THE EFFECTS OF PARAMETRIC UNCERTAINTIES IN SIMULATIONS OF A REACTIVE PLUME USING A LAGRANGIAN STOCHASTIC MODEL

A combined Lagrangian stochastic model with micro mixing and chemical sub-models is used to investigate a reactive plume of nitrogen oxides (NOx) released into a turbulent grid flow doped with ozone (O3). Sensitivities to the model input parameters are explored for high NOx model scenarios. A wind tunnel experiment is used to provide the simulation conditions for the first case study where photolysis reactions are not included and the main uncertainties occur in the parameters defining the turbulence scales, the source size and the reaction rate of NO (nitric oxide) with O3. Using nominal values of the parameters from previous studies, the model gives a good representation of the radial profile of the conserved scalar [NOx] compared to the experiments, although the width of the simulated profile is slightly smaller, especially at longer distances from the source. For this scenario, the Lagrangian velocity structure function coefficient has the largest impact on simulated [NOx] profiles. At the next stage photolysis reactions are included in a chemical scheme consisting of eight reactions between species NO, O, O3 and NO2. The high dimensional model representation (HMDR) method is used to investigate the effects of uncertainties in the various model inputs resulting from the parameterisation of important physical and chemical processes in the reactive plume model, on the simulation of primary and secondary chemical species concentrations. Both independent and interactive effects of the parameters are studied. In total 22 parameters are assumed to be uncertain, among them the turbulence parameters, temperature dependant rate parameters, photolysis rates, temperature, fraction of NO in total NOx at the source and background concentration of O3. Only uncertainties in the mixing time scale coefficient and the structure function coefficient are responsible for the variance in the [NOx] radial profile. On the other hand, the variance in the [O3] profile is caused by parameters describing both physical and chemical processes

Assessing the potential for crop albedo enhancement in reducing heatwave frequency, duration, and intensity under future climate change

Adapting to the impacts of future warming, and in particular the impacts of heatwaves, is an increasingly important challenge. One proposed strategy is land-surface radiation management via crop albedo enhancement. This has been argued to be an effective method of reducing daily hot temperature extremes regionally. However, the influence of crop albedo enhancement on heatwave events, which last three or more days, is yet to be explored and this remains an important knowledge gap. Using a fully coupled earth system model with 10 ensemble members, we show that crop albedo enhancement by up to ＋0.1 reduces the frequency of heatwave days over Europe and North America by 10 to 20 days; with a larger reduction over Europe under a future climate driven by SSP2-4.5. The average temperature anomaly during heatwaves (the magnitude of the event), is reduced by 0.8 °C to 1.2 °C where the albedo was enhanced, but reductions in mean heatwave duration are limited. There was a marked reduction in the mean annual cumulative heatwave intensity across most of Eurasia and North America, ranging from 32 °C to as high as 80 °C in parts of southern Europe. These changes were largely driven by a reduction in net radiation, decreasing the sensible heat flux, which reduces the maximum temperature, and therefore, heatwave frequency and intensity. These changes were largely localised to where the albedo enhancement was applied with no significant changes in atmospheric circulation or precipitation, which presents advantages for implementation. While our albedo perturbation of up to ＋0.1 is large and represents the likely upper limit of what is possible with more reflective crops, and we assume that more reflective crops are grown everywhere and instantly, these results provide useful guidance to policy makers and farmers on the maximum possible benefits of using more reflective crops in limiting the impacts of heatwaves under future climate

The Treatment of Uncertainties in Reactive Pollution Dispersion Models at Urban Scales

The ability to predict NO2 concentrations ([NO¬2]) within urban street networks is important for the evaluation of strategies to reduce exposure to NO2. However, models aiming to make such predictions involve the coupling of several complex processes: traffic emissions under different levels of congestion; dispersion via turbulent mixing; chemical processes of relevance at the street-scale. Parameterisations of these processes are challenging to quantify with precision. Predictions are therefore subject to uncertainties which should be taken into account when using models within decision making. This paper presents an analysis of mean [NO¬2] predictions from such a complex modelling system applied to a street canyon within the city of York, UK including the treatment of model uncertainties and their causes. The model system consists of a micro-scale traffic simulation and emissions model, a Reynolds Averaged turbulent flow model coupled to a reactive Lagrangian particle dispersion model. The analysis focuses on the sensitivity of predicted in-street increments of [NO¬2] at different locations in the street to uncertainties in the model inputs. These include physical characteristics such as background wind direction, temperature and background ozone concentrations; traffic parameters such as overall demand and primary NO2 fraction; as well as model parameterisations such as roughness lengths, turbulent time- and length-scales and chemical reaction rate coefficients. Predicted [NO¬2] is shown to be relatively robust with respect to model parameterisations, although there are significant sensitivities to the activation energy for the reaction NO+O3 as well as the canyon wall roughness length. Under off-peak traffic conditions, demand is the key traffic parameter. Under peak conditions where the network saturates, road-side [NO¬2] is relatively insensitive to changes in demand and more sensitive to the primary NO2 fraction. The most important physical parameter was found to be the background wind direction. The study highlights the key parameters required for reliable [NO¬2] estimations suggesting that accurate reference measurements for wind direction should be a critical part of air quality assessments for in-street locations. It also highlights the importance of street scale chemical processes in forming road-side [NO¬2], particularly for regions of high NOx emissions such as close to traffic queues

