scholarly journals Constraints and biases in a tropospheric two-box model of OH

2018 ◽  
Author(s):  
Stijn Naus ◽  
Stephen A. Montzka ◽  
Sudhanshu Pandey ◽  
Sourish Basu ◽  
Ed J. Dlugokencky ◽  
...  
Keyword(s):  
Hydroxyl Radical ◽  
3D Model ◽  
Model Simulation ◽  
Box Model ◽  
Surface Measurement ◽  
Model Parameters ◽  
Ch4 Emissions ◽  
Wide Range ◽  
The Impact ◽  
Bias Corrections

Abstract. The hydroxyl radical (OH) is the main atmospheric oxidant and the primary sink of the greenhouse gas CH4. In two recent studies, constraints on the hydroxyl radical (OH) were derived using a tropospheric two-box model of methyl chloroform (MCF) and CH4. When OH variations as derived in this set-up were propagated to the CH4 budget, the constraints on OH from MCF still allowed for a wide range of CH4 emission scenarios. This is important, because global CH4 emissions are generally considered best constrained by the global lifetime of CH4, which is determined mainly by OH. Here, we investigate how the use of a tropospheric two-box model in these studies can have affected derived constraints on OH, due to the simplifying assumptions inherent to a two-box model. First, instead of prescribing fixed model parameters for interhemispheric transport, chemical loss rates and loss to the stratosphere, we derive species- and time-dependent quantities from a full 3D transport model simulation. We find significant deviations between the magnitude and time-dependence of the parameters we derive, and the assumptions commonly reported and adopted in literature. Moreover, using output from the 3D model simulations, we investigated differences between the burden seen by the surface measurement network of the National Oceanic and Atmospheric Administration and the true tropospheric burden. Next, we accounted for these biases in a two-box model inversion of MCF and CH4, to investigate the impact of the biases on OH constraints. We find that the sensitivity of interannual OH anomalies to the biases is modest (1–2 %), relative to the significant uncertainties on derived OH (5–8 %). However, in an inversion where we implemented all four bias corrections simultaneously, we did find a shift to a positive OH trend over the 1994–2015 period. Moreover, the magnitude of derived global mean OH and by extent that of global CH4 emissions are affected much more strongly by the bias corrections than their anomalies (∼ 10 %). In this way, we identified and quantified direct limitations in the two-box model approach that can possibly be corrected for when a full 3D simulation is used to inform the two-box model. This derivation is, however, an extensive and species-dependent exercise. Therefore, a good alternative would be to move the inversion problem of OH to a 3D model completely. It is crucial to account for the limitations of two-box models in future attempts to constrain the atmospheric oxidative capacity, especially because though MCF and CH4 behave similarly in large parts of our analysis, it is not obvious that this should be the case for alternative tracers that potentially constrain OH, other than MCF.

scholarly journals Constraints and biases in a tropospheric two-box model of OH

2019 ◽  
Vol 19 (1) ◽  
pp. 407-424 ◽  
Author(s):  
Stijn Naus ◽  
Stephen A. Montzka ◽  
Sudhanshu Pandey ◽  
Sourish Basu ◽  
Ed J. Dlugokencky ◽  
...  
Keyword(s):  
Transport Model ◽  
Absolute Magnitude ◽  
Oxidative Capacity ◽  
Box Model ◽  
Surface Measurement ◽  
Positive Trend ◽  
Model Inversion ◽  
Methyl Chloroform ◽  
The Impact ◽  
Bias Corrections

