Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties
To better understand and predict changes to Earth’s energy balance, researchers must know the limitations of their climate projections. These limitations are largely related to dynamic processes inherent to natural systems, referred to as internal variability. Scientists at the University of Washington and Pacific Northwest National Laboratory analyzed climate projections for the west coast of North America and found that internal variability can be quantified consistently using a large single-model ensemble or an “ensemble of opportunity,” which consists of small ensembles from multiple models for multiple climate scenarios. This finding has important implications for designing ensemble modeling to quantify regional climate uncertainties.
Several modeling centers have devoted significant resources to creating large single-model ensembles to represent different realizations of possible climate scenarios due to internal variability. At the same time, multimodel ensembles have been produced to quantify uncertainty from multiple sources, such as models, scenarios, and internal variability. By comparing the internal variability estimated from a large single-model ensemble and a multimodel ensemble, this study provides important guidance for designing ensemble modeling to quantify multiple sources of uncertainty under the constraints of computational resources.
Internal variability in the Earth system can contribute substantial uncertainty in climate projections, particularly at regional scales. Internal variability can be quantified using large ensembles of simulations that are identical but for perturbed initial conditions. In this study, scientists compared methods for quantifying internal variability. They analyzed climate projections for the west coast of North America, which is strongly influenced by El Niño and other large-scale dynamics through their contribution to large-scale internal variability. Using a statistical framework to simultaneously account for multiple sources of uncertainty, researchers found that internal variability can be quantified consistently using a large single-model ensemble or an ensemble of opportunity that includes small ensembles from multiple models and climate scenarios. The latter ensemble also produces estimates of uncertainty due to model differences. These findings suggest that projection uncertainties are best assessed using small single-model ensembles from as many model/scenario pairings as are computationally feasible, which has implications for ensemble design in large modeling efforts.