Dominant Source Of Uncertainty Research Articles

Km‐Scale Simulations of Mesoscale Convective Systems Over South America—A Feature Tracker Intercomparison

AbstractMesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth's water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object‐based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluate MCSs in kilometer‐scale simulations with observational‐based MCSs over 3 years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems in space and time and the treatment of how MCSs are linked in time. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.

Pathological detonation in H[formula omitted]-Cl[formula omitted] mixtures: Uncertainty quantification and thermal non-equilibrium effects

Pathological detonations are detonations with a propagation velocity higher than the classical Chapman–Jouguet velocity and which depends on the wave structure. Such a behavior has been experimentally observed in low-pressure hydrogen-chlorine mixtures and important discrepancies were reported between the experimental and the theoretical velocities. Using the steady planar detonation model, along with a state-of-the-art chemical mechanism this work aims at investigating two aspects potentially responsible for these discrepancies: (i) the impacts of uncertainties in chemical kinetics mechanisms, and (ii) thermal non-equilibrium effects. The uncertainties in the rate constants of reactions R2: 2Cl+M=Cl2+M and R3: Cl2+H=HCl+Cl are found to be the dominant sources of uncertainty for the steady propagation velocity and the induction length of the pathological detonations. The multi-temperature, thermal non-equilibrium model is found to provide generally higher detonation velocity than the experimental value and the eigenvalue solutions obtained under thermal equilibrium. Nevertheless, for specific values of the initial vibrational temperature of HCl, the results can be closer to the experimental values. This latter aspect provides the motivation for future work that relies on more comprehensive thermal non-equilibrium modeling.

Globally consistent estimates of high-resolution Antarctic ice mass balance and spatially resolved glacial isostatic adjustment

Abstract. A detailed understanding of how the Antarctic ice sheet (AIS) responds to a warming climate is needed because it will most likely increase the rate of global mean sea level rise. Time-variable satellite gravimetry, realized by the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions, is directly sensitive to AIS mass changes. However, gravimetric mass balances are subject to two major limitations. First, the usual correction of the glacial isostatic adjustment (GIA) effect by modelling results is a dominant source of uncertainty. Second, satellite gravimetry allows for a resolution of a few hundred kilometres only, which is insufficient to thoroughly explore causes of AIS imbalance. We have overcome both limitations by the first global inversion of data from GRACE and GRACE-FO, satellite altimetry (CryoSat-2), regional climate modelling (RACMO2), and firn densification modelling (IMAU-FDM). The inversion spatially resolves GIA in Antarctica independently from GIA modelling jointly with changes of ice mass and firn air content at 50 km resolution. We find an AIS mass balance of −144 ± 27 Gt a−1 from January 2011 to December 2020. This estimate is the same, within uncertainties, as the statistical analysis of 23 different mass balances evaluated in the Ice sheet Mass Balance Inter-comparison Exercise (IMBIE; Otosaka et al., 2023b). The co-estimated GIA corresponds to an integrated mass effect of 86 ± 21 Gt a−1 over Antarctica, and it fits better with global navigation satellite system (GNSS) results than other GIA predictions. From propagating covariances to integrals, we find a correlation coefficient of −0.97 between the AIS mass balance and the GIA estimate. Sensitivity tests with alternative input data sets lead to results within assessed uncertainties.

Mathematical modeling applied to the uncertainty analysis of a tank prover calibration: Understanding the influence of calibration conditions on the GUM validation using the Monte Carlo method

This paper presents the calibration of the volume of a tank prover using the gravimetric method. A mathematical model of this calibration has been developed and it is non-linear. An evaluation of measurement uncertainty of the tank prover calibration has been carried out using the “Guide to the Expression of Uncertainty in Measurement” (GUM) and the Monte Carlo method. The measurement results have been compared. The influence of calibration conditions on the validation of GUM method by means of the Monte Carlo method (MCM) has been investigated computationally, as well as the behavior of the expanded uncertainty (GUM method) as a function of these calibration conditions. Seven case studies have been analyzed in this research work. In reference case, the tank prover calibration is carried out in the normal laboratory condition. In other case studies, the tank prover calibration is modeled in various hypothetical situations, such as change in tank prover scale quality, variation of calibration water density during calibration, large temperature gradient of water inside the tank prover and combination of the above conditions. In general, GUM method has not been validated by MCM due to one or more of the following conditions: a) the dominant source of uncertainty is a rectangular distribution; b) the uncertainty budget contains large uncertainty and the measurand model is non-linear; c) the measurand probability distribution is not normal.

