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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 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 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.

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.

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 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 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.

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

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.

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.

* This essay stems from international conferences on subseasonal to seasonal and seasonal to decadal prediction jointly convened by WWRP and WCRP in September 2018 in Boulder, Colorado: (www.wcrp-climate.org/s2s-s2d-2018-home). Adapted from “Current and Emerging Developments in Subseasonal to Decadal Prediction,”. Published Online in BAMS, June 2020. For the full, citable article, see DOI:10.1175 /BAMS-D-19-0037.1. © Copyright 2020 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). Tremendous recent progress in climate prediction on subseasonal to decadal time scales has been enabled by better observations, data assimilation, and models originating from the weather prediction and climate simulation communities together with ever-increasing computational power. World Climate Research Program (WCRP) efforts led initially to predictions one to two seasons ahead becoming part of the WMO operational infrastructure. More recently, a joint World Weather Research Program (WWRP) and WCRP Subseasonal to Seasonal Prediction Project has started tackling the weather–climate gap (from two weeks to a season). The NOAA-led Subseasonal Experiment project has similar aims. New frontiers have been enabled by Earth system models that represent the carbon and other biogeochemical cycles in addition to the physical climate system. As a result, skillful multiyear prediction is likely achievable for biogeochemical and ecological Earth system components. The ultimate collective subseasonal to seasonal (S2S; 2 weeks to season) and seasonal to decadal (S2D) endeavor is to improve the prediction of the spatial–temporal continuum connecting weather to climate through a coordinated, seamless, and integrated Earth system approach. The S2S and S2D communities share common scientific and technical challenges. This essay* synthesizes those commonalities across time scales and Earth system components, and from basic research to operational delivery. Peer Reviewed Article signat per 65 autors/es: William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia, Arun Kumar, Wolfgang A. Müller, Michel Rixen, Andrew W. Robertson, Doug M. Smith, Yuhei Takaya, Matthias Tuma, Frederic Vitart, Christopher J. White, Mariano S. Alvarez, Constantin Ardilouze, Hannah Attard, Cory Baggett, Magdalena A. Balmaseda, Asmerom F. Beraki, Partha S. Bhattacharjee, Roberto Bilbao, Felipe M. de Andrade, Michael J. DeFlorio, Leandro B. Díaz, Muhammad Azhar Ehsan, Georgios Fragkoulidis, Sam Grainger, Benjamin W. Green, Momme C. Hell, Johnna M. Infanti, Katharina Isensee, Takahito Kataoka, Ben P. Kirtman, Nicholas P. Klingaman, June-Yi Lee, Kirsten Mayer, Roseanna McKay, Jennifer V. Mecking, Douglas E. Miller, Nele Neddermann, Ching Ho Justin Ng, Albert Ossó, Klaus Pankatz, Simon Peatman, Kathy Pegion, Judith Perlwitz, G. Cristina Recalde-Coronel, Annika Reintges, Christoph Renkl, Balakrishnan Solaraju-Murali, Aaron Spring, Cristiana Stan, Y. Qiang Sun, Carly R. Tozer, Nicolas Vigaud, Steven Woolnough, and Stephen Yeager.

Current state-of-the-art dynamical seasonal prediction systems still show limited skill, particularly over Europe in summer. To circumvent this, we propose a neural network-based classification of individual ensemble members at the initialisation of summer climate predictions, prior to performing a skill analysis. Different from European winter climate, largely dominated by the North Atlantic Oscillation, predictability of European summer climate has been associated with several physical mechanisms, including teleconnections with the tropics. Recent studies have shown that predictive skill improves when the dominant physical processes in a given season are identified at the initialisation of a prediction. Each of these dominant physical processes is associated with large-scale circulation patterns, often depicted by modes of Empirical Orthogonal Functions (EOF). We argue that Self-Organising Maps (SOM), a type of neural network classifier, can provide further insight on interpreting the predictive skill of mixed resolution hindcast ensemble simulations generated by MPI-ESM. This is achieved by identifying which circulation patterns over the North Atlantic-European sector (NAE) at the initialisation of hindcasts lead to more predictable states than others, their preferable transition states, and whether the spatial structure of each SOM mode play a role in shaping climate over Europe. We train SOM networks on sea level pressure fields of ERA-20C reanalysis at the initialisation of the seasonal prediction system (every May) for the period of 1900-2010, covering NAE. We compare the SOM-derived modes with circulation patterns derived from EOF analysis, and characterise each class of circulation regime. This analysis is used to distinguish classes of predictions with two different sets of MPI-ESM initialised simulations with 10 and 30 members, covering the period of 1902-2008 and 1982-2016, respectively. We then discuss the differences and advantages of performing a neural interpretation of the skill of an ensemble prediction, over traditional skill analysis.

