Large Ensemble Simulations Research Articles

Future changes in North Atlantic winter cyclones in CESM-LE – Part 2: A Lagrangian analysis

Abstract. Future changes in extratropical cyclone structure and dynamics may lead to important impacts but are not yet fully understood. In the first part of this study, we have applied a composite approach together with potential vorticity (PV) inversion to study such changes in the dynamics of North Atlantic cyclones. Here, this is complemented with the help of a Lagrangian perspective, making use of air parcel trajectories to investigate the causes of altered PV anomalies as well as the role that cyclone airstreams play in shaping these changes. Intense cyclones in the extended winter seasons of two periods, 1990–2000 and 2091–2100, are studied in Community Earth System Model Large Ensemble (CESM-LE) simulations, and backward trajectories are calculated from the cyclone area as a basis to construct cyclone-centered composites of Lagrangian tendencies and their projected future changes. Our results show that diabatic processes on a timescale of 24 h shape the cyclones' low-level PV distribution and corroborate that the increasing moisture content along with enhanced ascent in warm conveyor belts leads to amplified latent heat release and larger low- and mid-level PV anomalies near the cyclone center in a warmer climate. In contrast, projected upper-level PV changes are due to a combination of several processes. These processes include cloud diabatic PV changes, anomalous PV advection, and likely also radiative PV generation in the lower stratosphere above the cyclone center. For instance, enhanced poleward advection is the primary reason for a projected decrease in upper-level PV anomalies south of the cyclone center. Warm conveyor belt outflow regions are projected to shift upward, but there is not robust change in the associated upper-level PV anomalies due to compensation between enhanced low-level PV generation and upper-level PV destruction. In summary, our two-part study points to future changes in the relative importance of different processes for the dynamics of intense North Atlantic cyclones in a warming climate, with important consequences for the near-surface wind pattern. In particular, a larger role of cloud diabatic processes is projected, affecting the cyclones through PV production in the lower troposphere. The role of other mechanisms, in particular radiative changes near the tropopause, should be investigated in more detail in future studies.

A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)

Abstract. Many scientific and societal questions that draw on ice sheet modeling necessitate sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. Here, we describe a method to construct a stochastic generator for ice sheet surface mass balance varying in time and space. We demonstrate the method with an application to Greenland Ice Sheet surface mass balance for 1980–2012. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedback from changing ice sheet surface elevation. The efficiency gained in the stochastic method supports large-ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications.

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.

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.

Responses of the Madden–Julian Oscillation to Global Warming: Impacts from Tropical Sea Surface Temperature Changes

Abstract The impacts of tropical sea surface temperature (SST) changes on the Madden–Julian oscillation (MJO) are investigated using the large-ensemble simulation from the Community Earth System Model version 2 (CESM2-LE) under the shared socioeconomic pathway (SSP370 scenario). Three SST change patterns are featured, distinguished by the zonal gradient of the change in the equatorial Pacific warming. MJO characteristics and its teleconnections responses are composited for the clusters, and their relationships to the zonal SST gradient changes are examined. Results show that the anomalously strong El Niño–like SST change pattern significantly intensifies the MJO amplitude and enhances its eastward extension compared to the anomalously weak El Niño–like SST change pattern. These changes in MJO amplitude are further interpreted through the α framework. We also found no statistically different extratropical geopotential height responses to MJO between the three SST warming patterns, possibly due to strong internal climate variability. Changes in Rossby wave source between clusters also show a weak relationship with the MJO teleconnections. Our results highlight the importance of Indo-Pacific zonal SST gradient changes on the changes of MJO but limited impacts on MJO teleconnections to the midlatitudes.

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.

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.

Atmospheric modes fiddling the simulated ENSO impact on tropical cyclone genesis over the Northwest Pacific

The El Niño-Southern Oscillation (ENSO) is crucial to the interannual variability of tropical cyclone (TC) genesis over the western North Pacific (WNP). However, most state-of-the-art climate models exhibit a consistent pattern of uncertainty in the simulated TC genesis frequency (TCGF) over the WNP in ENSO phases. Here, we analyze large ensemble simulations of TC-resolved climate models to identify the source of this uncertainty. Results show that large uncertainty appears in the South China Sea and east of the Philippines, primarily arising from two distinct atmospheric modes: the Matsuno-Gill-mode (MG-mode) and the Pacific-Japan-like pattern (PJ-mode). These two modes are closely associated with anomalous diabatic heating linked to tropical precipitation bias in model simulations. By conditionally constraining either of the modes, we can significantly reduce model uncertainty in simulating the dipole structure of the TCGF anomalies, confirming that it is the atmospheric circulation bias in response to tropical precipitation bias that causes uncertainty in the simulated WNP TCGF.

