Ensemble Simulations Research Articles

Assessing Radiative Feedbacks and Their Contribution to the Arctic Amplification Measured by Various Metrics

AbstractArctic amplification (AA), characterized by a more rapid surface air temperature (SAT) warming in the Arctic than the global average, is a major feature of global climate warming. Various metrics have been used to quantify AA based on SAT anomalies, trends, or variability, and they can yield quite different conclusions regarding the magnitude and temporal patterns of AA. This study examines and compares various AA metrics for their temporal consistency in the region north of 70°N from the early twentieth to the early 21st century using observational data and reanalysis products. We also quantify contributions of different radiative feedback mechanisms to AA based on short‐term climate variability in reanalysis and model data using the Kernel‐Gregory approach. Albedo and lapse rate feedbacks are positive and comparable, with albedo feedback being the leading contributor for all AA metrics. The net cloud feedback, which has large uncertainties, depends strongly on the data sets and AA metrics used. By quantifying the influence of internal variability on AA and related feedbacks based on global climate model ensemble simulations, we find that water vapor and cloud feedbacks are most heavily affected by internal variability.

Modulation of Northern Europe near-term anthropogenic warming and wettening assessed through internal variability storylines

Internal variability arising from the inherently chaotic nature of the climate system has amplified or obscured human-caused changes, especially at regional scales in the extratropics, where its contribution to climate variability is the largest. It is virtually certain that this will continue in the near-term. We here focus on the Northern Europe region, whose variability is largely controlled by the North Atlantic Oscillation (NAO) and the Atlantic Meridional Overturning Circulation (AMOC) through remote dynamical and thermodynamic processes, and introduce the concept of internal variability storylines (IVS) to explore, understand, and quantify the role of the two combined drivers of internal variability in the modulation of the anthropogenic warming by 2040 in winter. Based on a large ensemble of historical-scenario simulations, we show that the high-impact IVS, characterised by weak AMOC decline and a decadal shift of the NAO toward dominant positive phase, leads faster to warmer-wetter conditions independently of actual and future greenhouse gases emissions. By contrast, amplified AMOC reduction and more recurrent negative NAO can considerably damp both warming and wettening at near-term. In the latter IVS, we provide evidence that winter-severe conditions similar to those in 2010, that had been responsible for widespread socio-economic disruptions, remain almost as likely to occur by 2040. Reframing the uncertain climate outcomes into the physical science space in a conditional form through the prism of IVS makes climate information relevant for accurate risk assessments and adaptation planning.

Molecular Modeling of Glycosylated Catalytic Domain of Human Carbonic Anhydrase IX.

Glycans exhibit significant structural diversity due to the flexibility of glycosidic bonds linking their constituent monosaccharides and the formation of numerous hydrogen bonds. The present work searches a simulated ensemble of glycan chain conformations attached to the catalytic domain of N-glycosylated human carbonic anhydrase IX (HCA IX-c) to identify conformations pointed away or back-folded toward the protein surface guided by different amino acid residues. A series of classical molecular dynamics (MD) simulation studies for a total of 30 μs followed by accelerated MD simulations for a total of 2 μs have been performed using two different force fields to capture varying degrees of fluctuations of both glycan chain and HCA IX. From the underlying free energy profile and kinetics derived using hidden Markov state model, several stable glycan orientations are identified that extend away from the protein surface and convert among each other with rate constants of the order 107-1010 S-1. Most importantly, we have identified a rare glycan conformation which reaches close to a catalytically important amino acid residue, Glu-106. We further enlist the protein residues that couple such less frequent event of the glycan chain back-folding toward the surface of the protein.

The Significance of Internal Variability for Numerical Experimentation and Analysis

When regional (limited-area) models of the hydrodynamics of the atmosphere and ocean are run over an extended time, variability unrelated to external “drivers” emerges: this variability is colloquially named “hydrodynamical noise” or just “noise”. This article summarises what we have learned in the past few years about the properties of such noise and its implications for numerical experimentation and analysis. The presence of this noise can be identified easily in ensembles of numerical simulations, and it turns out that the intensity of the noise is closely linked to scale-dependent “memory”. The “memory” in the atmosphere and ocean describes the persistence of atmospheric and oceanic conditions, usually quantified by an autocorrelation function. At the system level, this “memory” term, as given by Hasselmann’s stochastic climate model, plays a key role. In the case of marginal seas, the process of baroclinic instability modulated by tides and the formation of seasonal thermoclines are significant aspects. Some more general aspects are discussed, such as the applicability of the stochastic climate model to systems outside of atmospheric and oceanic dynamics, for example, biogeochemical systems, the irreversibility of tipping points, the challenges of detecting changes beyond a noise level, and the attribution of causes of change.

