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We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960–2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. Further improvements of the simulated mean state of ICON-ESM, e.g. through fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean, are expected to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.

We use wind sensitivity experiments to understand the wind forcing dependencies of the level of no motion as the depth of maximum overturning and the e-folding pycnocline scale, as well as their relationship to northward transport of the mid-depth Atlantic meridional overturning circulation (AMOC) which extends vertically to the depth of maximum overturning of the upper AMOC cell. In contrast to previous studies, we investigate the interplay of nonlocal and local wind effects on a decadal timescale. We use 30-year simulations with a high-resolution ocean general circulation model (OGCM) which is an eddy-resolving version of the Max Planck Institute for Meteorology ocean model (MPIOM). Our findings deviate from the common perspective that the AMOC is a nonlocal phenomenon only, because northward transport and its depth scales depend on both nonlocal Southern Ocean wind effects and local wind effects in the Northern Hemisphere downwelling region where Ekman pumping takes place. Southern Ocean wind forcing predominantly determines the magnitude of the pycnocline scale throughout the basin, whereas Northern Hemisphere winds additionally influence the level of no motion locally. In that respect, the level of no motion is a better proxy for northward transport and mid-depth velocity profiles than the pycnocline scale, since the wind forcing dependencies of the level of no motion and maximum overturning are equal. The changes in maximum overturning with wind forcing are explained by the changes in the level of no motion only. This is because wind-driven Ekman compensation is baroclinic and occurs above the level of no motion, and the internal vertical velocity shear that is not influenced by the external Ekman cells stays approximately constant. The analysis of the wind experiments suggests a hemisphere-dependent scaling of the strength of AMOC. We put forward the idea that the ability of numerical models to capture the spatial and temporal variations of the level of no motion is crucial to reproduce the mid-depth cell in an appropriate way both quantitatively and dynamically.

The target of sequential reverse engineering is to extract the state machine of a design. Sequential reverse engineering of a gate-level netlist consists of the identification of so-called state flip-flops (sFFs), as well as the extraction of the state machine. The second step can be solved with an exact approach if the correct sFFs and the correct reset state are provided. For the first step, several more or less heuristic approaches exist. This work investigates sequential reverse engineering with the objective of a human-readable state machine extraction. A human-readable state machine reflects the original state machine and is not overloaded by additional design information. For this purpose, the work derives a systematic categorization of sFF sets, based on properties of single sFFs and their sets. These properties are determined by analyzing the degrees of freedom in describing state machines as the well-known Moore and Mealy machines. Based on the systematic categorization, this work presents an sFF set definition for a human-readable state machine, categorizes existing sFF identification strategies, and develops four post-processing methods. The results show that post-processing predominantly improves the outcome of several existing sFF identification algorithms.

Ocean heat content observations in the Indian Ocean have revealed distinctive periods of significant multi-decadal trends — for example a cooling between 1990 and 1999 followed by an unprecedented warming between 2000 and 2009. However, a systematic assessment of the relative importance of anthropogenic forcings versus natural variability in driving such trends is still missing. Here, we utilise four state-of-the-art Single Model Initial- Condition Large Ensembles with MPI-ESM1.2-LR containing different factual and counterfactual forcing scenarios to address the problem. We are able to robustly attribute the unprecedented warming of the Indian Ocean between 2000 and 2009 to the increasing anthropogenic greenhouse gas emissions. Our results also reveal that the preceding cooling is likely to be intrinsic to Indian Ocean heat content variability, since none of the applied counterfactual scenarios exhibits such an observed decrease in Indian Ocean heat content. Furthermore, we trace the underlying reasons for the observed inherent cooling between 1990 and 1999 to a significant reduction in heat transported into the Indian Ocean from the Pacific Ocean by the Indonesian Throughflow. These results have implications for decadal predictions of Indian Ocean heat content.

