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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.

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.

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.

The Standardized Precipitation Index (SPI) is awidely accepted drought index. Its calculation algorithm nor-malizes the index via a distribution function. Which distribu-tion function to use is still disputed within the literature. Thisstudy illuminates that long-standing dispute and proposes asolution that ensures the normality of the index for all com-mon accumulation periods in observations and simulations.We compare the normality of SPI time series derived withthe gamma, Weibull, generalized gamma, and the exponenti-ated Weibull distribution. Our normality comparison is basedon a complementary evaluation. Actual compared to theo-retical occurrence probabilities of SPI categories evaluatethe absolute performance of candidate distribution functions.Complementary, the Akaike information criterion evaluatescandidate distribution functions relative to each other whileanalytically punishing complexity. SPI time series, span-ning 1983–2013, are calculated from the Global PrecipitationClimatology Project’s monthly precipitation dataset, and sea-sonal precipitation hindcasts are from the Max Planck Insti-tute Earth System Model. We evaluate these SPI time seriesover the global land area and for each continent individuallyduring winter and summer. While focusing on regional per-formance disparities between observations and simulationsthat manifest in an accumulation period of 3 months, we ad-ditionally test the drawn conclusions for other common ac-cumulation periods (1, 6, 9, and 12 months).Our results suggest that calculating SPI with the com-monly used gamma distribution leads to deficiencies in theevaluation of ensemble simulations. Replacing it with theexponentiated Weibull distribution reduces the area of thoseregions where the index does not have any skill for precip-itation obtained from ensemble simulations by more than one magnitude. The exponentiated Weibull distribution max-imizes also the normality of SPI obtained from observationaldata and a single ensemble simulation. We demonstrate thatcalculating SPI with the exponentiated Weibull distributiondelivers better results for each continent and every investi-gated accumulation period, irrespective of the heritage of theprecipitation data. Therefore, we advocate the employmentof the exponentiated Weibull distribution as the basis for SPI.

A finite impulse response (FIR) filter is a ubiquitous block in digital signal processing applications. Its characteristics are determined by its coefficients, which are the intellectual property (IP) for its designer. However, in a hardware efficient realization, its coefficients become vulnerable to reverse engineering. This paper presents a filter design technique that can protect this IP, taking into account hardware complexity and ensuring that the filter behaves as specified only when a secret key is provided. To do so, coefficients are hidden among decoys, which are selected beyond possible values of coefficients using three alternative methods. As an attack scenario, an adversary at an untrusted foundry is considered. A reverse engineering technique is developed to find the chosen decoy selection method and explore the potential leakage of coefficients through decoys. An oracle-less attack is also used to find the secret key. Experimental results show that the proposed technique can lead to filter designs with competitive hardware complexity and higher resiliency to attacks with respect to previously proposed methods.

Over the last decade, the northeast Pacific (NP) experienced strong marine heatwaves (MHWs) that produced devastating marine ecological impacts and received major societal concerns. Here, we assess the link between the well-mixed greenhouse gas (GHG) forcing and the occurrence probabilities of the duration and intensity of the NP MHWs. To begin with, we apply attribution technique on the SST time series, and detect a region of systematically and externally-forced SST increase -- the long-term warming pool -- co-located with the past notably Blob-like SST anomalies. The anthropogenic signal has recently emerged from the natural variability of SST over the warming pool, which we attribute primarily to increased GHG concentrations, with anthropogenic aerosols playing a secondary role. With extreme event attribution technique, we further show that GHG forcing is a necessary, but not a sufficient, causation for the multi-year persistent MHW events in the current climate, such as that happened in 2019/2020 over the warming pool. However, the occurrence of the 2019/2020 MHW was extremely unlikely in the absence of GHG forcing. Thus, as GHG emissions continue to firmly rise, it is very likely that GHG forcings will become a sufficient cause for events of the magnitude of the 2019/2020 record event.