Regionalized ensemble estimation of wave periods for assessing wave energy resources across Canada. Part I: Improved wave-period modelling methodology

Large-scale estimations of wave periods are desired for wave energy assessment, ocean engineering, and wave climate research. Long-term global wave data from satellite altimeters are routinely applied to the estimations. However, this is challenged by uncertainties in wave-period models (WPMs), inaccuracies in data, and simplifications in modeling. Additionally, there exists a gap in the comprehensive examination of the variational mechanisms governing wave periods or model performances. As an effort to address them, we innovate a macroscale regionalized ensemble wave-period modeling (MREWPM) method by optimizing four wave-period models, driven by enhanced altimeter-based REWS (regionalized ensemble wave simulation) estimates of wave heights and wind speeds, within a regionalization framework in macroscale water environments. Results show that MREWPM driven by REWS dataset outperforms existing methods and performs better at larger scales (e.g., in eliminating local-scale overestimation). WPMs are more accurate over remote, deep, and windy regions in cool seasons under metrics-, scale- and data-dependent variations of performances with driving factors (mainly geographical features). This study serves as a foundational contribution towards the enhancement of wave-period simulations, the advancement of understanding wave-period dynamics, and the scientific evaluation of wave energy at macroscales.

Projected Changes of the Warm Arctic‐Cold North American Pattern

AbstractThe Warm Arctic‐Cold North American (WACNA) pattern features opposing surface temperature anomalies, with centers over the Chukchi‐Bering Seas (CBS) and the North American Great Plains. The pattern is mainly driven by meridional heat transport and damped by the generation of available potential energy through diabatic heating. The Canadian Earth System Model CanESM5, part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), reasonably reproduces this pattern and its formation mechanisms. Future projections under the Shared Socioeconomic Pathway 8.5 (SSP5‐8.5) suggest a significant weakening of WACNA with global warming. Notable changes in pattern intensity and spatial structure are anticipated, particularly a decrease in intensity over the CBS. WACNA changes are also found in 31 CMIP6 multi‐model ensemble simulations. These changes are attributed to Arctic amplification associated with global warming, which diminishes the equator‐to‐pole temperature gradient and consequently reduces meridional heat transport, particularly over the CBS region.

Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability

Observed North Atlantic sea-surface temperatures are modulated by a recurrent alternation of anomalously warm and cold interdecadal phases known as Atlantic Multidecadal Variability. Here we use observations and a multi-model ensemble of climate simulations to demonstrate an ongoing acceleration of North Atlantic surface warming, which implies a smaller contribution of the Atlantic Multidecadal Variability to 21st century North Atlantic sea-surface temperature anomalies than previously thought. Future projections of the Atlantic Multidecadal Variability from realistic climate simulations are poorly constrained, yet a relaxation to a neutral phase by the mid-21st century emerges as the most probable evolution of the Atlantic Multidecadal Variability. In the simulations, the mitigating effects of a less likely upcoming cold phase of the Atlantic Multidecadal Variability are overwhelmed by fast North Atlantic surface warming, which is robustly projected to persist in upcoming decades independent of emission scenarios. Sustained North Atlantic surface warming is therefore expected to continue in the near future.

