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We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice–Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model.

In this study, we present a variety of parameterizations for simulating ocean eddy dynamics including novel viscous and kinetic energy backscatter closures. Their effect is analyzed using new diagnostics that allow for application on unstructured meshes.Ocean mesoscale eddy dynamics play a crucial role for large-scale ocean currents as well as for the variability in the ocean and climate. The interactions between eddies and the mean flow affect strength, position and variations of ocean currents. Mesoscale eddies have a substantial impact on oceanic heat transport and the coupling between the atmosphere and ocean. However, at so-called eddy-permitting model resolutions around ¼°, eddy kinetic energy and variability is often substantially underestimated due to excessive dissipation of energy. Despite ever-increasing model resolutions, eddy-permitting simulations will still be used in uncoupled and coupled climate and Earth system simulations for years to come.To improve the presentation of eddy dynamics in such resolution regimes, we present and systematically compare a set of viscous and kinetic energy backscatter parameterization with different complexity. These schemes are implemented in the unstructured grid, finite volume ocean model FESOM2 and tested in both idealized channel and global ocean simulations. We show that kinetic energy backscatter and adjusted viscosity parameterizations can alleviate some of the substantial eddy related biases, for example biases in sea surface height variability, mean currents and in water mass properties. We then further analyze the effect of these schemes on energy and dissipation spectra using new diagnostics that can be extended to the unstructured grid used by FESOM2. The rigorous intercomparison allows to make informed decisions on which schemes are the most suitable for a given application, considering the complexity of the schemes, their computational costs, their adaptability to various model resolutions and any simulation improvements related to a specific scheme. We will show that novel viscous and kinetic energy backscatter schemes outperform previously used, classical viscous closures. Furthermore, when compared to higher resolution simulations, they are computationally less expensive but achieve similar results.

Parametrizing physical processes in the ocean is a universal approach to overcome resolution limitations across different scales. Parametrizations represent the mean effect of processes occurring on the scales less than the grid scale (i.e. on the subgrid) on the resolved mean flow through parametric equations. In this work, a viscous momentum closure, equipped with a backscatter operator that returns excessively dissipated energy back to the system, is used to parametrize mesoscale range processes on eddy-permitting mesh resolutions.The part of the variability that is not represented by the deterministic backscatter operator is modelled stochastically. We propose a stochastic field component, based on the patterns of variability extracted from the output of model simulations with different grid resolutions.As a continuation of this work, we propose an interaction of the backscatter parametrization with the Gent-McWilliams parametrization which is generally applied for coarser grids corresponding to non-eddy resolving resolutions. This connection is relevant to link kinetic and potential energy backscatter.The implementations are tested on two intermediate complexity setups of the global ocean model FESOM2: a doubly-periodic channel and a double-gyre box model. In this contribution, we present an increase of eddy activity and show that a greater complexity of setup enhances response to the implementation. Keywords: mesoscale eddies, parametrization, backscatter, stochastic parametrization, GM parametrization.

We present intermediate results as well as ongoing work on improving the post-CMIP6 climate model of the Alfred Wegener Institute, AWI-CM3. A baseline version of the model was completed in 2021 with an above average performance when compared to other CMIP6 models. Close investigation of surfaces fluxes revealed that v3.0 can be further improved in a number of ways which will also benefit other climate models.As a first step we reevaluated old coupling simplifications and assumptions made years ago. At the air-sea ice interface we corrected the gradient of surface sensible heat flux / wind speed vs. (2m air temp - sea ice surf temp), by using a nudged version of AWI-CM3 and evaluating against in situ data from the MOSAiC-Expedition. We added the coupling of ocean current feedback, as well as new latent and sensible heat fluxes resulting from precipitation entering the ocean with a different temperature and state than the ocean surface. As a precursor to subsequent Earth System Model (ESM) development we included the coupling of mass and heat fluxes of snow falling on ice-sheets. The resulting AWI-CM3 v3.1 shows increased ability to represent the current climate and historic climate change.Furthermore we present ongoing work towards AWI-CM3 v3.2, where among other improvements we will include the coupling of sub atmospheric gridscale information from the higher resolution ocean grid, via a stochastic method. Finally we give a brief outlook on our efforts to link up with the EC-Earth climate and earth system model community for common cryosphere, vegetation and isotope developments towards two comprehensive ESMs.

