Impact of the COVID-19 pandemic on levels of deep-ocean acoustic noise.

The extraordinary circumstances of the COVID-19 pandemic led to measures to mitigate the spread of the disease, with lockdowns and mobility restrictions at national and international levels. These measures led to sudden and sometimes dramatic reductions in human activity, including significant reductions in ship traffic in the maritime sector. We report on a reduction of deep-ocean acoustic noise in three ocean basins in 2020, based on data acquired by hydroacoustic stations in the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty. The noise levels measured in 2020 are compared with predicted levels obtained from modelling data from previous years using Gaussian Process regression. Comparison of the predictions with measured data for 2020 shows reductions of between 1 and 3 dB in the frequency range from 10 to 100 Hz for all but one of the stations.

What Drives Symbiotic Calcium Signalling in Legumes? Insights and Challenges of Imaging.

We review the contribution of bioimaging in building a coherent understanding of Ca 2 + signalling during legume-bacteria symbiosis. Currently, two different calcium signals are believed to control key steps of the symbiosis: a Ca 2 + gradient at the tip of the legume root hair is involved in the development of an infection thread, while nuclear Ca 2 + oscillations, the hallmark signal of this symbiosis, control the formation of the root nodule, where bacteria fix nitrogen. Additionally, different Ca 2 + spiking signatures have been associated with specific infection stages. Bioimaging is intrinsically a cross-disciplinary area that requires integration of image recording, processing and analysis. We used experimental examples to critically evaluate previously-established conclusions and draw attention to challenges caused by the varying nature of the signal-to-noise ratio in live imaging. We hypothesise that nuclear Ca 2 + spiking is a wide-range signal involving the entire root hair and that the Ca 2 + signature may be related to cytoplasmic streaming.

Analysis of Primary Care Computerised Medical Records with Deep Learning.

The analysis of primary care data plays an important role in understanding health at an individual and population level. Currently the utilization of computerized medical records is low due to the complexities, heterogeneities and veracity associated with these data. We present a deep learning methodology that clusters 11,000 records in an unsupervised manner identifying non-linear patterns in the data. This provides a useful tool for visualization as well as identify features driving the formation of clusters. Further analysis reveal the features that differentiate sub-groups that can aid clinical decision making. Our results uncover subsets that contain the highest proportion of missing data, specifically Episode type, as well as the sources that provide the most complete data.

A statistical method for the evaluation of long term trends in underwater noise measurements.

Deep ocean ambient sound levels have been the subject of recent studies, with particular interest in the identification of long term trends. This paper describes a statistical method for performing long term trend analysis and uncertainty evaluation of the estimated trends. Uncertainties are needed if the quality of the estimates are to be assessed and if the results from different studies or different methods are to be compared. The measured data span 14 years, from 2003 to 2017, and originate from the Southern Ocean close to Cape Leeuwin, Australia. The method uses a flexible discrete model incorporating terms that capture seasonal variations in the data and a moving-average statistical model to describe the serial correlation of residual deviations, with uncertainties validated using bootstrap resampling. The method is applied to time series representing monthly and daily aggregated statistical levels for five frequency bands to obtain estimates for the change in sound pressure level over the examined period with associated uncertainties. The results show a statistically significant reduction in sound pressure levels over the examined period at that location. Possible explanations for these changes are postulated, including the effects of shipping, wind speed, sea surface temperature, and changes in Antarctic ice volume.

Automated Differentiation of Incident and Prevalent Cases in Primary Care Computerised Medical Records (CMR).

Identifying incident (first or new) episodes of illness is critical in sentinel networks to inform about the seasonal onset of diseases and to give early warning of epidemics, as well as differentiating change in health service utilization from change in pattern of disease. The most reliable way of differentiating incident from prevalent cases is through the clinician assigning episode type to the patient's computerized medical record (CMR). However, episode type assignment is often made inconsistently. The objective of this collaborative study between the Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), University of Surrey and the National Physical Laboratory (NPL) is to develop a methodology to reconstruct missing or miscoded episode types. The data, gathered from the RCGP RSC network of over 230 practices, are analyzed and poor episode typing reconstructed by disease type. The methodology is tested in practices with good episode type data quality. This method could be used to improve prediction of epidemics, and to improve the quality of historical rates retrospectively.

The human physiological impact of global deoxygenation.

There has been a clear decline in the volume of oxygen in Earth's atmosphere over the past 20 years. Although the magnitude of this decrease appears small compared to the amount of oxygen in the atmosphere, it is difficult to predict how this process may evolve, due to the brevity of the collected records. A recently proposed model predicts a non-linear decay, which would result in an increasingly rapid fall-off in atmospheric oxygen concentration, with potentially devastating consequences for human health. We discuss the impact that global deoxygenation, over hundreds of generations, might have on human physiology. Exploring the changes between different native high-altitude populations provides a paradigm of how humans might tolerate worsening hypoxia over time. Using this model of atmospheric change, we predict that humans may continue to survive in an unprotected atmosphere for ~3600 years. Accordingly, without dramatic changes to the way in which we interact with our planet, humans may lose their dominance on Earth during the next few millennia.

