Climate change detection and attribution using proxy system models

Abstract

Until now, pre-instrumental climate change detection and attribution studies were based on the regression of statistical reconstructions on simulations. This approach is limited by stationarity assumptions and the univariate linear response of the underlying paleoclimatic observations. Here, we present a new procedure, in which we model paleoclimate data observations as a function of paleoclimatic data simulations using a proxy system model. Specifically, we detect and attribute tree-ring width (TRW) observations as a linear function of TRW simulations. These are nonlinear and multivariate TRW simulation driven by climate simulations with single or multiple external forcing.  Temperature- and moisture-sensitive TRW simulations detect distinct patterns in time and space. Northern Hemisphere averages of temperature-sensitive TRW observations and simulations are significantly correlated. We can attribute their variation to volcanic forcing. In decadally smoothed temporal fingerprints, we find the observed responses to be significantly larger and/or more persistent than the simulated responses. The pattern of simulated TRW of moisture-limited trees is consistent with the observed anomalies in the two years following major volcanic eruptions. We can for the first time attribute this spatiotemporal fingerprint in moisture-limited tree-ring records to volcanic forcing. These results suggest that the use of nonlinear and multivariate proxy system models in paleoclimatic detection and attribution studies may permit more realistic, spatially resolved and multivariate fingerprint detection studies and evaluation of the climate sensitivity to external radiative forcing than has previously been possible.