Marine risk embraces an assessment of likelihoods and consequences of impacts from climate fluctuations in order to identify time and regions vulnerable to climate hazards. This information can support sustainable and safe marine activities. The marine risk assessment is a part of the marine service provided by the DNV GL (short for Det Norske Veritas and Germanischer Lloyd). In their current risk application, likelihoods of extreme conditions on the sea are based on historical observations and atmospheric reanalyses. We assess predicted likelihoods of extreme conditions over 1990-2017 in the boreal summer (prediction months 2-4) from the seasonal forecast system provided by the German Meteorological Service (DWD). We chose summer as it represents the time of the open-water season, when the highest marine activity in the Barents Sea takes place. We selected three indicators from the marine risk assessment. Two of them represent meteorological properties such as wind speed and 2-meter temperature (T2m). The third indicator – the wind chill index (WCI) is a combination of the previous two and represents heat loss from the human body to its surroundings during cold and windy weather. As expected, the prediction skill assessment suggests different levels of predictability for the three indicators, with T2m having the highest skill followed by WCI and wind speed. The prediction skill represents the "trust layer" superimposed on the predicted likelihoods and used as input fields for marine risk assessment. From the likelihood maps for the test period of summer 2020 follows that large areas of the Barents Sea represent favorable conditions for marine operations considering high prediction skill and low likelihood for extreme WCI (>1000 W/m2) and T2m (13.9 m/s) is poorly predictable beyond the first lead month. Thus, if risk assessment is based on a suite of climate indicators with the heterogeneous prediction skill, the total risk assessment might be limited by the skill of the indicator with the lowest prediction skill. However, not all climate indicators are equally contributing to the risk assessment. The study describes a workflow for application of seasonal climate predictions and points to a few lessons learned, which can be useful to future climate services.

Marine cold air outbreaks (MCAOs) in the northeastern North Atlantic occur due to the advection of extremely cold air over an ice-free ocean. MCAOs are associated with a range of severe weather phenomena, such as polar lows, strong surface winds and intense cooling of the ocean surface. Given these extreme impacts, the identification of precursors of MCAOs is crucial for improved long-range prediction of associated impacts on Arctic infrastructure and human lives. MCAO frequency has been linked to the strength of the stratospheric polar vortex, but the study of connections to the occurrence of extreme stratospheric events, known as sudden stratospheric warmings (SSWs), has been limited to cold extremes over land. Here, the influence of SSW events on MCAOs over the North Atlantic ocean is studied using reanalysis datasets. Overall, SSW events are found to be associated with more frequent MCAOs in the Barents Sea and the Norwegian Sea compared to climatology and less frequent MCAOs in the Labrador Sea. In particular, SSW events project onto an anomalous dipole pattern of geopotential height 500 hPa, which consists of a ridge anomaly over Greenland and a trough anomaly over Scandinavia. By affecting the variability of the large-scale circulation patterns in the North Atlantic, SSW events contribute to the strong northerly flow over the Barents and Norwegian seas and thereby increase the likelihood of MCAOs in these regions. In contrast, the positive geopotential height anomaly over Greenland reduces the probability of MCAOs in the Labrador Sea after SSW events. As SSW events tend to have a long-term influence on surface weather, these results are expected to benefit the predictability of MCAOs in the Nordic Seas for winters with SSW events.

Arctic coastal erosion damages infrastructure, threatens coastal communities, and releases organic carbon from permafrost. However, the magnitude, timing and sensitivity of coastal erosion increase to global warming remain unknown. Here, we project the Arctic-mean erosion rate to roughly double by 2100 and very likely exceed its historical range of variability by mid-21st century. The sensitivity of erosion to warming also doubles, reaching 0.4-0.5 m year-1 oC-1 and 2.3-2.8 TgC year-1 oC-1 by the end of the century under moderate and high-emission scenarios. Our first 21st-century pan-Arctic coastal erosion rate projections should inform policy makers on coastal conservation and socioeconomic planning. Our organic carbon flux projections also lay out the path for future work to investigate the impact of Arctic coastal erosion on the changing Arctic Ocean, on its role as a global carbon sink, and on the permafrost-carbon feedback.