Earth system models are now regularly being used in inter-annual to decadal climate prediction. Such prediction systems based on CMIP5-generation Earth system models had demonstrated an overall positive impact of initialization, i.e. deriving initial conditions of retrospective forecasts from a separate data assimilation experiment, on decadal prediction skill. This view is now being increasingly challenged in the context of improvements both in CMIP6-generation Earth system models and CMIP6-evaluation of external forcing as well as in the context of ongoing transient climate change. In this study we re-evaluate the impact of atmospheric and oceanic initialization on decadal prediction skill of North Atlantic upper ocean heat content (0-700m) in a CMIP6-generation decadal prediction system based on the Max Planck Institute Earth system model (MPI-ESM). We compare the impact of initial conditions derived through full-field atmospheric nudging with those derived through an additional assimilation of observed oceanic temperature and salinity profiles using an ensemble Kalman filter. Our experiments suggest that assimilation of observed oceanic temperature and salinity profiles into the model reduces the warm bias in the subpolar North Atlantic heat content, and improves the modelled variability of the Atlantic meridional overturning circulation and ocean heat transport. These improvements enable a proper initialization of model variables which leads to an improved decadal prediction of surface temperatures. Our results reveal an important role of subsurface oceanic observations in decadal prediction of surface temperatures in the subpolar North Atlantic even in CMIP6-generation decadal prediction system.

We analyze the evolution of heat fluxes and the resulting surface energy balance at the Arctic sea-ice edge in CMIP6 model simulations. We build on the study of Notz and Stroeve (2016), in which they show the existence of a strong linear relationship between Arctic sea-ice area and cumulative anthropogenic CO2 emissions. In explaining this linear relationship, the authors claim that the surface energy balance at the sea-ice edge remains constant, following the conceptual idea of the sea-ice edge retreating northwards to compensate for the increasing longwave radiative input due to global warming by a decrease in shortwave radiation at higher latitudes. We examine the validity of this hypothesis by first identifying the sea-ice edge in the model data, and then scrutinize whether or not the surface energy balance at that location stays constant under future sea-ice retreat. Furthermore, we decompose the energy balance into its constituents to explore dynamical effects and oceanic influence.We find that the annual mean surface energy balance shows stronger spatial than temporal variations. Looking at individual months, we find that the surface energy balance is negative in winter and positive in summer along the ice edge. Towards the end of the 21st century, the surface energy balance enters a new regime, becoming less negative in winter, and more positive in summer. By showing a consistently negative relation between downwelling shortwave radiation and downwelling longwave radiation, we finally confirm the idea of the compensation of increasing longwave input by a decrease in shortwave incoming radiation due to a northward migration of the sea-ice edge.

Slowly varying ocean heat content is one of the most important variables when describing cli-mate variability on interannual to decadal time scales. Since observation-based estimates ofocean heat content require extensive observational coverage, incomplete observations are oftencombined with numerical models via data assimilation to simulate the evolution of oceanic heat.However, incomplete observations, particularly in the subsurface ocean, lead to large uncertain-ties in the resulting model-based estimate. As an alternative approach, Kadow et al (2020) haveproven that artificial intelligence can successfully be utilized to reconstruct missing climate in-formation for surface temperatures. In the following, we investigate the possibility to train theirthree-dimensional convolutional neural network to reconstruct missing subsurface temperaturesto obtain ocean heat content estimates with a focus on the North Atlantic ocean.The network is trained and tested to reconstruct a 16 member Ensemble Kalman Filter assimi-lation ensemble constructed with the Max-Planck Institute Earth System Model for the periodfrom 1958 to 2020. Specifically, we examine whether the partial convolutional U-net representsa valid alternative to the Ensemble Kalman Filter assimilation to estimate North Atlantic sub-polar gyre ocean heat content.The neural network is capable of reproducing the assimilation reduced to datapoints with ob-servational coverages within its ensemble spread with a correlation coefficient of 0.93 over theentire time period and of 0.99 over 2004 – 2020 (the Argo-Era). Additionally, the network isable to reconstruct the observed ocean heat content directly from observations for 12 additionalmonths with a correlation of 0.97, essentially replacing the assimilation experiment by an extrap-olation. When reconstructing the pre-Argo-Era, the network is only trained with assimilationsfrom the Argo-Era. The lower correlation in the resulting reconstruction indicates higher un-certainties in the assimilation outside of its ensemble spread at times with low observationaldensity. These uncertainties are highlighted by inconsistencies in the assimilation’s represen-tations of the North Atlantic Current at times and grid points without observations detectedby the neural network. Our results demonstrate that a neural network is not only capable ofreproducing the observed ocean heat content over the training period, but also before and aftermaking the neural network a suitable candidate to step-wise extend or replace data assimilation.