Abstract. The hydroxyl radical (OH) is the main atmospheric oxidant and the primary sink of the greenhouse gas CH4. In an attempt to constrain atmospheric levels of OH, two recent studies combined a tropospheric two-box model with hemispheric-mean observations of methyl chloroform (MCF) and CH4. These studies reached different conclusions concerning the most likely explanation of the renewed CH4 growth rate, which reflects the uncertain and underdetermined nature of the problem. Here, we investigated how the use of a tropospheric two-box model can affect the derived constraints on OH due to simplifying assumptions inherent to a two-box model. To this end, we derived species- and time-dependent quantities from a full 3-D transport model to drive two-box model simulations. Furthermore, we quantified differences between the 3-D simulated tropospheric burden and the burden seen by the surface measurement network of the National Oceanic and Atmospheric Administration (NOAA). Compared to commonly used parameters in two-box models, we found significant deviations in the magnitude and time-dependence of the interhemispheric exchange rate, exposure to OH, and stratospheric loss rate. For MCF these deviations can be large due to changes in the balance of its sources and sinks over time. We also found that changes in the yearly averaged tropospheric burden of CH4 and MCF can be obtained within 0.96 ppb yr−1 and 0.14 % yr−1 by the NOAA surface network, but that substantial systematic biases exist in the interhemispheric mixing ratio gradients that are input to two-box model inversions. To investigate the impact of the identified biases on constraints on OH, we accounted for these biases in a two-box model inversion of MCF and CH4. We found that the sensitivity of interannual OH anomalies to the biases is modest (1 %–2 %), relative to the uncertainties on derived OH (3 %–4 %). However, in an inversion where we implemented all four bias corrections simultaneously, we found a shift to a positive trend in OH concentrations over the 1994–2015 period, compared to the standard inversion. Moreover, the absolute magnitude of derived global mean OH, and by extent, that of global CH4 emissions, was affected much more strongly by the bias corrections than their anomalies (∼10 %). Through our analysis, we identified and quantified limitations in the two-box model approach as well as an opportunity for full 3-D simulations to address these limitations. However, we also found that this derivation is an extensive and species-dependent exercise and that the biases were not always entirely resolvable. In future attempts to improve constraints on the atmospheric oxidative capacity through the use of simple models, a crucial first step is to consider and account for biases similar to those we have identified for the two-box model.

A Sensitivity Study of the Porcine Head Subjected to Bump Impact

2016 ◽  
Author(s):  
Kimberly A. Thompson ◽  
Adam C. Sokolow ◽  
Juliana Ivancik ◽  
Timothy G. Zhang ◽  
William H. Mermagen ◽  
...  
Keyword(s):  
Human Brain ◽  
3D Model ◽  
Porcine Model ◽  
Load Transfer ◽  
Numerical Study ◽  
Sensitivity Study ◽  
Complex Problem ◽  
Model Parameters ◽  
Skin Thickness ◽  
The Impact

Understanding load transfer to the human brain is a complex problem that has been a key subject of recent investigations [4–6]. Because the porcine is a gyrencephalic species, having greater structural and functional similarities to the human brain than other lower species outlined in the literature, it is commonly chosen as a surrogate for human brain studies [7]. Consequently, we have chosen to use a porcine model in this work. To understand stress wave transfer to and through the brain, it is important to fully characterize the nature of the impact (i.e. source, location, and speed) as well as the response of the constituent tissues under such impact. We suspect the material and topology of these tissues play an important role in their response. In this paper, we report on a numerical study assessing the sensitivity of model parameters for a 6-month old Gottingen mini-pig model, under bump loading. In this study, 2D models are used for computational simplicity. While a 3D model is more realistic in nature, a 2D representation is still valuable in that it can provide trends on parameter sensitivity that can help steer the development of the 3D model. In this work, we investigate the variation of skull and skin thickness, evaluate material variability of the skull, and consider the effects of nasal cavities on load transfer. Eighty simulations are computed in LS-DYNA and analyzed in MATLAB. The results of this study will provide useful knowledge on the necessary components and parameters of the porcine model and therefore provide more confidence in the analysis. This is an essential first step as we look toward bridging the gap between correlates of injury in animal and human models.

scholarly journals Modelling the impact of curtailing antibiotic usage in food animals on antibiotic resistance in humans

2017 ◽  
Vol 4 (4) ◽  
pp. 161067 ◽  
Author(s):  
B. A. D. van Bunnik ◽  
M. E. J. Woolhouse
Keyword(s):  
Bacterial Infections ◽  
Antibiotic Consumption ◽  
Antibiotic Usage ◽  
Model Parameters ◽  
Simple Mathematical Model ◽  
Quantitative Evidence ◽  
Wide Range ◽  
Food Animals ◽  
The Impact ◽  
Equivalent Reduction