To investigate the inherent predictability of sea ice and its representation in climate models, we compare the seasonal-to-interannual memory of Arctic sea ice as given by lagged correlations of sea-ice area anomalies in large model ensembles (Max Planck Institute Grand Ensemble and Coupled Model Intercomparison Project phase 6) and multiple observational products. We find that state-of-the-art climate models significantly overestimate the memory of pan-Arctic sea-ice area from the summer months into the following year. This cannot be explained by internal variability. We further show that the observed summer memory can be disentangled regionally into a reemergence of positive correlations in the perennial ice zone and negative correlations in the seasonal ice zone; the latter giving rise to the discrepancy between observations and model simulations. These findings could explain some of the predictability gap between potential and operational forecast skill of Arctic sea-ice area identified in previous studies.

We improve seasonal hindcast skill of European summer climate in an ensemble based coupled seasonal prediction system by selecting individual ensemble members based on their respective consistent chain of processes that describe a physical mechanism. This mechanism is associated with the second mode of seasonal climate variability in the North-Atlantic-European sector and is contrary to the summer North Atlantic Oscillation. We initially analyse the mechanism in the ERA-Interim reanalysis and then test the influence of the mechanism on European hindcast skill in an initialised coupled seasonal climate model. We show that the mechanism originates in the tropical North Atlantic in spring, where either warm or cold sea surface temperature anomalies (SSTs) are connected with the European climate by an upper-level wave-train. This wave-train is accompanied by a zonal pressure gradient, that in turn influences the climate over central Europe in the following summer. We analyse the seasonal summer hindcast skill in a mixed resolution hindcast ensemble simulation generated by MPI-ESM, with 30 members starting every year in May. While the mean over the full ensemble shows no seasonal hindcast skill in summer, we achieve significant hindcast skill through forming a new mean over subselected ensemble members. For this selection, we test every ensemble member for the proposed consistent chain of connections between the wave-train, the zonal pressure gradient and their impact on European summer temperatures, and find that the processes that describe the mechanism are not represented in every ensemble member. Due to its influence on European summer climate, we use the condition of the persistent spring SSTs to anticipate the phase of the mechanism in each considered year. We thus use statistical relations to select ensemble members generated by a dynamical prediction system. With this approach, we significantly enhance the seasonal hindcast skill and the reliability of the hindcasts in the North-Atlantic-European sector, especially in the areas where the mechanism is showing a prominent signal. Since we only use knowledge that would be available in a real forecast set-up, this approach can potentially be applied in operational ensemble prediction systems.

The Atlantic multidecadal variability (AMV) is known to affect the central to eastern (C--E) European summer climate through an associated atmospheric baroclinic response called North-Atlantic-European East West mode as demonstrated in the twentieth century reanalysis (20CRv2). Here, using the atmospheric model ECHAM6.3, we perform sensitivity experiments with prescribed sea surface temperature (SST) anomalies that are representative of the observed positive and the negative AMV phases and investigate the model response to the observed AMV pattern for European summer climate. The results from the experiments reveal that in the negative phase of AMV, the North-Atlantic-European (NAE) climate is mainly governed by the extra-tropical branch of the AMV through a baroclinic-like response. This response brings negative surface air temperature (SAT) anomalies over C--E Europe. The response and its influence are similar to what is found in the 20CRv2. In contrast, in the positive phase of the AMV, the NAE climate in the model experiments is mainly influenced by the tropical branch of the AMV. A stationary Rossby wave response excited in the tropics is associated with negative SAT anomalies over C--E Europe, which is opposite to what is found in the 20CRv2. The model response from the tropical part of the AMV SST is unlikely to be realistic due to the lack of coupled air--sea interaction, when SST is specified. Hence, the results demonstrate that ECHAM6.3 can simulate the observed linear baroclinic response, but only in the negative phase of the AMV. For the positive phase, in agreement with the previous findings, the model response is very sensitive to the tropical branch of the AMV and unrealistic.