The long term variability and its predictability of the monthly mean oceanic surface net freshwater fluxes is compared in a set of retrospective predictions. All are using the same model setup, and only differ in the implemented ocean initialisation method and ensemble generation method. The basic aim is to deduce the differences between the initialization/ensemble generation methods in view of the uncertainty of the verifying observational data sets. The analysis will give an approximation of the uncertainties of the net freshwater fluxes, which up to now appear to be one of the most uncertain products in observational data and model outputs. All ensemble generation methods are implemented into the MPI-ESM earth system model in the framework of the ongoing MiKlip project (www.fona-miklip.de). Hindcast experiments are initialised annually between 2000–2004, and from each start year 8 ensemble members are run for 10 years forward. Four different ensemble generation methods are compared: (i) a method based on the Anomaly Transform method in which the initial oceanic perturbations represent orthogonal and balanced anomaly structures in space and time and between the variables taken from a control run, (ii) one-day-lagged ocean states from the MPI-ESM-LR Baseline 1 system, (iii) one-day-lagged ocean and atmospheric states with preceding full-field nudging to re-analysis in the atmospheric and anomaly nudging in the oceanic component of the system – the Baseline MPI-ESM-LR system, (iv) an Ensemble Kalman Filter (EnKF) implemented into oceanic part of MPI-ESM, assimilating monthly subsurface oceanic temperature and salinity using the Parallel Data Assimilation Framework and full-field nudging in the atmosphere. The hindcasts are evaluated probabilistically using freshwater flux data set from NCEP-R2. On the global scale the physically motivated methods (i) and (iv) provide probabilistic hindcasts to some extent higher correlation and reliability than the lagged initialization methods (ii)/(iii) despite the large uncertainties in the verifying observations and in the simulations. We suggest similar approaches for further evaluations of other variables of decadal hindcasts systems.

International audience; We demonstrate in this paper that conventional time-averaged decadal hindcast skill estimates can overestimate or underestimate the credibility of an individual decadal climate forecast. We show that hindcast skill in a long period can be higher or lower than skill in its subperiods. Instead of using time-averaged hindcast skill measures, we propose to use the physical state of the climate system at the beginning of the forecast to judge its credibility. We analyze hindcasts of North Atlantic sea surface temperature (SST) in an initialized prediction system based on the MPI-ESM-LR for the period 1901-2010. Subpolar North Atlantic Ocean heat transport (OHT) strength at hindcast initialization largely determines the skill of these hindcasts: We find high skill after anomalously strong or weak OHT, but low skill after average OHT. This knowledge can be used to constrain conventional hindcast skill estimates to improve the assessment of credibility for a decadal forecast. Plain Language Summary Credible predictions of climate up to 10 years into the future, so-called decadal climate predictions, can be a potent tool for decision makers. However, previous work indicated that such predictions are sometimes credible and sometimes not. This study illustrates that knowing the physical state of the climate system at the start of a decadal climate prediction helps assessing the credibility of that prediction. Analyzing sea surface temperature in the North Atlantic as a case study, we show that northward heat transport in the ocean provides a good indicator of the credibility of decadal predictions in the North Atlantic. Unlike previous studies, we do not only analyze time-averaged prediction credibility, but look at individual predictions in the past. This makes our findings particularly relevant for individual forecasts and decision makers.

