Uncertainty In Simulations Research Articles

Improved simulation of compound drought and heat extremes in Eastern China through CWRF downscaling

Abstract Given their profound socio-economic impact and increasing occurrence, compound drought and heat extremes (CDHEs) have become a focal point of widespread concern. Studies have attempted to reproduce and predict these extremes using general circulation models (GCMs); however, the performance of these models in capturing compound events remains controversial. This study presents an improved simulation of CDHE trends over eastern China by using the regional Climate-Weather Research and Forecasting model (CWRF) to downscale the projections of two GCMs that participated in the Coupled Model Intercomparison Project Phase 6. The results show that CWRF downscaling significantly improved the underestimation of CDHE trends in GCM historical simulations, aligning better with observed trends. Moreover, the improvements of CWRF downscaling in simulating CDHEs are more pronounced than those for univariate events, i.e., extreme drought and extreme heat events. This enhancement largely results from CWRF’s better representation of land-atmosphere interaction processes, as indicated by the more realistic spatial distributions and intensities of the land-atmosphere coupling strength index. Under the SSP245 and SSP585 scenario, CWRF downscaling again predicts a more rapid increase in regional mean CDHE frequency compared to GCMs, with values nearing or exceeding 0.4 by the mid-21st century, suggesting a significant future threat to the study region. This study highlights the important role of land-atmosphere interactions in shaping CDHEs and the efficacy of regional climate models to reduce uncertainty in compound event simulations.

Can the young water fraction reduce predictive uncertainty in water transit time estimations?

Transit time distributions (TTDs) of streamflow are informative descriptors of catchment hydrological functioning and solute transport mechanisms. Conventional methods for estimating TTDs generally require model calibration against extensive tracer data time series, which are often limited to well-studied experimental catchments. We challenge this limitation and propose an alternative approach that uses the young water fraction (Fywobs), an increasingly used water age metric which represents the proportion of streamflow with a transit time younger than 2-3 months, and that can be robustly estimated with sparsely measured tracer data. To this end, we conducted a proof of concept study by modelling TTDs using StorAge Selection (SAS) functions with oxygen isotopes (δ18O) measurements for 23 diverse catchments in Germany. In a Monte-Carlo approach, we computed the (averaged) marginal TTDs of a prior parameter distribution and derived a model-based Fyw (Fywsim). We compared Fywsim with Fywobs, obtained from δ18O measurements, and constrained the prior SAS parameters distribution. Subsequently, we derived a posterior distribution of parameters and resulting model simulations. Our findings showed that using Fywobs to constrain the model effectively reduced parameter equifinality and simulation uncertainty. However, the value of Fywobs on reducing model uncertainty varied across sites, with larger values (Fywobs≥0.10) leading to simulations with a narrower uncertainty band and higher model efficiency, whilst smaller values (Fywobs≤0.05) had limited influence on reducing model output uncertainty. We discussed the potential and limitations of combining SAS functions with Fywobs, and considered broader implications of this approach for enhancing our understanding of catchment functioning and water quality status.

The Impact of Drought on Terrestrial Carbon in the West African Sahel: Implications for Natural Climate Solutions

AbstractTerrestrial ecosystems store more than twice the carbon of the atmosphere, and are critical to climate change mitigation efforts. This has led to a proliferation of land‐based carbon sequestration efforts, such as re/afforestation associated with the Great Green Wall in the West African Sahel (WAS GGW). However, we currently lack comprehensive assessments of the long‐term viability of these ecosystems' carbon storage in the context of increasingly severe climate extremes. The WAS is particularly prone to recurrent and disruptive extremes, exemplified by the persistent and severe late‐20th century drought. We assessed the response and recovery of WAS GGW carbon stocks and fluxes to this late‐20th century drought, and the subsequent rainfall recovery, by leveraging a suite of terrestrial ecosystem models. While multi‐model mean carbon fluxes (e.g., gross primary production, respiration) partly recovered to pre‐drought levels, modeled total (above and below ground) ecosystem carbon stock falls to as much as two standard deviations below pre‐drought levels and does not recover even ∼20 years after the maximum drought anomaly. Furthermore, to the extent that the modeled regional carbon stock recovers, it is nearly entirely driven by atmospheric CO2 trends rather than the precipitation recovery. Uncertainties in regional ecosystem carbon simulation are high, as the models' carbon responses to drought displayed a nearly 10‐standard deviation spread. Nevertheless, the multi‐model average response highlights the strong and persistent impact of drought on terrestrial carbon storage, and the potential risks of relying on terrestrial ecosystems as a “natural climate solution” for climate change mitigation.