On the global scale, the frequency of marine heatwaves (MHWs) is projected to increase further in the twenty-first Century. In our earlier study we demonstrate that the high-impact major marine heatwaves over the northeast Pacific are co-located with a systematically-forced long-term warming pool, which we attribute to forcing by elevated greenhouse gases levels (GHG), and the recent industrial aerosol-load decrease (Barkhordarian et al., 2022). In current study we show that the magnitude of Arctic MHWs has significantly increased since 2006, and has exceeded the pre-industrial climate bounds since then. We here perform statistical attribution methodologies, and provide a quantitative assessment of whether GHG forcing was necessary for the Arctic MHWs to occur, and whether it is a sufficient cause for such events to continue to repeatedly occur in the future.The probability of necessary causation of Arctic MHWs intensity, increases with increasing the severity of MHWs, and saturate to 1.0 by the time MHWs intensity exceeds the 2°C, indicating that any MHWs over the Arctic with an intensity higher than 2°C is entirely attributable to the inclusion of GHG forcing. These amplified extreme MHWs in the Arctic have each been accompanied by a record decline in Arctic Sea ice, in particular in the years 2007, 2012, 2016 and 2020. Over the last decade, MHWs occur in the Arctic where sea ice melt in June is 4 %/year faster, the ice-free season is ~3 months longer, the ocean heat-uptake is 50 W/m2 higher, and the sea surface temperature is ~2°C warmer, in comparison with the previous decade. In autumn surface evaporation rate is increasing, the increased low clouds favor more sea ice melt via emitting stronger longwave radiation. In summary, prolonged Arctic marine heatwaves, triggered by faster early summer sea ice melt, will accelerate Arctic warming, and cause Arctic Sea ice extent to shrink even faster in the near future.Barkhordarian, A., Nielsen, D.M. & Baehr, J. Recent marine heatwaves in the North Pacific warming pool can be attributed to rising atmospheric levels of greenhouse gases. Nature Communications Earth & Environment, 3, 131 (2022). https://doi.org/10.1038/s43247-022-00461-2

We present the CMIP6 version of the Max Planck Institute-Grand Ensemble (MPI-GE CMIP6) with 30 realisations for the historical period and five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE by providing high-frequency model output, the full range of emission scenarios including the highly policy relevant scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to high resolution simulations of the same model version. We demonstrate with six novel application examples how to use the power of MPI-GE CMIP6 to better quantify and understand present and future extreme events in the Earth system, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heat waves are projected to occur every year in 2071-2100 in high-emission scenarios, that the storm activity in most tropical to mid-latitude oceans is projected to decrease, and that the ensemble is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Current sub-seasonal prediction systems are traditionally based on models developed for numerical weather prediction. We present a different approach wherein we develop a sub-seasonal prediction system using a coupled Earth system model, the Max-Planck-Institute Earth system model (MPI-ESM), developed primarily for the use in climate prediction. We present results from initialized sub-seasonal reforecasts for the time period 1993-2017 from a 1st generation (CMIP5) seasonal-turned-sub-seasonal prediction system based on MPI-ESM including different components of the Earth system: atmosphere, land surface, ocean, and marine ecosystems. With our system we find (1) that atmospheric variables can be predicted with a quality and prediction horizon similar to what is found within the range of current sub-seasonal to seasonal prediction systems, (2) that extreme events as diverse as heatwaves over land, storm severity over Europe, and sudden stratospheric warmings can be skilfully predicted one to a few weeks ahead, (3) that sea surface temperatures can be skilfully predicted in the majority of large marine ecosystems for several weeks ahead, and (4) that sea ice area in the majority of Arctic seas can be skillfully predicted several weeks ahead. Our findings indicate that a coupled Earth system model like MPI-ESM can already be seamlessly used for sub-seasonal to seasonal (to decadal) climate predictions of different domains of the Earth system. Ultimately these results ask for the seamless approach to be embedded into the development of future coupled Earth system models.