While the influence of the subpolar gyre (SPG) on thermohaline variability in the eastern North Atlantic is well documented, the extent and timescale of the influence of the SPG on North Sea is not well understood. This is primarily because earlier investigations on the causes of variability in the North Sea water properties mostly focused on the role of atmosphere and deployed regional models. Here using a historical simulation with the Max Planck Institute Earth System Model (MPI-ESM), we investigate circulation and water mass variability in key regions, namely, the Rockall Trough and the Faroe-Scotland Channel, which link the North Atlantic to the North Sea. We find that salinity covaries with advective lags in these three regions and that the northern North Sea salinity follows the Rockall Trough with a lag of 1 year. We show that recurring and persistent excursions of salinity anomalies into the northern North Sea are related to the SPG strength and not to the local acceleration of the inflow. Furthermore, we illustrate that the SPG signal is more pronounced in salinity than in temperature and that this simulated SPG signal has a period of 30–40 years. Overall, our study suggests that, at low frequency, water mass variability originating in the North Atlantic dominates changes in the North Sea water properties over those due to local wind-driven volume transport. ©2019. American Geophysical Union. All Rights Reserved.

Our decadal climate prediction system, which is based on the Max-Planck-Institute Earth System Model, is initialized from a coupled assimilation run that utilizes nudging to selected state parameters from reanalyses. We apply full-field nudging in the atmosphere and either full-field or anomaly nudging in the ocean. Full fields from two different ocean reanalyses are considered. This comparison of initialization strategies focuses on the North Atlantic Subpolar Gyre (SPG) region, where the transition from anomaly to full-field nudging reveals large differences in prediction skill for sea surface temperature and ocean heat content (OHC). We show that nudging of temperature and salinity in the ocean modifies OHC and also induces changes in mass and heat transports associated with the ocean flow. In the SPG region, the assimilated OHC signal resembles well OHC from observations, regardless of using full fields or anomalies. The resulting ocean transport, on the other hand, reveals considerable differences between full-field and anomaly nudging. In all assimilation runs, ocean heat transport together with net heat exchange at the surface does not correspond to OHC tendencies, the SPG heat budget is not closed. Discrepancies in the budget in the cases of full-field nudging exceed those in the case of anomaly nudging by a factor of 2–3. The nudging-induced changes in ocean transport continue to be present in the free running hindcasts for up to 5 years, a clear expression of memory in our coupled system. In hindcast mode, on annual to inter-annual scales, ocean heat transport is the dominant driver of SPG OHC. Thus, we ascribe a significant reduction in OHC prediction skill when using full-field instead of anomaly initialization to an initialization shock resulting from the poor initialization of the ocean flow.

An analysis of a three-member ensemble of initialized coupled simulations with the MPI-ESM-LR covering the period 1901–2010 shows that Atlantic northward ocean heat transport (OHT) at 50°N influences surface temperature variability in the North Atlantic region for several years. Three to ten years after strong OHT phases at 50°N, a characteristic pattern of sea surface temperature (SST) anomalies emerges: warm anomalies are found in the North Atlantic and cold anomalies emerge in the Gulf Stream region. This pattern originates from persistent upper-ocean heat content anomalies that originate from southward-propagating OHT anomalies in the North Atlantic. Interannual-to-decadal SST predictability of yearly initialized hindcasts is linked to this SST pattern: when ocean heat transport at 50°N is strong at the initialization of a hindcast, SST anomaly correlation coefficients in the northeast Atlantic at lead years 2–9 are significantly higher than when the ocean heat transport at 50°N is weak at initialization. Surface heat fluxes that mask the predictable low-frequency oceanic variability that influences SSTs in the northwest Atlantic after strong OHT phases, and in the northwest and northeast Atlantic after weak OHT phases at 50°N lead to zonally asymmetrically predictable SSTs 7–9 years ahead. This study shows that the interannual-to-decadal predictability of North Atlantic SSTs depends strongly on the strength of subpolar ocean heat transport at the start of a prediction, indicating that physical mechanisms need to be taken into account for actual temperature predictions.

