final_program_abstracts[1]

11 IMSC Session Program
Statistical evidence of recent climate change: An analysis of
Arctic seawater data
Friday - Parallel Session 6
Snigdhansu Chatterjee
School of Statistics, University of Minnesota, Canada
We perform a statistical analysis on a dataset on seawater pattern over the last few
decades. For specificity, we restrict attention to temperature measures in the Arctic
Ocean region for this talk. Our goal is to investigate whether there is a significant
change of pattern in the Arctic Ocean seawater temperature, thus detecting recent
climate change, after accounting for the systematic factors like location, depth,
season, and the temporal and spatial dependence pattern of the observations. In a
major departure from many climate data modeling studies, we do not explicitly model
the spatio-temporal dependency pattern of the observations, but treat it as an
extremely high dimensional nuisance parameter, and use techniques for estimation
and inference that are insensitive to it. We use nonparametric curve fitting for weakly
dependent observations, sequential tests, complex resampling-based robustness
studies, block bootstrap based two-regime analysis as a part of our statistical
approach. The methodology used in this paper is applicable for any sequence of
dependent observations on the climate, and does not rely on climate models or
reanalysis, nor on indirect historical data, nor on technical assumptions like linearity,
Gaussian nature of random variables, specific dependency patterns, and so on. This is
joint work with Qiqi Deng and Jie Xu.
Abstracts 355