Report from the PAGES/CLIVAR Workshop: Reducing and representing uncertainties in high-resolution proxy climate data (02 Dec 2008)

A report from the PAGES/CLIVAR Workshop: Reducing and representing uncertainties in high-resolution proxy climate data held in June 2008 in Trieste, Italy has been published (Cobb et al., 2008).

High-resolution paleoclimatic data play an increasingly critical role in the detection and attribution of regional, and global climate changes associated with anthropogenic warming, and in testing and validating global climate models for 21st century climate prediction. Such applications, which typically involve using high-resolution paleoclimate data in multi-proxy syntheses and data-model comparison efforts, demand a quantitative, transparent, and thorough representation of uncertainties associated with such high-resolution proxy climate data. Some uncertainties are specific to a particular source of proxy climate data (i.e. corals, ice cores, sediments, tree rings, documentary, etc), while others are common to all proxy archives (e.g. limited number of records, regional-scale representativeness, etc).

The report presents a discussion of the following three breakout sessions:

1. What are the major areas of uncertainty identified in IPCC AR4 that could be addressed by reducing uncertainties in high-resolution proxy climate data?

2. What strategies for the representation and reduction of proxy uncertainties could be used to make rapid and sizeable progress towards the climate questions posed in breakout session 1?

3. How can the data archival process be improved to encourage more thorough documentation of proxy uncertainties and the adoption of ?best practices? in the high-resolution paleoclimate community?

The workshop participants converged on a set of recommendations and there will be various follow-up activities, including the completion of white papers for discussion by the wider community, an EOS publication and a proposal for an AGU 2009 fall meeting session on model-based and observation-based approaches to quantifying and representing uncertainties in high-resolution paleoclimate data.