Addressing Model Uncertainties in Finite Element Simulation of Electrically Stimulated Implants for Critical-Size Mandibular Defects.

Electrical stimulation is known to enhance bone healing. Novel electrostimulating devices are currently being developed for the treatment of critical-size bone defects in the mandible. Previous numerical models of these devices did not account for possible uncertainties in the input data. We present the numerical model of an electrically stimulated minipig mandible, including optimization and uncertainty quantification (UQ) methods that allow us to determine the most influential parameters. Uncertainties in the optimized finite element model are quantified using the polynomial chaos method that is implemented in the open-source Python toolbox Uncertainpy. The volumes of understimulated, beneficially stimulated, and overstimulated tissue are considered quantities of interest because they may significantly impact the expected healing success. Further, the current is a substantial quantity, limiting the lifetime of a battery-driven stimulation unit. With sensitivity analyses, the most critical parameters in the numerical model can be identified. Thus, we can learn which parameters are particularly relevant, for example, when conceptualizing the stimulation unit or planning the manufacturing process. The results of this study show that the parameters of the electrode-tissue interface (ETI), as well as the conductivity within the defect volume, have the most significant impact on the model results. The UQ results suggest that careful characterization of the ETI and the dielectric tissue properties is crucial to reduce these uncertainties. The numerical model regarding uncertainties yields important implications for reliable implant design and clinical translation.

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers

Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework. An ensemble MIMR-optimized method is developed to provide more comprehensive monitoring information and avoid missing key information due to the randomness of stochastic response datasets in entropy analysis. A numerical case of solute transport with log-Gaussian permeability fields is presented, with twelve scenarios designed by combining different measurement types and monitoring strategies. The results demonstrated that the proposed ensemble MIMR-optimized method significantly improved the k-field estimates compared to the conventional MIMR method. Additionally, high prediction accuracy in forward modeling is essential for ensuring reliable inversion results, especially for observation data with strong nonlinearity. The findings of this study enhance our understanding and management of k-field estimation in heterogeneous aquifers, contributing to the development of more robust inversion frameworks for general data assimilation tasks.

Uncertainty Propagation of Monte Carlo Cross-Section Generation for Graphite-Moderated Pebble Bed Reactors

Monte Carlo codes have become increasingly popular for generating homogenized few-group cross-section data, especially for advanced reactor designs that have complex geometries and nontraditional compositions. However, the stochastic nature of Monte Carlo processes has the potential to introduce additional statistical uncertainties in the overall uncertainty in the prediction of core behavior. The work performed in this research quantified the additional uncertainty introduced by the use of Monte Carlo multigroup cross sections into the analysis of graphite-moderated pebble bed reactors. In this research, the objective was achieved by performing uncertainty quantification for the key output parameters in deterministic steady-state and transient safety calculations. The results show that when the homogenized multigroup cross sections are generated with a sufficient number of neutron histories in the Monte Carlo calculation, the uncertainties in the subsequent deterministic simulations caused by the Monte Carlo cross-section uncertainty are negligible compared to the contributions from the uncertainties of other input parameters.

