Griffies, Stephen M., Biastoch, Arne, Böning, Claus, Bryan, Frank, Danabasoglu, Gokhan, Chassignet, Eric P., England, Matthew H., Gerdes, Rüdiger, Haak, Helmuth, Hallberg, Robert W., Hazeleger, Wilco, Jungclaus, Johann, Large, William G., Madec, Gurvan, Pirani, Anna, Samuels, Bonita L., Scheinert, Markus, Gupta, Alex Sen, Severijns, Camiel A., Simmons, Harper L., Treguier, Anne Marie, Winton, Mike, Yeager, Stephen and Yin, Jianjun (2009) Coordinated Ocean-ice Reference Experiments (COREs). Ocean Modelling, 26 (1-2), 1-46. (doi:10.1016/j.ocemod.2008.08.007).
Abstract
Coordinated Ocean-ice Reference Experiments (COREs) are presented as a tool to explore the behaviour of global ocean-ice models under forcing from a common atmospheric dataset. We highlight issues arising when designing coupled global ocean and sea ice experiments, such as difficulties formulating a consistent forcing methodology and experimental protocol. Particular focus is given to the hydrological forcing, the details of which are key to realizing simulations with stable meridional overturning circulations.
The atmospheric forcing from [Large, W., Yeager, S., 2004. Diurnal to decadal global forcing for ocean and sea-ice models: the data sets and flux climatologies. NCAR Technical Note: NCAR/TN-460+STR. CGD Division of the National Center for Atmospheric Research] was developed for coupled-ocean and sea ice models. We found it to be suitable for our purposes, even though its evaluation originally focussed more on the ocean than on the sea-ice. Simulations with this atmospheric forcing are presented from seven global ocean-ice models using the CORE-I design (repeating annual cycle of atmospheric forcing for 500 years). These simulations test the hypothesis that global ocean-ice models run under the same atmospheric state produce qualitatively similar simulations. The validity of this hypothesis is shown to depend on the chosen diagnostic. The CORE simulations provide feedback to the fidelity of the atmospheric forcing and model configuration, with identification of biases promoting avenues for forcing dataset and/or model development.
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