An evaluation of the CESM Decadal Climate Prediction Large Ensemble with application to regional studies

The decadal climate prediction (DCP) is one of the major challenges addressed by the research community focused on climate studies during the last years. DCPs try to fill the gap between seasonal-to-interannual predictions and multidecadal-to-centennial climate change projections by taking advance of not only the forced climate change signal provided by boundary information, but also the initialization of the climate system components which exhibit longer memory, such as the ocean.

Climate modelling for DCP is a very expensive activity in terms of computing resources since many initialized experiments are needed to properly assess the predictive skill at such time scales. In the context of dynamical downscaling (DS), this problem becomes even more important. Hence, the aim of this study is to evaluate some output variables from the Decadal Climate Prediction Large Ensemble (DPLE) and to explore the issue of reducing the number of ensemble members in consideration to make DS more affordable. The DPLE is a set of decadal simulations carried out at NCAR by using the Community Earth System Model (CESM). The DPLE encompasses 62 decadal experiments initialized every year (from 1954 to 2015) for each of the 40 members of the ensemble. Despite the ensemble size, only 10 members provide an adequate set of variables with the proper time aggregation to run a regional model.

Results obtained from this study could be helpful for those researchers who decide to address the regional DCP through a DS approach. Because of high computing resources, conducting DS simulations is restricted to a small number of research groups or institutes which can afford that large investment. It potentially limits the progress on this important and relatively recent branch of the climate science.

 

ACKNOWLEDGEMENTS: JJRC acknowledges the Spanish Ministry of Science, Innovation and Universities for the predoctoral fellowship (grant code: PRE2018-083921). This research has been carried out in the framework of the project CGL2017-89836-R, funded by the Spanish Ministry of Economy and Competitiveness with additional FEDER funds.