Flow‐dependent stochastic coupling for climate models with high ocean‐to‐atmosphere resolution ratio

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

"Wissenschaft fürs Wohnzimmer" – two years of interactive, scientific livestreams weekly on YouTube

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

AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model

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 has the multi-resolution functionality typical for unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of 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 on-going research activities with AWI-CM3. This includes the exploration of high and variable resolution, 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 in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model

AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model

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

Stochastic Parameterization Toward a New View of Weather and Climate Models

Peer reviewe

Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

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

Repräsentation von Unsicherheiten in globalen Klimamodellen mittels stochastischer Meereisparametrisierungen

This dissertation deals with the representation of uncertainties in sea ice modelling, especially within the sea ice dynamics. An important term of the momentum balance for computing the evolution of sea ice drift is the viscous-plastic sea ice rheology. It describes the deformation of sea ice under convergent drift. In this context, an ice strength parameter determines the internal ice strength, which counteracts plastic deformation and hence piling up of sea ice. Uncertainties in the choice of the parameter are simulated in this study by application of symmetric perturbation schemes. Temporal as well as spatial correlations are included in the generation of continuously applied stochastic perturbations. The parameter perturbations are implemented in an ocean-sea ice and in a coupled atmosphere-ocean-sea ice model. Results show that including these uncertainty estimates leads to a change in the mean sea ice distribution, especially in the Arctic. A randomly reduced ice strength parameter results in a relative acceleration of sea ice drift under convergence, which cannot be reverted by a randomly increased ice strength in the subsequent course of the simulation. This is caused by the highly nonlinear formulation of the sea ice rheology and results in a general acceleration of sea ice owing to the symmetric perturbations. As a result, the amount of thick, ridged sea ice in regions of predominantly convergent drift is increased. In the Arctic this increase accumulates slowly, but continuously over decades. Antarctic sea ice on the other hand exhibits relatively small changes in the mean sea ice distribution. In the coupled atmosphere-ocean-sea ice model ice strength perturbations lead to increased drift as well, although the impact on the sea ice thickness distribution is reduced. The reason are coupled feedback mechanisms, which counteract a general thickness increase. Finally, ensemble simulations are conducted with the coupled model in the context of sea ice predictions. Comparing ensembles with parameter perturbations and ensembles with atmospheric initial condition perturbations shows that the inclusion of model uncertainty leads to increased ensemble spread for the sea ice distribution of the central Arctic during the first weeks of the simulation. This has important implications for uncertainty estimations in data assimilation and forecasts for the polar regions

Representing uncertainty in global climate models using stochastic sea ice parameterizations

This dissertation deals with the representation of uncertainties in sea ice modelling, espe- cially within the sea ice dynamics. An important term of the momentum balance for computing the evolution of sea ice drift is the viscous–plastic sea ice rheology. It describes the deformation of sea ice under convergent drift. In this context, an ice strength parameter determines the internal ice strength, which counteracts plastic deformation and hence piling up of sea ice. Un- certainties in the choice of the parameter are simulated in this study by application of symmetric perturbation schemes. Temporal as well as spatial correlations are included in the generation of continuously applied stochastic perturbations. The parameter perturbations are implemented in an ocean–sea ice and in a coupled atmosphere–ocean–sea ice model. Results show that in- cluding these uncertainty estimates leads to a change in the mean sea ice distribution, especially in the Arctic. A randomly reduced ice strength parameter results in a relative acceleration of sea ice drift under convergence, which cannot be reverted by a randomly increased ice strength in the subsequent course of the simulation. This is caused by the highly nonlinear formulation of the sea ice rheology and results in a general acceleration of sea ice owing to the symmetric perturbations. As a result, the amount of thick, ridged sea ice in regions of predominantly convergent drift is increased. In the Arctic this increase accumulates slowly, but continuously over decades. Antarctic sea ice on the other hand exhibits relatively small changes in the mean sea ice distribution. In the coupled atmosphere–ocean–sea ice model ice strength perturbations lead to increased drift as well, although the impact on the sea ice thickness distribution is reduced. The reason are coupled feedback mechanisms, which counteract a general thickness increase. Finally, ensemble simulations are conducted with the coupled model in the context of sea ice predictions. Comparing ensembles with parameter perturbations and ensembles with atmospheric initial condition perturbations shows that the inclusion of model uncertainty leads to increased ensemble spread for the sea ice distribution of the central Arctic during the first weeks of the simulation. This has important implications for uncertainty estimations in data assimilation and forecasts for the polar regions

FESOM2 Channel Data

Channel simulation data generated by the FESOM2 ocean model. LEITH uses a Leith viscosity parameterization and KBACK a kinematic backscatter eddy parameterization. Includes u and v velocities for 10 years (first year should be excluded due to spin up) as well as u and v dissipation tendencies via Leith viscosity (for LEITH) or both backscatter and dissipation tendencies of the kinematic backscatter parameterization (KBACK). For more details see Juricke, S., Danilov, S., Koldunov, N., Oliver, M., Sein, D. V., Sidorenko, D., et al. (2020). A kinematic kinetic energy backscatter parametrization: From implementation to global ocean simulations. Journal of Advances in Modeling Earth Systems, 12, e2020MS002175. https://doi.org/10.1029/2020MS002175This data is a contribution to projects M3, L4, S1, and S2 of the Collaborative Research Centre TRR 181 "Energy Transfer in Atmosphere and Ocean" funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Project 274762653. The computational resources were supplied by the supercomputing facilities at the Alfred Wegener Institute in Bremerhaven

Potential sea ice predictability and the role of stochastic sea ice strength perturbations

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