THE EFFECTS OF PARAMETRIC UNCERTAINTIES IN SIMULATIONS OF A REACTIVE PLUME USING A LAGRANGIAN STOCHASTIC MODEL

A combined Lagrangian stochastic model with micro mixing and chemical sub-models is used to investigate a reactive plume of nitrogen oxides (NOx) released into a turbulent grid flow doped with ozone (O3). Sensitivities to the model input parameters are explored for high NOx model scenarios. A wind tunnel experiment is used to provide the simulation conditions for the first case study where photolysis reactions are not included and the main uncertainties occur in the parameters defining the turbulence scales, the source size and the reaction rate of NO (nitric oxide) with O3. Using nominal values of the parameters from previous studies, the model gives a good representation of the radial profile of the conserved scalar [NOx] compared to the experiments, although the width of the simulated profile is slightly smaller, especially at longer distances from the source. For this scenario, the Lagrangian velocity structure function coefficient has the largest impact on simulated [NOx] profiles. At the next stage photolysis reactions are included in a chemical scheme consisting of eight reactions between species NO, O, O3 and NO2. The high dimensional model representation (HMDR) method is used to investigate the effects of uncertainties in the various model inputs resulting from the parameterisation of important physical and chemical processes in the reactive plume model, on the simulation of primary and secondary chemical species concentrations. Both independent and interactive effects of the parameters are studied. In total 22 parameters are assumed to be uncertain, among them the turbulence parameters, temperature dependant rate parameters, photolysis rates, temperature, fraction of NO in total NOx at the source and background concentration of O3. Only uncertainties in the mixing time scale coefficient and the structure function coefficient are responsible for the variance in the [NOx] radial profile. On the other hand, the variance in the [O3] profile is caused by parameters describing both physical and chemical processes

Transdiagnostic Processes as Mediators of Change in an Internet-Delivered Intervention Based on the Unified Protocol

Background Transdiagnostic treatments target shared mechanisms between disorders to facilitate change across diagnoses. The Unified Protocol (UP) aims at changing dysfunctional reactions towards emotions by increasing mindful emotion awareness and cognitive flexibility, as well as decreasing anxiety sensitivity and emotion avoidance. Method We investigated whether these transdiagnostic processes were malleable by treatment and mediated the relationship between treatment and outcome in an internet-delivered adaptation of the UP. N = 129 participants with mixed anxiety, depressive, and somatic symptom disorders were randomized to treatment or waitlist. Results The treatment yielded significant changes in all transdiagnostic processes over time in comparison to a waitlist condition. In separate mediator models, significant mediating effects were found for mindfulness, cognitive flexibility, behavioral activation, and experiential avoidance. When all mediators were combined in a multiple mediator model, the indirect effects through mindfulness and cognitive flexibility emerged as significant. Conclusion These findings add to the growing body of research on transdiagnostic processes as mediators of change and emphasize mindfulness and cognitive flexibility as a transdiagnostic treatment target. However, these results should be interpreted cautiously, as temporal precedence could not be established

Irreversible loss in marine ecosystem habitability after a temperature overshoot

Anthropogenic warming of the oceans and associated deoxygenation are altering marine ecosystems. Current knowledge suggests these changes may be reversible on a centennial timescale at the ocean surface but irreversible at deeper depths even if global warming were to ameliorate. In contrast, the marine ecosystem’s response to these persistent changes remains poorly elucidated. Here we explore to what extent global warming may drive alterations in marine habitats by exploring the evolution of a metabolic index that captures marine organisms’ ecophysiological response to both temperature and oxygen changes, throughout an idealised ramp-up/ramp-down atmospheric carbon dioxide concentration and an overshoot scenarios. Using a multi-model approach; we find that changes in ocean temperature and oxygen drive a centuries-long irreversible loss in the habitable volume of the upper 1000 m of the world ocean. These results suggest that the combined effect of warming and deoxygenation will have profound and long-lasting impacts on the viability of marine ecosystems, well after global temperatures have peaked.publishedVersio

