Abstract
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.
Highlights
Climate models are used to simulate the climate system by numerically solving the fundamental governing equations on supercomputers
Climatological 2-m temperatures (2mT) for boreal winter and summer as simulated by ECHAM6–finite element sea ice-ocean model (FESOM) are shown in Fig. 3 together with corresponding systematic errors
The largest biases of around 10 K are found over Antarctica with colder temperatures occurring in austral summer in ECHAM6–FESOM compared to ECMWF reanalysis (ERA)-40
Summary
Climate models are used to simulate the climate system by numerically solving the fundamental governing equations on supercomputers. They are becoming more and more important to a wider group of users. Hawkins and Sutton 2009); and model error has a detrimental influence on the skill of seasonal and decadal climate predictions (Kirtman and Pirani 2008). Recent progress in advancing the fidelity of climate models has become incremental. From this development it has been argued that radically new approaches are required to significantly advance the field of climate modeling and prediction (Shukla et al 2009; Slingo et al 2009)
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