The Value of Multi-proxy Reconstruction of Past Climate
نویسندگان
چکیده
Understanding the dynamics of climate change in its full richness requires the knowledge of long temperature time series. Although long-term, widely distributed temperature observations are not available, there are other forms of data, known as climate proxies, that can have a statistical relationship with temperatures and have been used to infer temperatures in the past before direct measurements. We propose a Bayesian hierarchical model to reconstruct past temperatures that integrates information from different sources, such as proxies with different temporal resolution and forcings acting as the external drivers of large scale temperature evolution. Additionally, this method allows us to quantify the uncertainty of the reconstruction in a rigorous manner. The reconstruction method is assessed, using a global climate model as the true climate system and with synthetic proxy data derived from the simulation. The target is to reconstruct Northern Hemisphere temperature from proxies that mimic the sampling and errors from tree ring measurements, pollen indices and borehole temperatures. The forcing series used as covariates are solar irradiance, volcanic aerosols and green house gas concentrations. The Bayesian model was successful in integrating these different sources of information in creating a coherent reconstruction. Within the context of this numerical testbed, a statistical process model that includes the external forcings can improve the quality of a hemispheric reconstruction when long time scale proxy information is not available. Some key words: Bayesian hierarchical model; Forcings; Global climate model; Past temperature reconstruction; Proxies Short title: Past Temperature Reconstruction Department of Statistics, Purdue University, West Lafayette, IN 47906. Email: [email protected] National Center for Atmospheric Research, Boulder, CO 80307-3000. Email:{nychka,ammann}@ucar.edu
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