Multi-model hydrological reference dataset over continental Europe and an African basin

Although Essential Climate Variables (ECVs) have been widely adopted as important metrics for guiding scientific and policy decisions, the Earth Observation (EO) and Land Surface and Hydrologic Model (LSM/HM) communities have yet to treat terrestrial ECVs in an integrated manner. To develop consistent terrestrial ECVs at regional and continental scales, greater collaboration between EO and LSM/HM communities is needed. An essential first step is assessing the LSM/HM simulation uncertainty. To that end, we introduce a new hydrological reference dataset that comprises a range of 19 existing LSM/HM simulations that represent the current state-of-the-art of our LSM/HMs. Simulations are provided on a daily time step, covering Europe, notably the Rhine and Po river basins, alongside the Tugela river basin in Africa, and are uniformly formatted to allow comparisons across simulations. Furthermore, simulations are comprehensively validated with discharge, evapotranspiration, soil moisture and total water storage anomaly observations. Our dataset provides valuable information to support policy development and serves as a benchmark for generating consistent terrestrial ECVs through the integration of EO products.

CMIP6 models underestimate Arctic sea ice loss during the Early Twentieth-Century Warming, despite simulating large low-frequency sea ice variability

Abstract The variability of Arctic sea ice extent (SIE) on interannual and multi-decadal timescales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in 20th-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850-1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE), but exhibit pronounced inter-model spread in multi-decadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the sub-polar North Atlantic. We find that this is associated with differences in models’ sensitivity to northern hemispheric sea surface temperatures (SST). Additionally, we show that while CMIP6 models are generally capable of simulating multi-decadal changes in Arctic sea ice from the mid-20th century to present day, they tend to underestimate the observed sea ice decline during the Early Twentieth-Century Warming (ETCW; 1915-1945). These results suggest the need for an improved characterization of the sea ice response to multi-decadal climate variability, in order to address the sources of model bias and reduce the uncertainty in future projections arising from inter-model spread.

Robust optimization of geometrical properties of flow diverter stents for treating cerebral aneurysm: A proof-of-concept study

This study presents a novel approach to optimize the design of flow diverter (FD) stents for cerebral aneurysm (CA) treatment. By addressing sources of uncertainty in cardiovascular simulations, including geometrical and physical properties and boundary conditions, we aim to assess the applicability of robust optimization techniques to the FD design, establishing a foundation for acquiring robust FDs that are capable of operating consistently under various real-world scenarios. Blood flow in a simplified 2-dimensional CA and FD model was simulated through computational fluid dynamics (CFD). A design space exploration method, incorporating Latin hypercube sampling and Kriging surrogate models, was employed to obtain the optimal solution. The objective was to maximize the reduction in velocity and vorticity within the CA sac. This study used non-intrusive polynomial chaos expansion (PCE) to quantify and propagate the input uncertainties through the computational model and compute the statistical moments of velocity and vorticity reductions. To assess the effect of uncertain sources on objective functions, a sensitivity analysis method based on Sobol indices was applied. Robust optimization involved simultaneously optimizing the mean and standard deviation of velocity reduction. Additionally, we accounted for patients’ specific conditions and repeated the robust optimization. The results indicate that blood Hematocrit and inlet velocity are the most impactful uncertain sources in FD optimization. Moreover, the obtained Pareto front shows that in robust designs, FD struts are concentrated in the distal region of the CA neck, while optimal designs have more struts in the proximal region. This study proposes an FD that compromises robustness and optimality with a velocity reduction of 72.31 % and a standard deviation of 0.00343.

