Ensemble Simulations Research Articles

The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model

Abstract. Understanding the future evolution of permafrost requires a better understanding of its climatological past. This requires permafrost models to efficiently simulate the thermal dynamics of permafrost over the past centuries to millennia, taking into account highly uncertain soil and snow properties. In this study, we present a computationally efficient numerical permafrost model which satisfactorily reproduces the current ground temperatures and active layer thicknesses of permafrost in the Arctic and their trends over recent centuries. The performed simulations provide insights into the evolution of permafrost since the 18th century and show that permafrost on the North American continent is subject to early degradation, while permafrost on the Eurasian continent is relatively stable over the investigated 300-year period. Permafrost warming since industrialization has occurred primarily in three “hotspot” regions in northeastern Canada, northern Alaska, and, to a lesser extent, western Siberia. We find that the extent of areas with a high probability (p3 m>0.9) of near-surface permafrost (i.e., 3 m of permafrost within the upper 10 m of the subsurface) has declined substantially since the early 19th century, with loss accelerating during the last 50 years. Our simulations further indicate that short-term climate cooling due to large volcanic eruptions in the Northern Hemisphere in some cases favors permafrost aggradation within the uppermost 10 m of the ground, but the effect only lasts for a relatively short period of a few decades. Despite some limitations, e.g., with respect to the representation of vegetation, the presented model shows great potential for further investigation of the climatological past of permafrost, especially in conjunction with paleoclimate modeling.

Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multi-decadal trend in the Warm Arctic Cold Eurasia pattern

The observed winter Barents-Kara Sea (BKS) sea ice concentration (SIC) has shown a close association with the second empirical orthogonal function (EOF) mode of Eurasian winter surface air temperature (SAT) variability, known as Warm Arctic Cold Eurasia (WACE) pattern. However, the potential role of BKS SIC on this WACE pattern of variability and on its long-term trend remains elusive. Here, we show that from 1979 to 2022, the winter BKS SIC and WACE association is most prominent and statistically significant for the variability at the sub-decadal time scale for 5–6 years. We also show the critical role of the multi-decadal trend in the principal component of the WACE mode of variability for explaining the overall Eurasian winter temperature trend over the same period. Furthermore, a large multi-model ensemble of atmosphere-only experiments from 1979 to 2014, with and without the observed Arctic SIC forcing, suggests that the BKS SIC variations induce this observed sub-decadal variability and the multi-decadal trend in the WACE. Additionally, we analyse the model simulated first or the leading EOF mode of Eurasian winter SAT variability, which in observations, closely relates to the Arctic Oscillation (AO). We find a weaker association of this mode to AO and a statistically significant positive trend in our ensemble simulation, opposite to that found in observation. This contrasting nature reflects excessive hemispheric warming in the models, partly contributed by the modelled Arctic Sea ice loss.

Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy.

Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model's integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field.

Deep Learning Ensembles for Improved Atmospheric Composition Modeling

Better forecasting of atmospheric composition is a critical aspect of environmental and climate monitoring. Among climate and weather numeric modeling, often ensembles are used to improve the forecasting power and to quantify the uncertainty of the model. However, the numerical simulation of atmospheric chemistry, critical for composition simulations, is computationally too expensive to generate numerical composition ensembles. One way to address this problem is to use deep learning to emulate the slow physical model. In this work we study the feasibility of two different deep learning methods and show how an emulator could be used to realistically estimate uncertainties of atmospheric composition forecasts, bypassing the need to run costly numerical ensemble simulations. One of the methods builds upon Fourier neural operators and the NVIDIA FourCastNet architecture and the second method builds on conditional Generative Adversarial Networks. We design the models to respond to perturbations to the most important drivers of air pollution, including meteorology and pollutant emissions. We apply this framework to the NASA GEOS Composition Forecast System (GEOS-CF), which produces daily global composition forecasts at approximately 25 km^2 horizontal resolution. Due to computational constraints, GEOS-CF currently has limited capability to produce probabilistic estimates or to optimally assimilate trace gas observations. We show how a deep learning emulator has the potential to improve composition forecasts produced by GEOS-CF or other, similar types of applications. These methods could be applied to other types of ensemble-based models, potentially providing a large speed-up in overall modeling time.

