The present paper studies the relative efficiency between hotels operating under a brand and hotels operating independently, in the island of Crete, Greece, using the Data Envelopment Analysis. Interestingly enough, we find that nationally branded hotels are the relatively most efficient; internationally branded are the least efficient, while those operating under a local brand and the independent ones lie in between. This efficiency ranking can be explained by the interplay between operating under a brand and being to changes in the local market's conditions. We also investigate the inefficiency causes and make suggestions for improvements, in the transformation of inputs to outputs, for each type of hotels studied.

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Abstract. State-of-the-art Earth system models typically employ grid spacings of O(100 km), which is too coarse to explicitly resolve main drivers of the flow of energy and matter across the Earth system. In this paper, we present the new ICON-Sapphire model configuration, which targets a representation of the components of the Earth system and their interactions with a grid spacing of 10 km and finer. Through the use of selected simulation examples, we demonstrate that ICON-Sapphire can (i) be run coupled globally on seasonal timescales with a grid spacing of 5 km, on monthly timescales with a grid spacing of 2.5 km, and on daily timescales with a grid spacing of 1.25 km; (ii) resolve large eddies in the atmosphere using hectometer grid spacings on limited-area domains in atmosphere-only simulations; (iii) resolve submesoscale ocean eddies by using a global uniform grid of 1.25 km or a telescoping grid with the finest grid spacing at 530 m, the latter coupled to a uniform atmosphere; and (iv) simulate biogeochemistry in an ocean-only simulation integrated for 4 years at 10 km. Comparison of basic features of the climate system to observations reveals no obvious pitfalls, even though some observed aspects remain difficult to capture. The throughput of the coupled 5 km global simulation is 126 simulated days per day employing 21 % of the latest machine of the German Climate Computing Center. Extrapolating from these results, multi-decadal global simulations including interactive carbon are now possible, and short global simulations resolving large eddies in the atmosphere and submesoscale eddies in the ocean are within reach.

We present a fully automated method for the optimal state space reconstruction from univariate and multivariate time series. The proposed methodology generalizes the time delay embedding procedure by unifying two promising ideas in a symbiotic fashion. Using non-uniform delays allows the successful reconstruction of systems inheriting different time scales. In contrast to the established methods, the minimization of an appropriate cost function determines the embedding dimension without using a threshold parameter. Moreover, the method is capable of detecting stochastic time series and, thus, can handle noise contaminated input without adjusting parameters. The superiority of the proposed method is shown on some paradigmatic models and experimental data from chaotic chemical oscillators.

Agent-based modeling is a simulation method in which autonomous agents interact with their environment and one another, given a predefined set of rules. It is an integral method for modeling and simulating complex systems, such as socio-economic problems. Since agent-based models are not described by simple and concise mathematical equations, the code that generates them is typically complicated, large, and slow. Here we present Agents.jl, a Julia-based software that provides an ABM analysis platform with minimal code complexity. We compare our software with some of the most popular ABM software in other programming languages. We find that Agents.jl is not only the most performant but also the least complicated software, providing the same (and sometimes more) features as the competitors with less input required from the user. Agents.jl also integrates excellently with the entire Julia ecosystem, including interactive applications, differential equations, parameter optimization, and so on. This removes any “extensions library” requirement from Agents.jl, which is paramount in many other tools.

Doing scientific work always involves a lot of focus and scrutiny, since producing a scientific result requires several levels of depth of analysis, all of which must be as accurate and as reproducible as possible. All this required scrutiny should be naturally translated into the codebase of the scientific project. One should strive for a code that is doing what it is supposed to, is reproducible, doesn't break over time, is sufficiently clear of bugs, and with simulation results that are appropriately labelled, and more. The challenges associated with carrying out scientific work should not be made any worse by the difficulties of managing the codebase and resulting data/simulations. An unfortunate but likely outcome of this stress is that scientific codebases tend to be sloppy: folders are not organized, there is no version control, data are not provenanced properly, most scripts break over time, and the whole project is very hard, if not impossible, to reproduce. We have created the software DrWatson to make the process of scientific project management easier. In this paper we will describe how DrWatson results in an efficient scientific workflow, taking time away from project management and giving it to doing science.

We present a fully automated method that identifies attractors and their basins of attraction without approximations of the dynamics. The method works by defining a finite state machine on top of the dynamical system flow. The input to the method is a dynamical system evolution rule and a grid that partitions the state space. No prior knowledge of the number, location, or nature of the attractors is required. The method works for arbitrarily high-dimensional dynamical systems, both discrete and continuous. It also works for stroboscopic maps, Poincaré maps, and projections of high-dimensional dynamics to a lower-dimensional space. The method is accompanied by a performant open-source implementation in the DynamicalSystems.jl library. The performance of the method outclasses the naïve approach of evolving initial conditions until convergence to an attractor, even when excluding the task of first identifying the attractors from the comparison. We showcase the power of our implementation on several scenarios, including interlaced chaotic attractors, high-dimensional state spaces, fractal basin boundaries, and interlaced attracting periodic orbits, among others. The output of our method can be straightforwardly used to calculate concepts, such as basin stability and final state sensitivity.

The properties of Earth's albedo and its symmetries are analyzed using twenty years of space‐based Energy Balanced And Filled product of Clouds and the Earth's Radiant Energy System measurements. Despite surface asymmetries, top of the atmosphere temporally & hemispherically averaged reflected solar irradiance R appears symmetric over Northern/Southern hemispheres. This is confirmed with the use of surrogate time‐series, which provides margins of 0.1±0.28normalWm−2 for possible hemispheric differences supported by Clouds and Earth's Radiant System data. R time‐series are further analyzed by decomposition into a seasonal (yearly and half yearly) cycle and residuals. Variability in the reflected solar irradiance is almost entirely (99%) due to the seasonal variations, mostly due to seasonal variations in insolation. The residuals of hemispherically averaged R are not only small, but also indistinguishable from noise, and thus not correlated across hemispheres. This makes yearly and sub‐yearly timescales unlikely as the basis for a symmetry‐establishing mechanism. The residuals however contain a global trend that is large, as compared to expected albedo feedbacks. It is also hemispherically symmetric, and thus indicates the possibility of a symmetry enforcing mechanism at longer timescales. To pinpoint precisely which parts of the Earth system establish the hemispheric symmetry, we create an energetically consistent cloud‐albedo field from the data. We show that the surface albedo asymmetry is compensated by asymmetries between clouds over extra‐tropical oceans, with southern hemispheric storm‐tracks being 11% cloudier than their northern hemisphere counterparts. This again indicates that, assuming the albedo symmetry is not a result of chance, its mechanism likely operates on large temporal and spatial scales.