The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations

Abstract. Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2–temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Revisiting the Relationship between the North Pacific High and Upwelling Winds along the West Coast of North America in the Present and Future Climate

Abstract The wind-driven circulation is an important driver of upwelling in the California Current System, a key factor in maintaining a productive ecosystem. In summer, the North Pacific high (NPH) dominates the atmospheric circulation, including the nearshore winds. The impact of the NPH on the surface winds along the North American west coast during summer is examined using the ECMWF Reanalysis v5 (ERA5) and the Community Earth System Model version 1 (CESM1) large ensemble of simulations. The strength, latitude, and longitude of the sea level pressure (SLP) and subsidence at 500 hPa are used to assess the NPH and its variability. While both the surface high pressure cell and subsidence are related to the interannual variability of the surface winds over the North Pacific, the strength of subsidence has a much larger effect on the coastal winds than the variability in SLP. Based on the mean of the 40 CESM simulations, future changes in upwelling also more strongly coincide with changes in subsidence than in SLP. Subsidence and southward upwelling-favorable winds increase off the Canadian coast, with the reverse occurring off the U.S. West Coast, by the end of the twenty-first century. In particular, the intermember correlation between the changes in the nearshore surface winds and the 500-hPa pressure vertical velocity reaches 0.75 and 0.87 in the southern and northern portions of the northeast Pacific, respectively. The effect of the subsidence on upwelling winds in the future is confirmed by the CESM2 large ensemble.

Using Large Ensembles to Examine Historical and Projected Changes in Record‐Breaking Summertime Temperatures Over the Contiguous United States

AbstractThe frequency and intensity of heat extremes over the United States have increased since the mid‐20th century and are projected to increase with additional anthropogenic greenhouse gas forcing. We define heat extremes as summertime (June–August) daily maximum 2m temperatures that exceed historical records. We examine characteristics of historical and near‐future heat extremes using observations and past and future projections using 100 ensemble members from three coupled global climate models large ensemble simulations. We find that the large ensembles capture the trend and variability of heat extremes over the period 2006–2020 relative to the 1991–2005 climatology but overestimate the frequency at which the heat extremes occur. In future warming scenarios, heat extremes continue to increase over the next 30 years, with high amplitude records in the Northwest and Central US. After 2050, we find there is a spread in the frequency of heat extremes that is dependent on the emissions scenario, with a high emissions until mid‐century followed by a high mitigation scenario showing a decrease in heat extremes by the end of the century. Although the frequency of future heat extremes is likely overestimated in the large ensembles, they are still a powerful tool for researching extreme temperatures in the climate system.

Assessing extreme flood inundation and demographic exposure in climate change using large ensemble climate simulation data in the Lower Chao Phraya River Basin of Thailand

Study regionLower Chao Phraya River Basin (LCPRB), Thailand. Study focusThis study aims to make a robust assessment based on a large ensemble d4PDF dataset and a flood-inundation model Inundation Model Coupling Rainfall-runoff (IMCR) for the LCPRB. Inundation area and depth for 100-year flooding are evaluated for the flood volume of capacity greater than 2000 m3/s for both past (1951–2010) and future (2051–2100) climates. This study also evaluates the affected population exposure in the region for both past and future climate scenarios. New hydrological insights for the regionThe IMCR inundation simulation findings indicate that compared to the historical climate, the inundation area increases by an average of 1.0–1.4 times, and the critical area (depth >3 m) increases by an average of 1.1–1.3 times. On the other hand, the exposed population in the future, with respect to the SSP5 scenario "Taking the Highway," is expected to decrease on average by 0.7–0.9 times in comparison to the past climate for depth > 0 m. However, keeping the population constant as in the past, the exposed population is likely to increase on average by 1.3–1.4 times in comparison to the past climate for depth > 0 m.

Future projections of wet snow accretion and snowfall in Kanto Plain, Japan, using a large ensemble climate simulation

AbstractSnowfall and wet snow accretion in the Kanto Plain, including the Tokyo Metropolitan Area, significantly impact the lives of people there. This study used a large ensemble simulation to predict future changes in the frequency of snowfall and wet snow accretion, and the accumulated snowfall and wet snow accretion during an event. We assessed wet snow accretion using a model that considers the effects of temperature, precipitation, wind speed, and humidity. The future snowfall event frequency and accumulated snowfall were predicted to decrease by 0.73 year−1 (61%) and 0.69 mm (11%), respectively, under +2‐K future climate projections from those under the present climate. 87% and 13% reduction in future snowfall events was due to increasing temperature and reduced frequency of extratropical cyclone passages, respectively. Moreover, the future frequency of wet snow accretion events and accumulated wet snow accretion are predicted to decrease by 0.84 year−1 (90%) and 0.73 kg m−1 (29%), respectively, from those in the present. 91% and 9% reduction in future wet snow accretion events was due to increasing temperature and reduced frequency of extratropical cyclone passages, respectively. Snowfall and wet snow accretion risk are predicted to decline under the +2‐K future climate projections from those under the current climate. The risk can decrease more significantly in the coastal areas than in the inland areas. We hope that the information provided in this study will help policymakers of local governments in the Kanto Plain to implement appropriate measures against future snowfall and wet snow accretion.