An ensemble docking-based virtual screening and molecular dynamics simulation of phytochemical compounds from Malaysian Kelulut Honey (KH) against SARS-CoV-2 target enzyme, human angiotensin-converting enzyme 2 (ACE-2)

The human angiotensin-converting enzyme 2 (ACE-2) receptor is a metalloenzyme that plays an important role in regulating blood pressure by modulating angiotensin II. This receptor facilitates SARS-CoV-2 entry into human cells via receptor-mediated endocytosis, causing the global COVID-19 pandemic and a major health crisis. Kelulut honey (KH), one of Malaysian honey recently gained attention for its distinct flavour and taste while having many nutritional and medicinal properties. Recent study demonstrates the antiviral potential of KH against SARS-CoV-2 by inhibiting ACE-2 in vitro, but the bioactive compound pertaining to the ACE-2 inhibition is yet unknown. An ensemble docking-based virtual screening was employed to screen the phytochemical compounds from KH with high binding affinity against the 10 best representative structures of ACE-2 that mostly formed from MD simulation. From 110 phytochemicals previously identified in KH, 27 compounds passed the ADMET analysis and proceeded to docking. Among the docked compound, SDC and FMN consistently exhibited strong binding to ACE-2's active site (-9.719 and −9.473 kcal/mol) and allosteric site (-7.305 and −7.464 kcal/mol) as compared to potent ACE-2 inhibitor, MLN 4760. Detailed trajectory analysis of MD simulation showed stable binding interaction towards active and allosteric sites of ACE-2. KH's compounds show promise in inhibiting SARS-CoV-2 binding to ACE-2 receptors, indicating potential for preventive use or as a supplement to other COVID-19 treatments. Additional research is needed to confirm KH's antiviral effects and its role in SARS-CoV-2 therapy, including prophylaxis and adjuvant treatment with vaccination. Communicated by Ramaswamy H. Sarma

How Does the Pinatubo Eruption Influence Our Understanding of Long‐Term Changes in Ocean Biogeochemistry?

AbstractPinatubo erupted during the first decadal survey of ocean biogeochemistry, embedding its climate fingerprint into foundational ocean biogeochemical observations and complicating the interpretation of long‐term biogeochemical change. Here, we quantify the influence of the Pinatubo climate perturbation on externally forced decadal and multi‐decadal changes in key ocean biogeochemical quantities using a large ensemble simulation of the Community Earth System Model designed to isolate the effects of Pinatubo, which cleanly captures the ocean biogeochemical response to the eruption. We find increased uptake of apparent oxygen utilization and preindustrial carbon over 1993–2003. Nearly 100% of the forced response in these quantities are attributable to Pinatubo. The eruption caused enhanced ventilation of the North Atlantic, as evidenced by deep ocean chlorofluorocarbon changes that appear 10–15 years after the eruption. Our results help contextualize observed change and contribute to improved constraints on uncertainty in the global carbon budget and ocean deoxygenation.

Assessment of Suitability Area for Maize Production in Poland Related to the Climate Change and Water Stress

In this study, we identify the spatial distribution of water deficits in Poland. The analyses considered expert knowledge in soil categories importance in water stress evaluation influencing the climate suitability for maize production using the analytical hierarchy process (AHP). The Climatic Water Balance was calculated from April to September, for the baseline (BL) period (1981–2010) and two future periods of 2041–2070 (2050s) and 2071–2100 (2080s) using a six-member ensemble of GCM-RCM chain simulations under two representative concentration pathways (RCP) scenarios: low emissions (LE, RCP4.5) and high emissions (HE, RCP8.5). Taking into consideration water deficiency for the BL period, about 81% of Poland proved highly suitable, 18% moderately suitable and 1% marginally suitable for maize cultivation. According to LE and HE scenarios, the area of Poland that is highly suitable for maize production would decrease to 67 and 69% by the 2050s, and to 64 and 44% by the 2080s. By the 2080s, under the HE scenario, rain-fed maize production would become risky, as 21% of Poland would be marginally suitable, while 11% would not be suitable. According to our findings, supplemental irrigation is one of the effective adaptation strategies to maintain the production potential of maize in Poland.

