Reconstructing past temperatures from natural proxies and estimated climate forcings using short- and long-memory models

rst linking the latent temperature series to three main external forcings (solar irradiance, greenhouse gas concentration, and volcanism), and the second linking the observed temperature proxy data (tree rings, sediment record, ice cores, etc.) to the unobserved temperature series. Uncertainty is captured with additive noise, and a rigorous statistical investigation of the correlation structure in the regression errors motivates the use of long memory fractional Gaussian noise models for the error terms. We use Bayesian estimation to t the model parameters and to perform separate reconstructions of land-only and combined land-and-marine temperature anomalies. We quantify the eects of including the forcings and long memory models