North Atlantic Subtropical Mode Water properties: intrinsic and atmospherically forced interannual variability

Abstract. This study investigates the contributions of the ocean's chaotic intrinsic variability (CIV) and atmospherically forced variability to the interannual fluctuations in the North Atlantic Subtropical Mode Water (STMW) properties. Utilizing a 0.25° regional 50-member ocean–sea-ice ensemble simulation driven by an original surface forcing method and perturbed initially, the forced variability in STMW properties is estimated from ensemble mean fluctuations, while CIV is determined from deviations around the ensemble mean within each member. The model successfully captures the main features of STMW, showing correct agreement with observation-based ARMOR3D data in terms of location, seasonality, mean temperature and volume, as well as interannual variance of STMW's main properties. CIV significantly impacts STMW, explaining 10 %–13 % and 28 %–44 % of the interannual variance of its geometric and thermohaline mean properties, respectively, with a maximum imprint on STMW temperature. Observation-based and simulated intrinsic-to-total variance ratios are mostly consistent, dispelling concerns about a signal-to-noise paradox. This study also illustrates the advantages of ensemble simulations over single simulations in understanding oceanic fluctuations and attributing them to external drivers, while also cautioning against overreliance on individual simulation assessments.

Contrasting Domestic and Global Impacts of Emission Reductions in China on Tropospheric Ozone

AbstractSustained near‐surface ozone pollution despite stringent emission controls in China has drawn wide attention. However, how such rapid regional emission reductions affect global tropospheric ozone remains unexplored. Here, using an ensemble of measurements and global model simulations, we find Chinese emission reductions during 2013–2020 have increased domestic surface ozone; particularly in North China, they have decreased the global tropospheric ozone burden by 2.7 Tg (54% of China's anthropogenic contribution and 7% of global anthropogenic contribution in 2019) and ozone radiative forcing by 8.8 mW m−2 (64% of China's anthropogenic contribution and 9% of global anthropogenic contribution in 2019). Nitrogen oxides (NOx) associated nonlinear chemistry and height‐dependent atmospheric transport patterns primarily cause the contrasting impacts. Future Chinese NOx emission controls would continue to decrease global tropospheric ozone, while regional surface ozone may still increase unless NOx reductions exceed ∼53% of the 2019 level. Our results reveal the substantial benefit of regional emission reductions in China on global tropospheric ozone mitigation.

Quantum Simulations of Radiation Damage in a Molecular Polyethylene Analog.

An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. Ensembles of quantum simulations of radiation damage in a polyethylene analog are performed using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products are computed. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. The results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. Ultimately, the work provides a computational framework for determining the coupling between nonequilibrium chemistry in polymers and potential changes to macro-scale properties that can aid in the interpretation of future radiation damage experiments on plasticmaterials.

Constrained cosmological simulations of the Local Group using Bayesian hierarchical field-level inference

We present a novel approach based on Bayesian field-level inference that provides representative initial conditions for simulation of the Local Group (LG) of galaxies and its neighbourhood, constrained by present-day observations. We extended the Bayesian Origin Reconstruction from Galaxies ( algorithm with a multi-resolution approach, allowing us to reach the smaller scales needed to apply the constraints. Our data model simultaneously accounts for observations of mass tracers within the dark haloes of the Milky Way (MW) and M31, for their observed separation and relative velocity, and for the quiet surrounding Hubble flow, represented by the positions and velocities of 31 galaxies at distances between one and four megaparsec. Our approach delivers representative posterior samples of realisations that are statistically and simultaneously consistent with all of these observations, leading to significantly tighter mass constraints than found if the individual datasets are considered separately. In particular, we estimate the virial masses of the MW and M31 to be $ (M_ 200c /M_ and $12.33 respectively, their sum to be $ 200c /M_ and the enclosed mass within spheres of radius $R$ to be $ (M(R)/M_ and $12.96 for $R=1$ Mpc and $3$ Mpc, respectively. The M31-MW orbit is nearly radial for most of our realisations, and most of them feature a dark matter sheet aligning approximately with the supergalactic plane, despite the surrounding density field not being used explicitly as a constraint. High-resolution, high-fidelity resimulations from initial conditions identified using the approximate simulations of our inference scheme continue to satisfy the observational constraints, demonstrating a route to future high-resolution, full-physics simulations of ensembles of LG look-alikes, all of which closely mirror the observed properties of the real system and its immediate environment.

