Uncertainty In Simulations Research Articles

Improvement and Uncertainties of Global Simulation of Sulfate Concentration and Radiative Forcing in CESM2

AbstractSulfate is a major atmospheric pollutant and radiative forcing (RF) factor that influences air quality, cloud microphysics, and climate. Therefore, a better evaluation of sulfate concentrations and RF patterns is essential for policy‐making and the management of air pollution and climate change. This study comprehensively estimates the global distribution of sulfate concentrations and RFs and analyzes the sources of uncertainty in the Community Earth System Model version 2 and the Parallel Offline Radiative Transfer model. Compared with the observations, the incorporation of detailed in‐cloud aqueous‐phase chemistry and the enhanced wet deposition (WD) flux of sulfate significantly improved the simulations of sulfur species both near the ground and at high altitudes, which is beneficial for a more accurate estimation of the global RF of sulfate. The improved simulated RF of sulfate from 1850 to 2015 is −0.502 Wˑm−2. This study finds that WD is the key process governing both the horizontal and vertical distributions of sulfate concentrations. The overestimation of surface sulfate and the underestimation of high‐altitude sulfate made by the model are essential uncertainty factors of the sulfate RF estimation. This study emphasizes the importance of improving the simulation of global sulfate distribution as well as its RF, which may strongly pressure the near‐future warming potential when witnessing a rapid transition to a carbon neutral world that is phasing out fossil fuel. A more accurate assessment of sulfate levels and radiation effects will play a remarkable guiding role in the formulation of global emission reduction‐related policies in the future.

High-resolution drought simulations and comparison to soil moisture observations in Germany

Abstract. Germany's 2018–2020 consecutive drought events resulted in multiple sectors – including agriculture, forestry, water management, energy production, and transport – being impacted. High-resolution information systems are key to preparedness for such extreme drought events. This study evaluates the new setup of the one-kilometer German drought monitor (GDM), which is based on daily soil moisture (SM) simulations from the mesoscale hydrological model (mHM). The simulated SM is compared against a set of diverse observations from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations, and lysimeters at 40 sites in Germany. Our results show that the agreement of simulated and observed SM dynamics in the upper soil (0–25 cm) are especially high in the vegetative active period (0.84 median correlation R) and lower in winter (0.59 median R). The lower agreement in winter results from methodological uncertainties in both simulations and observations. Moderate but significant improvements between the coarser 4 km resolution setup and the ≈ 1.2 km resolution GDM in the agreement to observed SM dynamics is observed in autumn (+0.07 median R) and winter (+0.12 median R). Both model setups display similar correlations to observations in the dry anomaly spectrum, with higher overall agreement of simulations to observations with a larger spatial footprint. The higher resolution of the second GDM version allows for a more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality, observational soil moisture database.

Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6

The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.

Bone remodelling-based numerical evaluation of personalized masticatory forces from CT-scans

In recent years, the patient specific medical scenario has gained increasing popularity in health and engineering research. Unlike the standard medical paradigm (generalized treatment), personalized health techniques are oriented towards suiting the specific demands of a given patient, resulting in solutions that increase the success of the medical procedure. The idea of computer oriented, or in silico, medical experimentation has proven to be an effective tool to permit the implementation of patient specific medicine.Bone remodelling (BR) simulation intends to predict bone distribution changes due to the specific living conditions of a certain bone under the patient’s loading conditions. Unfortunately, it is usually difficult to obtain the simulation conditions affecting this process which are particular in each case. In this sense, the objective of this work is to present a procedure to adjust the patient-dependent biomechanical factors affecting bone growth and ingrowth, in order to reduce the computational uncertainty of the BR simulation. Assuming that the medical image represents a bone distribution in remodelling homeostasis, the optimization of parameters is driven by the search of a stationary remodelling state of the bone. The methodology is tested in a synthetic problem and also applied in the context of a human mandible in which mastication forces are obtained.