We inspect the 3-D structure of Marine Cold Air Outbreaks (MCAOs) and Polar Lows (PLs) in ERA5 data with the aim of improving diagnostic indices to capture these weather events in long-term assessments on seasonal and climatological time-scales. For this study, we designed a workflow that starts with the interactive 3-D visual exploration of single MCAO and PL events, using an extended version of the open-source visualization framework Met.3D, followed by the design and statistical testing of new diagnostic indices in long-term assessments. Results from the interactive visual data exploration provide insights into the complex 3-D shape and dynamics of MCAOs and PLs in ERA5 data. Motivated by the visual analysis of single cases, we extend widely-used diagnostics by conceptualizing a simple index to capture the vertical extent of the lower-level instability induced by MCAOs. Testing the association of diagnostic indices with observed PLs in the Barents and the Nordic Seas (STARS data) shows that, the new MCAO index introduced here has an important advantage: it is a more skillful indicator for distinguishing the times and locations of PLs, compared with previously-used indices. We thus propose the new index for further analyses in seasonal climate predictions and climatological studies. The methods for interactive 3-D visual data analysis presented here are available as a generic open-source tool for investigating atmospheric processes in ERA5 and other gridded meteorological data.

Seasonal prediction systems based on Earth System Models exhibit a lower proportion of predictable signal to unpredictable noise than the actual world. This puzzling phenomena has been widely referred to as the signal‐to‐noise paradox (SNP). Here, we investigate the SNP in a conceptual framework of a seasonal prediction system based on the Lorenz, 1963 Model (L63). We show that the SNP is not apparent in L63, if the uncertainty assumed for the initialization of the ensemble is equal to the uncertainty in the starting conditions. However, if the uncertainty in the initialization overestimates the uncertainty in the starting conditions, the SNP is apparent. In these experiments the metric used to quantify the SNP also shows a clear lead‐time dependency on subseasonal timescales. We therefore, formulate the alternative hypothesis to previous studies that the SNP could also be related to the magnitude of the initial ensemble spread., Plain Language Summary: Comprehensive Earth System Models seem to be better at predicting the real observed climate system than expected based on their ability to predict their own modelled climate system. This puzzling phenomena is known as the signal‐to‐noise paradox (SNP) and its origin is still under intensive scientific debate with some studies pointing to deficiencies in the model formulation. In this study we investigate under which conditions the SNP can be obtained using a simple conceptual framework for a climate prediction system based on a simple dynamical model. Our results show that the SNP can be reproduced in the absence of model deficiencies if the model overestimates the observational uncertainty. We also investigate the development of the SNP on subseasonal timescales and find a clear dependency on the lead‐time of the prediction. Our results lead us to formulate an alternative hypothesis to previous studies on the origin of the SNP., Key Points: Whether forecasts in the Lorenz Model are reliable or not depends on the ratio of initial ensemble spread to observational uncertainty Until predictability is lost in the Lorenz Model the level of over‐or underconfidence increases with increasing lead‐time, Copernicus Climate Change Service, Marine Institute and the European Regional Development fund, Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)

Seasonal prediction is one important element in a seamless prediction chain between weather forecasts and climate projections. After several years of development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the configuration of the previous system GCFS1.0 and the current GCFS2.0 are described and the performance of the two systems is compared over the common hindcast period of 1990–2014. In GCFS2.0, the forecast skill is improved compared to GCFS1.0 during boreal winter, especially for the Northern Hemisphere where the Pearson correlation has increased for the North Atlantic Oscillation index. Overall, a similar performance of GCFS2.0 in comparison to GCFS1.0 is assessed during the boreal summer. Future developments for climate forecasts need a stronger focus on the performance of interannual variability in a model system., Journal of Advances in Modeling Earth Systems, 13 (2), ISSN:1942-2466

The north Atlantic subpolar gyre (SPG) has been widely implicated as the source of large-scale changes in the subpolar marine environment. However, inconsistencies between indices of SPG-strength have raised questions about the active role SPG-strength and size play in determining water properties in the eastern subpolar North Atlantic (ENA). Here, by analyzing various SPG indices derived from observations and a global coupled model, we show that the choice of the SPG index dictates the interpretation of SPG strength-salinity relationship in the ENA. Variability in geostrophic currents derived from observed hydrography and model based Lagrangian trajectories reveal zonal shifts of advective pathways in the ENA and meridional shifts in the western intergyre region. Such shifts in advective pathways are manifestations of variability in the size and strength of the SPG, and they impact salinity by modulating the proportion of subpolar and subtropical waters reaching the ENA. SPG indices based on subsurface density and principal component analysis of sea surface height variability capture these shifts in advective pathways, and are therefore best suited to describe SPG-salinity relationship in the ENA. Our results establish the dynamical constraints on the choice of the SPG index and emphasize that SPG indices should be cautiously interpreted.