Optimizing an airborne mass-balance methodology for accurate emission rate quantification of industrial facilities: A case study of industrial facilities in South Korea

Accurate estimation of emissions from industrial point sources is crucial in understanding the effectiveness of reduction efforts and establishing reliable emission inventories. In this study, we employ an airborne Chemical Ionization Mass Spectrometry (CIMS) instrument to quantify sulfur dioxide (SO2) emissions from prominent industrial facilities in South Korea, including power plants, a steel mill, and a petrochemical facility. Our analysis utilizes the box mass balance technique to derive SO2 emissions and associated uncertainty. We evaluate the interpolation methods between 2D kriging and 3D radial basis function. The results demonstrate that the total uncertainty of the box mass balance technique ranges from 5 % to 28 %, with an average of 20 %. Mixing ratio ground extrapolation from the lowest altitude of the airborne sampling to the ground emerges as the dominant source of uncertainty, followed by the determination of the boundary layer height. Adequate sampling at multiple altitudes is found to be essential in reducing the overall uncertainty by capturing the full extent of the plume. Furthermore, we assess the uncertainty of the single-height transect mass balance method commonly employed in previous studies. Our findings reveal an average precision of 47 % for this method, with the potential for overestimating emissions by up to 206 %. Samplings at fewer altitudes or with larger altitude gaps increase the risk of under-sampling and elevate method uncertainties. Therefore, this study provides a quantitative basis to evaluate previously airborne observational emission constraints.

Uncertainty quantification of bank vegetation impacts on the flood flow field in the American River, California, using large‐eddy simulations

AbstractBank vegetation plays a key role in both hydrodynamics and morphodynamics of natural rivers; however, these effects are often unaccounted for in the computational flow dynamics of natural waterways. Recent studies using the large‐eddy simulation (LES), however, have attempted to gain insights into the impacts of bank vegetation on the mean flow field of the natural rivers using a vegetation model, which applies a sink term to the momentum equations of motion. This approach accounts for the effects of the vegetation and provides a practical approach to account for the complex patches of bank vegetation in large‐scale rivers. To implement the vegetation model, a drag coefficient reflecting the overall resistance of vegetal structures to the flow is needed, but due to the lack of calibrated data and range of size, density and type of vegetation, this parameter can be a significant source of uncertainty in the model results. In this study, we use uncertainty quantification (UQ) to investigate the hydrodynamics and bed shear results when a bank vegetation is incorporated in an LES model. To this end, we used the polynomial chaos expansion and Monte Carlo sampling techniques to determine the uncertainties associated with the drag coefficient in the vegetation model and from uncertainties in the bed roughness and inflow discharge. The UQ analysis provided spatially varying confidence levels for the spanwise and vertical distribution of velocity magnitude and for the bed shear stress distributions. In addition, Sobol indices were computed to indicate the relative influence that each parameter had on the overall uncertainty. In general, it was found that uncertainty in flow discharge was the dominant source of uncertainty; however, the drag coefficient in the vegetation model and the bed roughness parameter also made significant contribution to the uncertainty near the banks and bed, respectively.

Third order QCD predictions for fiducial W-boson production

Measurements of W-boson production at the LHC have reached percent-level precision and impose challenging demands on theoretical predictions. Such predictions directly limit the precision of measurements of fundamental quantities such as the W-boson mass and the weak mixing angle. A dominant source of uncertainty in predictions is from higher-order QCD effects. We present a calculation of W-boson production at the level of {alpha}_s^3 at fixed order and including transverse-momentum resummation. We further show predictions for a direct comparison with low-pileup ATLAS transverse-momentum and fiducial cross-section measurements at sqrt{s} = 5.02 TeV. We discuss in detail the impact of modern PDFs. Our calculation including the matching to W+jet production at NNLO will be publicly available the upcoming CuTe-MCFM release and allows for theory-data comparison at the state-of-the-art level.