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Very large (~2000 members) initial-condition ensemble simulations have been performed to advance understanding of mean climate and extreme weather responses to projected Arctic sea-ice loss under 2 °C global warming above preindustrial levels. These simulations better sample internal atmospheric variability and extremes for each model compared to those from the Polar Amplification Model Intercomparison Project (PAMIP). The mean climate response is mostly consistent with that from the PAMIP multi-model ensemble, including tropospheric warming, reduced midlatitude westerlies and storm track activity, an equatorward shift of the eddy-driven jet and increased mid-to-high latitude blocking. Two resolutions of the same model exhibit significant differences in the stratospheric circulation response; however, these differences only weakly modulate the tropospheric response. The response of temperature and precipitation extremes largely follows the seasonal-mean response. Sub-sampling confirms that large ensembles (e.g. ≥400) are needed to robustly estimate the seasonal-mean large-scale circulation response, and very large ensembles (e.g. ≥1000) for regional climate and extremes.

Improved estimation of extreme floods with data pooling and mixed probability distribution

The accurate estimation of flood probability is crucial for designing water storage and flood retention structures. However, the assumption of identical distribution in flood samples is unrealistic, given the influence of various flood mechanisms. To address this challenge, we proposed a novel framework based on flood clustering and data pooling that encompasses the key steps such as 1) flood event separation based on a peak-detection flood separation algorithm, 2) grouping flood events using the k-prototypes algorithm, 3) application of the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach to pool reforecast ensemble datasets, and 4) statistical mixing approach to derive common quantiles from all the flood groups. We applied the framework to the Dresden gauge in the Elbe River for a detailed case study. Various tests have been performed to assess the applicability of the UNSEEN approach and the reforecast dataset consistently shows the potential for data pooling. The proposed methodology outperformed the classical approach in terms of goodness-of-fit. The relative difference between the classical and the proposed approach ((classical-proposed)/proposed) for the 100-year return level is 0.16, with a reduction in root mean square error (RMSE) value from 163 to 98 m3/s. Further, replication of the approach to the gauges in North Germany exhibited a relative difference ranging from −0.3 to +0.15 and produced better estimates in terms of RMSE compared with the traditional model. In summary, the proposed framework offers a better estimation of flood probability by addressing the inherent sample inhomogeneity along with the inclusion of unprecedented flood samples.

Projection of future heat-related morbidity in three metropolitan prefectures of Japan based on large ensemble simulations of climate change under 2 °C global warming scenarios

Recently, global warming has become a prominent topic, including its impacts on human health. The number of heat illness cases requiring ambulance transport has been strongly linked to increasing temperature and the frequency of heat waves. Thus, a potential increase in the number of cases in the future is a concern for medical resource management. In this study, we estimated the number of heat illness cases in three prefectures of Japan under 2 °C global warming scenarios, approximately corresponding to the 2040s. Based on the population composition, a regression model was used to estimate the number of heat illness cases with an input parameter of time-dependent meteorological ambient temperature or computed thermophysiological response of test subjects in large-scale computation. We generated 504 weather patterns using 2 °C global warming scenarios. The large-scale computational results show that daily amount of sweating increased twice and the core temperature increased by maximum 0.168 °C, suggesting significant heat strain. According to the regression model, the estimated number of heat illness cases in the 2040s of the three prefectures was 1.90 (95%CI: 1.35–2.38) times higher than that in the 2010s. These computational results suggest the need to manage ambulance services and medical resource allocation, including intervention for public awareness of heat illnesses. This issue will be important in other aging societies in near future.

