Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion

Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +14.7 ppb), the cause of which remains largely unknown. Here, we demonstrate a high-resolution (~80 km), short-window (24-hour) 4D-Var global inversion system based on the ECMWF Integrated Forecasting System (IFS) and newly available satellite observations. The largest national disagreement found between prior (63.1 Tg yr−1) and posterior (59.8 Tg yr−1) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated form our global system agree well with previous basin-wide regional studies and point source specific studies. Emission events (leaks/blowouts) >10 t hr−1 were detected, but without accurate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for 2020 were 5.7 Tg yr−1 (+1.6 %) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest 2020 increases were seen from China (+2.6 Tg yr−1, 4.3 %), with smaller increases from India (+0.8 Tg yr−1, 2.2 %) and Indonesia (+0.3 Tg yr−1, 2.6 %). Results show the rise in emissions, and subsequent atmospheric growth, would have occurred with or without the COVID-19 slowdown. During the onset of the global slowdown (March–April, 2020) energy sector CH4 emissions from China increased; however, during later months (May–June, 2020) emissions decreased below expected pre-slowdown levels. The accumulated impact of the slowdown on CH4 emissions from March–June 2020 is found to be small. Changes in atmospheric chemistry, not investigated here, may have contributed to the observed growth in 2020. Future work aims to develop the global IFS inversion system and to extend the 4D-Var window-length using a hybrid ensemble-variational method.

Co-producing Sea-Ice Predictions with Stakeholders Using Simulation

Forecasts of sea-ice evolution in the Arctic region for several months ahead can be of considerable socio-economic value for a diverse range of marine sectors and for local community supply logistics. However, subseasonal-to-seasonal (S2S) forecasts represent a significant technical challenge, while translating user needs into scientifically manageable procedures and robust user confidence requires collaboration among a range of stakeholders. We developed and tested a novel, transdisciplinary co-production approach that combined socio-economic scenarios and participatory, research-driven simulation-gaming to test a new S2S sea-ice forecast system with experienced mariners in the cruise tourism sector. Our custom-developed computerized simulation-game ICEWISE integrated sea-ice parameters, forecast technology and human factors, as a participatory environment for stakeholder engagement. We explored the value of applications-relevant S2S sea-ice prediction and linked uncertainty information. Results suggest that the usefulness of S2S services is currently most evident in schedule-dependent sectors but expected to increase due to anticipated changes in the physical environment and continued growth in Arctic operations. Reliable communication of uncertainty information in sea-ice forecasts must be demonstrated and trialed before users gain confidence in emerging services and technologies. Mariners’ own intuition, experience, and familiarity with forecast service provider reputation impact the extent to which sea-ice information may reduce uncertainties and risks for Arctic mariners. Our insights into the performance of the combined foresight/simulation co-production model in brokering knowledge across a range of domains demonstrates promise. We conclude with an overview of the potential contributions from S2S sea-ice predictions and from experiential co-production models to the development of decision-driven and science-informed climate services.

Future Directions for Human-Centered Transparent Systems for Engine Room Monitoring in Shore Control Centers

Many autonomous ship projects have reflected the increasing interest in incorporating the concept of autonomy into the maritime transportation sector. However, autonomy is not a silver bullet, as exemplified by many incidents in the past involving human and machine interaction; rather it introduces new Human Factor (HF) challenges. These challenges are especially critical for Engine Room Monitoring (ERM) in Shore Control Centre (SCCs) due to the system’s complexity and the absence of human senses in the decision-making process. A transparent system is one of the potential solutions, providing a rationale behind its suggestion. However, diverse implementations of transparency schemes have resulted in prevalent inconsistencies in its effects. This literature review paper investigates 17 transparency studies published over the last eight years to identify (a) different approaches to developing transparent systems, (b) the effects of transparency on key HFs, and (c) the effects of information presentation methods and uncertainty information. The findings suggest that the explicit presentation of information could strengthen the benefits of the transparent system and could be promising for performance improvements in ERM tasks in the SCC.

A preliminary classification of different tasks that make use of uncertainty visualizations

Individuals often struggle with tasks that involve uncertainty. Uncertainty visualizations are a type of cognitive aid that provides uncertainty information to help people with performing these tasks. However, the literature has shown that uncertainty visualizations differ in the extent they improve individuals’ task performance. We hypothesize that differences in the tasks can account for some of this variability. In this study, we aimed to create an initial classification of task types based on studies on uncertainty visualizations by reviewing a diverse set of recent research involving uncertainty visualizations. We classified the experimental tasks found in these papers into four groups: uncertainty assessment, forecasting, decision making, and metacognition. Then, we reviewed the result of the experiments in terms of the similarities and differences in the use of uncertainty visualizations within and between tasks. This classification serves as a starting point for further research into the effective design of visualizations of uncertainty.

A preliminary classification of different tasks that make use of uncertainty visualizations

Individuals often struggle with tasks that involve uncertainty. Uncertainty visualizations are a type of cognitive aid that provides uncertainty information to help people with performing these tasks. However, the literature has shown that uncertainty visualizations differ in the extent they improve individuals’ task performance. We hypothesize that differences in the tasks can account for some of this variability. In this study, we aimed to create an initial classification of task types based on studies on uncertainty visualizations by reviewing a diverse set of recent research involving uncertainty visualizations. We classified the experimental tasks found in these papers into four groups: uncertainty assessment, forecasting, decision making, and metacognition. Then, we reviewed the result of the experiments in terms of the similarities and differences in the use of uncertainty visualizations within and between tasks. This classification serves as a starting point for further research into the effective design of visualizations of uncertainty.

Enhanced functional binning for one- and two-point statistics using a posteriori Uncertainty Quantification of LPT data

In the evaluation of Lagrangian particle tracking (LPT) measurement data the use of spatially binned flow statistics in the form of one, two or multi-point statistics is often an essential step towards better understanding of the measured flow fields. Increasingly there is a focus towards uncertainty quantification of the measurement system however these evaluations are seldom used to directly improve the statistics by directly involving them into the calculation. We present our Functional Binning approach which makes use of such uncertainty information as a core component for the calculation of improved statistics. The improvements towards prior approaches are shown utilizing synthetic data as well as data from a real-world subsonic jet experiment. Beyond the initial formulation for one-point statistics, we show that this approach is readily extended towards two-point statistics and explore more advanced utilizations of uncertainty information for the optimal selection of particle pairs. Furthermore, the benefits of more individualized particle error estimations are investigated and some strategies for archiving such information are investigated.

CBD: A New Divergence Measure for Complex Mass Function and its Application in Pattern Recognition

The theory of complex mass function is an effective method to deal with uncertainty information, and it is a generalized of Dempster-Shafer evidence theory. However, divergence measure is still an open issue in the realm of complex mass function theory. The main contribution of our paper is to propose a generalized divergence measure for complex mass function that is called complex belief divergence (CBD),which has the properties of symmetry, nonnegativity, nondegeneracy. When complex mass function degenerates into classical mass function, the CBD will degenerate into classical belief divergence, which has a better ability to measure uncertainty of information. Finally, a pattern recognition algorithm based on CBD is designed and applied to a medical diagnosis problem, which proves its practical prospect.