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Statistical methods in the detection and attribution of long-term climate changes

Banff International Research Station for Mathematical Innovation and Discovery

Detection and attribution of climate change has been a growing activity since the 90's when the question of a possible human influence on the observed climate arose. I will first briefly introduce this theme together with the standard definitions of detection and attribution. Second, I will review the statistical models that have been used over the last 20 years to deal with these questions. Those models were predominantly linear regression models where the observations are regressed onto expected response patterns to different external forcings. Several levels of complexity have been proposed, from usual linear regression models to sophisticated error-in-variable models where observational and climate modelling uncertainties are accounted for. A recent, simple alternative proposes to avoid using linear regression based on similar assumption regarding uncertainties. Third, I will discuss a few statistical issues common to those models. A first issue involves the estimation of internal variability and its covariance matrix, which is required to carry out optimal inference. A second issue involves the estimation of climate modelling uncertainty, and the underlying assumptions. A third issue involves the data preprocessing and the dimension reduction that is needed to use those models.

Mathematics; Statistics; Geophysics; Mathematics of planet earth

video/mp4

Author affiliation: Météo France - CNRS

2016-12-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60053

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/