Future Extreme Precipitation in Summer Will Become More Widespread in China Depending on Level of Warming

AbstractIn this study, CMIP6 and single model initial‐condition large ensemble (SMILE) simulations were applied to reveal possible changes in the spatial coverage (RSC) of summer extreme precipitation (≥99th percentile) in China. Four different approaches were applied to provide a comprehensive assessment of RSC changes. Results indicate that the trend of RSC in summer for the period 1961–2020 in China is underestimated by raw simulations of CMIP6 multi‐model ensembles. Further analyses suggest that the confidence in model simulations reflecting the observed change can be improved based on the two observation constraint approaches and SMILE‐based approach. In addition, the reliability of the spatial distribution can also be improved. Projection results indicate that the RSC of summer extreme precipitation increases consistently with the increment of global warming across the different approaches. Among them, the results based on observational constraints and the SMILE‐based approach show enhanced reliability and present larger RSC changes than by directly using CMIP6 ensembles. In conclusion, extreme precipitation in summer is expected to become more widespread in China as the level of global warming increases.

Analyzing uncertainty in probable maximum precipitation estimation with large ensemble climate simulation data

AbstractThis study aimed to evaluate probable maximum precipitation (PMP) estimated using surface dew points (SDP) or actual precipitable water obtained from upper‐air data (UAD) in the moisture‐maximization method with the help of sufficient extreme precipitation events using large‐scale climate ensemble simulation data (d4PDF). The deviations between the PMP variables estimated by the SDP and UAD approaches were analyzed for southern and northern areas of Japan to consider the regional characteristics of the deviations. We found that the deviations were high in northern areas where the SDPs are relatively low during precipitation events. The PMPs estimated using each approach were also compared to the extreme‐scale reference precipitation proposed in this study. The SDP approach overestimated the PMPs by over 20% compared to the reference precipitation in the northern region. However, the UAD approach showed very low average errors in all southern and northern areas. This tendency of the SDP approach was significantly related to the regional climatic characteristics of the SDP, which indicated that the SDP approach may estimate an uncertain PMP value depending on each regional climatic characteristic compared to the UAD approach. Regional climatic characteristics should be considered when using the SDP approach to estimate the PMP.

Extreme and compound ocean events are key drivers of projected low pelagic fish biomass.

Ocean extreme events, such as marine heatwaves, can have harmful impacts on marine ecosystems. Understanding the risks posed by such extreme events is key to develop strategies to predict and mitigate their effects. However, the underlying ocean conditions driving severe impacts on marine ecosystems are complex and often unknown as risks to marine ecosystems arise not only from hazards but also from the interactions between hazards, exposure and vulnerability. Marine ecosystems may not be impacted by extreme events in single drivers but rather by the compounding effects of moderate ocean anomalies. Here, we employ an ensemble climate-impact modeling approach that combines a global marine fish model with output from a large ensemble simulation of an Earth system model, to identify the key ocean ecosystem drivers associated with the most severe impacts on the total biomass of 326 pelagic fish species. We show that low net primary productivity is the most influential driver of extremely low fish biomass over 68% of the ocean area considered by the model, especially in the subtropics and the mid-latitudes, followed by high temperature and low oxygen in the eastern equatorial Pacific and the high latitudes. Severe biomass loss is generally driven by extreme anomalies in at least one ocean ecosystem driver, except in the tropics, where a combination of moderate ocean anomalies is sufficient to drive extreme impacts. Single moderate anomalies never drive extremely low fish biomass. Compound events with either moderate or extreme ocean conditions are a necessary condition for extremely low fish biomass over 78% of the global ocean, and compound events with at least one extreme variable are a necessary condition over 61% of the global ocean. Overall, our model results highlight the crucial role of extreme and compound events in driving severe impacts on pelagic marine ecosystems.

