Description: Exploiting the complementary character of CryoSat-2 and Soil Moisture and Ocean Salinity satellite sea ice thickness products, daily Arctic sea ice thickness estimates from October 2010 to December 2016 are generated by an Arctic regional ice-ocean model with satellite thickness assimilated. The assimilation is performed by a Local Error Subspace Transform Kalman filter coded in the Parallel Data Assimilation Framework. The new estimates can be generally thought of as combined model and satellite thickness (CMST). It combines the skill of satellite thickness assimilation in the freezing season with the model skill in the melting season, when neither CryoSat-2 nor Soil Moisture and Ocean Salinity sea ice thickness is available. Comparisons with in situ observations from the Beaufort Gyre Exploration Project, Ice Mass Balance Buoys, and the NASA Operation IceBridge demonstrate that CMST reproduces most of the observed temporal and spatial variations. Results also show that CMST compares favorably to the Pan-Arctic Ice-Ocean Modeling and Assimilation System product and even appears to correct known thickness biases in the Pan-Arctic Ice-Ocean Modeling and Assimilation System. Due to imperfect parameterizations in the sea ice model and satellite thickness retrievals, CMST does not reproduce the heavily deformed and ridged sea ice along the northern coast of the Canadian Arctic Archipelago and Greenland. With the new Arctic sea ice thickness estimates sea ice volume changes in recent years can be further assessed.

Description: Data assimilation combines observational information with numerical models taking into account the errors in both the observations and the model. In ensemble data assimilation the errors in the model state are dynamically estimated using an ensemble of model states. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. The coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled atmosphere-ocean models like the AWI Climate Model (AWI-CM), simulate the physics in both compartments and fluxes in between then. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, but have a high high computing cost. To allow us to efficiently perform the data assimilation, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation on the examples of the data assimilative model system of AWI-CM coupled to PDAF and a coupled ocean-biogeochemical model consistent of the ocean circulation model MITgcm and the ecosystem model REcoM2.

Description: 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.

Description: Sea ice data assimilation can greatly improve forecasts of Arctic sea ice evolution. Many previous sea ice data assimilation studies were conducted without assimilating ocean state variables, even though the sea ice evolution is closely linked to the oceanic conditions, both dynamically and thermodynamically. Based on the method of a localized ensemble error subspace transform Kalman filter, satellite‐retrieved sea ice concentration and sea ice thickness are assimilated into an Arctic sea ice‐ocean model. As a new addition, sea surface temperature (SST) data are also assimilated. The additional assimilation of SST improves not only the simulated ocean temperature in the mixed layer of the ocean substantially but also the accuracy of sea ice edge position, sea ice extent, and sea ice thickness in the marginal sea ice zone. The improvement in the simulated potential temperature in the upper 1,000 m can be attributed to the enhanced vertical convection processes in the regions where the assimilated observational SST is colder than the simulated SST without assimilation. The improvements in the sea ice edge position and sea ice thickness simulations are primarily caused by the SST data assimilation reducing biases in the simulated SST and the associated coupled ocean‐sea ice processes. Our investigation suggests that, due to the complex interaction between the sea ice and ocean, assimilating ocean data should be an indispensable component of numerical polar sea ice forecasting systems.

Description: We discuss how to build an ensemble data assimilation system using a direct connection between a coupled model system and the ensemble data assimilation software PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de). The direct connection results in a data assimilation program with high flexibility, efficiency, and parallel scalability. For this we augment the source code of the coupled model by data assimilation routines and hence create an online-coupled assimilative model. This first modifies the coupled model to be able to simulate an ensemble. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard we can further add the analysis step of ensemble-based filter methods, which compute the assimilation of observations, without the need to stop and restart the whole coupled model system. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler that couples the different model compartments. This strategy to build the assimilation system allows us to perform both weakly coupled (in-compartment) and strongly coupled (cross-compartment) assimilation. The assimilation frequency can be kept flexible, so that the assimilation of observations from different compartments can be performed at different intervals. Further, the reading and writing of disk files is minimized. The resulting assimilative model can be run in the same way as the regular coupled model, but with additional parameters controlling the assimilation and with a higher number of processors to simulate the ensemble. Using the example of the coupled climate model AWI-CM that contains the FESOM model for the ocean and sea ice and ECHAM6 for the atmosphere, both coupled through the OASIS-MCT coupler, we discuss the features of the online assimilation coupling strategy and the performance of the resulting assimilative model.