climatology

Energy and momentum deposition from planetary-scale Rossby waves as well as from small-scale gravity waves (GWs) largely control stratospheric dynamics. Interactions between these different wave types, however, complicate the quantification of their individual contribution to the overall dynamical state of the middle atmosphere. In state-of-the-art general circulation models (GCMs), the majority of the GW spectrum cannot be resolved and therefore has to be parameterised. This is commonly implemented in two discrete schemes, one for GWs that originate from flow over orographic obstacles and one for all other kinds of GWs (non-orographic GWs). In this study, we attempt to gain a deeper understanding of the interactions of resolved with parameterised wave driving and of their influence on the stratospheric zonal winds and on the Brewer–Dobson circulation (BDC). For this, we set up a GCM time slice experiment with two sensitivity simulations: one without orographic GWs and one without non-orographic GWs. Our findings include an acceleration of the polar vortices, which has historically been one of the main reasons for including explicit GW parameterisations in GCMs. Further, we find inter-hemispheric differences in BDC changes when omitting GWs that can be explained by wave compensation and amplification effects. These are partly evoked through local changes in the refractive properties of the atmosphere caused by the omitted GW drag and a thereby increased planetary wave propagation. However, non-local effects on the flow can act to suppress vertical wave fluxes into the stratosphere for a very strong polar vortex. Moreover, we study mean age of stratospheric air to investigate the impact of missing GWs on tracer transport. On the basis of this analysis, we suggest that the larger ratio of planetary waves to GWs leads to enhanced horizontal mixing, which can have a large impact on stratospheric tracer distributions

Can feedback analysis be used to uncover the physical origin of climate sensitivity and efficacy differences?

Different strengths and types of radiative forcings cause variations in the climate sensitivities and efficacies. To relate these changes to their physical origin, this study tests whether a feedback analysis is a suitable approach. For this end, we apply the partial radiative perturbation method. Combining the forward and backward calculation turns out to be indispensable to ensure the additivity of feedbacks and to yield a closed forcing-feedbackbalance at top of the atmosphere

Relative Bedeutung chemischer und physikalischer Rückkopplungen in Klimasensitivitätsstudien mit dem Klima-Chemie-Modellsystem EMAC/MLO

Gekoppelte Klima-Chemie-Modelle erweitern die in einem System wirkenden Rückkopplungen, indem neben den physikalischen auch chemische Rückkopplungen berücksichtigt werden. Die vorliegende Arbeit hat zum Ziel chemische Rückkopplungen erstmals zu quantifizieren und ihren Einfluss auf die Klimasensitivität zu bestimmen. Durch die Kopplung des Deckschichtozeanmodells MLO an das Klima-Chemie-Modell EMAC wird es ermöglicht, Klimagleichgewichtssimulationen mit interaktiver Chemie durchzuführen. Für Gleichgewichtssimulationen, die durch eine Erhöhung der CO2-Konzentrationen angetrieben werden, zeigt sich eine signifikante Dämpfung der Klimasensitivität unter Berücksichtigung chemischer Rückkopplungen. Hierfür verantwortlich sind die negative Rückkopplung über stratosphärisches Ozon und die negative Rückkopplungsänderung über stratosphärischen Wasserdampf. Im Falle von Gleichgewichtssimulationen, die durch eine Erhöhung der anthropogenen NOx- und CO-Emissionen angetrieben werden, ist die Klimasensitivität infolge interaktiver Chemie nicht signifikant erhöht. Der Vergleich mit dem CO2-Experiment zeigt, dass die Variation der Antriebsart unterschiedliche dominierende Rückkopplungs-prozesse auslöst und somit die Klimasensitivität in verschiedenartiger Weise beeinflusst wird

A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor

Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach

A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor

Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach

Feedback Analyses of Equilibrium Climate Change Simulations

Performing equilibrium climate change simulations is a standard method to study the forcing-response relationship within the climate system. An important finding is th different climate models simulate a considerably different global mean response to the same kind of forcing, and previous studies have focussed to explore the respective reason. Radiative feedbacks are essential in controlling the global response and a model's distinctive climate sensitivity. The cloud feedback has been identified to be of particular importance. Respective evidence in equilibrium simulations is valid for transient model simulations as well. Another key issue is to explain the distinctive global mean response of one and the same model to the same amount of forcing induced by different forcings mechanisms. This so-called efficacy characterising a certain forcing can also be related to the specific acting of feedbacks, and not necessarily to the cloud feedback only. Several ways to explore different efficacy of different forcings have been developed, and it is not clear which method is best suited for the purpose. In this talk we will point out some merits and shortcomings of the "partial radiative perturbation method". It has been applied to equilibrium climate change simulations with a coupled chemistry-climate model. The particular focus will be on CO2 increase simulations

