Estimating subseasonal variability and trends in global atmosphere using reanalysis data

A new measure of subseasonal variability is introduced that provides a scale-dependent estimation of vertically and meridionally integrated atmospheric variability in terms of the normal modes of linearized primitive equations. Applied to the ERA-Interim data, the new measure shows that subseasonal variability decreases for larger zonal wave numbers. Most of variability is due to balanced (Rossby mode) dynamics but the portion associated with the inertio-gravity (IG) modes increases as the scale reduces. Time series of globally integrated variability anomalies in ERA-Interim show an increase in variability after year 2000. In recent years the anomalies have been about 2% above the 1981–2010 average. The relative increase in variability projecting on the IG modes is larger and more persistent than for the Rossby modes. Although the IG part is a small component of the subseasonal variability, it is an important effect likely reflecting the observed increase in the tropical precipitation variability. ©2018. The Authors

Energy Spectra of Rossby and Gravity Waves

The characteristics of energy spectrum based on 3D normal mode energetics are investigated with the global analysis dataset provided by Japan Meteorological Agency (JMA) with the resolution of TL959L60. The energy spectrum of gravity modes exactly follows the -5/3 power law in the synoptic and mesoscales. In the synoptic scale, the spectral slope of total energy follows the -3 power law because Rossby waves are dominant compared to gravity waves. The energy level of gravity modes becomes larger than that of Rossby modes around the zonal wavenumber k = 80. This scale corresponds to 350 km in 45°circle. The total energy spectrum does not show a clear transition from -3 power slope to -5/3 power slope because the energy level of Rossby and gravity modes become comparable near the transition wavenumbers

An assessment of scale-dependent variability and bias in global prediction models

The paper presents a method for the scale-dependent validation of the spatio-temporal variability in global weather or climate models and for their bias quantification in relation to dynamics. The method provides a relationship between the bias and simulated spatial and temporal variance by a model in comparison with verifying reanalysis data. For the low resolution (T30L8) subset of ERA-20C data, it was found that 80–90 (depending on season) of the global interannual variance is at planetary scales (zonal wavenumbers k = 0−3), and only about 1 of the variance is at scales with k> 7. The reanalysis is used to validate a T30L8 GCM in two configurations, one with the prescribed sea-surface temperature (SST) and another using a slab ocean model. Although the model with the prescribed SST represents the average properties of surface fields well, the interannual variability is underestimated at all scales. Similar to variability, model bias is strongly scale dependent. Biases found in the experiment with the prescribed SST are largely increased in the experiment using a slab ocean, especially in k= 0 , in scales with missing variability and in seasons with poorly simulated energy distribution. The perfect model scenario (a comparison between the GCM coupled to a slab ocean vs. the same model with prescribed SSTs) shows that the representation of the ocean is not critical for synoptic to subsynoptic variability, but essential for capturing the planetary scales. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

Overweight and Fatness in Dalmatia, Croatia: Comparison with the U.S. Population Reference

Subscapular skinfold, elbow breadth and upper arm indicators of nutritional status were studied in the population of Dalmatia in Croatia. Age- and sex-specific percentiles were obtained from 4373 subjects, 18 to 74 years of age, and compared to the U.S. NHANES I and II reference data. There were significant differences between these data sets in all studied variables. The results complement those reported previously for BMI and triceps skinfold and indicate that high prevalence of overweight in Dalmatians largely reflects their muscularity and skeletal robustness rather than excess body fatness. The findings suggest that the U.S. upper percentiles of BMI and skinfolds are inadequate for the assessment of excess body fatness in Dalmatian population. The obtained population-specific percentile distributions should be used provisionally as the reference data for group comparisons in the Dalmatian region

