Music is what people do in 2020 & beyond. Produsing, prosuming & the diversification of musical frames

The authors from Germany present a reflection on musical practices that have been paramount during the COVID-19-crisis, potentially impacting musical practices in the future. The main part of the paper descriptively maps the different manifestations displayed or generated in the context of the EAS conference. This article is part of the anthology European Perspectives on Music Education, Volume 11, which focusses on music practices in the classroom, diversity in music making, learning and teaching and praxeological perspectives on music education. (DIPF/Orig.

Doing music. Musikvereine and their concept(s) of community

The authors show how doing music and community are related to each other in the practice of Musikvereine (amateur wind orchestras) in Germany. With the Documentary Method they reconstruct the implicit and explicit knowledge that guides the everyday practice of members of Musikvereine. Members highlight the importance of social aspects and the intergenerational community within the orchestra. However, the reconstructions also point to exclusive logics and homogeneous structures within the community. This article is part of the anthology European Perspectives on Music Education, Volume 11, which focusses on music practices in the classroom, diversity in music making, learning and teaching and praxeological perspectives on music education. (DIPF/Orig.

Forecast-Oriented Assessment of Decadal Hindcast Skill for North Atlantic SST

We demonstrate in this paper that conventional time-averaged decadal hindcast skill estimates can overestimate or underestimate the credibility of an individual decadal climate forecast.We show that hindcast skill in a long period can be higher or lower than skill in its subperiods. Instead of using time-averaged hindcast skill measures, we propose to use the physical state of the climate system at the beginning of the forecast to judge its credibility.We analyze hindcasts of North Atlantic sea surface temperature (SST) in an initialized prediction system based on the MPI-ESM-LR for the period 1901–2010. Subpolar North Atlantic Ocean heat transport (OHT) strength at hindcast initialization largely determines the skill of these hindcasts:We find high skill after anomalously strong or weak OHT, but low skill after average OHT. This knowledge can be used to constrain conventional hindcast skill estimates to improve the assessment of credibility for a decadal forecast

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis

Abschätzung der Quality of Experience von Geschäftsanwendungen - Ein crowdsourcing-basierter Ansatz

Nowadays, employees have to work with applications, technical services, and systems every day for hours. Hence, performance degradation of such systems might be perceived negatively by the employees, increase frustration, and might also have a negative effect on their productivity. The assessment of the application's performance in order to provide a smooth operation of the application is part of the application management. Within this process it is not sufficient to assess the system performance solely on technical performance parameters, e.g., response or loading times. These values have to be set into relation to the perceived performance quality on the user's side - the quality of experience (QoE). This dissertation focuses on the monitoring and estimation of the QoE of enterprise applications. As building models to estimate the QoE requires quality ratings from the users as ground truth, one part of this work addresses methods to collect such ratings. Besides the evaluation of approaches to improve the quality of results of tasks and studies completed on crowdsourcing platforms, a general concept for monitoring and estimating QoE in enterprise environments is presented. Here, relevant design dimension of subjective studies are identified and their impact of the QoE is evaluated and discussed. By considering the findings, a methodology for collecting quality ratings from employees during their regular work is developed. The method is realized by implementing a tool to conduct short surveys and deployed in a cooperating company. As a foundation for learning QoE estimation models, this work investigates the relationship between user-provided ratings and technical performance parameters. This analysis is based on a data set collected in a user study in a cooperating company during a time span of 1.5 years. Finally, two QoE estimation models are introduced and their performance is evaluated.Heutzutage sind Geschäftsanwendungen und technische Systeme aus dem Arbeitsalltag vieler Menschen nicht mehr wegzudenken. Kommt es bei diesen zu Performanzproblemen, wie etwa Verzögerungen im Netzwerk oder Überlast im Datenzentrum, kann sich dies negativ auf die Effizienz und Produktivität der Mitarbeiter auswirken. Daher ist es wichtig aus Sicht der Betreiber die Performanz der Anwendungen und Systeme zu überwachen. Hierbei ist es allerdings nicht ausreichend die Qualität lediglich anhand von technischen Performanzparametern wie Antwortzeiten zu beurteilen. Stattdessen sollten diese Werte in Relation zu der von den Mitarbeitern wahrgenommenen Performanz oder Quality of Experience (QoE) gesetzt werden. Diese Dissertation beschäftigt sich mit dem Monitoring und der Abschätzung der QoE von Geschäftsanwendungen. Neben der Präsentation eines generellen Konzepts zum Monitoring und der Abschätzung der QoE im Geschäftsumfeld, befasst sich die Arbeit mit Aspekten der Erfassung von Qualitätsbewertungen durch die Nutzer. Dies umfasst einerseits die Evaluation von Ansätzen zur Verbesserung der Qualität von Aufgaben- und Studienergebnissen auf Crowdsourcing-Plattformen. Andererseits werden relevante Dimensionen des Designs von Studien zur Untersuchung der QoE von Geschäftsanwendungen aufgezeigt und deren Einfluss auf die QoE diskutiert und evaluiert. Letztendlich wird eine Methodik zur Erfassung von Qualitätsbewertungen durch Mitarbeiter während ihrer regulären Arbeit vorgestellt, welche implementiert und in einem kooperierenden Unternehmen ausgerollt wurde. Als Grundlage der Entwicklung eines QoE Abschätzungsmodells, untersucht diese Arbeit den Zusammenhang zwischen Bewertungen durch die Nutzer und technischen Performanzparametern. Die Untersuchungen erfolgen auf einem Datensatz, welcher in einer Studie über 1.5 Jahre in einem kooperierenden Unternehmen gesammelt wurde. Des Weiteren werden zwei Methoden zur Abschätzung der QoE präsentiert und deren Performanz evaluiert