A 1D RCE study of factors affecting the tropical tropopause layer and surface climate

There are discrepancies between global climate models regarding the evolution of the tropical tropopause layer (TTL) and also whether changes in ozone impact the surface under climate change. We use a 1D clear-sky radiative–convective equilibrium model to determine how a variety of factors can affect the TTL and how they influence surface climate. We develop a new method of convective adjustment, which relaxes the temperature profile toward the moist adiabat and allows for cooling above the level of neutral buoyancy. The TTL temperatures in our model are sensitive to CO2 concentration, ozone profile, the method of convective adjustment, and the upwelling velocity, which is used to calculate a dynamical cooling rate in the stratosphere. Moreover, the temperature response of the TTL to changes in each of the above factors sometimes depends on the others. The surface temperature response to changes in ozone and upwelling at and above the TTL is also strongly amplified by both stratospheric and tropospheric water vapor changes. With all these influencing factors, it is not surprising that global models disagree with regard to TTL structure and evolution and the influence of ozone changes on surface temperatures. On the other hand, the effect of doubling CO2 on the surface, including just radiative, water vapor, and lapse-rate feedbacks, is relatively robust to changes in convection, upwelling, or the applied ozone profile

pForest: In-Network Inference with Random Forests

The concept of "self-driving networks" has recently emerged as a possible solution to manage the ever-growing complexity of modern network infrastructures. In a self-driving network, network devices adapt their decisions in real-time by observing network traffic and by performing in-line inference according to machine learning models. The recent advent of programmable data planes gives us a unique opportunity to implement this vision. One open question though is whether these devices are powerful enough to run such complex tasks? We answer positively by presenting pForest, a system for performing in-network inference according to supervised machine learning models on top of programmable data planes. The key challenge is to design classification models that fit the constraints of programmable data planes (e.g., no floating points, no loops, and limited memory) while providing high accuracy. pForest addresses this challenge in three phases: (i) it optimizes the features selection according to the capabilities of programmable network devices; (ii) it trains random forest models tailored for different phases of a flow; and (iii) it applies these models in real time, on a per-packet basis. We fully implemented pForest in Python (training), and in P4_16 (inference). Our evaluation shows that pForest can classify traffic at line rate for hundreds of thousands of flows, with an accuracy that is on-par with software-based solutions. We further show the practicality of pForest by deploying it on existing hardware devices (Barefoot Tofino)

Flying low and slow: Application of algorithmic climate change functions to assess the climate mitigation potential of reduced cruise altitudes and speeds on different days

The climate effect from aviation’s non-CO2 emissions such as contrail cirrus, water vapor and nitrogen oxide induced ozone and methane changes depend on emission location and time. Among other approaches, the resulting climate effect can be reduced by lowering cruise flight levels. However, aircraft typically aim to fly at optimum altitudes and perform step climbs with increasing flight length to enhance fuel efficiency and reduce operating cost, what also limits climate effects from CO2 emissions. To account for this and to reduce the overall climate effect of flights, the higher fuel consumption at lower flight altitudes can be compensated by also reducing flight speeds. Therefore, this study analyzes the mitigation potential of flying lower and slower with regard to the overall climate effect along flight trajectories. Specifically, actually flown point profiles are combined with related meteorological parameters to evaluate the effect from reduced cruise altitudes and speeds with an updated set of prototype algorithmic climate change functions. Different case studies show varying effects for individual days during different seasons, and significant mitigation potentials due to flying lower and slower can be observed (up to 9 % on a summer day and 16 % on a winter day). A sensitivity study to explore uncertainties with regard to the quantification of contrail effects is performed as well as an investigation on possible economic consequences in terms of changes in direct operating cost and eco-efficient solutions

Robust 4D Climate Optimal Flight Planning in Structured Airspace using Parallelized Simulation on GPUs: ROOST V1.0

The climate impact of the non-CO2 emissions, being responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in the standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic opti- mization computationally fast. The effectiveness of our proposed strategy to plan robust climate optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with large contrails&rsquo; climate impact and a scenario with no formation of persistent contrails. It is shown that, for a night-time flight from Frankfurt to Kyiv, a 55 % reduction in climate impact can be achieved at the expense of a 4 % increase in cost.</p