Nonlinear changes in urban heat island intensity, urban breeze intensity, and urban air pollutant concentration with roof albedo

This study systematically examines how the urban heat island (UHI) and urban breeze circulation (UBC) respond to an increase in roof albedo (αr) and its influence on urban air pollutant dispersion. For this, idealized ensemble simulations are performed using the Weather Research and Forecasting (WRF) model. The increase in αr from 0.20 to 0.65 decreases the UHI intensity, UBC intensity, and urban planetary boundary layer (PBL) height in the daytime (from 1200 to 1700 LST) by 47%, 36%, and 6%, respectively. As both UBC intensity and urban PBL height decrease, the daytime urban near-surface passive tracer concentration increases by 115%. The daytime UHI intensity, UBC intensity, and urban tracer concentration nonlinearly change with αr: For 0.10 ≤ αr < 0.80, the rates of changes in the UHI intensity, UBC intensity, and urban tracer concentration with αr overall increase as αr increases. For αr ≥ 0.80, the daytime roof surface temperature is notably lower than the daytime urban near-surface air temperature, the UHI intensity, UBC intensity, and urban tracer concentration very slightly changing with αr. This study provides insights into the associations between changes in roof surface temperature and roof surface energy fluxes with αr and those in UHI intensity.

A computational framework to support probabilistic criticality modelling for the geological disposal of radioactive waste

Nuclear Waste Services is tasked with disposal of the UK’s higher-activity radioactive waste in a Geological Disposal Facility. The disposal of fissile nuclides requires a demonstration that there is no significant concern from criticality, i.e. a fission chain reaction. While waste packages will initially be emplaced in a subcritical configuration, over the long timescales following closure there is potential for waste packages to degrade and for nuclides to be dispersed in the subsurface by groundwater, leading to the potential for a critical system forming. To facilitate modelling, a codebase has been developed which interfaces a probabilistic simulation tool (GoldSim) with a neutron transport code (MONK/MCNP). This allows large ensemble simulations to be run iteratively to determine limiting fissile masses which satisfy a criticality safety criterion. This paper documents the main algorithms and methodologies implemented within this framework, and provides background and example results illustrating the application to post-closure criticality modelling.

Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution.Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O(nlogn) instead of the typical O(n3) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.

A framework for visual comparison of scalar fields with uncertainty

AbstractScientists working with uncertain data, such as climate simulations, medical images, or ensembles of physical simulations, regularly confront the problem of comparing observations, e.g., to identify similarities, differences, or patterns. Current approaches in comparative visualization of uncertain scalar fields mainly rely on juxtaposition of both data and uncertainties, where each is represented using, e.g., color mapping or volume rendering. While interpretation of uncertain scalar data from visual encodings is already cognitively challenging, comparison of uncertain fields without explicit visualization support adds a further layer of complexity. In this paper, we present a theoretical framework to devise and describe a class of techniques that directly visualize differences between two or more uncertain scalar fields in a single image. We model each such technique as a combination of one or more interpolation stages, with the application of distance measures on random variables to the resulting distributions, and an appropriate visual encoding. Our framework captures existing methods and lends itself well to formulating new comparative visualization techniques for uncertain data for different visualization scenarios. Furthermore, by modeling uncertain scalar field differences as random variables themselves, we enable additional opportunities for comparison. We demonstrate the usefulness of our framework and its properties by applying it to effective comparative visualization techniques for several synthetic and real-world data sets.

Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors.

α7 nicotinic acetylcholine receptors (nAChRs) are homopentameric ligand-gated ion channels with critical roles in the nervous system. Recent studies have resolved and functionally annotated closed, open, and desensitized states of these receptors, providing insight into ion permeation and lipid binding. However, the process by which α7 nAChRs transition between states remains unclear. To understand gating and lipid modulation, we generated two ensembles of molecular dynamics simulations of apo α7 nAChRs, with or without cholesterol. Using symmetry-adapted Markov state modeling, we developed a five-state gating model. Free energies recapitulated functional behavior, with the closed state dominating in absence of agonist. Open-to-nonconducting transition rates corresponded to experimental open durations. Cholesterol relatively stabilized the desensitized state, and reduced open-desensitized barriers. These results establish plausible asymmetric transition pathways between states, define lipid modulation effects on the α7 nAChR conformational cycle, and provide an ensemble of structural models applicable to rational design of lipidic pharmaceuticals.

