Title: On constraining projections of future climate using observations and simulations from multiple climate models,
Abstract: A new Bayesian framework is presented that can constrain projections of
future climate using historical observations by exploiting robust estimates of
emergent relationships between multiple climate models. We argue that emergent
relationships can be interpreted as constraints on model inadequacy, but that
projections may be biased if we do not account for internal variability in
climate model projections. We extend the previously proposed coexchangeable
framework to account for natural variability in the Earth system and internal
variability simulated by climate models. A detailed theoretical comparison with
previous multi-model projection frameworks is provided.
The proposed framework is applied to projecting surface temperature in the
Arctic at the end of the 21st century. A subset of available climate models are
selected in order to satisfy the assumptions of the framework. All available
initial condition runs from each model are utilized in order maximize the
utility of the data. Projected temperatures in some regions are more than 2C
lower when constrained by historical observations. The uncertainty about the
climate response is reduced by up to 30% where strong constraints exist.