planning around transport. The nature and scale of changes in movement and transport resulting from the pandemic have shown we can shift travel behaviour with strong policy responses, which is especially important in the concurrent climate change crisis.

Turbulence in Large-Scale Two-Dimensional Balanced Hard Sphere Gas Flow

In recent works, we developed a model of balanced gas flow, where the momentum equation possesses an additional mean field forcing term, which originates from the hard sphere interaction potential between the gas particles. We demonstrated that, in our model, a turbulent gas flow with a Kolmogorov kinetic energy spectrum develops from an otherwise laminar initial jet. In the current work, we investigate the possibility of a similar turbulent flow developing in a large-scale two-dimensional setting, where a strong external acceleration compresses the gas into a relatively thin slab along the third dimension. The main motivation behind the current work is the following. According to observations, horizontal turbulent motions in the Earth atmosphere manifest in a wide range of spatial scales, from hundreds of meters to thousands of kilometers. However, the air density rapidly decays with altitude, roughly by an order of magnitude each 15–20 km. This naturally raises the question as to whether or not there exists a dynamical mechanism which can produce large-scale turbulence within a purely two-dimensional gas flow. To our surprise, we discover that our model indeed produces turbulent flows and the corresponding Kolmogorov energy spectra in such a two-dimensional setting.

Subgrid-scale parametrization of unresolved scales in forced Burgers equation using generative adversarial networks (GAN)

Stochastic subgrid-scale parametrizations aim to incorporate effects of unresolved processes in an effective model by sampling from a distribution usually described in terms of resolved modes. This is an active research area in climate, weather and ocean science where processes evolved in a wide range of spatial and temporal scales. In this study, we evaluate the performance of conditional generative adversarial network (GAN) in parametrizing subgrid-scale effects in a finite-difference discretization of stochastically forced Burgers equation. We define resolved modes as local spatial averages and deviations from these averages are the unresolved degrees of freedom. We train a Wasserstein GAN (WGAN) conditioned on the resolved variables to learn the distribution of subgrid flux tendencies for resolved modes and, thus, represent the effect of unresolved scales. The resulting WGAN is then used in an effective model to reproduce the statistical features of resolved modes. We demonstrate that various stationary statistical quantities such as spectrum, moments, autocorrelation, etc. are well approximated by this effective model.

NIRVP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales

Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Therefore, we investigated relationships between SIF and variables that at least partly capture the canopy structure component of SIF. For this, we relied on high-quality SIF observations from ground-based instruments, high-resolution airborne SIF imagery and the most recent satellite SIF products to cover large ranges in spatial and temporal resolution and diverse ecosystems. We found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRVP) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to the structure and radiation components. Also, NIRVP captured spatio-temporal patterns of canopy photosynthesis better than SIF, which seems to be mostly due to the greater retrieval noise of SIF. Compared to other relevant structural SIF proxies, NIRVP showed more robust relationships to SIF, especially at the global scale. Our results highlight the promise of using widely available NIRVP data for vegetation monitoring and also indicate the potential of using SIF and NIRVP in combination to extract physiological information from SIF.

Integrating hazard, exposure, vulnerability and resilience for risk and emergency management in a volcanic context: the ADVISE model

Risk assessments in volcanic contexts are complicated by the multi-hazard nature of both unrest and eruption phases, which frequently occur over a wide range of spatial and temporal scales. As an attempt to capture the multi-dimensional and dynamic nature of volcanic risk, we developed an integrAteD VolcanIc risk asSEssment (ADVISE) model that focuses on two temporal dimensions that authorities have to address in a volcanic context: short-term emergency management and long-term risk management. The output of risk assessment in the ADVISE model is expressed in terms of potential physical, functional, and systemic damage, determined by combining the available information on hazard, exposed systems and vulnerability. The ADVISE model permits qualitative, semi-quantitative and quantitative risk assessment depending on the final objective and on the available information. The proposed approach has evolved over a decade of study on the volcanic island of Vulcano (Italy), where recent signs of unrest combined with uncontrolled urban development and significant seasonal variations of exposed population result in highly dynamic volcanic risk. For the sake of illustration of all the steps of the ADVISE model, we focus here on the risk assessment of the transport system in relation to the tephra fallout associated with a long-lasting Vulcanian cycle.