An ensemble of bias-adjusted CMIP6 climate simulations based on a high-resolution North American reanalysis

ESPO-G6-R2 v1.0 is a set of statistically downscaled and bias-adjusted climate simulations based on the Coupled Model Intercomparison Project 6 (CMIP6) models. The dataset is composed of daily timeseries of three variables: daily maximum temperature, daily minimum temperature and daily precipitation. Data are available from 1950 to 2100 over North America. The simulation ensemble is comprised of 14 models driven by two emissions scenarios (SSP2-4.5 and SSP3-7.0). In this paper, we describe the workflow used for the bias-adjustment, which relies on the detrended quantile mapping method and the Regional Deterministic Reforecast System (RDRS) v2.1 reference dataset. Using the framework defined in the VALUE project, we show the improvements made by the bias-adjustment on marginal, temporal and multivariate aspects of the data. We also verify that the bias-adjusted climate data have similar climate change signal to the original climate model simulations. Finally, we provide guidance to users on how to use this dataset.

Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects

The model internal year-to-year variability (hereafter, internal variability) is a significant source of uncertainty when estimating anthropogenic aerosol effective radiative forcing (ERF). In this study, we investigate the impact of internal variability using large ensemble simulations (600 years in total) with the same climate model under prescribed anthropogenic aerosol forcings. A comparison of the magnitudes (i.e., standard deviation, Std) of these influences confirms that internal variability has negligible impacts on the instantaneous radiative forcing (RF) diagnosed by double radiation calls but has considerable impacts on estimating ERF through rapid adjustments (ADJ). Approximately half of the model grids exhibit a strong internal variability influence on ERF (Std > 5 W m−2). These strong internal variabilities lead to a 50% probability that the 30-year linear change can reach 2 W m−2 and the 10-year linear change can reach 4 W m−2. A 50-year simulation can provide a relatively stable annual mean map of ERF (ERF = ADJ + RF), but it fails for ADJ. The statistically significant areas in the annual mean maps of both ERF and ADJ from a 10-year simulation exhibit instability with evident chaotic features. The insights derived from our analysis aid in assessing the stability of modeled ERF and contribute to the design of comparative experiments.

ModE-RA: a global monthly paleo-reanalysis of the modern era 1421 to 2008

The Modern Era Reanalysis (ModE-RA) is a global monthly paleo-reanalysis covering the period between 1421 and 2008. To reconstruct past climate fields an offline data assimilation approach is used, blending together information from an ensemble of transient atmospheric model simulations and observations. In the early period, ModE-RA utilizes natural proxies and documentary data, while from the 17th century onward instrumental measurements are also assimilated. The impact of each observation on the reconstruction is stored in the observation feedback archive, which provides additional information on the input data such as preprocessing steps and the regression-based forward models. The monthly resolved reconstructions include estimates of the most important climate fields. Furthermore, we provide a reconstruction, ModE-RAclim, which together with ModE-RA and the model simulations allows to disentangle the role of observations and model forcings. ModE-RA is best suited to study intra-annual to multi-decadal climate variability and to analyze the causes and mechanisms of past extreme climate events.

Constant inner potential DFT for modelling electrochemical systems under constant potential and bias

Electrochemical systems play a decisive role in, e.g. clean energy conversion but understanding their complex chemistry remains an outstanding challenge. Constant potential and grand canonical ensemble (GCE) simulations are indispensable for unraveling the properties of electrochemical processes as a function of the electrode potential. Currently, GCE calculations performed at the density functional theory (DFT) level require fixing the Fermi level within the simulation cell. Here, we illustrate that this method is inadequate when modeling outer sphere reactions and a biased two-electrode cell. For these systems, the Fermi level obtained from DFT calculations does not accurately present the experimentally controlled electrode potential or describe the thermodynamic independent variable in GCE-DFT. To address this limitation, we developed and implemented a constant inner potential (CIP) method offering a more robust and general approach to conducting GCE-DFT simulations of electrochemical systems under constant potential or bias conditions. The primary advantage of CIP is that it uses the local electrode inner potential as the thermodynamic parameter for the electrode potential, as opposed to the global Fermi level. Through numerical and analytical studies, we demonstrate that the CIP and Fermi level GCE-DFT approaches are equivalent for metallic electrodes and inner-sphere reactions. However, CIP proves to be more versatile, as it can be applied to outer-sphere and two-electrode systems, addressing the limitations of the constant Fermi-level approach in these scenarios. Altogether, the CIP approach stands out as a general and efficient GCE-DFT method simulating electrochemical interfaces from first principles.