Non‐Local Parameterization of Atmospheric Subgrid Processes With Neural Networks

AbstractSubgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine‐learning (ML) parameterizations based on high‐resolution model output data could be superior to traditional parameterizations. Currently, both traditional and ML parameterizations of subgrid processes in the atmosphere are based on a single‐column approach, which only use information from single atmospheric columns. However, single‐column parameterizations might not be ideal since certain atmospheric phenomena, such as organized convective systems, can cross multiple grid boxes and involve slantwise circulations that are not purely vertical. Here we train neural networks (NNs) using non‐local inputs spanning over 3 × 3 columns of inputs. We find that including the non‐local inputs improves the offline prediction of a range of subgrid processes. The improvement is especially notable for subgrid momentum transport and for atmospheric conditions associated with mid‐latitude fronts and convective instability. Using an interpretability method, we find that the NN improvements partly rely on using the horizontal wind divergence, and we further show that including the divergence or vertical velocity as a separate input substantially improves offline performance. However, non‐local winds continue to be useful inputs for parameterizating subgrid momentum transport even when the vertical velocity is included as an input. Overall, our results imply that the use of non‐local variables and the vertical velocity as inputs could improve the performance of ML parameterizations, and the use of these inputs should be tested in online simulations in future work.

Morning Transition of the Boundary Layer over Dallas–Fort Worth

Abstract Temperature profiles of the lower atmosphere (<3 km) over complex urban areas are related to health risks, including heat stress and respiratory illness. This complexity leads to uncertainty in numerical simulations, and many studies call for more observations of the lower atmosphere over cities. Using 20 years of observations from the Aircraft Meteorological Data Relay (AMDAR) program over Dallas–Fort Worth, Texas, average profiles every 0.5 h are created from the 1.5 million individual soundings. Dallas–Fort Worth is ideal because it is a large urban area in the central Great Plains, has no major topographic or coastal influences, and has two major airports near the center of the urban heat island. With frequent and high-quality measurements over the city, we investigate the evolution of the lower atmosphere around sunrise to quantify the stability, boundary layer height, and duration of the morning transition when there are southerly winds, few clouds, and no precipitation so as to eliminate transient synoptic events. Characteristics of the lower atmosphere are separated by season and maximum wind speed because the the Great Plains low-level jet contributes to day-to-day variability. In all seasons, stronger wind over the city leads to a weaker nocturnal temperature inversion at sunrise and a shorter morning transition period, with the greatest difference during autumn and the smallest difference during summer. During summer, the boundary layer height at sunrise is higher on average, deepens the most as wind strengthens, and has the fewest days exhibiting a surface temperature inversion over the city. Significance Statement Cities impact health by creating an urban heat island caused by more heating at the surface, less evaporative cooling, and increased anthropogenic waste heat, and they can have high pollution. Cooling overnight stabilizes the lower atmosphere and traps pollutants near the surface until surface heating after sunrise mixes them away. Inadequate pollution observations make it difficult to study these issues. The greatest mixing occurs about 2 h after sunrise but can be modulated by wind speed. Observations from 1.5 million aircraft landing and taking off over Dallas–Fort Worth, Texas, reveal that strong low-level wind leads to morning transitions ending 0.84 h earlier on average than with light wind. Details from this vast dataset contribute to improved understanding of the lower atmosphere over cities and provide a baseline for simulations.

Probabilistic eutrophication risk mapping in response to reservoir remediation

Reservoir eutrophication is a critical natural hazard in countries with rapid agricultural and industrial development. To simulate the eutrophication of the Fengshan Reservoir, the algal bloom occurrence patterns in the reservoir were modeled using the Environmental Fluid Dynamics Code (EFDC) for understanding the algal dynamics. The calibration and validation of the EFDC were performed by comparing the model results with field data. The model was calibrated against the hydrodynamic data, including the water level and water temperature, and water quality data from four monitoring stations in the study area. Uncertainty analysis was conducted using a perturbance moment method (PMM) with the EFDC model. Parameter uncertainty was considered in this study. The order of importance of model parameters in terms of their uncertainty contribution to the overall uncertainty of water quality simulations was obtained. The calibrated and validated model was then used to investigate three contaminant remediation scenarios: (a) increasing the water level, (b) dilution, and (c) reaeration. Operational uncertainty was also considered, as there exists variability in the remediation effort and project execution for a complex engineering system like reservoirs. Finally, probabilistic eutrophication risk mapping was established for each remediation effort to facilitate decision-making in eutrophication risk assessment.

Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the BGC model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-day satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for 3 years to jointly estimate 10 preselected key BGC parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values.

Poro-Mechanical Coupling for Flow Diagnostics

Accounting for poro-mechanical effects in full-field reservoir simulation studies and uncertainty quantification workflows using complex reservoir models is challenging, mainly because of the high computational cost. We hence introduce an alternative approach that couples hydrodynamics through existing flow diagnostics simulations with poro-mechanics to screen the impact of coupled poro-mechanical processes on reservoir performance without significantly increasing computational overheads. In flow diagnostics, time-of-flight distributions and influence regions can be used to characterise the flow field in the reservoir, which depends on the distribution of petrophysical properties that are altered due to production-induced changes in pore pressure and effective stress. These extended flow diagnostics calculations hence enable us to quickly screen how the dynamics in the reservoirs (e.g. reservoir connectivity, displacement efficiency, and well allocation factors) are affected by the complex interactions between poro-mechanics and hydrodynamics. Our poro-mechanically informed flow diagnostics account for steady-state and single-phase flow conditions based on the poro-elastic theory and assume that the reservoir does not contain fractures. Fluid flow and rock deformation calculations are coupled sequentially. The equations are discretised using a finite-volume method with two-point flux-approximation and the virtual element method, respectively. The solution of the coupled system considers stress-dependent permeabilities. Due to the steady-state nature of the calculations and the effective proposed coupling strategy, these calculations remain computationally efficient while providing first-order approximations of the interplay between poro-mechanics and hydrodynamics, as we demonstrate through a series of case studies. The extended flow diagnostic approach hence provides an efficient complement to traditional reservoir simulation and uncertainty quantification workflows and enable us to assess a broader range of reservoir uncertainties.

CFD code comparison, verification and validation for decay tests of a FOWT semi-submersible floater

With the advancement of high-performance computation capabilities in recent years, high-fidelity modelling tools such as computational fluid dynamics are becoming increasingly popular in the offshore renewable sector. To justify the credibility of the numerical simulations, thorough verification and validation is essential. In this work, preparatory heave decay tests for a freely floating single cylinder are modelled. Subsequently, the surge and sway decays of a linearly moored floating offshore wind turbine model of the OC4 (Offshore Code Comparison Collaboration Continuation) phase II semi-submersible platform are simulated. Two different viscous-flow CFD codes are used: OpenFOAM (open-source), and ReFRESCO (community-based open-usage). Their results are compared against each other and with water tank experiments. For the single-cylinder decay simulations, it is found that the natural period is accurately modelled compared to the experimental results. Regarding the damping, both CFD codes are overly dissipative. Differences and their potential explanations become apparent in the analysis of the flow field data. Meanwhile, large numerical uncertainties especially in later oscillations make a distinct conclusion difficult. For the OC4 semi-submersible decay simulations, a better agreement in damping can be achieved, however discrepancies in results are observed when restricting the degrees of freedom of the platform. Flow field data again reveals differences between the CFD codes. Meanwhile, through the effort to use similar numerical settings and quantify the numerical uncertainties of the CFD simulations, this work represents a stepping stone towards fairer and more accurate comparison between CFD and experimental results.

Assessment of the variation of failure probability of upgraded rubble-mound breakwaters due to climate change

The effects of climate change on coastal areas are expected to significantly influence the risk for port operations. In the present work, a novel methodology for the quantitative assessment of the performances of upgraded rubble-mound breakwaters under a changing climate is proposed. For each considered upgrading option, the failure probability related to a certain limit state is calculated through the implementation of Monte Carlo (MC) simulations, using the factor of change (FoC) method to include the projected future climate. Three indexes are defined for the immediate and intuitive interpretation of the results: i) the ratio between the calculated and the maximum acceptable failure probability during lifetime (r); ii) the rate of the growth of the failure probability during lifetime (s); iii) the coefficient of variation of the failure probability due to both the intrinsic uncertainty of the MC simulation and the variability of future climate (v). The methodology was applied to the case study of the Catania harbor breakwater, considering the failure of different upgrading solutions due to the collapse of the outer armor layer and to excessive mean overtopping discharge. The results revealed the acceptability of the structural and hydraulic performances of all the tested configurations, under both present and future climate. Moreover, a high climate-related variability of the future failure probability was found. The usefulness of the proposed indexes for designer and decision-makers was also demonstrated. In particular, r gives direct information about the acceptability of the structure performances, enabling the immediate comparison between different configurations and climate scenarios. The index s is fundamental to calculate the appropriate times to implement repair interventions during the structure lifetime. Finally, v allows the identification of those situations which requires the design of highly flexible maintenance plans, able to adapt to a very variable climate avoiding excessive costs.