We investigate the impact of the strength of the Atlantic Meridional Overturning Circulation (AMOC) at 26° N on the prediction of North Atlantic sea surface temperature anomalies (SSTA) a season ahead. We consider the physical mechanism proposed by Duchez et al. (2016a) and test the dependence of SST predictive skill in initialised hindcasts on the phase of AMOC at 26° N. We use initialised simulations with the MPI-ESM-MR seasonal prediction system. First, we use the assimilation experiment between 1979–2014 to confirm that the AMOC leads a SSTA dipole pattern in the tropical and subtropical North Atlantic, with strongest AMOC fingerprints after 2–4 months. Going beyond previous studies, we find that the AMOC fingerprint has a seasonal dependence, and is sensitive to the length of the observational window used, i.e. stronger over the last decade than for the entire time series back to 1979. We then use a set of ensemble hindcast simulations with 30 members, starting each February, May, August and November between 1982 and 2014. We compare the changes in skill between composites based on the AMOC phase a month prior to each start date to simulations without considering the AMOC phase. We find higher SST hindcast skill at 2–4 months lead time for SSTA composites based on the AMOC phase for February, May and November start dates. Our method shows major benefit for May start dates, where mean summer SST hindcast skill over the subtropics increase by a factor of 2, reaching up to 80 % agreement with ERA-Interim SST.

Significant hindcast skill for the 3-month standardized precipitation index (SPI$_{3M}$) has been so far limited to one lead month. To increase that lead time, we propose to exploit well-known El N...

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis. Fil: Merryfield, William J.. Canadian Centre For Climate Modelling And Analysis; Canadá Fil: Baehr, Johanna. Universitat Hamburg; Alemania Fil: Batté, Lauriane. Centre National de Recherches Météorologiques; Francia Fil: Becker, Emily J.. No especifíca; Fil: Butler, Amy H.. State University of Colorado at Boulder; Estados Unidos Fil: Coelho, Caio A.S.. Instituto Nacional de Pesquisas Espaciais; Brasil Fil: Danabasoglu, Gokhan. National Center for Atmospheric Research; Estados Unidos Fil: Dirmeyer, Paul A.. No especifíca; Fil: Doblasreyes, Francisco. No especifíca; Fil: Domeisen, Daniela I.V.. No especifíca; Fil: Ferranti, Laura. No especifíca; Fil: Ilynia, Tatiana. No especifíca; Fil: Kumar, Arun. No especifíca; Fil: Müller, Wolfgang A.. No especifíca; Fil: Rixen, Michel. World Meteorological Organization; Estados Unidos Fil: Robertson, Andrew W.. International Research Institute For Climate And Society; Estados Unidos Fil: Smith, Doug M.. No especifíca; Fil: Takaya, Yuhei. Japan Meteorological Agency; Japón Fil: Tuma, Matthias. World Meteorological Organization; Estados Unidos Fil: Vitart, Frederic. No especifíca; Fil: White, Christopher J.. University of Strathclyde; Reino Unido Fil: Alvarez, Mariano Sebastián. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Ardilouze, Constantin. No especifíca; Fil: Attard, Hannah. No especifíca; Fil: Baggett, Cory. State University of Colorado - Fort Collins; Estados Unidos Fil: Balmaseda, Magdalena A.. No especifíca; Fil: Beraki, Asmerom F.. University Of Pretoria; Sudáfrica Fil: Bhattacharjee, Partha S.. National Oceanic And Atmospheric Administration; Estados Unidos Fil: Bilbao, Roberto. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España Fil: De Andrade, Felipe M.. University of Reading; Reino Unido Fil: DeFlorio, Michael J.. No especifíca; Fil: Díaz, Leandro Baltasar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Ehsan, Muhammad Azhar. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; Italia Fil: Fragkoulidis, Georgios. Johannes Gutenberg Universitat Mainz; Alemania Fil: Grainger, Sam. No especifíca; Fil: Green, Benjamin W.. State University of Colorado at Boulder; Estados Unidos Fil: Hell, Momme C.. No especifíca; Fil: Infanti, Johnna M.. National Oceanic And Atmospheric Administration; Estados Unidos Fil: Isensee, Katharina. No especifíca; Fil: Kataoka, Takahito. No especifíca; Fil: Kirtman, Ben P.. No especifíca; Fil: Klingaman, Nicholas P.. University of Reading; Reino Unido Fil: Lee, June Yi. Pusan National University; Corea del Sur Fil: Mayer, Kirsten. State University of Colorado at Boulder; Estados Unidos Fil: McKay, Roseanna. Monash University; Australia Fil: Mecking, Jennifer V.. University of Southampton; Reino Unido Fil: Miller, Douglas E.. University of Illinois. Urbana - Champaign; Estados Unidos Fil: Neddermann, Nele. No especifíca; Fil: Ng, Ching Ho Justin. University of Princeton; Estados Unidos Fil: Ossó, Albert. University of Graz; Austria Fil: Pankatz, Klaus. Max Planck Institute For Meteorology; Estados Unidos Fil: Peatman, Simon. No especifíca; Fil: Pegion, Kathy. George Mason University; Estados Unidos Fil: Perlwitz, Judith. State University of Colorado at Boulder; Estados Unidos Fil: Recalde Coronel, G. Cristina. Escuela Superior Politecnica del Litoral ; Ecuador Fil: Reintges, Annika. Geomar-Helmholtz Centre for Ocean Research Kiel; Alemania Fil: Renkl, Christoph. Dalhousie University Halifax; Canadá Fil: Solaraju-Murali, Balakrishnan. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España Fil: Spring, Aaron. No especifíca; Fil: Stan, Cristiana. George Mason University; Estados Unidos Fil: Sun, Y. Qiang. University of Princeton; Estados Unidos Fil: Tozer, Carly R.. No especifíca; Fil: Vigaud, Nicolas. No especifíca; Fil: Woolnough, Steven. University of Reading; Reino Unido Fil: Yeager, Stephen. National Center for Atmospheric Research; Estados Unidos