The MPI‐ESM1.2 is the latest version of the Max Planck Institute Earth System Model and is the baseline for the Coupled Model Intercomparison Project Phase 6 and current seasonal and decadal climate predictions. This paper evaluates a coupled higher‐resolution version (MPI‐ESM1.2‐HR) in comparison with its lower‐resolved version (MPI‐ESM1.2‐LR). We focus on basic oceanic and atmospheric mean states and selected modes of variability, the El Niño/Southern Oscillation and the North Atlantic Oscillation. The increase in atmospheric resolution in MPI‐ESM1.2‐HR reduces the biases of upper‐level zonal wind and atmospheric jet stream position in the northern extratropics. This results in a decrease of the storm track bias over the northern North Atlantic, for both winter and summer season. The blocking frequency over the European region is improved in summer, and North Atlantic Oscillation and related storm track variations improve in winter. Stable Atlantic meridional overturning circulations are found with magnitudes of ~16 Sv for MPI‐ESM1.2‐HR and ~20 Sv for MPI‐ESM1.2‐LR at 26°N. A strong sea surface temperature bias of ~5°C along with a too zonal North Atlantic current is present in both versions. The sea surface temperature bias in the eastern tropical Atlantic is reduced by ~1°C due to higher‐resolved orography in MPI‐ESM‐HR, and the region of the cold‐tongue bias is reduced in the tropical Pacific. MPI‐ESM1.2‐HR has a well‐balanced radiation budget and its climate sensitivity is explicitly tuned to 3 K. Although the obtained reductions in long‐standing biases are modest, the improvements in atmospheric dynamics make this model well suited for prediction and impact studies.

The observed prominent multidecadal variations in the central to eastern (C--E) European summer temperature are closely related to the Atlantic multidecadal variability (AMV). Using the Twentieth Century Reanalysis project version 2 data for the period of 1930--2012, we present a mechanism by which the multidecadal variations in the C--E European summer temperature are associated to a linear baroclinic atmospheric response to the AMV-related surface heat flux. Our results suggest that over the north-western Atlantic, the positive heat flux anomaly triggers a surface baroclinic pressure response to diabatic heating with a negative surface pressure anomaly to the east of the heat source. Further downstream, this response induces an east-west wave-like pressure anomaly. The east-west wave-like response in the sea level pressure structure, to which we refer as North-Atlantic-European East West (NEW) mode, is independent of the summer North Atlantic Oscillation and is the principal mode of variations during summer over the Euro-Atlantic region at multidecadal time scales. The NEW mode causes warming of the C--E European region by creating an atmospheric blocking-like situation. Our findings also suggest that this NEW mode is responsible for the multidecadal variations in precipitation over the British Isles and north-western Europe.

We investigate changes in the seasonal cycle of the Atlantic Ocean meridional heat transport (OHT) in a climate projection experiment with the Max Planck Institute Earth System Model (MPI-ESM) performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). Specifically, we compare a Representative Concentration Pathway (RCP) RCP 8.5 climate change scenario, covering the simulation period from 2005 to 2300, to a historical simulation, covering the simulation period from 1850 to 2005. In RCP 8.5, the OHT declines by 30–50 % in comparison to the historical simulation in the North Atlantic by the end of the 23rd century. The decline in the OHT is accompanied by a change in the seasonal cycle of the total OHT and its components. We decompose the OHT into overturning and gyre component. For the OHT seasonal cycle, we find a northward shift of 5° and latitude-dependent shifts between 1 and 6 months that are mainly associated with changes in the meridional velocity field. We find that the changes in the OHT seasonal cycle predominantly result from changes in the wind-driven surface circulation, which projects onto the overturning component of the OHT in the tropical and subtropical North Atlantic. This leads in turn to latitude-dependent shifts between 1 and 6 months in the overturning component. In comparison to the historical simulation, in the subpolar North Atlantic, in RCP 8.5 we find a reduction of the North Atlantic Deep Water formation and changes in the gyre heat transport result in a strongly weakened seasonal cycle with a weakened amplitude by the end of the 23rd century. ISSN:2190-4987 ISSN:2190-4979

We evaluate the ensemble spread at seasonal-to-interannual timescales for two perturbation techniques implemented in the ocean component of a coupled model: (1) lagged initial conditions as commonly used for decadal predictions; (2) bred vectors as commonly used for weather and seasonal forecasting. We show that relative to an uninitialized reference simulation the implementation for bred vectors can improve the ensemble spread compared to lagged initialization at timescales from one month up to three years. As bred vectors have so far mostly been used at short timescales, we initially focus on the implementation of the bred vectors in the ocean component. We introduce a depth-dependent vertical rescaling norm, accounting for the vertical dependence of the variability, and extending the commonly used upper-ocean rescaling norm to the full water column. We further show that it is sufficient for the (sub-surface) ocean to breed temperature and salinity (i.e., scalar quantities), and rely on the governing physics to carry the temperature and salinity perturbations to the flow field. Using these bred vectors with a rescaling interval of 12 months, we initialize hindcast simulations and compare them to hindcast simulations initialized with lagged initial conditions. We quantify the ensemble spread by analyzing Talagrand diagrams and spread–error ratios. For both temperature and salinity, the lagged initialized ensemble is particularly under-dispersive for the first few months of predictable lead time. The ensemble initialized with bred vectors improves the spread for temperature and salinity for the 0–700 m and 1000–3500 m means, compared to the lagged ensemble at lead times of several months to one year. As the lead time increases to years, the differences between the two ensemble initialization techniques become more difficult to discern. While the results need to be confirmed in an initialized framework, the present analysis represents a first step towards improved ensemble generation at the transition from seasonal to interannual timescales, in particular at lead times up to one year.

We analyse the hindcast skill for the Atlantic meridional overturning circulation (AMOC) against 10 years of RAPID/MOCHA AMOC observations, which are now long enough to remove the mean seasonal cycle prior to the hindcast skill analysis. We analyse AMOC hindcast skill in two hindcast ensembles generated with two differently initialised decadal prediction systems that are both based on the earth system model MPI-ESM. We evaluate the hindcast skill for the AMOC and its components in both prediction systems against RAPID/MOCHA observations both with and without the mean seasonal cycle removed using anomaly correlation (COR) and root-mean-square error as skill measures. We find significant hindcast skill for most lead years up to 5 for monthly-mean AMOC variations only in the newer of the two prediction systems and only using COR, but with and without the mean seasonal cycle removed. In both systems and for all analysed lead years, the two geostrophic transport components (the upper-mid-ocean transport and Florida Strait combined, that is: AMOC minus Ekman) are the main source of hindcast skill. In the present model setup and with the currently available observational time series, we cannot relate AMOC hindcast skill to the upper-mid-ocean transport alone. Yet, we can show that the seasonal variability of the upper-mid-ocean transport in the free coupled model originates from eastern boundary density variability. Overall, our results indicate modest yet robust AMOC hindcast skill above the uninitialized simulation, independent of the treatment of the seasonal cycle, although we cannot directly link this hindcast skill to the initialisation of the density field with either initialisation method.

Three methods are analyzed for the design of ocean observing systems to monitor the meridional overturning circulation (MOC) in the North Atlantic. Specifically, a continuous monitoring array to monitor the MOC at 1000 m at different latitudes is “deployed” into a numerical model. The authors compare array design methods guided by (i) physical intuition (heuristic array design), (ii) sequential optimization, and (iii) global optimization. The global optimization technique can recover the true global solution for the analyzed array design, while gradient-based optimization would be prone to misconverge. Both global optimization and heuristic array design yield considerably improved results over sequential array design. Global optimization always outperforms the heuristic array design in terms of minimizing the root-mean-square error. However, whether the results are physically meaningful is not guaranteed; the apparent success might merely represent a solution in which misfits compensate for each other accidentally. Testing the solution gained from global optimization in an independent dataset can provide crucial information about the solution’s robustness.

An analysis of a three-member ensemble of initialized coupled simulations with the MPI-ESM-LR covering the period 1901–2010 shows that Atlantic northward ocean heat transport (OHT) at 50°N influences surface temperature variability in the North Atlantic region for several years. Three to ten years after strong OHT phases at 50°N, a characteristic pattern of sea surface temperature (SST) anomalies emerges: warm anomalies are found in the North Atlantic and cold anomalies emerge in the Gulf Stream region. This pattern originates from persistent upper-ocean heat content anomalies that originate from southward-propagating OHT anomalies in the North Atlantic. Interannual-to-decadal SST predictability of yearly initialized hindcasts is linked to this SST pattern: when ocean heat transport at 50°N is strong at the initialization of a hindcast, SST anomaly correlation coefficients in the northeast Atlantic at lead years 2–9 are significantly higher than when the ocean heat transport at 50°N is weak at initialization. Surface heat fluxes that mask the predictable low-frequency oceanic variability that influences SSTs in the northwest Atlantic after strong OHT phases, and in the northwest and northeast Atlantic after weak OHT phases at 50°N lead to zonally asymmetrically predictable SSTs 7–9 years ahead. This study shows that the interannual-to-decadal predictability of North Atlantic SSTs depends strongly on the strength of subpolar ocean heat transport at the start of a prediction, indicating that physical mechanisms need to be taken into account for actual temperature predictions.