Scaling up statistical model checking of cyber-physical systems via algorithm ensemble and parallel simulations over HPC infrastructures

Model-based formal verification of industry-relevant Cyber-Physical Systems (CPSs) is often a computationally prohibitive task. In most cases, the complexity of the models precludes any prospect of symbolic analysis, leaving numerical simulation as the only viable option. Unfortunately, exhaustive simulation of a CPS model over the entire set of plausible operational scenarios is rarely possible in practice, and alternative strategies such as Statistical Model Checking (SMC) must be used instead.In this article, we show that the number of model simulations (samples) required by SMC techniques to converge can be significantly reduced by considering multiple (an ensemble of) Adaptive Stopping Algorithms (SAs) at once, and that the simulations themselves (by far the most expensive step of the entire workload) can be efficiently sped up by exploiting massively parallel platforms.With three industry-scale CPS models, we experimentally show that the use of an ensemble of two state-of-the-art SAs (AA and EBGStop) may require dozens of millions fewer samples when compared to running a single algorithm, with reductions in sample size of up to 78%. Furthermore, we show that our implementation, by massively parallelizing system model simulations on a HPC infrastructure, yields speed-ups for the completion time of the verification tasks which are practically linear with respect to the number of computational nodes, thus achieving an efficiency of virtually 100%, even on very large platforms. This makes it possible to complete tasks of model-based SMC verification for complex CPSs in a matter of hours or days, whereas a naïve sequential execution would require from months to many years.

Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRAS G12D.

Identification of cryptic pockets has the potential to open new therapeutic opportunities by discovering ligand binding sites that remain hidden in static apo structures of a target protein. Moreover, allosteric cryptic pockets can become valuable for designing target-selective ligands when the natural ligand binding sites are conserved in variants of a protein. For example, before an allosteric cryptic pocket was discovered, KRAS was considered undruggable due to its smooth surface and conservation of the GDP/GTP binding pocket across the wild type and oncogenic isoforms. Recent identification of the Switch-II cryptic pocket in the KRASG12C mutant and FDA approval of anticancer drugs targeting this site underscores the importance of cryptic pockets in solving pharmaceutical challenges. Here, we present a newly developed approach for the exploration of cryptic pockets using weighted ensemble molecular dynamics simulations with inherent normal modes as progress coordinates applied to the wild type KRAS and the G12D mutant. We performed extensive all-atomic simulations (>400 μs) with and without several cosolvents (xenon, ethanol, benzene), and analyzed trajectories using three distinct methods to search for potential binding pockets. These methods have been applied as a proof-of-concept to KRAS and have shown they can predict known cryptic binding sites. Furthermore, we performed ligand-binding simulations of a known inhibitor (MRTX1133) to shed light on the nature of cryptic pockets in KRASG12D and the role of conformational selection vs induced-fit mechanism in the formation of these cryptic pockets.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Bayesian estimates for changes of the Russian river runoff in the 21st century as based on the CMIP6 model ensemble simulations

Based on ensemble calculations with the CMIP6 (Coupled Model Intercomparison Project, phase 6) climate models and using Bayesian averaging, an analysis was conducted on the changes in the 21st century runoff of several Russian rivers – the Volga, Ob, Yenisei, Lena, Amur, and Selenga. Bayesian weights considered the quality of models’ reproduction of runoff (long-term average runoff, linear runoff trend over the time interval with available runoff observations, interannual and interdecadal variability). The quality of runoff characteristics reproduction by individual models in the CMIP6 ensemble varies most significantly for the long-term average runoff, runoff trend, and, to a lesser extent, for interannual variability. In the 21st century, the ensemble average runoff increases for most of the analyzed rivers, except for the Volga. This increase is more pronounced under scenarios with larger anthropogenic impacts. It is especially significant for the SSP5-8.5 scenario (Shared Socioeconomic Pathways, 5-8.5), under which the runoff increase trend from 2015 to 2100 relative to its current long-term average is (10 ± 4)% for the Ob, (16 ± 3)% for the Yenisei, (39 ± 7)% for the Lena, (36 ± 7)% for the Amur, and (18 ± 6)% for the Selenga. The primary reason for the change in ensemble average runoff in the 21st century in models under all SSP scenarios is the change in precipitation. Accounting for differences in model quality in reproducing river runoff on average for 2015–2100 reduces inter-model deviations relative to the corresponding values with uniform weighting of model results by 6–26%, depending on the SSP scenario and river basin.