Assessing geochemical carbon management

The challenge of reversing rising atmospheric CO₂ concentrations is growing with the continued expansion of CO₂-emitting energy infrastructure throughout the world and with the lack of coordinated, effective measures to manage and reduce emissions. Given this situation, it is prudent for society to explore all potential carbon management options, including those with seemingly low probability for success. Recent initiatives for advancing and enhancing carbon storage options have focused primarily on the physical trapping of CO₂ in underground geologic formations and on the biological uptake of CO₂; less attention has been given to approaches that rely primarily on geochemical reactions that enhance transformation of CO₂ gas into dissolved or solid phase carbon by liberating cations to neutralize carbonic acid. This paper provides a structured review of the technical status of these geochemical approaches, and also presents a simple framework for assessing the potential and limitations of various proposed geochemical approaches to assist prioritizing future research in this area. Despite major limitations, geochemical approaches have unique potential to contribute to CO₂ reductions in ways that neither physical nor biological carbon storage can by allowing for the direct removal of CO₂ from the atmosphere with minimal requirements for integrating with existing infrastructure. Recognizing the severity and urgency of the need for carbon management options, we argue for an increase in research activity related to geochemical approaches to carbon management.     

Exploring multi-model atmospheric GCM ensembles with ANOVA

Analysis of variance (ANOVA) is a powerful statistical technique for making inferences about experiments that are influenced by multiple factors. Whilst common in many other scientific fields, its use within the climate community has been limited to date. Here we review the basis for ANOVA and how, in particular, it can be applied to partition the variance in a multi-model ensemble of Atmospheric General Circulation Model simulations. We examine an ensemble of four AGCMs forced with observed twentieth century sea surface temperatures (SST). We show that the dominant contributions to the total variance of seasonal mean sea level pressure arise from between-model differences (the bias term) and internal noise (the noise term). However, which term is most important varies from region to region. Of particular interest is the interaction term, which describes differences between the models in their responses to common SST forcing. The interaction term is found to be largest over the Indian Ocean (in all seasons), and over the subtropical Northwest Pacific in boreal summer. The differences between the model responses in these regions suggest differences in their simulation of atmospheric teleconnections, with potentially important implications, e.g. for seasonal predictions of the South and East Asian Monsoons. Examination of these differences may lead to an understanding of the reasons why models respond differently to common forcing, and ultimately to improvements in the performance of climate models.     

Evangelical Protestantism,Politics, and the Environment: When and How Do Biblical Beliefs Matter?

Abstract

Literature on environment-related outcomes highlights the role of political and religious factors—in particular, political orientation, party affiliation and biblical beliefs. Less is known about how these factors independently and interactively shape attitudes towards policy and environmental behaviors including how political factors may be moderated by religious beliefs. Using structural equation modeling and data from the 2010 GSS, we find that in the United States political orientation and party affiliation independently affect willingness to pay for the environment and to engage in pro-environmental behaviors. Biblical beliefs have no effect on behaviors, but strongly influence willingness to pay. Moreover, biblical beliefs moderate the effects of political orientation: the more politically conservative biblical literalist are the more willing they are willing to pay for the environment. We conclude with a discussion of these surprising results by calling for more intersectional approaches to environment-related behaviors and attitudes.     

Survey of larval Euphausia superba lipid content along the western Antarctic Peninsula during late autumn 2006

A survey of larval Euphausia superba (furcilia stages four and six) was conducted in waters along the western Antarctic Peninsula during late autumn (May and June 2006). Larvae were collected from stations in four regions to estimate dry weight and lipid content. There were no statistically significant differences in the dry weight or lipid content among the regions sampled. The overall average (±S.D.) dry weight was 1.51 ± 0.32 mg indiv.⁻¹ and 0.85 ± 0.12 mg indiv.⁻¹ for F6 and F4 larvae, respectively. The average (±S.D.) lipid content was 21.6 ± 9.6 %DW and 27.9 ± 13.7 %DW for F6 and F4 larvae, respectively.     

Processes governing the predictability of the Atlantic meridional overturning circulation in a coupled GCM

The processes that govern the predictability of decadal variations in the North Atlantic meridional overturning circulation (MOC) are investigated in a long control simulation of the ECHO-G coupled atmosphere?Cocean model. We elucidate the roles of local stochastic forcing by the atmosphere, and other potential ocean processes, and use our results to build a predictive regression model. The primary influence on MOC variability is found to come from air?Csea heat fluxes over the Eastern Labrador Sea. The maximum correlation between such anomalies and the variations in the MOC occurs at a lead time of 2?years, but we demonstrate that the MOC integrates the heat flux variations over a period of 10?years. The corresponding univariate regression model accounts for 74.5% of the interannual variability in the MOC (after the Ekman component has been removed). Dense anomalies to the south of the Greenland-Scotland ridge are also shown to precede the overturning variations by 4?C6?years, and provide a second predictor. With the inclusion of this second predictor the resulting regression model explains 82.8% of the total variance of the MOC. This final bivariate model is also tested during large rapid decadal overturning events. The sign of the rapid change is always well represented by the bivariate model, but the magnitude is usually underestimated, suggesting that other processes are also important for these large rapid decadal changes in the MOC.