Professor Myles Allen CBE FRS

Abstract:

The selection of climate policies should be an exercise in risk management reflecting the many relevant sources of uncertainty. Studies of climate change and its impacts rarely yield consensus on the distribution of exposure, vulnerability or possible outcomes. Hence policy analysis cannot effectively evaluate alternatives using standard approaches, such as expected utility theory and benefit-cost analysis. This Perspective highlights the value of robust decision-making tools designed for situations such as evaluating climate policies, where consensus on probability distributions is not available and stakeholders differ in their degree of risk tolerance. A broader risk-management approach enables a range of possible outcomes to be examined, as well as the uncertainty surrounding their likelihoods. © 2013 Macmillan Publishers Limited. All rights reserved.

Abstract:

This paper aims to test the robustness of the detection and attribution of anthropogenic climate change using four different empirical models that were previously developed to explain the observed global mean temperature changes over the last few decades. These studies postulated that the main drivers of these changes included not only the usual natural forcings, such as solar and volcanic, and anthropogenic forcings, such as greenhouse gases and sulfates, but also other known Earth system oscillations such as El Niño Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO). In this paper, we consider these signals, or forced responses, and test whether or not the anthropogenic signal can be robustly detected under different assumptions for the internal variability of the climate system. We assume that the internal variability of the global mean surface temperature can be described by simple stochastic models that explore a wide range of plausible temporal autocorrelations, ranging from short memory processes exemplified by an AR(1) model to long memory processes, represented by a fractional differenced model. In all instances, we conclude that human-induced changes to atmospheric gas composition is affecting global mean surface temperature changes. ©2013. American Geophysical Union. All Rights Reserved.