Climate change detection and attribution using observed and simulated tree-ring width

Abstract

The detection and attribution (D&A) of paleoclimatic change to external radiative forcing relies on regression of statistical reconstructions on simulations. However, this procedure may be biased by assumptions of stationarity and univariate linear response of the underlying paleoclimatic observations. Here we perform a D&A study, modeling paleoclimate data observations as a function of paleoclimatic data simulations. Specifically, we detect and attribute tree-ring width (TRW) observations as a linear function of TRW simulations, which are themselves a nonlinear and multivariate TRW simulation driven with singly forced and cumulatively forced climate simulations for the period 1401–2000 CE. Temperature- and moisture-sensitive TRW simulations detect distinct patterns in time and space. Temperature-sensitive TRW observations and simulations are significantly correlated for Northern Hemisphere averages, and their variation is attributed 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 2 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.