Taming the tyranny of scales: models and scale in the geosciences

While the predominant focus of the philosophical literature on scientific modeling has been on single-scale models, most systems in nature exhibit complex multiscale behavior, requiring new modeling methods. This challenge of modeling phenomena across a vast range of spatial and temporal scales has been called the tyranny of scales problem. Drawing on research in the geosciences, I synthesize and analyze a number of strategies for taming this tyranny in the context of conceptual, physical, and mathematical modeling. This includes several strategies that can be deployed in physical (table-top) modeling, even when strict dynamical scaling fails. In all cases, I argue that having an adequate conceptual model—given both the nature of the system and the particular purpose of the model—is essential. I draw a distinction between depiction and representation, and use this research in the geosciences to advance a number of debates in the philosophy of modeling.

Prediction of Sunspot and Plage Coverage for Solar Cycle 25

Abstract Solar variability occurs over a broad range of spatial and temporal scales, from the Sun’s brightening over its lifetime to the fluctuations commonly associated with magnetic activity over minutes to years. The latter activity includes most prominently the 11 yr sunspot solar cycle and its modulations. Space weather events, in the form of solar flares, solar energetic particles, coronal mass ejections, and geomagnetic storms, have long been known to approximately follow the solar cycle occurring more frequently at solar maximum than solar minimum. These events can significantly impact our advanced technologies and critical infrastructures, making the prediction for the strength of future solar cycles particularly important. Several methods have been proposed to predict the strength of the next solar cycle, cycle 25, with results that are generally not always consistent. Most of these methods are based on the international sunspot number time series, or other indicators of solar activity. We present here a new approach that uses more than 100 yr of measured fractional areas of the visible solar disk covered by sunspots and plages and an empirical relationship for each of these two indices of solar activity in even–odd cycles. We anticipate that cycle 25 will peak in 2024 and will last for about 12 yr, slightly longer than cycle 24. We also found that, in terms of sunspot and plage areas coverage, the amplitude of cycle 25 will be substantially similar or slightly higher than cycle 24.

The Application of Complexity Analysis in Brain Blood-Oxygen Signal.

One of the daunting features of the brain is its physiology complexity, which arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various cognitive functions. As a reflection of the complexity of brain physiology, the complexity of brain blood-oxygen signal has been frequently studied in recent years. This paper reviews previous literature regarding the following three aspects: (1) whether the complexity of the brain blood-oxygen signal can serve as a reliable biomarker for distinguishing different patient populations; (2) which is the best algorithm for complexity measure? And (3) how to select the optimal parameters for complexity measures. We then discuss future directions for blood-oxygen signal complexity analysis, including improving complexity measurement based on the characteristics of both spatial patterns of brain blood-oxygen signal and latency of complexity itself. In conclusion, the current review helps to better understand complexity analysis in brain blood-oxygen signal analysis and provide useful information for future studies.

Global patterns of fern species diversity: An evaluation of fern data in GBIF

Despite that several studies have shown that data derived from species lists generated from distribution occurrence records in the Global Biodiversity Information Facility (GBIF) are not appropriate for those ecological and biogeographic studies that require high sampling completeness, because species lists derived from GBIF are generally very incomplete, Suissa et al. (2021) generated fern species lists based on data with GBIF for 100 km × 100 km grid cells across the world, and used the data to determine fern diversity hotspots and species richness–climate relationships. We conduct an evaluation on the completeness of fern species lists derived from GBIF at the grid–cell scale and at a larger spatial scale, and determine whether fern data derived from GBIF are appropriate for studies on the relations of species composition and richness with climatic variables. We show that species sampling completeness of GBIF is low (<40%) for most of the grid cells examined, and such low sampling completeness can substantially bias the investigation of geographic and ecological patterns of species diversity and the identification of diversity hotspots. We conclude that fern species lists derived from GBIF are generally very incomplete across a wide range of spatial scales, and are not appropriate for studies that require data derived from species lists in high completeness. We present a map showing global patterns of fern species diversity based on complete or nearly complete regional fern species lists.