Atmospheric energy spectra in global kilometre-scale models

Eleven 40-day long integrations of five different global models with horizontal resolutions of less than 9 km are compared in terms of their global energy spectra. The method of normal-mode function decomposition is used to distinguish between balanced (Rossby wave; RW) and unbalanced (inertia-gravity wave; IGW) circulation. The simulations produce the expected canonical shape of the spectra, but their spectral slopes at mesoscales, and the zonal scale at which RW and IGW spectra intersect differ significantly. The partitioning of total wave energies into RWs an IGWs is most sensitive to the turbulence closure scheme and this partitioning is what determines the spectral crossing scale in the simulations, which differs by a factor of up to two. It implies that care must be taken when using simple spatial filtering to compare gravity wave phenomena in storm-resolving simulations, even when the model horizontal resolutions are similar. In contrast to the energy partitioning between the RWs and IGWs, changes in turbulence closure schemes do not seem to strongly affect spectral slopes, which only exhibit major differences at mesoscales. Despite their minor contribution to the global (horizontal kinetic plus potential available) energy, small scales are important for driving the global mean circulation. Our results support the conclusions of previous studies that the strength of convection is a relevant factor for explaining discrepancies in the energies at small scales. The models studied here produce the major large-scale features of tropical precipitation patterns. However, particularly at large horizontal wavenumbers, the spectra of upper tropospheric vertical velocity, which is a good indicator for the strength of deep convection, differ by factors of three or more in energy. High vertical kinetic energies at small scales are mostly found in those models that do not use any convective parameterisation

Atmospheric bias teleconnections in boreal winter associated with systematic sea surface temperature errors in the tropical Indian Ocean

Coupled climate models suffer from significant sea surface temperature (SST) biases in the tropical Indian Ocean (TIO), leading to errors in global climate predictions. In this study, we investigate the local and remote effects of the TIO SST bias on the simulated atmospheric circulation and spatio-temporal variability – bias teleconnections. A set of century-long simulations forced by idealized SST perturbations, which resemble various (monopolar or dipolar, positive or negative) TIO SST biases in coupled climate models, are conducted with an intermediate-complexity atmospheric model. Bias teleconnections with a focus on boreal wintertime are analysed using the normal-mode function (NMF) decomposition, which can differentiate between balanced and unbalanced flows across spatial scales. The results show that the atmospheric circulation biases caused by the TIO SST bias have the Gill–Matsuno-type pattern in the tropics and Rossby-wave-train structure in the extratropics, similar to the steady-state response to tropical heating perturbations. The teleconnections between the tropical and extratropical biases are set up by Rossby wave activity flux emanating from the subtropics. Over 90 % of the bias variance (i.e. the square of the bias amplitude) is contained in zonal wavenumbers k≤5. The northward shift of the SST bias away from the Equator weakens the amplitude but does not change the spatial structure of the atmospheric response. Besides, the positive SST bias produces stronger bias teleconnections than the negative one of the same size and magnitude. In the NMF framework, the change in the spatial variance of the time-mean state (i.e. energy) is equal to the sum of the bias variance and the covariance between the circulation bias and the reference state (i.e. bias covariance). Due to the TIO SST biases, the global unbalanced zonal-mean (k=0) flow energy decreases, whereas its balanced counterpart increases. These changes primarily arise from the strong bias covariance. For k&gt;0, both the global unbalanced and the tropical balanced energies increase in the case of a monopolar SST bias and decrease in the case of a dipolar SST bias. The increase appears mainly as the bias variance, whereas the decrease is associated with a strong negative bias covariance at k=1 and 2. In contrast, the extratropical balanced wave energy decreases (increases) when the TIO SST bias is positive (negative), which is mainly associated with the bias covariance at k=1. The change in the interannual variance (IAV) is contingent upon the sign of the TIO SST bias. A positive bias reduces, whereas a negative one increases, the IAV in both balanced and unbalanced flows. Geographically, large IAV changes are observed in the tropical Indo-West Pacific region, Australia, South and Northeast Asia, the Pacific-North America region, and Europe, where the background IAVs are strong.</p