We evaluate the prediction skill of the Max Planck Institute Earth System Model (MPI-ESM) decadal hindcast system for German Bight storm activity (GBSA) on a multiannual to decadal scale. We define GBSA every year via the most extreme 3-hourly geostrophic wind speeds, which are derived from mean sea-level pressure (MSLP) data. Our 64-member ensemble of annually initialized hindcast simulations spans the time period 1960–2018. For this period, we compare deterministically and probabilistically predicted winter MSLP anomalies and annual GBSA with a lead time of up to 10 years against observations. The model produces poor deterministic predictions of GBSA and winter MSLP anomalies for individual years but fair predictions for longer averaging periods. A similar but smaller skill difference between short and long averaging periods also emerges for probabilistic predictions of high storm activity. At long averaging periods (longer than 5 years), the model is more skillful than persistence- and climatology-based predictions. For short aggregation periods (4 years and less), probabilistic predictions are more skillful than persistence but insignificantly differ from climatological predictions. We therefore conclude that, for the German Bight, probabilistic decadal predictions (based on a large ensemble) of high storm activity are skillful for averaging periods longer than 5 years. Notably, a differentiation between low, moderate, and high storm activity is necessary to expose this skill.

Responses of El Niño–Southern Oscillation (ENSO) to global warming remain uncertain, which challenges ENSO forecasts in a warming climate. We investigate changes in ENSO characteristics and predictability in idealized simulations with quadrupled CO2 forcing from seven general circulation models. Comparing the warmer climate to control simulations, ENSO variability weakens, with the neutral state lasting longer, while active ENSO states last shorter and skew to favor the La Niña state. The 6-month persistence-assessed ENSO predictability slightly reduces in five models and increases in two models under the warming condition. While the overall changes in ENSO predictability are insignificant, we find significant relationships between changes in predictability and intensity, duration, and skewness of the three individual ENSO states. The maximal contribution to changes in the predictability of El Niño, La Niña and neutral states stems from changes in skewness and events' duration. Our findings show that a robust and significant decrease in ENSO characteristics does not imply a similar change in ENSO predictability in a warmer climate. This could be due to model deficiencies in ENSO dynamics and limitations in the persistence model when predicting ENSO.

Responses of El Niño–Southern Oscillation (ENSO) to global warming remain uncertain, which challenges ENSO forecasts in a warming climate. We investigate changes in ENSO characteristics and predictability in idealized simulations with quadrupled CO2 forcing from seven general circulation models. Comparing the warmer climate to control simulations, ENSO variability weakens, with the neutral state lasting longer, while active ENSO states last shorter and skew to favor the La Niña state. The 6-month persistence-assessed ENSO predictability slightly reduces in five models and increases in two models under the warming condition. While the overall changes in ENSO predictability are insignificant, we find significant relationships between changes in predictability and intensity, duration, and skewness of the three individual ENSO states. The maximal contribution to changes in the predictability of El Niño, La Niña and neutral states stems from changes in skewness and events' duration. Our findings show that a robust and significant decrease in ENSO characteristics does not imply a similar change in ENSO predictability in a warmer climate. This could be due to model deficiencies in ENSO dynamics and limitations in the persistence model when predicting ENSO.

Ensemble forecasting has become a standard means to obtain information about forecast uncertainties in meteorological centres across the world. The large datasets generated by ensemble prediction systems carry much information that is difficult to analyse manually – here, techniques from the field of artificial intelligence can be beneficial to aid the analysis. Cluster analysis is one commonly used (unsupervised machine learning) approach to automatically determine distinct scenarios in numerical weather forecasting ensembles, both in atmospheric research and operational forecasting. Typically, a cluster analysis focusses on a selected meteorological forecast variable, a specific region, and time (or a time window). The dimensionality of the data is reduced by techniques like principal component analysis, and a clustering algorithm – typically k-means – is applied to the reduced data set. Challenges with such an approach arise through the determined clusters often being sensitive to factors including the selected region, forecast variable, and algorithm parameters, and also through the employed algorithms often appearing as a “black box” to the user. In our work, we attempt to make the clustering process more transparent by providing a visual analysis framework to analyse the sensitivity of generated clusters with respect to various factors. The presented framework is coupled to the open-source meteorological ensemble visualization software Met.3D, allowing for interactive specification of clustering parameters and for interactive visual analysis, including 3-D elements. A case study using ensemble prediction data of sudden stratospheric warmings (SSWs) is presented, demonstrating how visualizing similarity between clusterings with different parameters can aid the interpretation of the data.

