Statistical palaeoclimate reconstruction: how fast can climate change?

Andrew Parnell (University College Dublin)

I will present the generic problem of reconstructing aspects of past climate based on proxy data from a statistical perspective. There are now numerous data sources to assist with such reconstructions, which all present their own advantages and issues. Whilst my group have developed one particular approach, based on Bayesian inversion of causal models of the climate-proxy relationship, we have currently only applied it to individual proxies (pollen) and individual sites. Our approach contrasts with much of traditional palaeoclimate reconstruction. I will point out the differences and explore some of the many possible extensions which require collaboration between climatologists, mathematicians, proxy specialists, and statisticians.

Evaluating weather and climate forecasts

Christopher Ferro (University of Exeter)

Probabilistic weather and climate forecasts are a key part of risk-based decision support, and assessments of forecast performance help to guide both our responses to forecasts and our development of forecasting systems. The definitive measures of forecast performance are proper scoring rules. We shall discuss the use of proper scoring rules in evaluating weather and climate forecasts and then introduce recent extensions that are suitable for measuring the performance of ensemble forecasts, and for measuring performance when the truth is uncertain.