Climate models are one of the most useful tools for predicting future climate states, which has become more important than ever in the ongoing climate crisis. However, due to their spatial and temporal resolutions, which are constrained by computing power and resources, climate models are not able to represent all processes in the ocean and atmosphere. Therefore, modelers need to estimate the effects unresolved processes have on the resolved processes.One such structure is turbulent mesoscale eddies in the ocean. It is known from observations that eddies carry a large amount of kinetic energy and play a significant role in transport of tracers such as temperature and salinity as well as in heat uptake from the atmosphere. Therefore, it is crucial that eddies and their effects on the processes mentioned above are represented accurately in climate models.To better estimate these effects in low-resolution simulations, high-resolution simulations can be used to constrain the parameters necessary for the estimates. However, tuning these parameters can be subjective and time-consuming. In this project, Machine Learning (ML) methods will be used to facilitate and speed up this process.In my PhD project, high-resolution data from the FESOM2 ocean model will be used. At low resolution, which is insufficient to represent eddies, FESOM2 estimates the effects of the missing eddies using the Gent-McWilliams (GM) parameterization containing a GM coefficient. With the help of Bayesian Neural Networks, a framework will be developed to calculate a predictor for this parameterization as well as its variability. Using this framework, maps of the GM coefficient for multiple setups with increasing complexity and data volume will be created. The presentation includes a project outline as well as preliminary results.

Science communication is becoming increasingly important to connect academia and society, and to counteract fake news among climate change deniers. Online video platforms, such as YouTube, offer great potential for low-threshold communication of scientific knowledge to the general public. In April 2020 a diverse group of researchers from the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research launched the YouTube channel “Wissenschaft fürs Wohnzimmer” (translated to “Sitting Room Science”) to stream scientific talks about climate change and biodiversity every Thursday evening. Here we report on the numbers and diversity of content, viewers, and presenters from 2 years and 100 episodes of weekly livestreams. Presented topics encompass all areas of polar research, social issues related to climate change, and new technologies to deal with the changing world and climate ahead. We show that constant engagement by a group of co-hosts, and presenters from all topics, career stages, and genders enable a continuous growth of views and subscriptions, i.e. impact. After 783 days the channel gained 30,251 views and 828 subscribers and hosted well-known scientists while enabling especially early career researchers to improve their outreach and media skills. We show that interactive and science-related videos, both live and on-demand, within a pleasant atmosphere, can be produced voluntarily while maintaining high quality. We further discuss challenges and possible improvements for the future. Our experiences may help other researchers to conduct meaningful scientific outreach and to push borders of existing formats with the overall aim of developing a better understanding of climate change and our planet.

The extent to which interannual variability in Arctic sea ice influences the mid-latitude circulation has been extensively debated. While observational data support the existence of a teleconnection between November sea ice in the Barents–Kara region and the subsequent winter North Atlantic Oscillation, climate models do not consistently reproduce such a link, with only very weak inter-model consensus. We show, using the EC-Earth3 climate model, that while an ensemble of coupled EC-Earth3 simulations shows no evidence of such a teleconnection, the inclusion of stochastic parameterizations to the ocean and sea ice component results in the emergence of a robust teleconnection comparable in magnitude to that observed. While the exact mechanisms causing this remain unclear, we argue that it can be accounted for by an improved ice–ocean–atmosphere coupling due to the stochastic perturbations, which aim to represent the effect of unresolved ice and ocean variability. In particular, the weak inter-model consensus may to a large extent be due to model biases in surface coupling, with stochastic parameterizations being one possible remedy.

Multi-scale systems, such as the climate system, the atmosphere, and the ocean, are hard to understand and predict due to their intrinsic nonlinearities and chaotic behavior. Here, we apply a physics-consistent machine learning method, the multi-resolution dynamic mode decomposition (mrDMD), to oceanographic data. mrDMD allows a systematic decomposition of high-dimensional data sets into time-scale dependent modes of variability. We find that mrDMD is able to systematically decompose sea surface temperature and sea surface height fields into dynamically meaningful patterns on different time scales. In particular, we find that mrDMD is able to identify varying annual cycle modes and is able to extract El Nino–Southern Oscillation events as transient phenomena. mrDMD is also able to extract propagating meanders related to the intensity and position of the Gulf Stream and Kuroshio currents. While mrDMD systematically identifies mean state changes similarly well compared to other methods, such as empirical orthogonal function decomposition, it also provides information about the dynamically propagating eddy component of the flow. Furthermore, these dynamical modes can also become progressively less important as time progresses in a specific time period, making them also state dependent.