Long-term climate change impacts on regional sterodynamic sea level statistics analyzed from the MPI-ESM large ensemble simulation

Statistics of regional sterodynamic sea level variability are analyzed in terms of probability density functions of a 100-member ensemble of monthly mean sea surface height (SSH) timeseries simulated with the low-resolution Max Planck Institute Grand Ensemble. To analyze the impact of climate change on sea level statistics, fields of SSH variability, skewness and excess kurtosis representing the historical period 1986–2005 are compared with similar fields from projections for the period 2081–2100 under moderate (RCP4.5) and strong (RCP8.5) climate forcing conditions. Larger deviations of the models SSH statistics from Gaussian are limited to the western and eastern tropical Pacific. Under future climate warming conditions, SSH variability of the western tropical Pacific appear more Gaussian in agreement with weaker zonal easterly wind stress pulses, suggesting a reduced El Niño Southern Oscillation activity in the western warm pool region. SSH variability changes show a complex amplitude pattern with some regions becoming less variable, e.g., off the eastern coast of the north American continent, while other regions become more variable, notably the Southern Ocean. A west (decrease)-east (increase) contrast in variability changes across the subtropical Atlantic under RCP8.5 forcing is related to changes in the gyre circulation and a declining Atlantic Meridional Overturning Circulation in response to external forcing changes. In addition to global mean sea-level rise of 16 cm for RCP4.5 and 24 cm for RCP8.5, we diagnose regional changes in the tails of the probability density functions, suggesting a potential increased in variability-related extreme sea level events under global warmer conditions.

Global Warming Increases Interannual and Multidecadal Variability of Subarctic Atlantic Nutrients and Biological Production in the CESM1‐LE

AbstractEarth system models indicate that the Subarctic Atlantic Ocean ecosystems are uniquely sensitive to global warming. Nutrients, phytoplankton biomass, and phytoplankton production all decline precipitously in global warming scenarios. Superimposed on this forced response is changing internal variability that is not fully understood. Here, a large ensemble of simulations with the Community Earth System Model is used to quantify how global warming affects the interannual and multidecadal variability of phytoplankton production integrated over the Subarctic Atlantic. Surprisingly, it is found that this variability increases non‐monotonically with global warming. The increased variability of production is caused by an elevated volatility of wintertime surface nutrient concentrations, which is a consequence of a rising sensitivity of these nutrients to winter mixing and overturning fluctuations, which overcomes a reduced amplitude of these physical fluctuations with warming. Future work is needed to fully understand how internal climate variability impacts ocean ecosystems in a warming climate.

High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems.

We propose a high-order stochastic-statistical moment closure model for efficient ensemble prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. The statistical moment equations are closed by a precise calibration of the high-order feedbacks using ensemble solutions of the consistent stochastic equations, suitable for modeling complex phenomena including non-Gaussian statistics and extreme events. To address challenges associated with closely coupled spatiotemporal scales in turbulent states and expensive large ensemble simulation for high-dimensional systems, we introduce efficient computational strategies using the random batch method (RBM). This approach significantly reduces the required ensemble size while accurately capturing essential high-order structures. Only a small batch of small-scale fluctuation modes is used for each time update of the samples, and exact convergence to the full model statistics is ensured through frequent resampling of the batches during time evolution. Furthermore, we develop a reduced-order model to handle systems with really high dimensions by linking the large number of small-scale fluctuation modes to ensemble samples of dominant leading modes. The effectiveness of the proposed models is validated by numerical experiments on the one-layer and two-layer Lorenz '96 systems, which exhibit representative chaotic features and various statistical regimes. The full and reduced-order RBM models demonstrate uniformly high skill in capturing the time evolution of crucial leading-order statistics, non-Gaussian probability distributions, while achieving significantly lower computational cost compared to direct Monte-Carlo approaches. The models provide effective tools for a wide range of real-world applications in prediction, uncertainty quantification, and data assimilation.

The warm Arctic-cold north american pattern in CanESM5 large ensemble simulations: Eurasian influence and uncertainty due to internal variability

This study examines the warm Arctic-cold North American pattern (WACNA) and its connection with the warm Arctic-cold Eurasia pattern (WACE) using ERA5 reanalysis and a 50-member ensemble of historical climate simulations produced by CanESM5, the Canadian model participated in CMIP6. The results indicate that a negative WACE-like pattern typically precedes a positive WACNA pattern by one month, and the presence of a negative Asian-Bering-North American (ABNA)-like circulation pattern connects Eurasia and North America, along with the Pacific-North American (PNA)-like pattern. The negative ABNA-like pattern can be attributed to anomalous heating in southern Siberia, which is associated with the negative WACE pattern and its featured Eurasian warming. The negative PNA-like pattern is influenced by negative SST anomalies in the tropical Pacific, resembling tropical ENSO variability. Anomalous temperature advection in the lower troposphere follows the circulation anomaly, which supports the formation of WACNA. Conversely, processes with circulation anomalies of opposite sign result in a negative WACNA pattern. The tropical ENSO variability does not significantly impact the WACNA pattern and its linkage with WACE. CanESM5 simulates the WACNA pattern and WACE-WACNA connection well, with some discrepancies in the magnitude of anomalies compared to ERA5 reanalysis. The uncertainty in the simulated WACNA pattern due to internal climate variability is dominated by two modes of inter-member variability: a southeast-northwest phase shift and a local variation in amplitude.