On constraining projections of future climate using observations and simulations from multiple climate models

Abstract

Appropriate statistical frameworks are required to make credible inferences about the future state of the climate from multiple climate models. The spread of projections simulated by different models is often a substantial source of uncertainty. This uncertainty can be reduced by identifying "emergent relationships" between future projections and historical simulations. Estimation of emergent relationships is hampered by unbalanced experimental designs and varying sensitivity of models to input parameters and boundary conditions. The relationship between the climate models and the Earth system is uncertain and requires careful modeling. Observation uncertainty also plays an important role when emergent relationships are exploited to constrain projections of future climate in the Earth system A new Bayesian framework is presented that can constrain projections of future climate using historical observations by exploiting robust estimates of emergent relationships while accounting for observation uncertainty. A detailed theoretical comparison with previous multi-model frameworks is provided. The proposed framework is applied to projecting surface temperature in the Arctic at the end of the 21st century. 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.