Estimating the influence of various factors on the uncertainty of light collection simulation in scintillators

The paper is dedicated to the evaluation of statistical uncertainty of simulating the process of light transfer in NaI (Tl) and BGO scintillators by Monte Carlo method. The DETECT2000 program with a unified surface model was used. The process of light transfer (“fate”) by light photons was traced from the moment of appearance in the scintillator to the moment of passing through its exit window. The light collection coefficient was determined as the ratio of the number of photons that passed through the exit window to a given number of emitted photons. Different values of the number of emitted photons, optical transparency coefficients and fractions of the surface diffuse reflection were set. Multiple repetitions of the simulation process for a different set of properties made it possible to evaluate the precision and type A uncertainty of simulating the light collection coefficient for all possible options. It is shown that the uncertainty decreases when the statistics of the emitted photons is increased, and increases when the transparency and diffuse reflection fraction are decreased.

Effect of stratigraphic model uncertainty at a given site on its liquefaction potential index: comparing two random field approaches

Random field theory is often used to model spatial variability of geo-material boundary and property. The results of random field generation based on different theories are quite different; however, few studies discuss the effects of adopting different random field approaches on the established stratigraphic models and their influence on engineering analysis. This article compares two random field approaches for evaluating liquefaction potential at a selected site. Here, based on the results of cone penetration tests (CPTs) at the study site, stratigraphic models are constructed using a continuous random field (conditional random field, CRF) and a discontinuous random field (Markov random field, MRF). Note that the MRF parameters were calibrated with the statistical parameters used in CRF. A series of geological profiles representing realizations of the derived CRF-based and MRF-based stratigraphic models are generated. Then, the liquefaction potential index (LPI) is calculated using the simplified procedure with a simulated geological profile and associated soil parameters. Finally, by repeating the analysis for all realizations of random stratigraphic models, the mean and the coefficient of variation of LPI are determined. Meanwhile, the uncertainty of stratigraphic models generated by CRF and MRF approaches is quantified and expressed as information entropy. Next, the relationship between stratigraphic model uncertainty (as an entropy) and LPI variation (or uncertainty) is established. The results show that: (1) the generation of the stratigraphic model is affected by the chosen random field approach, and the distribution of MRF-based strata is more continuous compared with that of CRF-based strata; (2) due to this effect, the strata uncertainty of CRF simulation is more uniform compared with that of MRF; (3) the information entropy and LPI uncertainty obtained using CRF exhibit moderate correlation, while these parameters obtained using MRF exhibit a strong positive correlation.

Uncertainty quantification of large-eddy simulation results of riverine flows: a field and numerical study

We present large-eddy simulations (LES) of riverine flow in a study reach in the Sacramento River, California. The riverbed bathymetry was surveyed in high-resolution using a multibeam echosounder to construct the computational model of the study area, while the topographies were defined using aerial photographs taken by an Unmanned Aircraft System (UAS). In a series of field campaigns, we measured the flow field of the river river across multiple transects throughout the field site using an acoustic Doppler current profiler (ADCP) and estimated using large-scale particle velocimetry of the videos taken during the operation UAS. We used the measured data of the river flow field to evaluate the accuracy of the LES-computed hydrodynamics. The propagation of uncertainties in the LES results due to the variations in the riverbed’s effective roughness height and the river’s inflow discharge was studied and showed that both parameters redistributed the flow distribution laterally and vertically in the velocity profile. For the uncertainty quantification (UQ) analyses, the polynomial chaos expansion (PCE) method was used to develop a surrogate model, which was randomly sampled sufficiently by the Monte Carlo Sampling (MCS) method to generate confidence intervals for the LES-computed velocity field. Also, Sobol indices derived from the PCE coefficients were calculated to help understand the relative influence of different input parameters on the global uncertainty of the results. The UQ analysis showed that uncertainties of LES results in the shallow near bank regions of the river were mainly related to the roughness, while the variation of inflow discharge leads to uncertainty in the LES results throughout the river, indiscriminately. Changes in discharge and bed roughness altered the flow distribution laterally and vertically in the vertical profile. Polynomial chaos expansions provided a practical approach for characterizing uncertainty in LES results of a natural river. Uncertainty in simulation results near the bank is mainly due to parameter uncertainty in bed roughness characterization. Uncertainty related to the inflow discharge dominated the overall uncertainty in LES results of the river.