The extent to which interannual variability in Arctic sea ice influences the midlatitude circulation has been extensively debated. While observational data supports the existence of a teleconnection between November sea ice in the Barents-Kara region and the subsequent winter circulation, climate models do not consistently reproduce such a link, with only very weak inter-model consensus. We show, using the EC-Earth3 climate model, that while a deterministic ensemble of coupled simulations shows no evidence of such a teleconnection, the inclusion of stochastic parameterizations to the ocean and sea ice component of EC-Earth3 results in the emergence of a robust teleconnection comparable in magnitude to that observed. We show that this can be accounted for entirely by an improved ice-ocean-atmosphere coupling due to the stochastic perturbations. In particular, the inconsistent signal in existing climate model studies may be due to model biases in surface coupling, with stochastic parameterizations being one possible remedy.

Using the depth (z) and density (ϱ) frameworks, we analyze local contributions to AMOC variability in a 900‐year simulation with the AWI climate model. Both frameworks reveal a consistent interdecadal variability; however, the correlation between their maxima deteriorates on year‐to‐year scales. We demonstrate the utility of analyzing the spatial patterns of sinking and diapycnal transformations through depth levels and isopycnals. The success of this analysis relies on the spatial binning of these maps which is especially crucial for the maps of vertical velocities which appear to be too noisy in the main regions of upwelling and downwelling because of stepwise bottom topography. Furthermore, we show that the AMOC responds to fast (annual or faster) fluctuations in atmospheric forcing associated with the NAO. This response is more obvious in the ϱ than in the z framework. In contrast, the link between AMOC and deep water production south of Greenland is found for slower fluctuations and is consistent between the frameworks.

This study introduces a new flow‐dependent distribution sampling (FDDS) scheme for air–sea coupling. The FDDS scheme is implemented in a climate model and used to improve the simulated mean and variability of atmospheric and oceanic surface fields and thus air–sea fluxes. Most coupled circulation models use higher resolutions in the sea ice and ocean compared to the atmospheric model component, thereby explicitly simulating the atmospheric subgrid‐scale at the interface. However, the commonly applied averaging of surface fields and air–sea fluxes tends to smooth fine‐scale structures, such as oceanic fronts. The stochastic FDDS scheme samples the resolved spatial ocean (and sea ice) subgrid distribution that is usually not visible to a coarser‐resolution atmospheric model. Randomly drawn nodal ocean values are passed to the corresponding atmospheric boxes for the calculation of surface fluxes, aiming to enhance surface flux variability. The resulting surface field perturbations of the FDDS scheme are based on resolved dynamics, displaying pronounced seasonality with realistic magnitude. The AWI Climate Model is used to test the scheme on interannual time‐scales. Our set‐up features a high ocean‐to‐atmosphere resolution ratio in the Tropics, with grid‐point ratios of about 60:1. Compared to the default deterministic averaging, changes are largest in the Tropics leading to an improved spatial distribution of precipitation with bias reductions of up to 50%. Enhanced sea‐surface temperature variability in boreal winter further improves the seasonal phase locking of temperature anomalies associated with the El Niño–Southern Oscillation. Mean 2m temperature, sea ice thickness and concentration react with a contrasting dipole pattern between hemispheres but a joint increase of monthly and interannual variability. This first approach to implement a flow‐dependent stochastic coupling scheme shows considerable benefits for simulations of global climate, and various extensions and modifications of the scheme are possible.