Consumption of antibiotics in food animals is increasing worldwide and is approaching, if not already surpassing, the volume consumed by humans. It is often suggested that reducing the volume of antibiotics consumed by food animals could have public health benefits. Although this notion is widely regarded as intuitively obvious there is a lack of robust, quantitative evidence to either support or contradict the suggestion. As a first step towards addressing this knowledge gap, we develop a simple mathematical model for exploring the generic relationship between antibiotic consumption by food animals and levels of resistant bacterial infections in humans. We investigate the impact of restricting antibiotic consumption by animals and identify which model parameters most strongly determine that impact. Our results suggest that, for a wide range of scenarios, curtailing the volume of antibiotics consumed by food animals has, as a stand-alone measure, little impact on the level of resistance in humans. We also find that reducing the rate of transmission of resistance from animals to humans may be more effective than an equivalent reduction in the consumption of antibiotics in food animals. Moreover, the response to any intervention is strongly determined by the rate of transmission from humans to animals, an aspect which is rarely considered.

scholarly journals Stationary Ca2+ nanodomains in the presence of buffers with two binding sites

2020 ◽  
Author(s):  
Y. Chen ◽  
V. Matveev
Keyword(s):  
Closed Form ◽  
Binding Sites ◽  
Interpolation Method ◽  
Secretory Vesicle ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Wide Range ◽  
Vesicle Exocytosis ◽  
Cell Processes ◽  
The Impact

ABSTRACTWe examine closed-form approximations for the equilibrium Ca2+ concentration near a point Ca2+ source representing a Ca2+ channel, in the presence of a mobile Ca2+ buffer with 2:1 Ca2+ binding stoichiometry. We consider buffers with two Ca2+ binding sites activated in tandem and possessing distinct binding affinities and kinetics. This allows to model the impact on Ca2+ nanodomains of realistic endogenous Ca2+ buffers characterized by cooperative Ca2+ binding, such as calretinin. The approximations we present involve a combination or rational and exponential functions, whose parameters are constrained using the series interpolation method that we recently introduced for the case of 1:1 Ca2+ buffers. We conduct extensive parameter sensitivity analysis and show that the obtained closed-form approximations achieve reasonable qualitative accuracy for a wide range of buffer’s Ca2+ binding properties and other relevant model parameters. In particular, the accuracy of the newly derived approximants exceeds that of the rapid buffering approximation in large portions of the relevant parameter space.STATEMENT OF SIGNIFICANCEClosed-form approximations describing equilibrium distribution of Ca2+ in the vicinity of an open Ca2+ channel proved useful for the modeling of local Ca2+ signals underlying secretory vesicle exocytosis, muscle contraction and other cell processes. Such approximations provide an efficient method for estimating Ca2+ and buffer concentrations without computationally expensive numerical simulations. However, while most biological buffers have multiple Ca2+ binding sites, much of prior modeling work considered Ca2+ dynamics in the presence of Ca2+ buffers with a single Ca2+ binding site. Here we extend modeling work on equilibrium Ca2+ nanodomains to the case of Ca2+ buffers with two binding sites, allowing to gain deeper insight into the impact of more realistic Ca2+ buffers, including cooperative buffers, on cell Ca2+ dynamics.