Deforestation-induced surface warming is influenced by the fragmentation and spatial extent of forest loss in Maritime Southeast Asia

Deforestation in the tropics causes warming which contributes to regional climate change. Forest loss occurs over a broad range of spatial scales, producing a variety of spatial patterns of cleared and forested land. Whether the spatial attributes of these patterns influence the resulting temperature change remains largely unknown. We adopted a differences-in-differences approach to analyse remotely-sensed forest loss and land surface temperature (LST) data in maritime Southeast Asia. We found that deforestation increased LST, as expected, but that the temperature increases were smaller when forest loss produced more fragmented landscapes in which non-forest and forest edges were heavily interlaced. Temperature increases were greater where the forest loss was more extensive. Warming also extended beyond the location of forest removal, so that forest loss increased temperatures in undisturbed locations up to 6 km away. Different spatial patterns of land clearing, for example, as might be produced by small-holder agriculture as opposed to large-scale deforestation, would therefore have different impacts on the local climate. Conserving forests within 4 km of farmland, urban areas or other sensitive environments may help to avoid temperature increases that reduce land productivity and worsen human health.

A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities.

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce massive ecoevolutionary synthesis simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: (i) species richness and abundances, (ii) population genetic diversities, and (iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community‐scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.

Sources of variation in search and foraging: A theoretical perspective.

Search-the problem of exploring a space of alternatives to identify target goals-is a fundamental behaviour for many species. Although its foundation lies in foraging, most studies of human search behaviour have been directed towards understanding the attentional mechanisms that underlie the efficient visual exploration of two-dimensional (2D) scenes. With this review, we aim to characterise how search behaviour can be explained across a wide range of contexts, environments, spatial scales, and populations, both typical and atypical. We first consider the generality of search processes across psychological domains. We then review studies of interspecies differences in search. Finally, we explore in detail the individual and contextual variables that affect visual search and related behaviours in established experimental psychology paradigms. Despite the heterogeneity of the findings discussed, we identify that variations in control processes, along with the ability to regulate behaviour as a function of the structure of search space and the sampling processes adopted, to be central to explanations of variations in search behaviour. We propose a tentative theoretical model aimed at integrating these notions and close by exploring questions that remain unaddressed.

A Novel Method for Gaining New Insight on Flows Over Inundated Landscapes

AbstractWe present a novel approach for detecting surface water flows over flooded terrain. The approach requires a minimum of three stage measurements and can be applied to any inundated surface, across a range of temporal and spatial scales. This method creates new and robust opportunities for field observation and for validation of numerical simulations and remote sensing analyses. In this proof‐of‐concept study, we found that floodplain free surface gradients were particularly variable during sub‐bankfull inundation, including abrupt current reversals. Also, flow direction varied with stage and the rate of change in stage. The “triangular facet” approach provides a new type of mesoscale insight of flow processes over inundated landscapes, highlighting flow complexity. Moreover, the approach provides information on floods that represent a compromise between point velocimeter measurements and satellite remote sensing and it can provide substantial benefits to society by aiding in flood hazard and mitigation assessment and planning.

Sodium channels and the intercalated disk - it is all about location, location, location.

Sodium channels and the intercalated disk - it is all about location, location, location.

Selective world-building: Collaboration and regional specificities in the marine biodiversity field

Over the past two decades, the scientific study of marine biodiversity developed into one of the most dynamic research fields within environmental research in general and ocean science in particular. Marine biodiversity research spans a broad range of spatial and temporal scales, scientific disciplines, and infrastructures for assessing patterns of change in marine biodiversity with different topics and regions contributing to the abstract construction of the field. Yet, beneath the surface of these abstract constructions and attributions such as “big science” to assess the international, interdisciplinary and data-driven nature marine biodiversity research, patterns of scientific collaboration emerge that shape the contributions of different world regions to the field. Furthermore, legal, political and territorial orders shape scientific collaboration by determining access to the marine environment and the study thereof. To understand these dynamics, this study analyses scientific publications on marine biodiversity using topic modeling methodology. Our approach provides the opportunity not only to describe large clusters within the field of marine biodiversity, but also to show how these topical clusters differ in time and across regions. By looking at the temporal and regional level, we wish to contribute to a deeper understanding of collaboration and regional specificity in the emergence of new research fields and – what we call – “selective world-building” across time and space.