On the assessment of the wave modeling uncertainty in wave climate projections

Abstract This study investigates the epistemic uncertainty associated with the wave propagation modeling in wave climate projections. A single-forcing, single-scenario, seven-member global wave climate projection ensemble is used, developed using three wave models with a consistent numerical domain. The uncertainty is assessed through projected changes in wave height, wave period, and wave direction. The relative importance of the wave model used and its internal parameterization are examined. The former is the dominant source of uncertainty in approximately two-thirds of the global ocean. The study reveals divergences in projected changes from runs of different models and runs of the same model with different parameterizations over 75% of the ensemble mean change in several ocean regions. Projected changes in the wave period shows the most significant uncertainties, particularly in the Pacific Ocean basin, while the wave height shows the least. Over 30% of global coastlines exhibit significant uncertainties in at least two out of the three wave climate variables analyzed. The coasts of western North America, the Maritime Continent and the Arabian Sea show the most significant wave modeling uncertainties.

Bayesian uncertainty quantification of tristructural isotropic particle fuel silver release: Decomposing model inadequacy plus experimental noise and parametric uncertainties

Tristructural isotropic (TRISO) particle fuel is one of the most promising fuel concepts enabling high temperature and high burnup reactor operation. One dominant source of radioactivity released from the TRISO particles is silver (Ag), which is subject to a high release fraction and long decay life compared to other fission products. Previous modeling efforts using the fuel performance code BISON indicated nonnegligible uncertainties in modeling the diffusion process of fission products in TRISO compared to the Advanced Gas Reactor experiments. The overall uncertainties observed when modeling the fission product diffusion can result from uncertainties in model parameters, noisy experimental measurements, and deficiencies in the developed models. The three types of underlying uncertainties have not yet been properly quantified in open literature. This paper presents the Bayesian uncertainty quantification (UQ) using massively parallelizable Markov chain Monte Carlo samplers. The uncertainties due to model parameters, model inadequacy, and experimental measurement noise are quantified, with the σ term used to represent the sum of the model inadequacy and measurement noise uncertainties. It is worth noting that this is the first time the σ term is inferred for nuclear fuel experiments, as compared to using prescribed values for uncertainty quantification in previous work. The parallelizable Markov chain Monte Carlo samplers efficiently infer the model parameters and the σ term, giving insight into physical parameters like diffusion coefficients and the combined model discrepancy and measurement noise. A subsequent forward uncertainty quantification (UQ) is also performed based on the calibration results to generate more accurate predictions of the Ag release. The model inadequacy plus experimental noise is the most dominant source of uncertainty compared to the parametric uncertainty. All the UQ analyses presented in this work are based on the second series of the irradiation experiments in the Advanced Gas Reactor program.

Modeling the R-ratio and hadronic contributions to g-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$g-2$$\\end{document} with a Treed Gaussian process

The BNL and FNAL measurements of the anomalous magnetic moment of the muon disagree with the Standard Model (SM) prediction by more than 4σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4\\sigma $$\\end{document}. The hadronic vacuum polarization (HVP) contributions are the dominant source of uncertainty in the SM prediction. There are, however, tensions between different estimates of the HVP contributions, including data-driven estimates based on measurements of the R-ratio. To investigate that tension, we modeled the unknown R-ratio as a function of CM energy with a treed Gaussian process (TGP). This is a principled and general method grounded in data-science that allows complete uncertainty quantification and automatically balances over- and under-fitting to noisy data. Our tool yields exploratory results are similar to previous ones and we find no indication that the R-ratio was previously mismodeled. Whilst we advance some aspects of modeling the R-ratio and develop new tools for doing so, a competitive estimate of the HVP contributions requires domain-specific expertise and a carefully curated database of measurements (github, https://github.com/qiao688/TGP_for_g-2).

Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations

Abstract. The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime-specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years (2001–2020) of near-global (60∘ N–60∘ S) satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R2=0.63) and longwave cloud radiative effects (CREs) (R2=0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CREs to selected CCFs is quantified and analyzed. CRE sensitivities to sea surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects the fact that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds, leading to overestimated aerosol sensitivities. Sensitivities of shortwave CREs to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol–cloud interactions using data from both observations and global climate models.

High-Pressure Gravimetric Measurements for Binary Gas Adsorption Equilibria and Comparisons with Ideal Adsorbed Solution Theory (IAST).

Measurements of gas mixture adsorption equilibria at high pressures are important for assessing actual adsorbent selectivities but are often out of reach, given the challenging nature of the required experiments. Here, we report a high-pressure gravimetric binary gas adsorption equilibrium measurement system based on simultaneous gas density and mixture adsorption measurements in a single gas cell coupled to a magnetic-suspension balance. Compared to traditional techniques which rely on analytical measurements of gas composition, this approach does not require any sampling. Adsorption measurements of two gas mixtures (0.500 N2 + 0.500 CH4 and 0.400 N2 + 0.600 CO2, mole fraction) on a commercially available molecular sieve (NaY, sodium molecular sieve type Y) were carried out in the temperature range 282 to 325 K with a pressure up to 10 MPa. A prediction method for the gas mixture adsorption equilibria in a closed system using the ideal adsorbed solution theory (IAST) model was used to compare the experimental results. For binary mixtures of components with similar adsorption capacities (here N2 and CH4), the system can measure the adsorption equilibria at pressures higher than 1.0 MPa and the result agrees well with the IAST model prediction. For two gases with very different adsorption capacities, the uncertainty in the adsorption equilibrium measurement is much larger. The dominant uncertainty source is the gas density measurement, whose uncertainty could potentially be cut to half if the current titanium sinker is replaced with a sinker made of single-crystal silicon and with a larger volume.

Climate change impacts on hydrometeorological and river hydrological extremes in Quito, Ecuador

Study regionQuito, Ecuador Study focusThe study region faces two water-related challenges, which, to date, have only been studied to a minimal extent: extreme precipitation events and water shortage in the dry season. This study investigates the current conditions and future changes in short-duration events, low river discharges and associated uncertainties. Daily precipitation and temperature projections from 19 state-of-the-art global climate models (CMIP6) and four future scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5) are downscaled with delta change and more advanced quantile perturbation methodologies. Temporal disaggregation is applied to obtain sub-daily precipitation with five extreme value distributions (EVDs). Three conceptual hydrological models are implemented to quantify the impacts on river flows. Variance decomposition is applied to estimate the uncertainty share of climate models, hydrological models, and EVDs in the results. New hydrological insightsThe results indicate an intensification of extreme precipitation events, with short-duration events expected to be more intense by up to 30% in the near future (2021–2050) and up to 60% in the far future (2070–2099). The river peak discharges are projected to increase by 5–20% and 10–50% in the near and far future, respectively. On the contrary, the low river flows are projected to decrease by 0–13% and 0–30% for the respective time horizons. Overall, climate models are the dominant source of uncertainty.

Heliocentric distance dependence of zodiacal light observed by Hayabusa2#

Zodiacal light (ZL) is sunlight scattered by interplanetary dust particles (IDPs) at optical wavelengths. The spatial distribution of IDPs in the Solar System may hold an important key to understanding the evolution of the Solar System and material transportation within it. The number density of IDPs can be expressed as n(r) sim r^{-alpha }, and the exponent alpha sim 1.3 was obtained by previous observations from interplanetary space by Helios 1/2 and Pioneer 10/11 in the 1970s and 1980s. However, no direct measurements of alpha based on ZL observations from interplanetary space outside Earth’s orbit have been performed since then. Here, we introduce initial results for the radial profile of the ZL at optical wavelengths observed over the range 0.76-1.06 au by ONC-T aboard the Hayabusa2# mission in 2021-2022. The ZL brightness we obtained is well reproduced by a model brightness, although there is a small excess of the observed ZL brightness over the model brightness at around 0.9 au. The radial power-law index we obtained is alpha = 1.30 pm 0.08, which is consistent with previous results based on ZL observations. The dominant source of uncertainty arises from the uncertainty in estimating the diffuse Galactic light (DGL).Graphical