We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

We present a new energy backscatter parametrization for primitive equation ocean models at eddy-permitting resolution, specifically for unstructured grids. Traditional eddy parametrizations in terms of viscosity closures lead to excessive dissipation of kinetic energy when used with eddy-permitting meshes. Implemented into the FESOM2 ocean model, the backscatter parametrization leads to a more realistic total dissipation of kinetic energy. It maintains a reservoir of dissipated energy and reinjects this subgrid energy at larger scales at a controlled rate. The separation between dissipation and backscatter scales is achieved by using different-order differential operators and/or spatial smoothing. This ensures numerical model stability. We perform sensitivity studies with different choices of parameter settings and viscosity schemes in a configuration with a baroclinically unstable flow in a zonally reentrant channel with a horizontally uniform mesh. The best backscatter setup substantially improves eddy-permitting simulations at 1/4° and 1/6° resolution, bringing them close to a 1/12° eddy-resolving reference. Improvements are largest for levels of kinetic energy and variability in temperature and vertical velocity. A selected optimal default scheme is then tested in a mixed resolution setup – a channel with narrow transitions between an eddy-permitting and an eddy-resolving subdomain. The backscatter scheme is able to adapt dynamically to the different resolutions and moves the diagnostics closer to the high resolution reference throughout the domain. Our study is a first step toward using backscatter in global variable-mesh ocean models and suggests potential for substantial improvements of ocean mean state and variability at reduced computational cost.

Capturing mesoscale eddy dynamics is crucial for accurate simulations of the large-scale ocean currents as well as oceanic and climate variability. Eddy-mean flow interactions affect the position, strength and variations of mean currents and eddies are important drivers of oceanic heat transport and atmosphere-ocean-coupling. However, simulations at eddy-permitting resolutions are substantially underestimating eddy variability and eddy kinetic energy many times over. Such eddy-permitting simulations will be in use for years to come, both in coupled and uncoupled climate simulations. We present a set of kinetic energy backscatter schemes with different complexity as alternative momentum closures that can alleviate some eddy related biases such as biases in the mean currents, in sea surface height variability and in temperature and salinity. The complexity of the schemes reflects in their computational costs, the related simulation improvements and their adaptability to different resolutions. However, all schemes outperform classical viscous closures and are computationally less expensive than a related necessary resolution increase to achieve similar results. While the backscatter schemes are implemented in the ocean model FESOM2, the concepts can be adjusted to any ocean model including NEMO.

Ocean models at eddy‐permitting resolution are generally overdissipative, damping the intensity of the mesoscale eddy field. To reduce overdissipation, we propose a simplified, kinematic energy backscatter parametrization built into the viscosity operator in conjunction with a new flow‐dependent coefficient of viscosity based on nearest neighbor velocity differences. The new scheme mitigates excessive dissipation of energy and improves global ocean simulations at eddy‐permitting resolution. We find that kinematic backscatter substantially raises simulated eddy kinetic energy, similar to an alternative, previously proposed dynamic backscatter parametrization. While dynamic backscatter is scale‐aware and energetically more consistent, its implementation is more complex. Furthermore, it turns out to be computationally more expensive, as it applies, among other things, an additional prognostic subgrid energy equation. The kinematic backscatter proposed here, by contrast, comes at no additional computational cost, following the principle of simplicity. Our primary focus is the discretization on triangular unstructured meshes with cell placement of velocities (an analog of B‐grids), as employed by the Finite‐volumE Sea ice‐Ocean Model (FESOM2). The kinematic backscatter scheme with the new viscosity coefficient is implemented in FESOM2 and tested in the simplified geometry of a zonally reentrant channel as well as in a global ocean simulation on a 1/4° mesh. This first version of the new kinematic backscatter needs to be tuned to the specific resolution regime of the simulation. However, the tuning relies on a single parameter, emphasizing the overall practicality of the approach.

Numerical weather prediction and climate models comprise a) a dynamical core describing resolved parts of the climate system and b) parameterizations describing unresolved components. Development of new subgrid-scale parameterizations is particularly uncertain compared to representing resolved scales in the dynamical core. This uncertainty is currently represented by stochastic approaches in several operational weather models, which will inevitably percolate into the dynamical core. Hence, implementing dynamical cores with excessive numerical accuracy will not bring forecast gains, may even hinder them since valuable computer resources will be tied up doing insignificant computation, and therefore cannot be deployed for more useful gains, such as increasing model resolution or ensemble sizes. Here we describe a low-cost stochastic scheme that can be implemented in any existing deterministic dynamical core as an additive noise term. This scheme could be used to adjust accuracy in future dynamical core development work. We propose that such an additive stochastic noise test case should become a part of the routine testing and development of dynamical cores in a stochastic framework. The overall key point of the study is that we should not develop dynamical cores that are more precise than the level of uncertainty provided by our stochastic scheme. In this way, we present a new paradigm for dynamical core development work, ensuring that weather and climate models become more computationally efficient. We show some results based on tests done with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) dynamical core.