Impacts of Precipitation–Evaporation–Salinity coupling on upper ocean stratification and momentum over the tropical pacific prior to onset of the 2018 El Niño

Precipitation affects upper ocean salinity and temperature over a wide range of temporal and spatial scales. Although the effects of freshwater on the diurnal mixing and formation of barrier layer have been shown in previous studies, the impacts of freshwater from the large-scale precipitation associated with the Madden–Julian Oscillation (MJO) on the ocean from the western to central-eastern Pacific are not well understood. This study investigates the coupling of precipitation, evaporation, and salinity (P–E–S) and its impact on the upper ocean stratification and momentum on time scales from days to weeks (intraseasonal) and beyond. The Unified Wave INterface – Coupled Model (UWIN-CM) is used to simulate multiscale air–sea interactions during January–July 2018, including multiple MJO events leading up to the onset of the 2018–2019 El Niño. The atmospheric component of the UWIN-CM is constrained by reanalysis and reinitialized daily, while the ocean component evolves continuously and is fully coupled with the atmosphere. The initial and lateral boundary conditions for the atmosphere and ocean are from the ECMWF ERA5 and HYCOM global analysis, respectively. The UWIN-CM simulation captures the surface winds, rainfall, and the evolution of the upper ocean compared with in situ and satellite observations. To determine the effect of P–E–S coupling, the UWIN-CM simulation with full P–E–S coupling is compared with a model experiment in which the P–E input to the ocean is set to zero (NoPE). The difference between the two increases with time and becomes particularly large during the 3rd MJO event in April 2018. Two key results emerge. First, heavy precipitation and strong winds from the MJO events result in a locally fresher, warmer western Pacific warm pool with a shallower mixed layer and thicker barrier layer. Second, the fresh, warm water mass extends into the central-eastern Pacific from mid-April to late May with up to 0.5 m s−1 eastward currents. A well-defined salinity/density front, propagating eastward at about 0.3 m s−1, is located at the leading edge of the warm pool. The P–E–S coupling plays an important role in the eastward extension of the warm pool prior to the onset of El Niño in July 2018.

Memories of Migrations Past: Sociality and Cognition in Dynamic, Seasonal Environments

Seasonal migrations are a widespread and broadly successful strategy for animals to exploit periodic and localized resources over large spatial scales. It remains an open and largely case-specific question whether long-distance migrations are resilient to environmental disruptions. High levels of mobility suggest an ability to shift ranges that can confer resilience. On the other hand, a conservative, hard-wired commitment to a risky behavior can be costly if conditions change. Mechanisms that contribute to migration include identification and responsiveness to resources, sociality, and cognitive processes such as spatial memory and learning. Our goal was to explore the extent to which these factors interact not only to maintain a migratory behavior but also to provide resilience against environmental changes. We develop a diffusion-advection model of animal movement in which an endogenous migratory behavior is modified by recent experiences via a memory process, and animals have a social swarming-like behavior over a range of spatial scales. We found that this relatively simple framework was able to adapt to a stable, seasonal resource dynamic under a broad range of parameter values. Furthermore, the model was able to acquire an adaptive migration behavior with time. However, the resilience of the process depended on all the parameters under consideration, with many complex trade-offs. For example, the spatial scale of sociality needed to be large enough to capture changes in the resource, but not so large that the acquired collective information was overly diluted. A long-term reference memory was important for hedging against a highly stochastic process, but a higher weighting of more recent memory was needed for adapting to directional changes in resource phenology. Our model provides a general and versatile framework for exploring the interaction of memory, movement, social and resource dynamics, even as environmental conditions globally are undergoing rapid change.

Methods for quantifying methane emissions using unmanned aerial vehicles: a review.

Methane is an important greenhouse gas, emissions of which have vital consequences for global climate change. Understanding and quantifying the sources (and sinks) of atmospheric methane is integral for climate change mitigation and emission reduction strategies, such as those outlined in the 2015 UN Paris Agreement on Climate Change. There are ongoing international efforts to constrain the global methane budget, using a wide variety of measurement platforms across a range of spatial and temporal scales. The advancements in unmanned aerial vehicle (UAV) technology over the past decade have opened up a new avenue for methane emission quantification. UAVs can be uniquely equipped to monitor natural and anthropogenic emissions at local scales, displaying clear advantages in versatility and manoeuvrability relative to other platforms. Their use is not without challenge, however: further miniaturization of high-performance methane instrumentation is needed to fully use the benefits UAVs afford. Developments in the models used to simulate atmospheric transport and dispersion across small, local scales are also crucial to improved flux accuracy and precision. This paper aims to provide an overview of currently available UAV-based technologies and sampling methodologies which can be used to quantify methane emission fluxes at local scales.This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.