The AWI Climate Model (AWI-CM)
Fig. 1: Schematic of AWI-CM’s coupling procedure: ECHAM6 computes 12 air-sea fluxes based on 4 surface fields provided by FESOM. The 6-hourly averaged fields and 6-hourly accumulated fluxes are mapped between the model components every 6 hours using the OASIS3-MCT coupler (https://portal.enes.org/oasis). The newest version of AWI-CM couples the unstructured FESOM fields and the structured ECHAM fluxes directly, without the need to resort to an intermediate exchange grid, and increased the coupling frequency to 1 hour.
Fig. 2: Grids corresponding to (left) ECHAM6 at T63 ( ≈180 km) horizontal resolution and (right) FESOM with the REF87k grid. The grid resolution for FESOM is indicated through color coding (in km). Dark green areas of the T63 grid correspond to areas where the land fraction exceeds 50 %; areas with a land fraction between 0 and 50 % are shown in light green. Figure from Sidorenko et al. (2015)
Fig. 3: (Top) Standard deviation of non-seasonal monthly near-surface temperature anomalies from HADCRUT4 observations (1850 to 2012) based on its 1961–1990 climatology. Only grid boxes with at least 10 years of data are considered. White boxes represent data gaps. (Bottom) Average non-seasonal monthly surface temperature standard deviation in the last 1000 years of an AWI-CM low-resolution simulation, derived from six non-overlapping 163-year chunks as the square root of the ensemble-mean variance. The model data have been interpolated to the HADCRUT4 grid ( 5∘×5∘) prior to the analysis.
The AWI Climate Model (AWI-CM) consists of the atmospheric model ECHAM6, developed at the Max Planck Institute for Meteorology in Hamburg, and the Finite Element Sea ice-Ocean Model (FESOM), version 1.4 (link to this website!). FESOM and ECHAM6 are coupled every via the OASIS3-MCT coupler (Fig. 1).
Overall, in its low-resolution reference setup (Fig. 2), AWI-CM realistically simulates many aspects of the observed climate under constant present-day (1990) conditions. .More specifically, it is found that AWI-CM performs at least as well as some of the most sophisticated climate models participating in the fifth phase of the Coupled Model Intercomparison Project (Sidorenko et al., 2015). Regarding the simulated climate variability,AWI-CM also performs overall favourably compared to those models (Rackow et al.,2016). Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability.
Common atmospheric and oceanic variability patterns (e.g., the Atlantic Multi-decadal Oscillation, AMO, and the Pacific Decadal Oscillation, PDO) are simulated largely consistent with their real counterparts. Specifically, the coupled El Nino-Southern Oscillation phenomenon is particularly well simulated in AWI-CM with locally refined oceanic resolution of approx. 0.25° (Rackow et al.,2016), illustrating the potential of the novel unstructured-mesh method for global climate modeling.
Typical deficits that are known from other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean (Fig. 3) and episodic periods of almost absent deep-water formation in the Labrador Sea, resulting in overestimated North Atlantic SST variability.
We presented first evidence to correct the surface temperature biases (both mean and variability) by using locally eddy-resolving meshes (Sein et al., 2016, Sein et al. 2017, submitted). Here, the question is: What is the most efficient way of using model resolution, while keeping the total cost at a level that is affordable for climate simulations? We propose to base the spatial resolution on the observed eddy-variability pattern, but our group will further elaborate this approach to reach an even better efficiency.
The new ocean grids are already applied in the latest AWI-CM versions in medium and high resolution (MR and HR, respectively) and form the basis for AWI’s running CMIP6 simulations.
For applications in the context of the study of climate states that are very different from modern conditions, in particular for applications in paleo- and future climate, there is a dedicated version of AWI-CM with dynamic vegetation, named AWI-ESM. More information on this model can be found at the AWI-ESM website.