We present a simulation of Antarctic iceberg drift and melting that includes small, medium-sized, and giant tabular icebergs with a realistic size distribution. For the first time, an iceberg model is initialized with a set of nearly 7000 observed iceberg positions and sizes around Antarctica. The study highlights the necessity to account for larger and giant icebergs in order to obtain accurate melt climatologies. We simulate drift and lateral melt using iceberg-draft averaged ocean currents, temperature, and salinity. A new basal melting scheme, originally applied in ice shelf melting studies, uses in situ temperature, salinity, and relative velocities at an iceberg's bottom. Climatology estimates of Antarctic iceberg melting based on simulations of small (≤ 2.2 km), 'small-to-medium'-sized (≤ 10 km), and small-to-giant icebergs (including icebergs > 10 km) exhibit differential characteristics: successive inclusion of larger icebergs leads to a reduced seasonality of the iceberg meltwater flux and a shift of the mass input to the area north of 58 °S, while less meltwater is released into the coastal areas. This suggests that estimates of meltwater input solely based on the simulation of small icebergs introduce a systematic meridional bias; they underestimate the northward mass transport and are, thus, closer to the rather crude treatment of iceberg melting as coastal runoff in models without an interactive iceberg model. Future ocean simulations will benefit from the improved meridional distribution of iceberg melt, especially in climate change scenarios where the impact of iceberg melt is likely to increase due to increased calving from the Antarctic ice sheet.

The last decade has seen the success of stochastic parameterizations in short-term, medium-range and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy and improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing longstanding climate biases and relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups which show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface and cryosphere of comprehensive weather and climate models (a) gives rise to more reliable probabilistic forecasts of weather and climate and (b) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics and turbulence is reviewed, its relevance for the climate problem demonstrated, and future research directions outlined., Comment: 26 pages, 15 figures. Final published version

In this study we demonstrate the potential of a kinetic energy backscatter scheme for use in global ocean simulations. Ocean models commonly employ (bi)harmonic eddy viscosities causing excessive dissipation of kinetic energy in eddy‐permitting simulations. Overdissipation not only affects the smallest resolved scales but also the generation of eddies through baroclinic instabilities, impacting the entire wave number spectrum. The backscatter scheme returns part of this overdissipated energy back into the resolved flow. We employ backscatter in the FESOM2 multiresolution ocean model with a quasi‐uniform 1/4° mesh. In multidecadal ocean simulations, backscatter increases eddy activity by a factor 2 or more, moving the simulation closer to observational estimates of sea surface height variability. Moreover, mean sea surface height, temperature, and salinity biases are reduced. This amounts to a globally averaged bias reduction of around 10% for each field, which is even larger in the Antarctic Circumpolar Current. However, in some regions such as the coastal Kuroshio, backscatter leads to a slight overenergizing of the flow and, in the Antarctic, to an unrealistic reduction of sea ice. Some of the bias increases can be reduced by a retuning of the model, and we suggest related adjustments to the backscatter scheme. The backscatter simulation is about 2.5 times as expensive as a simulation without backscatter. Most of the increased cost is due to a halving of the time step to accommodate higher simulated velocities.

A new global climate model setup using FESOM2.0 for the sea ice-ocean component and ECHAM6.3 for the atmosphere and land-surface has been developed. Replacing FESOM1.4 by FESOM2.0 promises a higher efficiency of the new climate setup compared to its predecessor. The new setup allows for long-term climate integrations using a locally eddy-resolving ocean. Here it is evaluated in terms of (1) the mean state and long-term drift under pre-industrial climate conditions, (2) the fidelity in simulating the historical warming, and (3) differences between coarse and eddy- resolving ocean configurations. The results show that the realism of the new climate setup is overall within the range of existing models. In terms of oceanic temperatures, the historical warming signal is of smaller amplitude than the model drift in case of a relatively short spin-up. However, it is argued that the strategy of ‘de-drifting’ climateruns after the short spin-up, proposed by the HighResMIP protocol, allows one to isolate the warming signal. Moreover, the eddy-permitting/resolving ocean setup shows notable improvements regarding the simulation of oceanic surface temperatures, in particular in the Southern Ocean.