Early warning signals of ecological transitions: methods for spatial patterns.

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

Spontaneous abrupt climate change due to an atmospheric blocking-sea-ice-ocean feedback in an unforced climate model simulation.

Abrupt climate change is abundant in geological records, but climate models rarely have been able to simulate such events in response to realistic forcing. Here we report on a spontaneous abrupt cooling event, lasting for more than a century, with a temperature anomaly similar to that of the Little Ice Age. The event was simulated in the preindustrial control run of a high-resolution climate model, without imposing external perturbations. Initial cooling started with a period of enhanced atmospheric blocking over the eastern subpolar gyre. In response, a southward progression of the sea-ice margin occurred, and the sea-level pressure anomaly was locked to the sea-ice margin through thermal forcing. The cold-core high steered more cold air to the area, reinforcing the sea-ice concentration anomaly east of Greenland. The sea-ice surplus was carried southward by ocean currents around the tip of Greenland. South of 70 °N, sea ice already started melting and the associated freshwater anomaly was carried to the Labrador Sea, shutting off deep convection. There, surface waters were exposed longer to atmospheric cooling and sea surface temperature dropped, causing an even larger thermally forced high above the Labrador Sea. In consequence, east of Greenland, anomalous winds changed from north to south, terminating the event with similar abruptness to its onset. Our results imply that only climate models that possess sufficient resolution to correctly represent atmospheric blocking, in combination with a sensitive sea-ice model, are able to simulate this kind of abrupt climate change.

No increase in global temperature variability despite changing regional patterns.

Evidence from Greenland ice cores shows that year-to-year temperature variability was probably higher in some past cold periods, but there is considerable interest in determining whether global warming is increasing climate variability at present. This interest is motivated by an understanding that increased variability and resulting extreme weather conditions may be more difficult for society to adapt to than altered mean conditions. So far, however, in spite of suggestions of increased variability, there is considerable uncertainty as to whether it is occurring. Here we show that although fluctuations in annual temperature have indeed shown substantial geographical variation over the past few decades, the time-evolving standard deviation of globally averaged temperature anomalies has been stable. A feature of the changes has been a tendency for many regions of low variability to experience increases, which might contribute to the perception of increased climate volatility. The normalization of temperature anomalies creates the impression of larger relative overall increases, but our use of absolute values, which we argue is a more appropriate approach, reveals little change. Regionally, greater year-to-year changes recently occurred in much of North America and Europe. Many climate models predict that total variability will ultimately decrease under high greenhouse gas concentrations, possibly associated with reductions in sea-ice cover. Our findings contradict the view that a warming world will automatically be one of more overall climatic variation.

Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data.

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called 'early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Using GENIE to study a tipping point in the climate system.

We have used the Grid ENabled Integrated Earth system modelling framework to study the archetypal example of a tipping point in the climate system; a threshold for the collapse of the Atlantic thermohaline circulation (THC). eScience has been invaluable in this work and we explain how we have made it work for us. Two stable states of the THC have been found to coexist, under the same boundary conditions, in a hierarchy of models. The climate forcing required to collapse the THC and the reversibility or irreversibility of such a collapse depends on uncertain model parameters. Automated methods have been used to assimilate observational data to constrain the pertinent parameters. Anthropogenic climate forcing leads to a robust weakening of the THC and increases the probability of crossing a THC tipping point, but some ensemble members collapse readily, whereas others are extremely resistant. Hence, we test general methods that have been developed to directly diagnose, from time-series data, the proximity of a 'tipping element', such as the THC to a bifurcation point. In a three-dimensional ocean-atmosphere model exhibiting THC hysteresis, despite high variability in the THC driven by the dynamical atmosphere, some early warning of an approaching tipping point appears possible.

Memory in the occurrence of earthquakes.

We study the statistics of the recurrence times tau between earthquakes above a certain magnitude M in six (one global and five regional) earthquake catalogs. We find that the distribution of the recurrence times strongly depends on the previous recurrence time tau0, such that small and large recurrence times tend to cluster in time. This dependence on the past is reflected in both the conditional mean recurrence time and the conditional mean residual time until the next earthquake, which increase monotonically with tau0. As a consequence, the risk of encountering the next event within a certain time span after the last event depends significantly on the past, an effect that has to be taken into account in any effective earthquake prognosis.

Nonlinear volatility of river flux fluctuations.

We study the spectral properties of the magnitudes of daily river flux increments, the volatility. The volatility series exhibits (i) strong seasonal periodicity and (ii) power-law correlations for time scales less than 1 yr. We test the nonlinear properties of the river flux increment series by randomizing its Fourier phases and find that the surrogate volatility series (i) has almost no seasonal periodicity and (ii) is weakly correlated for time scales less than 1 yr. We quantify the degree of nonlinearity by measuring (i) the amplitude of the power spectrum at the seasonal peak and (ii) the correlation power-law exponent of the volatility series.