Mesoscale structures in the Orinoco basin during an extreme precipitation event in the tropical Andes

During the night of March 31st, 2017, a severe precipitation event affected the city of Mocoa, in the tropical Andes. Total 24-h accumulated precipitation during that day was the fourth largest on record. Satellite data shows that the event was associated with a Mesoscale Convective System (MCS) that formed over the Amazon and moved westward, reaching the tropical Andes. Reanalysis data suggests that a rapid intensification of the Orinoco Low-Level Jet (OLLJ) traveling southwestward parallel to the Andes was a precursor that favored the zones of convergence for MCS formation. Upstream intensification of the OLLJ was evident 8 h prior to the Mocoa precipitation event. Given the lack of a dense network of observations in this understudied region, we use the Weather Research and Forecasting model (WRF) to explore the plausible mesoscale structures in the OLLJ region associated with the initiation and development of the MCS. We study an ensemble of simulations with different grid spacings (12, 4 and 1.3 km) and Planetary Boundary Layer (PBL) schemes (YSU, MYNN and QNSE). The more realistic MCSs were obtained with the QNSE and YSU schemes, given that the corresponding simulations included a density current in the lowest levels moving parallel to the Andes, with a sharp line of convergence and large vertical velocities over the leading edge of the mesoscale disturbance. In contrast, the MYNN scheme produced a weaker OLLJ and no density current. It is suggested that the stronger vertical mixing in the MYNN scheme was associated with the vertical dilution of the OLLJ, and with a much weaker low-level traveling perturbation via the upward radiation of energy by gravity waves. Our results help to better understand flood-producing extreme events over the poorly studied Andes-Amazon region and provide the groundwork for improved predictability of such storms.

Increased Asian aerosols drive a slowdown of Atlantic Meridional Overturning Circulation

Observational evidence and climate model experiments suggest a slowdown of the Atlantic Meridional Overturning Circulation (AMOC) since the mid-1990s. Increased greenhouse gases and the declined anthropogenic aerosols (AAs) over North America and Europe are believed to contribute to the AMOC slowdown. Asian AAs continue to increase but the associated impact has been unclear. Using ensembles of climate simulations, here we show that the radiative cooling resulting from increased Asian AAs drives an AMOC reduction. The increased AAs over Asia generate circumglobal stationary Rossby waves in the northern midlatitudes, which shift the westerly jet stream southward and weaken the subpolar North Atlantic westerlies. Consequently, reduced transport of cold air from North America hinders water mass transformation in the Labrador Sea and thus contributes to the AMOC slowdown. The link between increased Asian AAs and an AMOC slowdown is supported by different models with different configurations. Thus, reducing emissions of Asian AAs will not only lower local air pollution, but also help stabilize the AMOC.

PROWIS: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime.

Weather forecasting is essential for decision-making and is usually performed using numerical modeling. Numerical weather models, in turn, are complex tools that require specialized training and laborious setup and are challenging even for weather experts. Moreover, weather simulations are data-intensive computations and may take hours to days to complete. When the simulation is finished, the experts face challenges analyzing its outputs, a large mass of spatiotemporal and multivariate data. From the simulation setup to the analysis of results, working with weather simulations involves several manual and error-prone steps. The complexity of the problem increases exponentially when the experts must deal with ensembles of simulations, a frequent task in their daily duties. To tackle these challenges, we propose ProWis: an interactive and provenance-oriented system to help weather experts build, manage, and analyze simulation ensembles at runtime. Our system follows a human-in-the-loop approach to enable the exploration of multiple atmospheric variables and weather scenarios. ProWis was built in close collaboration with weather experts, and we demonstrate its effectiveness by presenting two case studies of rainfall events in Brazil.