Responses of summer mesoscale convective systems to irrigation over the North China Plain based on convection-permitting model simulations

Extensive irrigation activities in the North China Plain (NCP) significantly influence regional weather and climate. However, previous studies focusing on the NCP were primarily based on coarse-resolution models, which are unable to explicitly resolve convection systems, causing large uncertainty in precipitation simulations. In this study, a convection-permitting model coupled with a dynamic irrigation scheme is utilized to investigate the impacts of irrigation on summertime mesoscale convective systems (MCSs) over the NCP. Sensitivity experiments with irrigation off and on are conducted for 5 summers and an MCS identification and tracking algorithm is applied to both satellite observations and model simulations. We find that incorporating irrigation in the model increases MCS precipitation, which agrees more with observations. The probability distributions of MCS lifetime, area, propagation speed, and intensity are all better simulated with irrigation. Irrigation increases the occurrence frequency of MCSs throughout the entire day. The nighttime increase is partly because of more frequent local initiation of MCS developed from isolated deep convection, while the daytime increase is mainly attributed to the changes in MCSs initiating elsewhere and then propagating to the NCP. On average, irrigation induces additional moisture that is more thermodynamically favorable for precipitation, but this effect is partially offset by the weakened ascending air motion primarily caused by irrigation surface cooling. Compared to weak MCS precipitation events, strong MCS precipitation events experience greater enhancement in precipitation intensity when including irrigation because the offset effect from the change in large-scale ascending air motion is insignificant. In addition, irrigation makes the variation of MCS precipitation intensity more correlated with the variation in ascending motion but less correlated with that in atmospheric moisture. Our results suggest the pronounced impacts of irrigation on MCSs over the NCP which should be included in numerical models to improve regional precipitation simulation and prediction.

UNCERTAINTY EVALUATION USING LAW OF PROPAGATION AND MONTE CARLO SIMULATION METHODS WITH THE AUTORFPOWER MEASUREMENT SOFTWARE

RF power measurement is essential in RF and microwave metrology. For reliable and accurate power measurement, automatic measurement is preferred. A software application in C#, named AutoRFPower, was developed for automatic RF power measurement and uncertainty calculations at this study. According to the GUM document, this application is enhanced for uncertainty calculations by utilizing the Law of Propagation method and the Monte Carlo Simulation method. Trial measurements were performed at different RF power levels and frequencies between 50 MHz and 18 GHz using the AutoRFPower software. Law of Propagation and Monte Carlo Simulation uncertainty calculations were carried out by AutoRFPower based on the trial measurements and by the Oracle Crystal Ball simulation application. All measurements and their uncertainty calculations were compared with each other, and this study validated the uncertainty calculation of AutoRFPower. In addition, it was observed that in the Monte Carlo Simulation, uncertainty calculation results were non-symmetrical normal distribution, contrary to the assumption of symmetrical normal distribution according to the Low of Propagation method. Moreover, it has been observed that the statistical distribution of uncertainty changes depending on the dominant component of the parameters in the model function used for the uncertainty calculation with the Monte Carlo Simulation method.

PNG-UNITsims: Halo clustering response to primordial non-Gaussianities as a function of mass

This paper presents the PNG-UNITSIMS suite, which includes the largest full N-body simulation to date with local primordial non-Gaussianities (local PNG), the PNG-UNIT. The amplitude of the PNGs is given by f localNL=100. The simulation follows the evolution of 40963 particles in a periodic box with Lbox = 1 h−1 Gpc, resulting in a mass resolution of mp = 1.24 × 109 h−1 M⊙, enough to finely resolve the galaxies targeted by stage-IV spectroscopic surveys. The PNG-UNIT has fixed initial conditions with phases also matching the pre-existing UNIT simulation with Gaussian initial conditions. The fixed and matched initial conditions reduce the simulation uncertainty significantly. In this first study of the PNG-UNITSIMS, we measure the PNG response parameter, p, as a function of the halo mass. halos with masses between 1 × 1012 and 5 × 1013 h−1 M⊙ are well described by the universality relation, given by p = 1. For halos with masses between 2 × 1010 and 1 × 1012 h−1 M⊙ we find that p < 1, at a significance between 1.5 and 3.1σ. Combining all the halos between 2 × 1010 and 5 × 1013 h−1 M⊙, we find p consistent with a value of 0.955 ± 0.013, which is 3σ away from the universality relation. We demonstrate that these findings are robust to mass resolution, scale cuts and uncertainty estimation. We also compare our measurements to separate universe simulations, finding that the PNG-UNITSIMS constraints outperform the former for the setup considered. Using a prior on p as tight as the one reported here for DESI-like forecast can result in fNL constraints comparable to fixing p. At the same time, fixing p to a wrong value (p = 1) may result in up to 2σ biases on fNL.