State-of-the-art global ocean circulation models used in climate studies are only passing the edge of becoming “eddy-permitting” or barely eddy-resolving. Such models commonly suffer from overdissipation of mesoscale eddies by routinely used subgrid dissipation (viscosity) operators and a resulting depletion of energy in the large-scale structures which are crucial for draining available potential energy into kinetic energy. More broadly, subgrid momentum closures may lead to both overdissipation or pileup of eddy kinetic energy and enstrophy of the smallest resolvable scales. The aim of this chapter is twofold. First, it reviews the theory of two-dimensional and geostrophic turbulence. To a large part, this is textbook material with particular emphasis, however, on issues relevant to modeling the global ocean in the eddy-permitting regime. Second, we discuss several recent parameterizations of subgrid dynamics, including simplified backscatter schemes by Jansen and Held, stochastic superparameterizations by Grooms and Majda, and an empirical backscatter scheme by Mana and Zanna.

A new global climate model setup using FESOM2.0 for the sea ice‐ocean component and ECHAM6.3 for the atmosphere and land surface has been developed. Replacing FESOM1.4 by FESOM2.0 promises a higher efficiency of the new climate setup compared to its predecessor. The new setup allows for long‐term climate integrations using a locally eddy‐resolving ocean. Here it is evaluated in terms of (1) the mean state and long‐term drift under preindustrial climate conditions, (2) the fidelity in simulating the historical warming, and (3) differences between coarse and eddy‐resolving ocean configurations. The results show that the realism of the new climate setup is overall within the range of existing models. In terms of oceanic temperatures, the historical warming signal is of smaller amplitude than the model drift in case of a relatively short spin‐up. However, it is argued that the strategy of “de‐drifting” climate runs after the short spin‐up, proposed by the HighResMIP protocol, allows one to isolate the warming signal. Moreover, the eddy‐permitting/resolving ocean setup shows notable improvements regarding the simulation of oceanic surface temperatures, in particular in the Southern Ocean.

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this paper. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving a greater attention than 5 to 10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and to other components of the Earth system as well as the overall computational efficiency of representing model uncertainty.

In global ocean models, the representation of small-scale, high-frequency processes considerably influences the large-scale oceanic circulation and its low-frequency variability. This study investigates the impact of stochastic perturbation schemes based on three different subgrid-scale parameterizations in multidecadal ocean-only simulations with the ocean model NEMO at 1° resolution. The three parameterizations are an enhanced vertical diffusion scheme for unstable stratification, the Gent–McWilliams (GM) scheme, and a turbulent kinetic energy mixing scheme, all commonly used in state-of-the-art ocean models. The focus here is on changes in interannual variability caused by the comparatively high-frequency stochastic perturbations with subseasonal decorrelation time scales. These perturbations lead to significant improvements in the representation of low-frequency variability in the ocean, with the stochastic GM scheme showing the strongest impact. Interannual variability of the Southern Ocean eddy and Eulerian streamfunctions is increased by an order of magnitude and by 20%, respectively. Interannual sea surface height variability is increased by about 20%–25% as well, especially in the Southern Ocean and in the Kuroshio region, consistent with a strong underestimation of interannual variability in the model when compared to reanalysis and altimetry observations. These results suggest that enhancing subgrid-scale variability in ocean models can improve model variability and potentially its response to forcing on much longer time scales, while also providing an estimate of model uncertainty.

The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth-System Model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 km up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PBytes of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Center (LRZ) in Garching, Germany. About 140 TBytes of post-processed data are stored on the CINECA supercomputing center archives and are freely accessible to the community thanks to an EUDAT Data Pilot project. This paper presents the technical and scientific setup of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given: an improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increases is observed. It is also shown that including stochastic parameterisation in the low resolution runs helps to improve some aspects of the tropical climate – specifically the Madden-Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small scale processes on the large scale climate variability either explicitly (with high resolution simulations) or stochastically (in low resolution simulations).