Marine cold air outbreaks (MCAOs) in the Arctic are associated with a range of severe weather phenomena, such as polar lows, strong surface winds and intense cooling of the ocean surface. While MCAO frequency has been linked to the strength of the stratospheric polar vortex, a connection to the occurrence of extreme stratospheric events, known as sudden stratospheric warmings (SSWs), has dominantly been investigated with respect to cold extremes over land. Here, the influence of SSW events on MCAOs in the Barents Sea is studied using observational and reanalysis datasets. Overall, more than a half of SSW events lead to more frequent MCAOs in the Barents Sea. SSW events with an enhanced MCAO response in the Barents Sea are associated with a ridge over Greenland and a trough over Scandinavia, leading to an anomalous dipole pattern of 500-hPa geopotential height and strong northerly flow over the Norwegian Sea. As SSW events tend to have a long-term influence on surface weather, these results can shed light on the predictability of MCAOs in the Arctic for winters with SSW events.

The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within literature. This study illuminates the long-standing dispute and proposes a solution which ensures the normality of the index for all common accumulation periods in observations and simulations.We compare the normality of SPI time-series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison evaluates actual against theoretical occurrence probabilities of SPI categories, and the quality of the fit of candidate distribution functions against their complexity with Akaike's Information Criterion. SPI time-series, spanning 1983–2013, are calculated from Global Precipitation Climatology Project's monthly precipitation data-set and seasonal precipitation hindcasts from the Max Planck Institute Earth System Model. We evaluate these SPI time-series over the global land area and for each continent individually during winter and summer. While focusing on an accumulation period of 3-months, we additionally test the drawn conclusions for other common accumulation periods (1-, 6-, 9-, and 12-months).Our results suggest to exercise caution when using the gamma distribution to calculate SPI; especially in simulations or their evaluation. Further, our analysis shows a distinctly improved normality for SPI time-series derived with the exponentiated Weibull distribution relative to other distributions. The use of the exponentiated Weibull distribution maximizes the normality of SPI time-series in observations and simulations both individual as well as concurrent. Its use further maximizes the normality of SPI time-series over each continent and for every investigated accumulation period. We, therefore, advocate to derive SPI with the exponentiated Weibull distribution, irrespective of the heritage of the precipitation data or the length of analyzed accumulation periods.