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

AbstractLarge‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles

This work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace reduced-order model for the canonical (NVT) and isobaric (NPT) ensembles. The symplectic reduced-order basis is randomized on the tangent space of the Stiefel manifold to provide topological relationships and capture model-form uncertainty. Using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), we assess the relevance of these stochastic reduced-order atomistic models on canonical problems involving a Lennard-Jones fluid and an argon crystal melt.

Research on the influence of different pyramid array structures on plane blackbody emissivity

The plane blackbody radiation source is pivotal to the infrared remote sensing detection system, with emissivity serving as a critical metric for its performance evaluation. This metric is predominantly influenced by the surface structure and coating properties. To improve emissivity, existing works have utilized pyramid structures to expand the contact area between the radiation surface and light, thereby augmenting light absorption. Nonetheless, there is relatively little research exploring the impact of pyramid structure on emissivity, leading to a scarcity of related emissivity data for plane blackbody. In this paper, we explored the impact on the blackbody emissivity induced by different pyramid structures, including the triangular pyramid array (TPA), the quadrangular pyramid array (QPA), the pentagonal pyramid array (PPA), and the hexagonal pyramid array (HPA). Also, the effects involving coating properties, the base-height ratio of the pyramid, and reflection characteristics on emissivity were investigated based on the Monte Carlo Method. Then, the specimens with diffuse reflection (DR) and near-specular reflection (NSR) were fabricated and tested experimentally. The uncertainties of simulation and experiment were maintained below 0.21 % and 0.50 %, respectively. Experimental results are well aligned with the corresponding simulation results, indicating that the normal emissivity of the TPA blackbody is 0.12 % higher than the QPA blackbody, and the directional emissivity uniform of the TPA blackbody is superior to that of the QPA blackbody for the DR model in the 8–14 μm waveband. Furthermore, the temperature field distributions of radiation surface for TPA and QPA blackbody were analysed, noting a maximum temperature difference of 50 mK and 30 mK, respectively. This paper provides experimental support and design references for further improving the emissivity of plane blackbodies.

The Role of Forcing and Parameterization in Improving Snow Simulation in the Upper Colorado River Basin Using the National Water Model

AbstractThis study assesses snow water equivalent (SWE) simulation uncertainty in the National Water Model (NWM) due to forcing and model parameterization, using data from 46 Snow Telemetry (SNOTEL) sites in the Upper Colorado River Basin (UCRB). We evaluated the newly developed Analysis of Record for Calibration (AORC) forcing data for SWE simulation and examined the impact of bias correction applied to AORC precipitation and temperature. Additionally, we investigated the sensitivity of SWE simulations to choices of physical parameterization schemes through 72 ensemble experiments. Results showed that NWM driven by AORC forcings captured the overall temporal variation of SWE but underestimated its amount. Adjusting AORC precipitation with SNOTEL observations reduced SWE root‐mean‐square error (RMSE) by 66%, adjusting temperature trimmed it by 10%, and adjusting both decreased it by 69%. Among the physical processes, the snow/soil temperature time scheme (STC) demonstrated the highest sensitivity, followed by the surface exchange coefficient for heat (SFC), snow surface albedo (ALB), and rainfall and snowfall partitioning (SNF), while the lower boundary of soil temperature (TBOT) proved to be insensitive. Further optimization of the parameterization combination resulted in a 12% SWE RMSE reduction. When combined with the bias‐corrected AORC precipitation and temperature, this optimization led to a remarkable 78% SWE RMSE reduction. Despite these enhancements, a persistent slow and late spring ablation suggests model deficiencies in snow ablation physics. The study emphasizes the critical need to enhance the accuracy of forcing data in mountainous regions and address model parameterization uncertainty through optimization efforts.