The PRACE Climate SPHINX project investigates the sensitivity of climate simulations to model resolution and stochastic parameterization. The EC-Earth Earth-System Model is used to explore the impact of stochastic physics in 30-years climate integrations as a function of model resolution (from 80km up to 16km for the atmosphere). The experiments include more than 70 simulations in both a historical scenario (1979-2008) and a climate change projection (2039-2068), using RCP8.5 CMIP5 forcing. A total amount of 20 million core hours will be used at end of the project (March 2016) and about 150 TBytes of post-processed data will be available to the climate community. Preliminary results show a clear improvement in the representation of climate variability over the Euro-Atlantic following resolution increase. More specifically, the well-known atmospheric blocking negative bias over Europe is definitely resolved. High resolution runs also show improved fidelity in representation of tropical variability - such as the MJO and its propagation - over the low resolution simulations. It is shown that including stochastic parameterization in the low resolution runs help to improve some of the aspects of the MJO propagation further. These findings show the importance of representing the impact of small scale processes on the large scale climate variability either explicitly (with high resolution simulations) or stochastically (in low resolution simulations).

A new climate model has been developed that employs a multi-resolution dynamical core for the sea ice-ocean component. In principle, the multi-resolution approach allows one to use enhanced horizontal resolution in dynamically active regions while keeping a coarse-resolution setup otherwise. The coupled model consists of the atmospheric model ECHAM6 and the finite element sea ice-ocean model (FESOM). In this study only moderate refinement of the unstructured ocean grid is applied and the resolution varies from about 25 km in the northern North Atlantic and in the tropics to about 150 km in parts of the open ocean; the results serve as a benchmark upon which future versions that exploit the potential of variable resolution can be built. Details of the formulation of the model are given and its performance in simulating observed aspects of the mean climate is described. Overall, it is found that ECHAM6–FESOM realistically simulates many aspects of the observed climate. More specifically it is found that ECHAM6–FESOM performs at least as well as some of the most sophisticated climate models participating in the fifth phase of the Coupled Model Intercomparison Project. ECHAM6–FESOM shares substantial shortcomings with other climate models when it comes to simulating the North Atlantic circulation.

Ensemble experiments with a climate model are carried out in order to explore how incorporating a stochastic ice strength parameterization to account for model uncertainty affects estimates of potential sea ice predictability on time scales from days to seasons. The impact of this new parameterization depends strongly on the spatial scale, lead time and the hemisphere being considered: Whereas the representation of model uncertainty increases the ensemble spread of Arctic sea ice thickness predictions generated by atmospheric initial perturbations up to about 4 weeks into the forecast, rather small changes are found for longer lead times as well as integrated quantities such as total sea ice area. The regions where initial condition uncertainty generates spread in sea ice thickness on subseasonal time scales (primarily along the ice edge) differ from that of the stochastic sea ice strength parameterization (along the coast lines and in the interior of the Arctic). For the Antarctic the influence of the stochastic sea ice strength parameterization is much weaker due to the predominance of thinner first year ice. These results suggest that sea ice data assimilation and prediction on subseasonal time scales could benefit from taking model uncertainty into account, especially in the Arctic.

The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere–sea ice–ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10–20% after an accumulation period of approximately 20–30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small.

The ice strength parameter P* is a key parameter in dynamic/thermodynamic sea ice models that cannot be measured directly. Stochastically perturbing P* in the Finite Element Sea Ice–Ocean Model (FESOM) of the Alfred Wegener Institute aims at investigating the effect of uncertainty pertaining to this parameterization. Three different approaches using symmetric perturbations have been applied: 1) reassignment of uncorrelated noise fields to perturb P* at every grid point, 2) a Markov chain time correlation, and 3) a Markov chain time correlation with some spatial correlation between nodes. Despite symmetric perturbations, results show an increase of Arctic sea ice volume and a decrease of Arctic sea ice area for all three approaches. In particular, the introduction of spatial correlation leads to a substantial increase in sea ice volume and mean thickness. The strongest response can be seen for multiyear ice north of the Greenland coast. An ensemble of eight perturbed simulations generates a spread in the multiyear ice comparable to the interannual variability of the model. Results cannot be reproduced by a simple constant global modification of P*.