Leveraging large ensembles for renewable resource assessments: how to subselect?

How climate impacts energy is widely recognised as a complex research area, given the diversity of phenomena and spatiotemporal scales at which climate and weather patterns influence the energy sector. While climate models have contributed to understanding climate risk in renewable energy implementation, the systematic use of large ensembles in the climate-energy interface still needs further assessment. This study therefore aims to evaluate changes and internal variability of the main resources for solar photovoltaic, wind, and hydropower energy generation based on large ensembles. To this end, we focus on the historical and SSP3-7.0 experiments from four Single Model Large Ensembles (SMILEs) that provide at least 40 realizations: CESM2, MPI-ESM1.2-LR, ACCESS-ESM1.5 and CanESM5. We evaluate solar radiation at surface and near-surface wind speed, and runoff across the globe because they are the primary resources for renewable energy generation. Given the different number of realizations per model, we identify the optimal ensemble size to assess trends and internal variability following the approach of Milinski et al. (2020).  As suggested therein, we use the pi-control simulation and extract 200, 100, and 40 time series of 20-year duration that we consider as different realizations of each model. We report that the optimal number of realizations varies depending on the variable, region, and maximum number of realizations available. For example, starting from a 100-member ensemble, the optimal number of realizations to assess internal variability in solar radiation can reach up to 60 for some models while 40 are sufficient for runoff.  Our findings provide additional insights into renewable energy resource changes around the world by leveraging multiple realizations of GCMs, which can increase our understanding of the impacts of climate variability and change on renewable energy resources. These results highlight the need to carefully consider the number of realizations when assessing large ensembles. 

Reference: Milinski, S., Maher, N., & Olonscheck, D. (2020). How large does a large ensemble need to be?. Earth System Dynamics, 11(4), 885-901. https://doi.org/10.5194/esd-11-885-2020