Enhanced SWAT calibration through intelligent range-based parameter optimization

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

Gaussian-based plug load profile prediction in non-residential buildings archetype

This paper presents an innovative approach to addressing the prevalent challenge of simulation uncertainty in urban building energy modeling (UBEM), focusing on accurately determining occupant-related input parameters. Traditional UBEM methods typically rely on standard schedules to create archetype models, which often fail to reflect the variability observed in real-world scenarios. To overcome this limitation, this research introduces a novel framework for generating electricity use profiles in institutional building archetypes across various climate zones. This framework integrates k-means clustering with Gaussian processes, effectively incorporating uncertainties into the prediction models. The evaluation of this stochastic model suggests that the methodology can give acceptable predictions on the electricity consumption of institutional buildings. The model demonstrates robust predictive capabilities, achieving a CVRMSE as low as 11% on weekdays and 8.7% on weekends, reflecting its strong predictive performance. However, its performance varies among different clusters and time periods, with specific clusters displaying more significant predictive inaccuracies at particular times. These results emphasize the importance of fine-tuning models and offer opportunities for improvement in predicting urban building energy consumption. This can be achieved by incorporating sensor-derived data to develop more detailed building profiles that include variable electricity usage patterns. This methodology has been integrated into a UBEM tool, enabling the generation of more realistic electricity load profiles.

Distinct sources of uncertainty in simulations of the ocean biological carbon pump at different depths

Model uncertainty in simulating the biological carbon pump was quantified and partitioned using 14 models from the Coupled Model Intercomparison Project Phase 6. Uncertainty increases with depth. On the global scale, uncertainty in carbon export dominates above 900 m and uncertainty in transfer efficiency below. Reducing model uncertainty in carbon export and transfer efficiency offers similar benefits for understanding century-scale carbon sequestration and climate. These models produce three different qualitative patterns in transfer efficiency: one where it is globally homogenous and two opposite latitudinal patterns due to different model structures and parameters. The exponent b of the Martin curve, which has long been used to compare different representations of transfer efficiency, is shown here to underestimate uncertainty in transfer efficiency. This highlights the importance of using vertical profiles of carbon flux rather than the single exponent b in model validation and intercomparison exercises.

Initial needle tracking with the first standalone combined infrared camera - CT system for brachytherapy-analysis of tracking accuracy and uncertainties.

Aprototype infrared camera - cone-beam computed tomography (CBCT) system for tracking in brachytherapy has recently been developed. We evaluated for the first time the corresponding tracking accuracy and uncertainties, and implemented atracking-based prediction of needles on CBCT scans. Amarker tool rigidly attached to needles was 3D printed. The precision and accuracy of tool tracking was then evaluated for both static and dynamic scenarios. Euclidean distances between the tracked and CBCT-derived markers were assessed as well. To implement needle tracking, ground truth models of the tool attached to 200 mm and 160 mm needles were matched to the tracked positions in order to project the needles into CBCT scans. Deviations between projected and actual needle tips were measured. Finally, we put our results into perspective with simulations of the system's tracking uncertainties. For the stationary scenario and dynamic movements, we achieved tool-tracking precision and accuracy of 0.04 ± 0.06 mm and 0.16 ± 0.18 mm, respectively. The tracked marker positions differed by 0.52 ± 0.18 mm from the positions determined via CBCT. In addition, the predicted needle tips in air deviated from the actual tip positions by only 1.62 ± 0.68 mm (200 mm needle) and 1.49 ± 0.62 mm (160 mm needle). The simulated tracking uncertainties resulted in tip variations of 1.58 ± 0.91 mm and 1.31 ± 0.69 mm for the 200 mm and 160 mm needles, respectively. With the innovative system it was possible to achieve ahigh tracking and prediction accuracy of marker tool and needles. The system shows high potential for applicator tracking in brachytherapy.