Do Multi-Model Ensembles Improve Reconstruction Skill in Paleoclimate Data Assimilation?

Reconstructing past climates remains a difficult task because pre-instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi-model ensembles produce lower error than reconstructions from single-model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid- and high-latitude ocean and sea-ice regions. Furthermore, we find that multi-model ensemble reconstructions outperform single-model reconstructions that use covariance localization. We propose that multi-model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables.

Duke Scholars

Author Luke Parsons