AMOC fingerprints influence seasonal SST predictability in the North Atlantic

We investigate the impact of the strength of the Atlantic Meridional Overturning Circulation (AMOC) at 26° N on the prediction of North Atlantic sea surface temperature anomalies (SSTA) a season ahead. We consider the physical mechanism proposed by Duchez et al. (2016a) and test the dependence of SST predictive skill in initialised hindcasts on the phase of AMOC at 26° N. We use initialised simulations with the MPI-ESM-MR seasonal prediction system. First, we use the assimilation experiment between 1979–2014 to confirm that the AMOC leads a SSTA dipole pattern in the tropical and subtropical North Atlantic, with strongest AMOC fingerprints after 2–4 months. Going beyond previous studies, we find that the AMOC fingerprint has a seasonal dependence, and is sensitive to the length of the observational window used, i.e. stronger over the last decade than for the entire time series back to 1979. We then use a set of ensemble hindcast simulations with 30 members, starting each February, May, August and November between 1982 and 2014. We compare the changes in skill between composites based on the AMOC phase a month prior to each start date to simulations without considering the AMOC phase. We find higher SST hindcast skill at 2–4 months lead time for SSTA composites based on the AMOC phase for February, May and November start dates. Our method shows major benefit for May start dates, where mean summer SST hindcast skill over the subtropics increase by a factor of 2, reaching up to 80 % agreement with ERA-Interim SST

North Atlantic subpolar gyre provides downstream ocean predictability

Abstract Slowly varying large-scale ocean circulation can provide climate predictability on decadal time scales. It has been hypothesized that the North Atlantic subpolar gyre (SPG) exerts substantial influence on climate predictability. However, a clear identification of the downstream impact of SPG variations is still lacking. Using the MPI-ESM-LR1.2 decadal prediction system, we show that along the Atlantic water pathway, a dynamical link to the SPG causes salinity to be considerably better predicted than temperature. By modulating the slow northward ocean propagation, the subsurface memory of SPG variations enables salinity to be skillfully predicted up to 8 years ahead. In contrast, the SPG loses influence on temperature before Atlantic water penetrates into the Nordic Seas, and in turn, limits temperature to be predicted only 2 years ahead. This study identifies the key role of SPG signals in downstream prediction and highlights how SPG signals determine prediction time scales for different quantities, opening the door for investigating potentially associated predictions in the subarctic for the earth system, marine ecosystems in particular

Subtle influence of the Atlantic Meridional Overturning Circulation (AMOC) on seasonal sea surface temperature (SST) hindcast skill in the North Atlantic

International audienceWe investigate the impact of the strength of the Atlantic Meridional Overturning Circulation (AMOC) at 26° N on the prediction of North Atlantic sea surface temperature anomalies (SSTAs) a season ahead. We test the dependence of sea surface temperate (SST) predictive skill in initialised hindcasts on the phase of the AMOC at 26°N, invoking a seesaw mechanism driven by AMOC fluctuations, with positive SSTAs north of 26° N and negative SSTAs south of 26° N after a strong AMOC and vice versa. We use initialised simulations with the MPI-ESM-MR (where MR is mixed resolution) seasonal prediction system. First, we use an assimilation experiment between 1979-2014 to confirm that the AMOC leads a SSTA dipole pattern in the tropical and subtropical North Atlantic, with the strongest AMOC fingerprints after 2-4 months. Going beyond previous studies, we find that the AMOC fingerprint has a seasonal dependence and is sensitive to the length of the observational window used, i.e. stronger over the last decade than for the entire time series back to 1979. We then use a set of ensemble hindcast simulations with 30 members, starting each February, May, August and November between 1982 and 2014. We compare the changes in skill between composites based on the AMOC phase a month prior to each start date to simulations without considering the AMOC phase and find subtle influence of the AMOC mechanism on seasonal SST prediction skill. We find higher subtropical SST hindcast skill at a 2-4-month lead time for June-July-August (JJA) SSTA composites based on the AMOC phase at May start dates than for the full time period. In other regions and seasons, we find a negligible impact of the AMOC seesaw mechanism on seasonal SST predictions due to atmospheric influence, calling for caution when considering such a mechanism. Our method shows that, for May start dates following strong AMOC phases, summer SST hindcast skill over the subtropics increases significantly compared to that of weak AMOC phases. This suggests that in the assessment of SST skill for a season ahead an eye should be kept on the initial AMOC state

Decadal Predictions of the Probability of Occurrence for Warm Summer Temperature Extremes

We use a decadal prediction system with the Coupled Model Intercomparison Project Phase 6 version of the coupled Max Planck Institute Earth System Model to predict the probability of occurrence for extremely warm summers in the Northern Hemisphere. An assimilation run with Max Planck Institute Earth System Model shows a robust response of summer temperature extremes in northern Europe and northeast Asia to North Atlantic sea surface temperature via a circumglobal Rossby wavetrain. When the North Atlantic is warm, warm summer temperature extremes occur with a probability of 20% and 24% in northern Europe and northeast Asia, respectively. In a cold North Atlantic phase, these probabilities are 0% and 8%. A similar difference in probability of occurrence is found in the initialized climate predictions. Consequently, the likelihood of a warm summer temperature extreme occurring in the examined regions in the next 10 years can be inferred from predictions of North Atlantic temperature