final_program_abstracts[1]

11 IMSC Session Program
Objective approaches to probabilistic climate forecasting
Wednesday - Parallel Session 2
Dan Rowlands 1 , Steve Jewson 2 and Myles Allen 1
1 AOPP, Oxford University, UK
2 Risk Management Solutions
Observations of the climate are one of the primary tools for assessing the performance
of climate models, the assumption being that if a climate model performs well in
simulating the present day climate we should place greater faith in future predictions
from the same model.
However there is currently little consensus on how to weight ensembles of climate
model simulations together in such a way for making future forecasts, and role of
prior assumptions has received much attention. While the Detection and Attribution
(D&A) community has often relied on Likelihood based statements, the Climate
Prediction community has often embraced subjective prior assumptions, thus resulting
in inconsistencies when one compares hind casts from the two approaches – should
we believe a model hind cast that is biased with respect to the very observations used
to constrain it?
In this work we seek to unite the two detailing three approaches, namely: Likelihood
Profiling, Transfer functions and Reference Priors, where observational constraints
can be used in Objective Probabilistic Forecasting. The key feature is that prior
assumptions are based on the observational constraints used, rather than climate
model parameters, which often do not correspond to any real world quantities.
We show that for a simple Energy Balance Model example the three converge in
terms of the uncertainty estimates produced, and discuss extensions to more complex
models using the climateprediction.net ensemble.
Abstracts 180