Multilevel Network Games

We consider a multilevel network game, where nodes can improve their communication costs by connecting to a high-speed network. The $n$ nodes are connected by a static network and each node can decide individually to become a gateway to the high-speed network. The goal of a node $v$ is to minimize its private costs, i.e., the sum (SUM-game) or maximum (MAX-game) of communication distances from $v$ to all other nodes plus a fixed price $\alpha > 0$ if it decides to be a gateway. Between gateways the communication distance is $0$, and gateways also improve other nodes' distances by behaving as shortcuts. For the SUM-game, we show that for $\alpha \leq n-1$, the price of anarchy is $\Theta(n/\sqrt{\alpha})$ and in this range equilibria always exist. In range $\alpha \in (n-1,n(n-1))$ the price of anarchy is $\Theta(\sqrt{\alpha})$, and for $\alpha \geq n(n-1)$ it is constant. For the MAX-game, we show that the price of anarchy is either $\Theta(1 + n/\sqrt{\alpha})$, for $\alpha\geq 1$, or else $1$. Given a graph with girth of at least $4\alpha$, equilibria always exist. Concerning the dynamics, both the SUM-game and the MAX-game are not potential games. For the SUM-game, we even show that it is not weakly acyclic.Comment: An extended abstract of this paper has been accepted for publication in the proceedings of the 10th International Conference on Web and Internet Economics (WINE

Evaluating Humidity and Sea Salt Disturbances on CO2 Flux Measurements

AbstractGlobal oceans are an important sink of atmospheric carbon dioxide (CO2). Therefore, understanding the air–sea flux of CO2 is a vital part in describing the global carbon balance. Eddy covariance (EC) measurements are often used to study CO2 fluxes from both land and ocean. Values of CO2 are usually measured with infrared absorption sensors, which at the same time measure water vapor. Studies have shown that the presence of water vapor fluctuations in the sampling air potentially results in erroneous CO2 flux measurements resulting from the cross sensitivity of the sensor. Here measured CO2 fluxes from both enclosed-path Li-Cor 7200 sensors and open-path Li-Cor 7500 instruments from an inland measurement site are compared with a marine site. Also, new quality control criteria based on a relative signal strength indicator (RSSI) are introduced. The sampling gas in one of the Li-Cor 7200 instruments was dried by means of a multitube diffusion dryer so that the water vapor fluxes were close to zero. With this setup the effect that cross sensitivity of the CO2 signal to water vapor can have on the CO2 fluxes was investigated. The dryer had no significant effect on the CO2 fluxes. The study tested the hypothesis that the cross-sensitivity effect is caused by hygroscopic particles such as sea salt by spraying a saline solution on the windows of the Li-Cor 7200 instruments during the inland field test. The results confirm earlier findings that sea salt contamination can affect CO2 fluxes significantly and that drying the sampling air for the gas analyzer is an effective method for reducing this signal contamination.</jats:p

High-resolution large-scale onshore wind energy assessments : A review of potential definitions, methodologies and future research needs

Funding Information: KG, MK, JS, OT and SW gratefully acknowledge support from the European Research Council (’‘reFUEL’’ ERC-2017-STG 758149). JL has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 715132). MJ and IS were funded by the Engineering and Physical Sciences Research Council [ EP/R045518/1 ] through the IDLES programme. JW is funded through an ETH Postdoctoral Fellowship and acknowledges support from the ETH foundation and the Uniscientia foundation. The authors gratefully acknowledge the helpful comments of three anonymous reviewers on an earlier version of this paper.Peer reviewedPublisher PD

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90ĝ€¯d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and "hotspots"of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean-atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question