A Hybrid Gain Analog Offline EnKF for Paleoclimate Data Assimilation

AbstractFor Paleoclimate data assimilation (PDA), a hybrid gain analog offline ensemble Kalman filter (HGAOEnKF) is proposed. It keeps the benefits of the analog offline ensemble Kalman filter (AOEnKF) that constructs analog ensembles from existing climate simulations with joint information of the proxies. The analog ensembles can provide more accurate prior ensemble mean and “flow‐dependent” error covariances than randomly sampled ensembles. HGAOEnKF further incorporates the benefits of static prior error covariances that better capture large‐scale error correlations and mitigate sampling errors than the sample prior error covariances, through a hybrid gain approach within an ensemble framework. Observing system simulation experiments are conducted for various data assimilation methods, using ensemble simulations from the Community Earth System Model‐Last Millennium Ensemble Project. Results show that using the static prior error covariances estimated from a sufficiently large sample set is beneficial for the traditional offline ensemble Kalman filter (OEnKF) and AOEnKF. HGAOEnKF method is superior to the OEnKF and AOEnKF with and without static prior error covariances, especially for the reconstruction of extreme events. The advantages of HGAOEnKF over OEnKF and AOEnKF with and without static prior error covariances are persistent with varying sample sizes and presence of model errors.

Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery

AbstractTropical cyclones are an important cause of forest disturbance, and major storms caused severe structural damage and elevated tree mortality in coastal tropical forests. Model capabilities that can be used to understand post‐hurricane forest recovery are still limited. We use a vegetation demography model, the Functionally Assembled Terrestrial Ecosystem Simulator, coupled with the Energy Exascale Earth System Model Land Model (ELM‐FATES) to study the processes and the key factors regulating post‐hurricane forest recovery. We implemented hurricane‐induced forest damage, including defoliation, structural biomass reduction, and tree mortality, performed ensemble model simulations, and used random forest feature importance. For the simulation in the Luquillo Experimental Forest, Puerto Rico, we identified factors controlling the post‐hurricane forest recovery, and quantified the sensitivity of key model parameters to the post‐hurricane forest recovery. The results indicate a tendency for the Bisley forests to shift toward the light demanding plant functional type (PFT) when the pre‐hurricane biomass between the light demanding and shade tolerant PFTs is nearly equal and forests experience hurricane disturbance with mortality >60% for both the two PFTs. Under more realistic conditions where the shade tolerant PFT is initially dominant, mortality >80% is required for a shift toward dominance of the light demanding PFT at Bisley. Hurricane mortality and background mortality are the two major factors regulating post‐hurricane forest recovery in simulations. This research improves understanding of the ELM‐FATES model behavior associated with hurricane disturbance and provides guidance for dynamic vegetation model development in representing hurricane induced forest damage with varied intensities.

Evaluating therapeutic potential of AYUSH-64 constituents against omicron variant of SARS-CoV-2 using ensemble docking and simulations

The COVID-19 pandemic in the later phase showed the presence of the B.1.1.529 variant of the SARS-CoV-2 designated as Omicron. AYUSH-64 a poly herbal drug developed by Central Council for Research in Ayurvedic Sciences (CCRAS) has been recommended by Ministry of Ayush in asymptomatic, mild to moderate COVID-19 patients. One of the earlier, in-silico study has shown the binding of the constituents of AYUSH-64 to the main protease (Mpro) of the SARS-CoV-2. This study enlisted four phytochemicals of AYUSH-64, which were found to have significant binding with the Mpro. In continuation to the same, the current study proposes to understand the binding of these four phytochemicals to main protease (Mpro) and receptor binding domain (RBD) of spike protein of the Omicron variant. An enhanced molecular docking methodology, namely, ensemble docking has been used to find the most efficiently binding phytochemical. Using molecular dynamics (MD) simulations and clustering approach it was observed that the Mpro and RBD Spike of Omicron variant of SARS-CoV-2 in complex with human ACE2 tends to attain 4 and 8 conformational respectively. Based on the docking studies, the best binding phytochemical of the AYUSH-64, akummicine N-oxide was selected for MD simulations. MD simulations of akummicine N-oxide bound to omicron variant of Mpro and RBD Spike-ACE complex was performed. The conformational, interaction and binding energy analysis suggested that the akummicine N-oxide binds well with Mpro and RBD Spike-ACE2 complex. The interaction between RBD Spike and ACE2 was observed to weaken in the presence of akummicine N-oxide. Hence, it can be inferred that, these phytochemicals from AYUSH-64 formulation may have the potential to act against the Omicron variant of SARS-CoV-2.