A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model

2011 ◽  
Vol 24 (23) ◽  
pp. 6210-6226 ◽  
Author(s):  
S. Zhang
Keyword(s):  
Time Scales ◽  
Parameter Optimization ◽  
Time Scale ◽  
Model Simulation ◽  
Coupled Model ◽  
State Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Long Time ◽  
The Impact

Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.

scholarly journals Agent-based modelling of complex factors impacting malaria prevalence

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Miracle Amadi ◽  
Anna Shcherbacheva ◽  
Heikki Haario
Keyword(s):  
Malaria Transmission ◽  
Field Data ◽  
Control Measures ◽  
Model Parameters ◽  
Economic Factors ◽  
Agent Based ◽  
Wide Range ◽  
The Impact ◽  
Socio Economic Factors

Abstract Background Increasingly complex models have been developed to characterize the transmission dynamics of malaria. The multiplicity of malaria transmission factors calls for a realistic modelling approach that incorporates various complex factors such as the effect of control measures, behavioural impacts of the parasites to the vector, or socio-economic variables. Indeed, the crucial impact of household size in eliminating malaria has been emphasized in previous studies. However, increasing complexity also increases the difficulty of calibrating model parameters. Moreover, despite the availability of much field data, a common pitfall in malaria transmission modelling is to obtain data that could be directly used for model calibration. Methods In this work, an approach that provides a way to combine in situ field data with the parameters of malaria transmission models is presented. This is achieved by agent-based stochastic simulations, initially calibrated with hut-level experimental data. The simulation results provide synthetic data for regression analysis that enable the calibration of key parameters of classical models, such as biting rates and vector mortality. In lieu of developing complex dynamical models, the approach is demonstrated using most classical malaria models, but with the model parameters calibrated to account for such complex factors. The performance of the approach is tested against a wide range of field data for Entomological Inoculation Rate (EIR) values. Results The overall transmission characteristics can be estimated by including various features that impact EIR and malaria incidence, for instance by reducing the mosquito–human contact rates and increasing the mortality through control measures or socio-economic factors. Conclusion Complex phenomena such as the impact of the coverage of the population with long-lasting insecticidal nets (LLINs), changes in behaviour of the infected vector and the impact of socio-economic factors can be included in continuous level modelling. Though the present work should be interpreted as a proof of concept, based on one set of field data only, certain interesting conclusions can already be drawn. While the present work focuses on malaria, the computational approach is generic, and can be applied to other cases where suitable in situ data is available.

scholarly journals Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

2019 ◽  
Vol 5 (12) ◽  
pp. 2738-2746
Author(s):  
Abdul Ghani Soomro ◽  
Muhammad Munir Babar ◽  
Anila Hameem Memon ◽  
Arjumand Zehra Zaidi ◽  
Arshad Ashraf ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Availability ◽  
Curve Number ◽  
Resource Planning ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Direct Runoff ◽  
Wide Range ◽  
The Impact ◽  
Scs Cn

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

scholarly journals Surface runoff simulation to mitigate the impact of soil erosion, case study of Třebsín (Czech Republic)

2012 ◽  
Vol 7 (No. 3) ◽  
pp. 85-96 ◽  
Author(s):  
P. Kovář ◽  
D. Vaššová ◽  
M. Janeček
Keyword(s):  
Shear Stress ◽  
Czech Republic ◽  
Soil Erosion ◽  
Surface Runoff ◽  
Model Simulation ◽  
Slope Angle ◽  
Model Parameters ◽  
Physically Based ◽  
The Impact ◽  
Experimental Plots

The relation between soil erosion and its redistribution on land strictly depends on the process of surface runoff formation during intensive rainfall. Therefore, interrupting and reducing continuous surface runoff, using adequate conservation measures, may be implemented in order to reduce the shear stress of flowing water. This paper describes the outcomes of the KINFIL model simulation in assessing the runoff from extreme rainfall on hill slopes. The model is a physically based and parameter distributed 3D model that was applied at the Třebsín experimental station in the Czech Republic. This model was used for the first time to simulate the impact of surface runoff caused by natural or sprinkler-made intensive rains on four of the seven different experimental plots. The plots involved in the analysis contain a variety of soils which are covered with different field crops. At this stage, the model parameters comprise saturated hydraulic conductivity, field capacity, sorptivity, plot geometry and surface roughness reflecting the Třebsín experimental plots. These parameters were verified on observed data. All seven plots had the same slope angle, but two of them were vulnerable to surface runoff due to their soil hydraulic parameters. There were rapidly increasing depths and velocities which consequently caused a higher shear stress for splashing soil particles downstream. The paper provides further information and data concerning the relationships between the depth of water and its velocity on the slopes of certain roughness. It also provides information concerning shear stress and shear velocity values, compared with their critical values depending on the soil particle distribution. This approach is more physically based than the traditional method of Universal Soil Loss Equation (USLE).

