MAĐARSKI SALTO Orban nakon sastanka s Plenkovićem nahvalio Hrvatsku: "Oni su naš povijesni partner i uvijek ćemo joj iskazivati poštovanje!"

Nakon što je mađarski ministar vanjskih poslova Péter Szijjártó Hrvatsku manje više proglasio ratnim profiterom koji naplaćuje previsoke tranzitne naknade za transport nafte preko Jadranskog naftovoda (JANAF-a), prvi je čovjek našeih sjevernih susjeda promijenio retoriku. Naglaso je da je interes njegove vlade osigurati što jeftinije izvore energije za mađarska kućanstva

Medium

Network percolation provides early warnings of abrupt changes in coupled oscillatory systems: An explanatory analysis

Functional networks are powerful tools to study statistical interdependency structures in spatially extended or multivariable systems. They have been used to get insights into the dynamics of complex systems in various areas of science. In particular, percolation properties of correlation networks have been employed to identify early warning signals of critical transitions. In this work, we further investigate the corresponding potential of percolation measures for the anticipation of different types of sudden shifts in the state of coupled irregularly oscillating systems. As a paradigmatic model system, we study the dynamics of a ring of diffusively coupled noisy FitzHugh-Nagumo oscillators and show that, when the oscillators are nearly completely synchronized, the percolation-based precursors successfully provide very early warnings of the rapid switches between the two states of the system. We clarify the mechanisms behind the percolation transition by separating global trends given by the mean-field behavior from the synchronization of individual stochastic fluctuations. We then apply the same methodology to real-world data of sea surface temperature anomalies during different phases of the El Niño-Southern Oscillation. This leads to a better understanding of the factors that make percolation precursors effective as early warning indicators of incipient El Niño and La Niña events.

Increased extinction probability of the Madden-Julian Oscillation after about 27 days

The Madden-Julian oscillation (MJO) is a tropical weather system having important influence in the tropics and beyond; however, many of its characteristics are poorly understood, including their initiation and completion. Here we define Madden-Julian events as the contiguous time periods with an active MJO, and we show that both the durations and the sizes of these events are well described by a double power-law distribution. Thus, small events have no characteristic scale, and the same for large events; nevertheless, both types of events are separated by a characteristic duration of about 27 days (this corresponds to half a cycle, roughly). Thus, after 27 days, there is a sharp increase in the probability that an event gets extinct. We find that this effect is independent of the starting and ending phases of the events, which seems to point to an internal mechanism of exhaustion rather than to the effect of an external barrier. Our results would imply an important limitation of the MJO as a driver of sub-seasonal predictability.

Assessing Long-Distance Atmospheric Transport of Soilborne Plant Pathogens

Pathogenic fungi are a leading cause of crop disease and primarily spread through microscopic, durable spores adapted differentially for both persistence and dispersal. Computational Earth System Models and air pollution models have been used to simulate atmospheric spore transport for aerial-dispersal-adapted (airborne) rust diseases, but the importance of atmospheric spore transport for soil-dispersal-adapted (soilborne) diseases remains unknown. This study adapts the Community Atmosphere Model, the atmospheric component of the Community Earth System Model, to simulate the global transport of the plant pathogenic soilborne fungus Fusarium oxysporum, F. oxy. Our sensitivity study assesses the model's accuracy in long-distance aerosol transport and the impact of deposition rate on long-distance spore transport in Summer 2020 during a major dust transport event from Northern Sub-Saharan Africa to the Caribbean and southeastern U.S. We find that decreasing wet and dry deposition rates by an order of magnitude improves representation of long distance, trans-Atlantic dust transport. Simulations also suggest that a small number of viable spores can survive trans-Atlantic transport to be deposited in agricultural zones. This number is dependent on source spore parameterization, which we improved through a literature search to yield a global map of F. oxy spore distribution in source agricultural soils. Using this map and aerosol transport modeling, we show how viable spore numbers in the atmosphere decrease with distance traveled and offer a novel danger index for viable spore deposition in agricultural zones.

Streamer propagation in humid air

We investigate the effect of humidity on the propagation of streamers in air. We present a minimal set of chemical reactions that takes into account the presence of water in a nonthermal air plasma and considers ionization, attachment, detachment, recombination and ion conversion including water cluster formation. We find differences in streamer propagation between dry and humid air that we attribute mostly to an enhanced effective attachment rate in humid air, leading to higher breakdown electric field and threshold field for propagation. This higher effective attachment rate in humid conditions leads to a faster decay of the conductivity in the streamer channel, which hinders the accumulation of charge in the streamer head. In some cases a propagating streamer solution still exists at the expense of a smaller radius and lower velocity. In other cases a high humidity leads to the stagnation of the streamer. We finally discuss how all these statements may affect streamer branching and the dimensions and lifetime of a streamer corona.

Post-processing output from ensembles with and without parametrised convection, to create accurate, blended, high-fidelity rainfall forecasts

Flash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address this, one can try to use output from convection-parametrising (global) ensembles, post-processed to forecast at point-scale, or convection-resolving limited area ensembles. In this study, we combine both. First, we apply the "ecPoint-rainfall" post-processing to the ECMWF global ensemble. Then, we use 2.2km COSMO LAM ensemble output (centred on Italy), and also post-process it using a scale-selective neighbourhood approach to compensate for insufficient members. The two components then undergo lead-time-weighted blending, to create the final probabilistic 6h rainfall forecasts. Product creation for forecasters constituted the "Italy Flash Flood use case" within the EU-funded MISTRAL project and it will be a real-time open-access product. One year of verification shows that ecPoint is the most skilful ensemble product. The post-processed COSMO ensemble adds most value to summer convective events in the evening, when the global model has an underprediction bias. In two heavy rainfall case studies we observed underestimation of the largest point totals in the raw ECMWF ensemble, and overestimation in the raw COSMO ensemble. However, ecPoint increase the value and highlighted best the most affected areas, whilst post-processing of COSMO diminished extremes by eradicating unreliable detail. The final merged products looked best from a user perspective and seemed to be the most skilful of all. Although our LAM post-processing does not implicitly include bias correction (a topic for further work) our study nonetheless provides a unique blueprint for successfully combining ensemble rainfall forecasts from global and LAM systems around the world. It also has important implications for forecast products as global ensembles move ever closer to having convection-permitting resolution.

The role of internal variability in global climate projections of extreme events

Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipitation extremes, which are defined as events that exceed the 99.97th percentile. This is done globally using initial-condition large ensembles. For maximum temperature extremes, internal variability dominates in the next two decades. Around the middle of the 21st century model and scenario uncertainty become the dominant contribution in the tropics but internal variability remains dominant in the extra-tropics. Towards the end of the century, model and scenario uncertainty increase to near equal contributions of ~40% each globally with large regional fluctuations. For precipitation extremes, internal variability dominates throughout the 21st century, except for some tropical regions, for example, West Africa. In regions where internal variability constitutes the major source of uncertainty, the potential impact of reducing model uncertainty on the signal-to-noise ratio of the climate projection is estimated to be small. We discuss the caveats of the methodology used and impact of our findings for the design of future climate models. The importance of internal variability found here emphasizes that large ensembles are a vital tool for understanding climate projections.