The obfuscation of a combinational logic circuit using logic locking is similar to symmetric cryptography and can be expressed in terms of a quantified Boolean function. Satisfiability checking algorithms are used to reveal the secret key of the logic locking (so-called SAT attacks). In this paper, we analyze the impact of quantum computers for these SAT-based attacks similar to the impact on symmetric cryptographic ciphers. For the first time to our knowledge, we propose modifications of existing logic locking schemes to achieve the same security level in a post-quantum era. Furthermore, we investigate dedicated countermeasures against SAT- based algorithms, suggest modifications and evaluate their effectiveness against quantum attacks.

Surface temperatures in the Arctic are increasing more than twice as fast as the global average due to Arctic amplification. This warming gives rise to new types of extreme events that can have particularly large impacts. Here, we study the interplay of mean warming and changes in internal variability to better understand and constrain the intensity and frequency of temperature extremes in the Arctic, both regionally and seasonally.For this study, we analyze projected mean and extreme surface air temperatures in the Arctic for different levels of global warming based on output data from multiple single-model initial-condition large ensembles, with the Max Planck Institute Grand Ensemble (MPI-GE) at the core of the analysis. We use a time-slice approach to construct representative samples of the pre-industrial climate and the climate at different levels of global warming, including the Paris Agreement targets of 1.5 °C and 2 °C.Considering pan-Arctic temperatures, we find that the mean warming is strongest in winter (~3.5 times annual mean global warming) and lowest in summer (~1.05 times annual mean global warming), which leads to a weakening of the Arctic seasonal cycle with global warming. Moreover, the change in the return levels of extreme temperatures is particularly strong for cold extremes, rendering extremely cold temperatures seldom in a warming Arctic. The level of global warming is strongly impacting the frequency of extreme events. For example, warm extremes that occur every 100 years at 1.5 °C of global warming, occur more than once in 10 years at 2 °C of global warming, and cold extremes that occur every 10 years at 1.5 °C global warming, occur only about every 200 years at 2 °C of global warming (based on MPI-GE data). The response of Arctic mean temperatures to global warming results from a local temperature response that varies substantially for different regions and types of surfaces (land, ice sheet, open ocean, sea ice). We find the most drastic warming, accompanied by a strong reduction of variability, in winter temperatures over the northern Barents Sea linked to its ‘Atlantification’. Lastly, we also note a considerable difference in the Arctic temperature response for the same level of global warming in a transient versus a quasi-equilibrium climate state.The results of our study allow us to quantify expected changes in the Arctic temperature range with global warming and also to determine when and where, for example, climate mitigation measures are most likely to be visible.

Despite the importance of the Atlantic Meridional Overturning Circulation (AMOC) to the climate on decadal and multidecadal timescales, Earth System Models (ESM) exhibit large differences in their estimation of the amplitude and spectrum of its variability. In addition, observational data is sparse and before the onset of the current century, many reconstructions of the AMOC rely on linear relationships to the more readily observed surface properties of the Atlantic rather than the less explored deeper ocean. Yet, it is conceptually well established that the density distribution is dynamically closely related to the AMOC, and in this contribution, we investigate this connection in model simulations to identify which density information is necessary to reconstruct the AMOC. We chose to establish these links in a data-driven approach. We use simulations from a historically forced large ensemble as well as abruptly forced long term simulations with varying strength of forcing and therefore comprising vastly different states of the AMOC. In a first step, we train uncertainty-aware neural networks to infer the state of the AMOC from the density information at different layers in the North Atlantic. In a second step, we compare the performance of the trained neural networks across depth and with their linear counterparts in simulations that were not part of the training process. Finally, we investigate how the networks arrived at their specific prediction using Layer-Wise-Relevance Propagation (LRP), a recently developed technique that propagates relevance backwards through the network to the input density field, effectively filtering out important from unimportant information and identifying regions of high relevance for the reconstruction of the AMOC.Our preliminary results show that in general, the information provided by only one density layer between the surface and 1100 m is sufficient to reconstruct the AMOC with high precision, and neural networks are capable of generalizing to unseen simulations. From the set of these neural networks trained on different layers, we choose the surface layer as well as one subsurface layer close to 1000 m for further investigation of their decision-making process using LRP. Our preliminary investigation reveals that the LRP in the subsurface layer identifies regions of potentially high physical relevance for the AMOC. By contrast, the regions identified in the surface layer show little physical relevance for the AMOC.

