Felix Pretis

DETECTING VOLCANIC ERUPTIONS IN TEMPERATURE RECONSTRUCTIONS BY DESIGNED BREAK-INDICATOR SATURATION

Abstract: We present a methodology for detecting structural breaks at any point in time-series regression models using an indicator saturation approach. Building on recent developments in econometric model selection for more variables than observations, we saturate a regression model with a full set of designed break functions. By selecting over these break functions using an extended general-to-specific algorithm, we obtain unbiased estimates of the break date and magnitude. Monte Carlo simulations confirm the approximate properties of the approach. We assess the methodology by detecting volcanic eruptions in a time series of Northern Hemisphere mean temperature spanning roughly 1200 years, derived from a fully-coupled global climate model simulation. Our technique demonstrates that historic volcanic eruptions can be statistically detected without prior knowledge of their occurrence or magnitude- and hence may prove useful for estimating the past impact of volcanic events using proxy-reconstructions of hemispheric or global mean temperature, leading to an improved understanding of the effect of stratospheric aerosols on temperatures. The break detection procedure can be applied to evaluate policy impacts as well as act as a robust forecasting device.

Testing competing models of the temperature hiatus: assessing the effects of conditioning variables and temporal uncertainties through sample-wide break detection

Explaining the recent slowdown in the rise of global mean surface temperature (the hiatus in warming) has become a major focus of climate research. Efforts to identify the causes of the hiatus that compare simulations from experiments run by climate models raise several statistical issues. Specifically, it is necessary to identify whether an experiment’s inability to simulate the hiatus is unique to this period or reflects a more systematic failure throughout the sample period. Furthermore, efforts to attribute the hiatus to a particular factor by including that mechanism in an experimental treatment must improve the model’s performance in a statistically significant manner at the time of the hiatus. Sample-wide assessments of simulation errors can provide an accurate assessment of whether or not the control experiment uniquely fails at the hiatus, and can identify its causes using experimental treatments. We use this approach to determine if the hiatus constitutes a unique failure in simulated climate models and to re-examine the conclusion that the hiatus is uniquely linked to episodes of La Niña-like cooling (Kosaka and Xie 2013). Using statistical techniques that do not define the hiatus a priori, we find no evidence that the slowdown in temperature increases are uniquely tied to episodes of La Niña-like cooling.

Carbon dioxide emission-intensity in climate projections: Comparing the observational record to socio-economic scenarios

The wide spread of projected temperature changes in climate projections does not predominately originate from uncertainty across climate models; instead it is the broad range of different global socio-economic scenarios and the implied energy production that results in high uncertainty about future climate change. It is therefore important to assess the observational tracking of these scenarios. Here we compare these socio-economic scenarios created in both 1992 and 2000 against the recent observational record to investigate the coupling of economic growth and fossil-fuel CO2 emissions. We find that global emission intensity (fossil fuel CO2 emissions per GDP) rose in the first part of the 21st century despite all major climate projections foreseeing a decline. Proposing a method to disaggregate differences between scenarios and observations in global growth rates to country-by-country contributions, we find that the relative discrepancy was driven by unanticipated GDP growth in Asia and Eastern Europe, in particular in Russia and China. The growth of emission intensity over the 2000s highlights the relevance of unforeseen local shifts in projections on a global scale.

Spatial heterogeneity of climate change as an experiential basis for skepticism

Significance We develop a simple heuristic to measure local changes in climate based on the timing of record high and low temperatures. The metric shows local cooling and warming in the United States and captures two aspects of experiential learning that influence how the public perceives a change in climate: recency weighting and an emphasis on extreme events. We find that skepticism about whether the Earth is warming is greater in areas exhibiting cooling relative to areas that have warmed and that recent cooling can offset historical warming. This experiential basis for skepticism of climate change identifies obstacles to communicating ongoing changes in climate to the public and how these communications might be improved. We postulate that skepticism about climate change is partially caused by the spatial heterogeneity of climate change, which exposes experiential learners to climate heuristics that differ from the global average. This hypothesis is tested by formalizing an index that measures local changes in climate using station data and comparing this index with survey-based model estimates of county-level opinion about whether global warming is happening. Results indicate that more stations exhibit cooling and warming than predicted by random chance and that spatial variations in these changes can account for spatial variations in the percentage of the population that believes that “global warming is happening.” This effect is diminished in areas that have experienced more record low temperatures than record highs since 2005. Together, these results suggest that skepticism about climate change is driven partially by personal experiences; an accurate heuristic for local changes in climate identifies obstacles to communicating ongoing changes in climate to the public and how these communications might be improved.