Compatible fossil fuel CO2 emissions in the CMIP6 earth system models' historical and shared socioeconomic pathway experiments of the twenty-first century

We present the compatible CO2 emissions from fossil fuel (FF) burning and industry, calculated from the historical and Shared Socioeconomic Pathway (SSP) experiments of nine Earth system models (ESMs) participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The multimodel mean FF emissions match the historical record well and are close to the data-based estimate of cumulative emissions (394 6 59 GtC vs 400 6 20 GtC, respectively). Only two models fall inside the observed uncertainty range; while two exceed the upper bound, five fall slightly below the lower bound, due primarily to the plateau in CO2 concentration in the 1940s. The ESMs' diagnosed FF emission rates are consistent with those generated by the integrated assessment models (IAMs) from which the SSPs' CO2 concentration pathways were constructed; the simpler IAMs' emissions lie within the ESMs' spread for seven of the eight SSP experiments, the other being only marginally lower, providing confidence in the relationship between the IAMs' FF emission rates and concentration pathways. The ESMs require fossil fuel emissions to reduce to zero and subsequently become negative in SSP1-1.9, SSP1-2.6, SSP4-3.4, and SSP5-3.4over. We also present the ocean and land carbon cycle responses of the ESMs in the historical and SSP scenarios. The models' ocean carbon cycle responses are in close agreement, but there is considerable spread in their land carbon cycle responses. Land-use and land-cover change emissions have a strong influence over the magnitude of diagnosed fossil fuel emissions, with the suggestion of an inverse relationship between the two. © 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses)

IoT-PMA: Patient Health Monitoring in Medical IoT Ecosystems

The emergence of the Internet of Things (IoT) and the increasing number of cheap medical devices enable geographically distributed healthcare ecosystems of various stakeholders. Such ecosystems contain different application scenarios, e.g., (mobile) patient monitoring using various vital parameters such as heart rate signals. The increasing number of data producers and the transfer of data between medical stakeholders introduce several challenges to the data processing environment, e.g., heterogeneity and distribution of computing and data, lowlatency processing, as well as data security and privacy. Current approaches propose cloud-based solutions introducing latency bottlenecks and high risks for companies dealing with sensitive patient data. In this paper, we address the challenges of medical IoT applications by proposing an end-to-end patient monitoring application that includes NebulaStream as the data processing system, an easy-to-use UI that provides ad-hoc views on the available vital parameters, and the integration of ML models to enable predictions on the patients' health state. Using our end-to-end solution, we implement a real-world patient monitoring scenario for hemodynamic and pulmonary decompensations, which are dynamic and life-threatening deteriorations of lung and cardiovascular functions. Our application provides ad-hoc views of the vital parameters and derived decompensation severity scores with continuous updates on the latest data readings to support timely decision-making by physicians. Furthermore, we envision the infrastructure of an IoT ecosystem for a multi-hospital scenario that enables geo-distributed medical participants to contribute data to the application in a secure, private, and timely manner

The decadal state of the terrestrial carbon cycle: global retrievals of terrestrial carbon allocation, pools and residence times

The terrestrial carbon cycle is currently the least constrained component of the global carbon budget. Large uncertainties stem from a poor understanding of plant carbon allocation, stocks, residence times, and carbon use efficiency. Imposing observational constraints on the terrestrial carbon cycle and its processes is, therefore, necessary to better understand its current state and predict its future state. We combine a diagnostic ecosystem carbon model with satellite observations of leaf area and biomass (where and when available) and soil carbon data to retrieve the first global estimates, to our knowledge, of carbon cycle state and process variables at a 1° × 1° resolution; retrieved variables are independent from the plant functional type and steady-state paradigms. Our results reveal global emergent relationships in the spatial distribution of key carbon cycle states and processes. Live biomass and dead organic carbon residence times exhibit contrasting spatial features (r = 0.3). Allocation to structural carbon is highest in the wet tropics (85–88%) in contrast to higher latitudes (73–82%), where allocation shifts toward photosynthetic carbon. Carbon use efficiency is lowest (0.42–0.44) in the wet tropics. We find an emergent global correlation between retrievals of leaf mass per leaf area and leaf lifespan (r = 0.64–0.80) that matches independent trait studies. We show that conventional land cover types cannot adequately describe the spatial variability of key carbon states and processes (multiple correlation median = 0.41). This mismatch has strong implications for the prediction of terrestrial carbon dynamics, which are currently based on globally applied parameters linked to land cover or plant functional types