The practice of initialized subseasonal, seasonal and decadal climate prediction has matured considerably in recent years, with real-time subseasonal and decadal multi-system ensembles joining those established previously for the seasonal to multi-seasonal range. However, substantial scientific, modelling, and informational challenges remain that must be overcome in order to more fully realize the potential for such predictions to serve societal needs. This presentation will examine five such challenges that the World Climate Research Programme’s Working Group on Subseasonal to Interdecadal Prediction (WGSIP) has identified as crucial for further advancing capabilities for translating the inherent predictability of the Earth system into actionable predictive information. Surmounting these challenges will bring nearer an envisaged future in which global users have access to such information specific to individual needs, across Earth system components and on a continuum of time scales, with degrees of confidence, limitations and uncertainties clearly indicated, as well as tools to guide optimal actions.

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial feature of Earth’s climate, and the possible recent AMOC weakening in association with anthropogenic emissions is a topic of current scientific discussion. To assess whether anthropogenic emissions affect the circulation, we examine low AMOC strengths with the framework of the causal counterfactual theory. We compare the occurrence of low strengths in a factual world with all forcings present against a counterfactual world, equivalent except for anthropogenic forcings. To represent these two worlds, we use past and future 30-member ensembles of simulations from the Max Planck Institute Earth System Model (MPI-ESM1.2-LR), whose AMOC characteristics at 26.5°N are in overall agreement with observations from the RAPID program.Considering the whole historical period (1850-2020), our results show that causation probabilities for anthropogenic forcings on low AMOC strengths at 26.5 °N are generally low. However, within future projections, we find that anthropogenic forcings will very likely be a necessary cause of any AMOC strength below 16.0 Sv by about 2040. These results are sensitive to the choice of attribution window: if the starting year is chosen closer to the present, all probabilities substantially increase. Within the analyzed simulations, the main attribution results found at 26.5°N are confirmed for other latitudes in the North Atlantic.

We investigate the potential for enhancing the seasonal prediction skill of mid-latitude cyclonic activity, represented by eddy kinetic energy (EKE) at 250 hPa over the North Atlantic and Europe, in hindcast simulations with the Max Planck Institute Earth System Model (MPI-ESM) against the ECMWF ERA5 reanalysis. Our analysis focuses on wintertime months (December-March) from 1982 to 2019, with a 30-member seasonal hindcast ensemble initialized every November 1st. Based on the initial confirmation that in both ERA5 reanalysis and MPI-ESM hindcasts, the eddy-driven jet stream and the wintertime North Atlantic Oscillation (NAO) play a significant role in wintertime's spatial and temporal distribution of mid-latitude cyclonic activity, we perform ensemble subsampling.Specifically, we sample each winter so that a northern position of the jet stream is consistent with a positive phase of the NAO and represents poleward enhanced EKE activity. In contrast, a southern position of the jet stream is consistent with a negative phase of the NAO and represents equatorward enhanced EKE activity. Preliminary analysis of the predictive skill of MPI-ESM hindcasts with respect to ERA5 shows that such subsampling with respect to a consistent representation of the jet stream position and the NAO phase leads to improvements over the skill from the 30-member ensemble mean, with significant correlations concentrated over areas of major frequency of storm tracks. Our results put into practical use that an enhanced representation of the large-scale climate variability plays a crucial role in the long-term prediction of high-frequency events such as mid-latitude cyclones.