Arctic coastal erosion experiences pronounced effects from ongoing climate change. The Laptev Sea figures among the Arctic regions with the most severe erosion rates. Here, we use unprecedentedly long records of almost 30 years of annual in-situ coastal erosion rate measurements from Bykovsky Peninsula and Muostakh Island to separate the main modes of variability, which we attribute to large-scale drivers. The first (lower-frequency) and second (higher-frequency) modes are associated with winter sea-ice cover in the Laptev Sea and with the Arctic Oscillation, respectively, which together account for (Formula presented.) of the total observed variance. Arctic coastal erosion has so far been neglected in Earth system models (ESMs). The proposed mechanisms set favorable conditions for coastal erosion at large scales (synoptic to planetary scales), compatible with those represented in modern ESMs. ©2020. American Geophysical Union. All Rights Reserved.

On interannual to decadal time scales, memory in the Earth's climate system resides to a large extent in the slowly varying heat content of the ocean, which responds to fast atmospheric variability and in turn sets the frame for large-scale atmospheric circulation patterns. This large-scale coupled atmosphere–ocean feedback is generally well represented in today's Earth system models. This may fundamentally change when data assimilation is used to bring such models close to an observed state to initialize interannual to decadal climate predictions. Here, we review how the large-scale coupled atmosphere–ocean feedback is preserved in common approaches to construct such initial conditions, with the focus on the initialized ocean state. In a set of decadal prediction experiments, ranging from an initialization of atmospheric variability only to full-field nudging of both atmosphere and ocean, we evaluate the variability and predictability of the Atlantic meridional overturning circulation, of the Atlantic multidecadal variability and North Atlantic subpolar gyre sea surface temperatures. We argue that the quality of initial conditions for decadal predictions should not purely be assessed by their closeness to observations, but also by the closeness of their respective predictions to observations. This prediction quality may depend on the representation of the simulated large-scale atmosphere–ocean feedback. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models

International audience; We investigate how ocean-driven multidecadal sea surface temperature (SST) variations force the atmosphere to jointly set the pace of Atlantic multidecadal variability (AMV). We generate periodic low-frequency Atlantic Meridional Overturning Circulation oscillations by implementing time-dependent deep-ocean-density restoring in MPI-ESM1.2 to explicitly identify variations driven by Atlantic Meridional Overturning Circulation without any perturbation at the ocean-atmosphere interface. We show in a coupled experiment that ocean heat convergence variations generate positive SST anomalies, turbulent heat release, and low sea level pressure in the subpolar North Atlantic (NA) and vice versa. The SST signal is communicated to the tropical NA by wind-evaporative-SST feedbacks and to the NorthEast Atlantic by enhanced northward atmospheric heat transport. Such atmospheric feedbacks and the characteristic AMV-SST pattern are synchronized to the multidecadal time scale of ocean circulation changes by air-sea heat exchange. This coupled ocean-atmosphere mechanism is consistent with observed features of AMV and thus supports a key role of ocean dynamics in driving the AMV.

Current state-of-the-art dynamical seasonal ensemble prediction systems (EPS) still show limited predictive skill, particularly over Europe in summer. We propose a neural network-based classification of individual ensemble members before performing a hindcast skill analysis. This classification targets high skill cases emerging from large-scale atmospheric regimes associated with the dominant modes of summertime low-frequency variability in the North Atlantic-European sector (NAE). This classification allows to then select those ensemble members that better predict the phase of the summer North Atlantic Oscillation (SNAO) and East Atlantic Pattern (EAP). Our baseline is a set of teleconnection patterns in NAE identified by Self-Organising Maps (SOM) using ERA-20C reanalysis data. We illustrate our methodology with an example with one set of hindcast ensemble simulations with 30-members generated by the MPI Earth System Model. We achieve better predictive skills at 3-4 months lead time for sea level pressure and geopotential height anomalies at 500 hPa. © 2020 Owner/Author.