Albedo measurements and energy yield estimation uncertainty for bifacial photovoltaic systems

AbstractThe deployment of bifacial photovoltaic (PV) modules for utility‐scale PV plants has quickly proliferated in recent years. As they can harness the irradiance received at the rear side of bifacial generators—which depends on the albedo of the reflector ground—bifacial modules provide higher productivity than monofacial modules, in exchange for a low increase in manufacturing costs. However, the reduction of PV performance estimations uncertainty still represents a main concern for both PV and financial actors, which seek to reduce financial risks of bifacial PV systems. The aim of this work is to provide a better understanding of the impact that albedo measurements have on PV yield estimations uncertainty. We analysed the albedo sensitivity coefficient relating albedo measurement and subsequent energy yield estimations' uncertainties through a representative yield simulation exercise performed for three different sites (Chile, Spain and Sweden) and two types of PV plants: static and single‐axis tracker. A formula considering the impact of Ground Coverage Ratio and Inverter Loading Ratio is proposed for this coefficient. We performed an experimental campaign to evaluate the uncertainty associated to different albedo measurement conditions. Surface reflectance of five different ground surfaces has been measured using four sensors, which allowed to obtain three useful types of albedos that we defined and described. A series of recommendations for short‐term ground‐based albedo measurements that entail a maximum of 1.5% yield uncertainty contribution in the frame of commercial PV plants are then derived. A representative case of actual albedo measurement practices at the current PV market scene is also presented in the form of a commercial albedo measurement project for a future 50 MW bifacial PV plant. Despite being a local characteristic, the most relevant uncertainty source found is associated to the non‐homogeneity of the future PV plant's surface. An important consideration is that changes in the use of land caused by the construction of a PV plant lead to an albedo uncertainty that predominates over all the uncertainty sources associated to the albedo measurement conditions. This can also be the case for long‐term seasonal variations, which can become the dominant uncertainty source for high latitude locations.

Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models.

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.

Primary microvibration standards down to 10−3 m s−2 at low frequency

The reliability of microvibration measurements is important in some applications, such as infrastructure health monitoring. Thus, it is necessary to develop a vibration standard based on ISO16063-11 for microvibrations. In this study, the low-frequency standard vibration calibration system in the National Metrology Institute of Japan was upgraded to be compatible with small input vibrations down to an amplitude of 10−3 m s−2. A low-noise reference vibration measurement system and a precise signal processing method were integrated to reduce the background noise contribution, which is a dominant uncertainty source in the field of microvibration calibration. The developed system could calibrate the sensitivity of a low-noise accelerometer down to 10−3 m s−2, between 0.1 Hz and 100 Hz. This paper reports the calibration demonstration using a servo accelerometer and the evaluated uncertainty budget. The estimated calibration uncertainty was 0.1% for a normal calibration process with an excitation of 10 m s−2, and it was 2.1% for a microvibration calibration process with an excitation of 10−3 m s−2.