Chemical recycling of plastics assisted by microwave multi-frequency heating

Handling plastic waste through recycling allows extending the life of polymeric materials, avoiding recurrence to incineration or landfilling. In contrast with traditional mechanical recycling technologies, chemical recycling enables the obtention of the virgin monomers by means of depolymerisation to create new polymers with the same mechanical and thermal properties as the originals. Research presented in this paper is part of the polynSPIRE project (Horizon 2020 European funding programme) and develops and scales-up a heated reactor to carry out the depolymerisation of polyamide-6 (PA6), polyamide-6, 6 (PA66) and polyurethane (PU) using microwave (MW) technology as the heating source. The purpose is to design and optimize a MW reactor using up to eight ports emitting electromagnetic waves. Finite element method (FEM) simulation and optimisation are used to design the reactor, considering as parameters the data obtained from experimental dielectric testing and lab-scale characterisation of the processes and materials studied. Two different COMSOL Multiphysics modules are involved in this work: Radio Frequency (RF) and Chemical Reaction Engineering (RE), to simulate the reactor cavity using two frequency levels (915 MHz and 2.45 GHz) with a power level of 46 kW, and the chemical depolymerisation process, respectively. A sensitivity study has been performed on key parameters such as the frequency, the number of ports, and position inside the reactor to consolidate the final design. It is expected that these results assist in the design and scale-up of microwave technology for the chemical recycling of plastics, and for the large-scale deployment of this sustainable recovery alternative. © 2021 The Author

Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions

An integrated modelling system based on the regional online coupled meteorology–atmospheric chemistry WRF-Chem model configured with two nested domains with horizontal resolutions of 11.1 and 3.7 km has been applied for numerical weather prediction and for air quality forecasts in Slovenia. In the study, an evaluation of the air quality forecasting system has been performed for summer 2013. In the case of ozone (O3) daily maxima, the first- and second-day model predictions have been also compared to the operational statistical O3 forecast and to the persistence. Results of discrete and categorical evaluations show that the WRF-Chem-based forecasting system is able to produce reliable forecasts which, depending on monitoring site and the evaluation measure applied, can outperform the statistical model. For example, the correlation coefficient shows the highest skill for WRF-Chem model O3 predictions, confirming the significance of the non-linear processes taken into account in an online coupled Eulerian model. For some stations and areas biases were relatively high due to highly complex terrain and unresolved local meteorological and emission dynamics, which contributed to somewhat lower WRF-Chem skill obtained in categorical model evaluations. Applying a bias correction could further improve WRF-Chem model forecasting skill in these cases

The Intricacies of Identifying Equatorial Waves

Equatorial waves (EWs) are synoptic- to planetary-scale propagating disturbances at low latitudes with periods from a few days to several weeks. Here, this term includes Kelvin waves, equatorial Rossby waves, mixed Rossby–gravity waves, and inertio-gravity waves, which are well described by linear wave theory, but it also other tropical disturbances such as easterly waves and the intraseasonal Madden–Julian Oscillation with more complex dynamics. EWs can couple with deep convection, leading to a substantial modulation of clouds and rainfall. EWs are amongst the dynamic features of the troposphere with the longest intrinsic predictability, and models are beginning to forecast them with an exploitable level of skill. Most of the methods developed to identify and objectively isolate EWs in observations and model fields rely on (or at least refer to) the adiabatic, frictionless linearized primitive equations on the sphere or the shallow-water system on the equatorial -plane. Common ingredients to these methods are zonal wave-number–frequency filtering (Fourier or wavelet) and/or projections onto predefined empirical or theoretical dynamical patterns. This paper gives an overview of six different methods to isolate EWs and their structures, discusses the underlying assumptions, evaluates the applicability to different problems, and provides a systematic comparison based on a case study (February 20–May 20, 2009) and a climatological analysis (2001–2018). In addition, the influence of different input fields (e.g., winds, geopotential, outgoing long-wave radiation, rainfall) is investigated. Based on the results, we generally recommend employing a combination of wave-number–frequency filtering and spatial-projection methods (and of different input fields) to check for robustness of the identified signal. In cases of disagreement, one needs to carefully investigate which assumptions made for the individual methods are most probably not fulfilled. This will help in choosing an approach optimally suited to a given problem at hand and avoid misinterpretation of the results

HopScotch - a low-power renewable energy base station network for rural broadband access

The provision of adequate broadband access to communities in sparsely populated rural areas has in the past been severely restricted. In this paper, we present a wireless broadband access test bed running in the Scottish Highlands and Islands which is based on a relay network of low-power base stations. Base stations are powered by a combination of renewable sources creating a low cost and scalable solution suitable for community ownership. The use of the 5~GHz bands allows the network to offer large data rates and the testing of ultra high frequency ``white space'' bands allow expansive coverage whilst reducing the number of base stations or required transmission power. We argue that the reliance on renewable power and the intelligent use of frequency bands makes this approach an economic green radio technology which can address the problem of rural broadband access