scholarly journals Impact of a Random Sequence of Debris Flows on Torrential Fan Formation

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
Author(s):  
Nejc Bezak ◽  
Jošt Sodnik ◽  
Matjaž Mikoš
Keyword(s):  
Debris Flow ◽  
Numerical Simulations ◽  
Rheological Properties ◽  
Debris Flows ◽  
Random Sequence ◽  
Rheological Parameters ◽  
Model Parameters ◽  
Maximum Height ◽  
Wide Range ◽  
The Impact

Debris flows with different magnitudes can have a large impact on debris fan characteristics such as height or slope. Moreover, knowledge about the impact of random sequences of debris flows of different magnitudes on debris fan properties is sparse in the literature and can be improved using numerical simulations of debris fan formation. Therefore, in this paper we present the results of numerical simulations wherein we investigated the impact of a random sequence of debris flows on torrential fan formation, where the total volume of transported debris was kept constant, but different rheological properties were used. Overall, 62 debris flow events with different magnitudes from 100 m3 to 20,000 m3 were selected, and the total volume was approximately 225,000 m3. The sequence of these debris flows was randomly generated, and selected debris fan characteristics after the 62 events were compared. For modeling purposes, we applied the Rapid Mass Movement Simulations (RAMMS) software and its debris flow module (RAMMS-DF). The modeling was carried out using (a) real fan topography from an alpine environment (i.e., an actual debris fan in north-west (NW) Slovenia formed by the Suhelj torrent) and (b) an artificial surface with a constant slope. Several RAMMS model parameters were tested. The simulation results confirm that the random sequence of debris flow events has only some minor effects on the fan formation (e.g., slope, maximum height), even when changing debris flow rheological properties in a wide range. After the 62 events, independent of the selected sequence of debris flows, the final fan characteristics were not significantly different from each other. Mann–Whitney (MW) tests and t-tests were used for this purpose, and the selected significance level was 0.05. Moreover, this conclusion applies for artificial and real terrain and for a wide range of tested RAMMS model rheological parameters. Further testing of the RAMMS-DF model in real situations is proposed in order to better understand its applicability and limitations under real conditions for debris flow hazard assessment or the planning of mitigation measures.

scholarly journals Residual Hole Concentration in Recombination Centres after Bleaching

2021 ◽  
Vol 48 (1) ◽  
pp. 415-427
Author(s):  
N. K. Pawlak ◽  
A. Timar-Gabor ◽  
A. Chruścińska
Keyword(s):  
Total Dose ◽  
Hole Concentration ◽  
Transport Processes ◽  
Point Of View ◽  
Model Parameters ◽  
Sediment Dating ◽  
The Past ◽  
Wide Range ◽  
Dating Method ◽  
The Impact

Abstract Trapped charge dating method using electron spin resonance (ESR) of quartz is progressively used for sediment dating. ESR signals can be used for accurate age estimation only when these signals are zeroed by sunlight exposure before the layer creation or when one knows their ESR residual level (the part of the signal that is not bleached). It is well known that the ESR signal related to the Al-hole centres in quartz used for sediment dating has a significant residual signal. From the point of view of luminescence models, as a hole trap, the Al-hole centre is considered as a recombination centre in quartz. Recently, it was demonstrated experimentally that the ESR signal of the Al-hole centre is dependent on the total dose absorbed by the quartz sample in the past. The same effect was confirmed by simulations of the charge transport processes for a model including two recombination centres. Here, the dependence of residual hole concentration (RHC) in the recombination centres on the total dose absorbed by a sample in the past is studied in detail by computer simulations for a wide range of model parameters. The impact that the various relations of centre parameters have on the dependence of the residual as a function of dose is investigated and the implications for the dating practice are discussed.

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