Fission Battery transportation and siting aspects

Fission Batteries (FBs) are envisioned to be highly mobile, autonomously operating, and economically competitive small nuclear power reactor systems useable for generating electric power or heat. Such novel systems will face unique challenges pertaining to siting and transportation. This paper presents the findings and recommendations of the 2021 Idaho National Laboratory FB Initiative workshop on the transportation and siting of FBs. Regulatory and technical subject matter experts on the transportation and storage of radioactive materials, licensing of nuclear power plants, innovative sensing technologies, etc., participated in this workshop and shared their professional experiences and perspectives. Based on these conversations, the paper describes the lessons learned from nuclear reactor siting and licensing, as well as from the siting and transportation of radioactive materials such as fresh or spent nuclear fuel. It then explores the applicability of these lessons and practices to FB siting and transportation. In addition, associated regulatory and technological challenges are identified, such as a changing FB source term, criticality safety aspects, neutron activation and embrittlement of structural components, site development, transportation uncertainties, the current regulatory framework, modeling and simulation uncertainties, and limited inspections. Additionally, research and development activities are suggested to address these gaps and challenges, including the development of modeling and simulation tools for FB design, strategies for rapid FB transportation, innovative FB shell materials, procedures for onsite handling of FBs, transportation-accident-proof criticality control mechanisms, self-monitoring and self-testing capabilities, regulatory requirements, and probabilistic risk assessment tools. Carrying out these research initiatives is expected to help accelerate the evolution of FBs and facilitate their design, licensing, and deployment.

Deep learning for downward longwave radiative flux forecasts in the Arctic

Downward longwave radiative flux (LWD), a key factor affecting sea ice properties and warming (i.e., Arctic amplification) in the Arctic, has large uncertainties in numerical weather prediction (NWP) model simulations over the Arctic. LWD estimated in the European Centre for Medium-Range Weather Forecasts (ECMWF)’s fifth-generation reanalysis (ERA5) underestimated the LWD observations at Hopen in Svalbard, Norway. Although LWD underestimation in the ERA5 reanalysis with respect to observations was improved in 24 h forecasts using the Polar Weather Research and Forecasting model (PWRF) without and with data assimilation (DA), 24 h LWD forecasts using PWRF continue to underestimate LWD observations. To improve LWD estimation in the Arctic, a deep learning post-processing model that corrects the bias of the LWD simulation was developed using convolutional neural network and ERA5 reanalysis (2016–2019) as training data. By applying the trained deep learning post-processing model to LWD from three independent datasets (i.e., ERA5 reanalysis data in 2020, 24 h forecasts in 2020 using PWRF without and with DA), the time-averaged root mean square errors (RMSEs) of LWD after deep learning post-processing were reduced by 17.62%, 14.98%, and 13.14%, respectively. Therefore, deep learning reduces uncertainties in LWD simulations in the Arctic. The deep learning model trained with ERA5 reanalysis (2016–2019) was able to correct the bias in LWD simulation from the same type of independent data (i.e., ERA5 reanalysis), as well as from different model type (i.e., PWRF forecasts without and with DA). Therefore, when several NWP models simulate the same atmospheric phenomena, a deep learning model trained with data from one NWP model can be applied to data from other NWP models to reduce uncertainties. Additionally, deep learning can further improve forecasts with DA. Therefore, it is expected that the cost required to generate training data will be reduced, and the efficiency of the deep learning model will increase.

