Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate
We analyze a time series of global temperature anomaly distributions to identify and estimate persistent features in climate change. Temperature densities from globally distributed data between 1850 and 2012 are treated as a time series of functional observations that change over time. We employ a formal test for the existence of functional unit roots in the time series of these densities. Further, we develop a new test to distinguish functional unit roots from functional deterministic trends or explosive behavior. Results suggest that temperature anomalies contain stochastic trends (as opposed to deterministic trends or explosive roots), two trends are present in the Northern Hemisphere while one stochastic trend is present in the Southern Hemisphere, and the probabilities of observing moderately positive anomalies have increased, but the probabilities of extremely positive anomalies has decreased. These results are consistent with the anthropogenic theory of climate change, in which a natural experiment causes human emissions of greenhouse gases and sulfur to be greater in the Northern Hemisphere and radiative forcing to be greater in the Southern Hemisphere.
Robert K. Kaufmann
Chang Sik Kim
Joon Y. Park