First observations of core-transiting seismic phases on Mars

We present the first observations of seismic waves propagating through the core of Mars. These observations, made using seismic data collected by the InSight geophysical mission, have allowed us to construct the first seismically constrained models for the elastic properties of Mars' core. We observe core-transiting seismic phase SKS from two farside seismic events detected on Mars and measure the travel times of SKS relative to mantle traversing body waves. SKS travels through the core as a compressional wave, providing information about bulk modulus and density. We perform probabilistic inversions using the core-sensitive relative travel times together with gross geophysical data and travel times from other, more proximal, seismic events to seek the equation of state parameters that best describe the liquid iron-alloy core. Our inversions provide constraints on the velocities in Mars' core and are used to develop the first seismically based estimates of its composition. We show that models informed by our SKS data favor a somewhat smaller (median core radius = 1,780 to 1,810 km) and denser (core density = 6.2 to 6.3 g/cm3) core compared to previous estimates, with a P-wave velocity of 4.9 to 5.0 km/s at the core-mantle boundary, with the composition and structure of the mantle as a dominant source of uncertainty. We infer from our models that Mars' core contains a median of 20 to 22 wt% light alloying elements when we consider sulfur, oxygen, carbon, and hydrogen. These data can be used to inform models of planetary accretion, composition, and evolution.

Can the Gravitational Effect of Planet X be Detected in Current-era Tracking of the Known Major and Minor Planets?

Using Fisher information matrices, we forecast the uncertainties σ M on the measurement of a “Planet X” at heliocentric distance d X via its tidal gravitational field’s action on the known planets. Using planetary measurements currently in hand, including ranging from the Juno, Cassini, and Mars-orbiting spacecraft, we forecast a median uncertainty (over all sky positions) of A 5σ detection of a 5 M ⊕ Planet X at d X = 400 au should be possible over the full sky but over only 5% of the sky at d X = 800 au. The gravity of an undiscovered Earth- or Mars-mass object should be detectable over 90% of the sky to a distance of 260 or 120 au, respectively. Upcoming Mars ranging improves these limits only slightly. We also investigate the power of high-precision astrometry of ≈8000 Jovian Trojans over the 2023–2035 period from the upcoming Legacy Survey of Space and Time (LSST). We find that the dominant systematic errors in optical Trojan astrometry (photocenter motion, nongravitational forces, and differential chromatic refraction) can be solved internally with minimal loss of information. The Trojan data allow cross-checks with Juno/Cassini/Mars ranging, but do not significantly improve the best achievable σ M values until they are ≳10× more accurate than expected from LSST. The ultimate limiting factor in searches for a Planet X tidal field is confusion with the tidal field created by the fluctuating quadrupole moment of the Kuiper Belt as its members orbit. This background will not, however, become the dominant source of uncertainty until the data get substantially better than they are today.

Global evaluation of model agreement and uncertainty in terrestrial water storage simulations from ISIMIP 2b framework

Terrestrial water storage (TWS) is a vital component in global hydrologic cycle with direct linkage to water resources availability and hydrologic extremes. Assessing the uncertainty in TWS projections are fundamentally important for developing counter-measures against impacts of climate change on water resources and disasters. This study presents a global-scale evaluation of model agreement and uncertainty in TWS anomaly (TWSA) simulations within the ISIMIP 2b framework by comparison with the 2006–2016 Gravity Recovery and Climate Experiment (GRACE) data. The total 24 members of ISIMIP2b ensembles considered here include the permutations of 6 global water models (GWMs: CLM4.5, H08, LPLmL, MATSIRO, PCR-GLOBWB, WaterGAP2) simulations forced by 4 global climate models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) under RCP2.6 and RCP6.0 scenarios. Results show that the fraction of global land area with the same sign between the ISIMIP2b ensemble mean and GRACE TWSA is 47.5 % and 47.3 % under RCP2.6 and RCP6.0, respectively. The ensemble mean reproduces the negative (positive) TWSA in southern North America, southwestern Asia, northern and central Africa (central South America and northern Europe) under both scenarios. More than 50 % of ensemble members agree with GRACE TWS better in the sign of drying trend than wetting trend over the global land. The interannual TWS variations among ensemble members show better agreement in North America and Europe than other continents. More than 80 % of ensemble members simulate larger (smaller) TWS seasonal amplitude than GRACE in North America, Europe, and Asia (South America, Africa, and Australia). GWM is found to be the main source of uncertainty in TWS simulations in North America, Europe, and Asia (>55 %), while GCM uncertainty is the dominant uncertainty source in South America, Africa, and Australia (>45 %).