Improving probabilistic monthly water quantity and quality predictions using a simplified residual-based modeling approach

Uncertainty quantification between simulated and observed water quality simulations needs to be improved. This study generated and evaluated probabilistic hydrologic and water quality predictions in 18 locations across the U.S. using residual-based modeling. A Box-Cox transformation scheme group provided the best predictive uncertainties for all case studies. The tradeoffs in the performance metrics for a single variable predictive uncertainty in a single study watershed were more obvious than those for all hydrologic or water quality cases. Compared to a single realization of simulations, the ensemble average of hydrologic and water quality simulations better represented the predictive uncertainty, especially for large watersheds. This study recommends various opportunities via residual error scheme selection, data monitoring improvement, and hydrologic model enhancement to robust hydrologic and water quality predictive uncertainties. The results could improve the quantification of the predictive uncertainty of hydrologic and water quality simulations and guide probabilistic prediction enhancement.

Refractory black carbon aerosols in rainwater in the summer of 2019 in Beijing: Mass concentration, size distribution and wet scavenging ratio

Black carbon (BC) aerosols in the atmosphere play a significant role in climate systems due to their strong ability to absorb solar radiation. The lifetime of BC depends on atmospheric transport, aging and consequently on wet scavenging processes (in-cloud and below-cloud scavenging). In this study, sequential rainwater samples in eight rainfall events collected in 2 mm interval were measured by a tandem system including a single particle soot photometer (SP2) and a nebulizer. The results showed that the volume-weighted average (VWA) mass concentrations of refractory black carbon (rBC) in each rainfall event varied, ranging from 10.8 to 78.9 µg/L. The highest rBC concentrations in the rainwater samples typically occurred in the first fraction from individual rainfall events. The geometric mean median mass-equivalent diameter (MMD) decreased under precipitation, indicating that rBC with larger sizes was relatively aged and preferentially removed by wet scavenging. A positive correlation (R2 = 0.73) between the VWA mass concentrations of rBC in rainwater and that in ambient air suggested the important contribution of scavenging process. Additionally, the contributions of in-cloud and below-cloud scavenging were distinguished and accounted for 74% and 26% to wet scavenging, respectively. The scavenging ratio of rBC particles was estimated to be 0.06 on average. This study provides helpful information for better understanding the mechanism of rBC wet scavenging and reducing the uncertainty of numerical simulations of the climate effects of rBC.

Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas

Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, and remote sensing. Each has its own sets of uncertainties and limitations. Therefore, evaluating these datasets is critical in assessing their applicability in various climatic regions. We used ten precipitation datasets, including measured (in situ), gauge-based, and satellite-based products, to assess their relevance for hydrologic modeling at the Bosque River Basin in North-Central Texas. Evaluated datasets include: (1) in situ station data from the Global Historical Climate Network (GHCN); (2) gauge-based dataset Daymet and the Parameter-elevation Regression on Independent Slope Model (PRISM); (3) satellite-based dataset Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Early and Late, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and PERSIANN-CCS (Cloud Classification System); (4) satellite-based gauge-corrected dataset IMERG-Final, PERSIANN-CDR (Climate Data Record), and CHIRPS (Climate Hazards Group Infrared Precipitation with Station data). Daily precipitation data (2000–2019) were used in the Soil and Water Assessment Tool (SWAT) for hydrologic simulations. Each precipitation dataset was used with measured monthly United States Geological Survey (USGS) streamflow data at three locations in the watershed for model calibration and validation. The SUFI-2 (Sequential Uncertainty Fitting) method on the SWAT-CUP (Calibration and Uncertainty Program) was used to quantify and compare the uncertainty in streamflow simulations from all precipitation datasets. The study has also analyzed the uncertainties in SWAT model parameter values under different gridded precipitation datasets. The results showed similar or better model calibration/validation statistics from gauge-based (Daymet and PRISM) and satellite-based gauge-corrected products (CHIRPS) compared with the GHCN data. However, satellite-based precipitation products such as PERSIANN-CCS and PERSIANN-CDR unveil comparatively inferior to capture in situ precipitation and simulate streamflow. The results showed that gauge-based datasets had comparable and even superior performances in some metrics compared with the GHCN data.

Modeling and propagating inventory‐based sampling uncertainty in the large‐scale forest demographic model “MARGOT”

AbstractModels based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug‐in option to any inventory‐based initial condition. Forty‐year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority.