The El Ni & ntilde;o-Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, yet accurately simulating ENSO in climate models remains a major challenge due to its complex coupled dynamics. In this study, we present a linear optimization framework and systematically adjust atmospheric parameters to improve ENSO fidelity in the Icosahedral Nonhydrostatic eXtended Predictions and Projections (ICON XPP) Earth System Model of the Max-Planck-Institute for Meteorology. The optimization approach is based on the superposition of parameter sensitivities and a Nelder-Mead algorithm that reduces the ENSO cost function. The cost function accounts for ENSO-related tropical climatology, variability, and feedbacks, which are estimated with the ENSO metric package. We first assess the sensitivity of ENSO metrics to 21 atmospheric parameters in atmosphere-only simulations. The optimization approach reduces the ENSO cost function by 30 % in the optimized atmosphere-only runs. Key improvements include reduced precipitation bias and strengthened atmospheric feedbacks such as the Bjerknes and thermal damping feedbacks. These results demonstrate the effectiveness of our method in improving ENSO metrics within the atmosphere-only configuration. Six parameters identified as most impactful from atmosphere-only tuning experiments are subsequently tuned in fully coupled simulations. The optimized fully coupled run yields moderate improvements in ENSO amplitude, cold tongue SST bias, seasonal phase-locking, ocean-atmosphere coupling and teleconnection patterns. However, isolated ENSO tuning introduces unrealistic global warming, which is further corrected by adjusting turbulence-related parameters without degrading ENSO skill. These results demonstrate that systematic ENSO tuning can yield performance gains but must be balanced with broader climate stability constraints. Our method offers a scalable, physically grounded optimization strategy, with strong potential for tuning ENSO in climate model configurations.

The origin of multi-decadal climate variability in the North Atlantic is under debate. The variability could be caused by oceanic internal variability or by external anthropogenic or natural forcing. We have produced a set of single-forcing historical simulations with the Max Planck Institute - Earth System Model (MPI-ESM) in low resolution (LR). The historical-like simulations consists of 30 ensemble members and the external forcing is from the Coupled Model Intercomparison phase 6 (CMIP6). Each set of simulation is forced by either only greenhouse-gases, total ozone, solar insolation, anthropogenic aerosols or volcanic aerosols. We present first results of our attribution of the climate signals in the North Atlantic region to the different single forcings.

Decadal predictions have advanced greatly in recent years: not only have they become operational worldwide and have been demonstrated to be skillful in various aspects of climate variability, including predicting changes in the atmospheric circulation and in the occurrence of extremes several years ahead, but —as such— they are also being used increasingly in climate services. Climate adaptation and policy making, however, also require climate predictions that go beyond the 10-year horizon. For climate information beyond 10 years into the future, uninitialized climate projections, which completely miss any predictability stemming from internal variability, have been the only available product. Trying to account for this lack of information in climate projections regarding any predictable components of internal variability, methods to constrain climate projections using information from large ensembles of initialized decadal predictions have been developed and have been shown to reduce the uncertainty and increase the skill of climate projections, even beyond the 10-year horizon. The demonstrated benefits of such indirect methods to account for predictable internal variability indicate that the latter remains significant beyond the 10-year limit of decadal predictions. Hence, directly harnessing this predictability through running initialized 20-year predictions emerges as a strategic endeavour.In this study a novel, multi-system ensemble of initialized extended-decadal predictions is assessed. These predictions consist of a grand ensemble of 71 members derived from 6 forecast systems. They are initialized every 5 years from 1960 onward and run ahead for 20 years. Our analysis uses an elaborate drift- and bias-correction method that accounts for the correct representation of trends. Importantly, we show significant skill against observations for a number of variables (fields and indices), even in the second decade of the forecasts. The origin of such predictability is discussed together with the limitations of these 20-year predictions. The respective experimental protocol was defined in the framework of the ASPECT EU project and has been proposed as a tier-2 Decadal Climate Prediction Project (DCPP) protocol for the Coupled Model Intercomparison Project phase 7 (CMIP7).

A wide range of important societal and economic applications on national and international levels strive for an integrated understanding and forecasting of weather and climate, at high spatial resolution ranging from days to decades. The global to regional model system Icosahedral Nonhydrostatic (ICON) has been applied to weather as well as to climate time scales with joint developments of the model infrastructure. However, ICON's model configurations share the same dynamical core but differ substantially in their physical parameterization and the coupling of Earth system components, depending on whether they were designed for numerical weather prediction (NWP) or climate applications. Starting in 2020, a new modeling initiative has been launched a joint project between climate modeling institutes and the Deutscher Wetterdienst. The initiative "vertically" integrates NWP, climate predictions, climate projections, and atmospheric composition modeling based on the ICON framework and targets a unified treatment of the respective subgrid-scale parameterizations. This initiative aims at the development of coupled model configurations of ICON to conduct operational weather and ocean forecasts for several days, climate predictions with time scales up to 10 years ahead as well as climate projections, and it provides model baseline for joint research for NWP and climate. This paper illustrates the strategic direction of this modeling initiative, isolates key challenges, and reports on first results.

We develop a new Earth System model configuration framed into the ICON architecture, which provides the baseline for the next generation of climate predictions and projections (hereafter ICON XPP - where XPP stands for eXtended Predictions and Projections). ICON XPP comprises the atmospheric component of the numerical weather prediction (ICON NWP), the ICON ocean and land surface components, and an ensemble-variational data assimilation system, all adjusted to an Earth System model for pursuing climate research and operational climate forecasting. Two baseline configurations are presented: (1) a 160 km atmosphere and a 40 km ocean resolution, and (2) 80 km atmosphere and 20 km ocean resolution. A CMIP DECK (Diagnostic, Evaluation and Characterization of Klima) experimentation framework is used for a first evaluation.ICON XPP depicts the basic properties of the coupled climate. The pre-industrial climate shows a top-of-atmosphere balanced radiation budget and a mean global near-surface temperature of 13.8-14.0 degrees C. The ocean shows circulation strengths in the range of the observed values, such as the AMOC at 16-18 Sv and the flows through the common passages. The current climate is characterized by a trend in the global mean temperature of similar to 1.2 degrees C since the 1850s, similar to reference datasets. Regionally, the hydroclimate differs greatly from observed conditions. For example, the inter-tropical convergence zone (ITCZ) has a double peak and a wet southern subtropical branch across the oceans. Further, the Southern Ocean sea surface temperature has a strong positive mean bias with temperatures up to 5 degrees C higher than observations.Dynamical processes, such as El Ni & ntilde;o/Southern Oscillation (ENSO) performs similarly to CMIP6-like coupled models. Tropical waves and the Madden-Julian Oscillation are well captured, and the 40 km atmospheric configuration has a spontaneous weak quasi-biennial oscillation. The atmospheric dynamics in the northern extra-tropics of both configurations represent well the position of the jet stream as well as the influences of the transient momentum transports and their feedbacks on the jet stream. Overall, ICON XPP performs similarly to climate models performed in CMIP6 making it a good basis for climate forecasts and projections, and climate research.

The El Niño/Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, profoundly influencing global weather and climate systems. However, accurately simulating ENSO in climate models remains a major scientific challenge due to the complex coupled ocean-atmosphere interactions involved. Utilizing the ENSO Metrics Package, which evaluates tropical climatology, ENSO performance, and feedback biases, twenty-one atmospheric parameters related to cloud physics, microphysics, and turbulence schemes were tuned for ENSO simulations in the next-generation Max-Planck-Institute for Meteorology Earth System model, ICON XPP. Initial parameter perturbations were performed in AMIP simulations to estimate model sensitivities to each parameter. The optimal parameter combination for ENSO simulations was estimated based on the Nelder-Mead optimization scheme using the linear superposition of the parameter sensitivities. This approach effectively reduced the ENSO metrics cost function by 40% in the optimized run within AMIP experiments, including very good simulations of the Bjerknes and atmospheric net heat flux feedbacks. However, applying the optimized parameter sets to fully coupled ocean-atmosphere simulations resulted in very different parameter sensitivities and much less improved ENSO simulations. This discrepancy in the coupled model is largely related to very strong mean state changes in the Sea Surface Temperatures (SST) in the tropical. Direct tuning of parameters in coupled ICON XPP simulations will be explored in subsequent studies.

We present the development of the initialisation strategy for seasonal to decadal climate predictions based on the ICON-XPP model, within DWD’s Innovation in the Applied Research and Development (IAFE) program. The ICON-XPP model is based on several well-established model components: the ICON-NWP, operational weather forecast model at the DWD, as atmospheric model, ICON-O as ocean model, JSBACH as land model and uses a hydrological discharge model. To develop a weakly coupled data assimilation system for ICON-XPP, we use the experience build on a former ICON-ESM version (Pohlmann et al 2023). In our weakly coupled data assimilation framework, we use two different assimilation methods for atmosphere and ocean. We initialize the ocean component of the climate system through a monthly assimilation of salinity and temperature profiles from the EN4 dataset. For this we use a localised singular evolutive interpolated Kalman filter implemented via the Parallel Data Assimilation Framework (PDAF, Nerger 2020). The atmosphere component is initialised by nudging temperature, pressure and horizontal wind fields of the ERA5 reanalysis. We conduct our experiments with a 25-member ensemble, which we put together from three different historical runs started from different states of our piControl run. In the atmosphere ICON-XPP is run as R2B5 (~80km resolution) with 130 vertical levels and in the ocean we use a resolution of ~40km (R2B6) with 72 vertical levels. Our assimilation experiments start from 1990 after a ten-year assimilation spin-up of the ocean. We show the results of our experiments with the weakly coupled data assimilation system and discuss its challenges.

Abstract. To better understand possible reasons for the diverse modeling results and large discrepancies of the detected solar fingerprints, we took one step back and assessed the "initial" solar signals in the middle atmosphere based on large ensemble simulations with multiple climate models — FOCI, EMAC, and MPI-ESM-HR. Consistent with previous work, we find that the 11-year solar cycle signals in the short wave heating rate (SWHR) and ozone anomalies are robust and statistically significant in all three models. These "initial" solar cycle signals in SWHR, ozone, and temperature anomalies are sensitive to the strength of the solar forcing. Correlation coefficients of the solar cycle with the SWHR, ozone, and temperature anomalies linearly increase along with the enhancement of the solar cycle amplitude, and this reliance becomes more complex when the solar cycle amplitude exceeds a certain threshold. In addition, the cold bias in the tropical stratopause of EMAC dampens the subsequent results of the "initial" solar signal. The warm pole bias in MPI-ESM-HR leads to a weak polar night jet (PNJ), which may limit the top-down propagation of the initial solar signal. Although FOCI simulated a so-called top-down response as revealed in previous studies in a period with large solar cycle amplitudes, its warm bias in the tropical upper stratosphere results in a positive bias in PNJ and can lead to a "reversed" response in some extreme cases. We suggest a careful interpretation of the single model result and further re-examination of the solar signal based on more climate models.

Following efforts from leading centres for climate forecasting, sustained routine operational near-term climate predictions (NTCP) are now produced that bridge the gap between seasonal forecasts and climate change projections offering the prospect of seamless climate services. Though NTCP is a new area of climate science and active research is taking place to increase understanding of the processes and mechanisms required to produce skillful predictions, this significant technical achievement combines advances in initialisation with ensemble prediction of future climate up to a decade ahead. With a growing NTCP database, the predictability of the evolving externally-forced and internally-generated components of the climate system can now be quantified. Decision-makers in key sectors of the economy can now begin to assess the utility of these products for informing climate risk and for planning adaptation and resilience strategies up to a decade into the future. Here, case studies are presented from finance and economics, water management, agriculture and fisheries management demonstrating the emerging utility and potential of operational NTCP to inform strategic planning across a broad range of applications in key sectors of the global economy.

Abstract. Studies concerning solar–terrestrial connections over the last decades claim to have found evidence that the quasi-decadal solar cycle can have an influence on the dynamics in the middle atmosphere in the Northern Hemisphere (NH) during the winter season. It has been argued that feedbacks between the intensity of the UV part of the solar spectrum and low-latitude stratospheric ozone may produce anomalies in meridional temperature gradients which have the potential to alter the zonal-mean flow in middle to high latitudes. Interactions between the zonal wind and planetary waves can lead to a downward propagation of the anomalies, produced in the middle atmosphere, down to the troposphere. More recently, it has been proposed that top-down-initiated decadal solar signals might modulate surface climate and synchronize the North Atlantic Oscillation. A realistic representation of the solar cycle in climate models was suggested to significantly enhance decadal prediction skill. These conclusions have been debated controversial since then due to the lack of realistic decadal prediction model setups and more extensive analysis. In this paper we aim for an objective and improved evaluation of possible solar imprints from the middle atmosphere to the surface and with that from head to toe. Thus, we analyze model output from historical ensemble simulations conducted with the state-of-the-art Max Planck Institute for Meteorology Earth System Model in high-resolution configuration (MPI-ESM-HR). The target of these simulations was to isolate the most crucial model physics to foster basic research on decadal climate prediction and to develop an operational ensemble decadal prediction system within the “Mittelfristige Klimaprognose” (MiKlip) framework. Based on correlations and multiple linear regression analysis we show that the MPI-ESM-HR simulates a realistic, statistically significant and robust shortwave heating rate and temperature response at the tropical stratopause, in good agreement with existing studies. However, the dynamical response to this initial radiative signal in the NH during the boreal winter season is weak. We find a slight strengthening of the polar vortex in midwinter during solar maximum conditions in the ensemble mean, which is consistent with the so-called “top-down” mechanism. The individual ensemble members, however, show a large spread in the dynamical response with opposite signs in response to the solar cycle, which might be a result of the large overall internal variability compensating for rather small solar imprints. We also analyze the possible surface responses to the 11-year solar cycle and review the proposed synchronization between the solar forcing and the North Atlantic Oscillation. We find that the simulated westerly wind anomalies in the lower troposphere, as well as the anomalies in the mean sea level pressure, are most likely independent from the timing of the solar signal in the middle atmosphere and the alleged top-down influences. The pattern rather reflects the decadal internal variability in the troposphere, mimicking positive and negative phases of the Arctic and North Atlantic oscillations throughout the year sporadically, which is then assigned to the solar predictor time series without any plausible physical connection and sound solar contribution. Finally, by applying lead–lag correlations, we find that the proposed synchronization between the solar cycle and the decadal component of the North Atlantic Oscillation might rather be a statistical artifact, affected for example by the internal decadal variability in the ocean, than a plausible physical connection between the UV solar forcing and quasi-decadal variations in the troposphere.

Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.

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.

A multi-model study is carried out to investigate the ability of models to predict the evolution of the quasi-biennial oscillation (QBO) up to 12 months in advance. All models are initialised from common reanalysis data, and forecasts run for a common set of 30 start dates over 15 years. All models have high skill in predicting the phase evolution of the QBO at 20-30 hPa, with slightly more variable results at higher and lower levels. Other aspects of the predicted QBO are of variable quality, and in some cases are consistently poor. QBO easterlies are too weak in all models at 20-50 hPa, while westerlies can be either too strong or too weak. This results in both a reduced amplitude of the QBO and a westerly bias in zonal-mean winds, notably at 30 hPa. At 70 hPa models tend to have reduced QBO amplitude and an easterly bias. Despite these failings, a multi-model ensemble of bias- and variance-corrected forecasts can be used to give accurate and reliable QBO forecasts up to at least a year ahead. Analysis of the zonal momentum budget during the first month of the forecast shows that large-scale forcing from Eliassen-Palm flux divergence and vertical advection are handled fairly well by the models, although vertical advection terms tend to be weaker than reanalysis estimates. Total tendencies show common errors, suggesting common failings in gravity-wave drag treatments. Teleconnections from the QBO to Northern Hemisphere winter circulation are also examined, and do not appear to be realistic beyond the first month. Analysis of initialised forecasts is a powerful tool for diagnosing the accuracy of model processes driving the QBO.

* This work documents ICON-ESM 1.0, the first version of a coupled model based 19 on the ICON framework 20 * Performance of ICON-ESM is assessed by means of CMIP6 DECK experiments 21 at standard CMIP-type resolution 22 * ICON-ESM reproduces the observed temperature evolution. Biases in clouds, winds, 23 sea-ice, and ocean properties are larger than in MPI-ESM. Abstract 25 This work documents the ICON-Earth System Model (ICON-ESM V1.0), the first cou-26 pled model based on the ICON (ICOsahedral Non-hydrostatic) framework with its un-27 structured, icosahedral grid concept. The ICON-A atmosphere uses a nonhydrostatic dy-28 namical core and the ocean model ICON-O builds on the same ICON infrastructure, but 29 applies the Boussinesq and hydrostatic approximation and includes a sea-ice model. The 30 ICON-Land module provides a new framework for the modelling of land processes and 31 the terrestrial carbon cycle. The oceanic carbon cycle and biogeochemistry are repre-32 sented by the Hamburg Ocean Carbon Cycle module. We describe the tuning and spin-33 up of a base-line version at a resolution typical for models participating in the Coupled 34 Model Intercomparison Project (CMIP). The performance of ICON-ESM is assessed by 35 means of a set of standard CMIP6 simulations. Achievements are well-balanced top-of-36 atmosphere radiation, stable key climate quantities in the control simulation, and a good 37 representation of the historical surface temperature evolution. The model has overall bi-38 ases, which are comparable to those of other CMIP models, but ICON-ESM performs 39 less well than its predecessor, the Max Planck Institute Earth System Model. Problem-40 atic biases are diagnosed in ICON-ESM in the vertical cloud distribution and the mean 41 zonal wind field. In the ocean, sub-surface temperature and salinity biases are of con-42 cern as is a too strong seasonal cycle of the sea-ice cover in both hemispheres. ICON-43 ESM V1.0 serves as a basis for further developments that will take advantage of ICON-44 specific properties such as spatially varying resolution, and configurations at very high 45 resolution. 46 Plain Language Summary 47 ICON-ESM is a completely new coupled climate and earth system model that ap-48 plies novel design principles and numerical techniques. The atmosphere model applies 49 a non-hydrostatic dynamical core, both atmosphere and ocean models apply unstruc-50 tured meshes, and the model is adapted for high-performance computing systems. This 51 article describes how the component models for atmosphere, land, and ocean are cou-52 pled together and how we achieve a stable climate by setting certain tuning parameters 53 and performing sensitivity experiments. We evaluate the performance of our new model 54 by running a set of experiments under pre-industrial and historical climate conditions 55 as well as a set of idealized greenhouse-gas-increase experiments. These experiments were 56 designed by the Coupled Model Intercomparison Project (CMIP) and allow us to

Within the project ICON-Seamless a new Earth System Model is developed for weather forecasts, seasonal and decadal climate predictions, as well as climate projections. In doing so, we use the expertise from the numerical weather prediction (NWP), which operates and maintains ICON-NWP, as well as the experience with the first ICON-Earth System Model version based on the physics of the MPI-M atmosphere model (ECHAM). The goal is to use common components for all time scales. As a first step we develop a model for seasonal and decadal predictions.ICON-Seamless builds on the coupling of the atmosphere (ICON-NWP) and ocean (ICON-O) components via the coupling software YAC. Sea ice as a further important component is thereby included. Furthermore, to have a closed hydrological cycle and to represent the carbon and other biogeochemical cycles comprehensively, a suitable soil model based on the ICON-Land framework as well as the TERRA and JSBACH/QUINCY land models, are or will be coupled to ICON-NWP. In addition, transient external fields for aerosol, greenhouse gases, ozone and solar irradiance are implemented in ICON-NWP to be able to simulate historical time periods and scenarios of the future. In parallel, the ART modules (Aerosol and Reactive Trace gases), which allow a dynamic treatment of gases and aerosol, are adapted to the modified model physics. Intensive model evaluation supports the tuning. For future use in the field of (weather and) climate predictions, coupled data assimilation is being developed as well.We give an overview of the current state of the development, experiments and potential areas of application.

Climate models are an important tool in our understanding of the climate system. Among other things, we use them together with initialisation procedures to predict the climate from a few weeks to more than a decade. While the community has demonstrated prediction skill for various climate modes on these time scales in the past years, we have also encountered problems. One is the non-stationarity of prediction skill over the past century in seasonal and decadal predictions. It was shown in multiple prediction systems and for multiple variables that prediction skill varies over time. Potential reasons for this non-stationarity was found in the changing state of the North Atlantic system on multi-decadal scales and the limited representation of physical processes within the model. While on the one side this feature of climate predictions leaves uncertainties for future predictions it also highlights windows of opportunity and challenges within climate models.We investigate the past century for this non-stationarity with a special focus on the North Atlantic Oscillation, and how the North Atlantic sector changes during these low prediction skill periods. We will demonstrate the limited predictability of features of the North Atlantic Oscillation, like the movement of its activity centres, as well as its implication for the Signal-to-Noise paradox. We also discuss the implications of non-stationarity model prediction skill for the development on future prediction systems and which processes are most likely the reason for the current challenges the community faces.

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.

ICON-seamless entwickelt ein neues Erdsystemmodell, als Grundlage für Wettervorhersage, saisonale und dekadische Klimavorhersagen, bis hin zu Klimaprojektionen. Dabei nutzen wir die Expertise, die ICON-NWV als zuverlässiges Modell für numerische Wettervorhersage (NWV) betreibt und pflegt sowie die Erfahrungen mit der ersten ICON-Erdsystemversion basierend auf der Physik der MPI-Atmosphäre ECHAM. Das Ziel ist, gemeinsame Komponenten für alle meteorologischen Zeitskalen nutzen zu können. Der erste Schritt entwickelt ein Modell für saisonale und dekadische Zeitskalen. ICON-seamless baut auf der Kopplung der Komponenten ICON-NWV (Atmosphäre) und ICON-O (Ozean) auf. Mit Hilfe des speziell entwickelten Kopplungs-Tools YAC können beide Komponenten Variablen austauschen, die für die Wechselwirkung zwischen Atmosphäre und Ozean wichtig sind. Auch die Parametrisierung von Meereis stellt einen wichtigen Baustein dar. Zur Wiedergabe eines geschlossenen hydrologischen Kreislaufs und um den Kohlenstoffkreislauf sauber darzustellen, wird ferner ein geeignetes Bodenmodell, ICON-L, an die Atmosphärenphysik von ICON-NWV gekoppelt. Zudem werden transiente Aerosolfelder, Treibhausgase, und Strahlungsantriebe neu in ICON-NWV eingelesen, um historische Zeiträume nachzuvollziehen. Parallel hierzu werden die ART Module (Aerosol and Reactive Trace gases), die eine dynamische Behandlung von Gasen und Aerosolen gestatten, an die modifizierte Modellphysik angepasst. Eine intensive Modelldiagnostik unterstützt das Tuning. Für die zukünftige Verwendung im Bereich der (Wetter- und) Klimavorhersagen wird parallel die gekoppelte Datenassimilation entwickelt. Wir geben einen Überblick über den aktuellen Stand der Entwicklung, der Experimente und potentieller Anwendungsbereiche.

Predicting the ambient environmental conditions in the coming several years to one decade is of key relevance for elucidating how deep-sea habitats, like for example sponge habitats, in the North Atlantic will evolve under near-future climate change. However, it is still not well known to what extent the deep-sea environmental properties can be predicted in advance. A regional downscaling prediction system is developed to assess the potential predictability of the North Atlantic deep-sea environmental factors. The large-scale climate variability predicted with the coupled Max Planck Institute Earth System Model with low-resolution configuration (MPI-ESM-LR) is dynamically downscaled to the North Atlantic by providing surface and lateral boundary conditions to the regional coupled physical-ecosystem model HYCOM-ECOSMO. Model results of two physical fields (temperature and salinity) and two biogeochemical fields (concentrations of silicate and oxygen) over 21 sponge habitats are taken as an example to assess the ability of the downscaling system to predict the interannual to decadal variations of the environmental properties based on ensembles of retrospective predictions over the period from 1985 to 2014. The ensemble simulations reveal skillful predictions of the environmental conditions several years in advance with distinct regional differences. In areas closely tied to large-scale climate variability and ice dynamics, both the physical and biogeochemical fields can be skillfully predicted more than 4 years ahead, while in areas under strong influence of upper oceans or open boundaries, the predictive skill for both fields is limited to a maximum of 2 years. The simulations suggest higher predictability for the biogeochemical fields than for the physical fields, which can be partly attributed to the longer persistence of the former fields. Predictability is improved by initialization in areas away from the influence of Mediterranean outflow and areas with weak coupling between the upper and deep oceans. Our study highlights the ability of the downscaling regional system to predict the environmental variations at deep-sea benthic habitats on time scales of management relevance. The downscaling system therefore will be an important part of an integrated approach towards the preservation and sustainable exploitation of the North Atlantic benthic habitats.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Advances in Modeling Earth Systems (JAMES). ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]The ICON Earth System Model Version 1.0AuthorsJohann HJungclausiDStephan J.LorenzHaukeSchmidtiDOliverGutjahriDHelmuthHaakiDCarolinMehlmanniDUweMikolajewiczDirkNotziDDianPutrashanJin-Songvon StorchLinardakisLeonidasiDVictorBrokviniDFatemehCheginiVeronikaGayleriDMarco A.GiorgettaStefanHagemannTatianaIlyinaiDPeterKorniDJürgenKrögeriDWolfgang A.MüllerHolgerPohlmanniDThomas JürgenRaddatzLennartRammeiDReick H.ChristianRainerSchneckReinerSchnuriDBjornStevensiDFlorian AndreasZiemenMartinClausseniDJochemMarotzkeiDFabianWachsmannMartinSchupfnerThomasRiddickiDKarl-HermannWienersiDNilsBrueggemannReneRedlerPhilippde VreseJulia Esther Marlene SophiaNabeliDTeffySamMoritzHankeSee all authors Johann H JungclausiDCorresponding Author• Submitting AuthorMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0002-3849-4339view email addressThe email was not providedcopy email addressStephan J. LorenzMax Planck Institute of Meteorologyview email addressThe email was not providedcopy email addressHauke SchmidtiDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0001-7977-5041view email addressThe email was not providedcopy email addressOliver GutjahriDUniversität HamburgiDhttps://orcid.org/0000-0002-3116-8071view email addressThe email was not providedcopy email addressHelmuth HaakiDMax-Planck-Institut fuer MeteorologieiDhttps://orcid.org/0000-0002-9883-5086view email addressThe email was not providedcopy email addressCarolin MehlmanniDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0001-7329-5178view email addressThe email was not providedcopy email addressUwe MikolajewiczMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressDirk NotziDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0003-0365-5654view email addressThe email was not providedcopy email addressDian PutrashanMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressJin-Song von StorchMax-Plank Institute for Meteorologyview email addressThe email was not providedcopy email addressLinardakis LeonidasiDMax Planck Institute for Meteorology (MPG)iDhttps://orcid.org/0000-0002-0531-6923view email addressThe email was not providedcopy email addressVictor BrokviniDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0001-6420-3198view email addressThe email was not providedcopy email addressFatemeh CheginiMax-Planck-Institute for Meteorologyview email addressThe email was not providedcopy email addressVeronika GayleriDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0003-4069-5444view email addressThe email was not providedcopy email addressMarco A. GiorgettaMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressStefan HagemannHelmholtz-Zentrum Hereonview email addressThe email was not providedcopy email addressTatiana IlyinaiDMax Planck Institute of MeteorologyiDhttps://orcid.org/0000-0002-3475-4842view email addressThe email was not providedcopy email addressPeter KorniDMPI-MetiDhttps://orcid.org/0000-0002-7525-5732view email addressThe email was not providedcopy email addressJürgen KrögeriDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0002-6815-4220view email addressThe email was not providedcopy email addressWolfgang A. MüllerMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressHolger PohlmanniDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0003-1264-0024view email addressThe email was not providedcopy email addressThomas Jürgen RaddatzMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressLennart RammeiDMax-Planck-Institute for MeteorologyiDhttps://orcid.org/0000-0002-8307-2493view email addressThe email was not providedcopy email addressReick H. ChristianMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressRainer SchneckMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressReiner SchnuriDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0002-7380-8313view email addressThe email was not providedcopy email addressBjorn StevensiDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0003-3795-0475view email addressThe email was not providedcopy email addressFlorian Andreas ZiemenDeutsches Klimarechenzentrumview email addressThe email was not providedcopy email addressMartin ClausseniDMax Planck Institute for Meteorology (MPG)iDhttps://orcid.org/0000-0001-6225-5488view email addressThe email was not providedcopy email addressJochem MarotzkeiDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0001-9857-9900view email addressThe email was not providedcopy email addressFabian WachsmannDKRZview email addressThe email was not providedcopy email addressMartin SchupfnerDKRZview email addressThe email was not providedcopy email addressThomas RiddickiDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0002-9364-0343view email addressThe email was not providedcopy email addressKarl-Hermann WienersiDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0003-3797-7694view email addressThe email was not providedcopy email addressNils BrueggemannMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressRene RedlerMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressPhilipp de VreseMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressJulia Esther Marlene Sophia NabeliDMax Planck Institute for MeteorologyiDhttps://orcid.org/0000-0002-8122-5206view email addressThe email was not providedcopy email addressTeffy SamMax Planck Institute for Meteorologyview email addressThe email was not providedcopy email addressMoritz HankeDKRZview email addressThe email was not providedcopy email address