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This paper continues the work of Kang and Larsson Theoretical and Applied Climatology 116:537–548 (2014) What is the link between temperature and carbon dioxide levels? A Granger causality analysis based on ice core data. Theoretical and Applied Climatology 116:537-548, by adding methane to the study of causality between temperature and carbon dioxide. The data used goes 800,000 years back in time which is possible due to it being extracted from ice cores located at Dome C in Antarctica. First we use linear interpolation to make the three data sets equidistant to be able to employ statistical methods. We adjust for a deterministic trend and find the best model fit to be an autoregressive model with lag two and a piecewise quadratic trend. By employing multivariate Granger causality tests we find strong evidence that temperature, carbon dioxide and methane all Granger cause each other in both directions i.e. carbon dioxide Granger causes temperature and temperature Granger causes carbon dioxide, etc. This is in accordance with the findings of Kang and Larsson, with the extension that we can add methane to establish a trivariate feedback system between temperature, carbon dioxide and methane. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to model numerically the dynamics of CO2 and 222Rn in cave atmospheres, particularly in the noteworthy Cave of Altamira (Spain). We aim to get a better understanding of the nature of these dynamics and their couplings with climatic controls, more specifically the soil water content, which role in the said dynamics poses some questions. For the first time, we apply the global modeling technique in the field of cave microclimate and atmospheric composition. The global modeling technique is a methodology based on the theory of nonlinear systems and designed to extract mathematical models directly from observational time series. We were able to extract four global models from our data. These models represent a step forward from the existent conceptual ones. They also show that CO2 and 222Rn dynamics can be approximated by low-dimensional, deterministic systems, which can be chaotic or, at least, close to chaos; this has decisive methodological consequences for future research. Moreover, the global modeling technique was used for the first time in a non-autonomous formulation; this enabled the possibility of studying the influence of the external forcing (soil water content) on the gas concentration in different scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates patterns of daily evolutions of CO in the lower atmosphere at a rural site over 2 years. The first part is devoted to observation analysis using a clustering procedure. However, direct application of the average-linkage method yielded undesirable results. In order to improve this procedure, data were previously processed using three smoothing procedures: the kernel smoothing method, the elliptical procedure, and the second-order cylindrical model. These procedures successfully revealed that clusters were based on daily concentration and range. However, the unequal distribution of frequencies in the clusters proved to be a noticeable disadvantage. Four alternative and simpler schemes for grouping observations were proposed in the second part of this paper. The first, comprising groups following fixed values of daily range and mean concentration, provided a sharp contrast between spring, with a marked daily cycle linked to the biological peak, and summer with a smooth daily cycle and low concentration when the biological minimum was reached. The second scheme is based on isopleth analysis and considers observation groups of similar frequencies following an increasing order of mean concentration and daily range. As a result, seasonal evolution was less marked. Straight lines were the borders for groups in the third scheme, which was similar and simpler than the second. The final scheme divided observations by means of equations of daily range as a quadratic function of daily concentration. The groups formed may be linked to seasons, with the group prevailing in summer presenting a noticeable daily range. [ABSTRACT FROM AUTHOR]

High and low CO2 partial pressure (Pco2) levels can induce consequences to the internal system of a karstic cave. This then can further affect the state of the cave. Subsequently, speleothem growths and the walls with prehistorical paintings and engravings will be affected with either calcite dissolution or precipitation. Thus, pinpointing the major components of Pco2 is found indispensable. In this paper, a method is proposed in order to (1) identify the major components of the Pco2 and (2) quantify each specific contribution to the Pco2. Pco2, air temperature, air pressure, and water flow were measured from 2008 to 2010 in Lascaux Cave (Southwest of France). Pco2 varied from 0.62% (6200 ppm) in summer to 0.04% (400 ppm) in winter. We identified three major components: (1) seasonal: component A; (2) low frequency: component B; and (3) high frequency: component C. The method is applied to estimate the contribution of each component. Component A was modelled with a sinusoid curve fitted to the Pco2 measures. This component was subtracted from the measured Pco2. Then, from the remaining value of Pco2, Fourier transform, frequency filtering, and inverse Fourier transform were applied to separate high- and low-frequency components. As a result, the seasonal component A contributes 77% of the measured Pco2, while components B and C supply 13 and 11%, respectively. The three identified components have links with environmental parameters such as temperature, water flow, and atmospheric pressure. Hypotheses were proposed to describe these links. Knowledge on these aspects can help in cave management and protection specifically in answering the question: which parameter should be given more attention.Separation and quantification of the major components of CO2 signal in Lascaux Cave [ABSTRACT FROM AUTHOR]

In this paper, we analyze the Granger causality from natural or anthropogenic forcings to global temperature anomalies. The lag-augmented Wald test is performed, and its robustness is also evaluated considering bootstrap method. The results show there is no-evidence of Granger causality from natural forcings to global temperature. On the contrary, a detectable Granger causality is found from anthropogenic forcings to global temperature confirming that greenhouse gases have an important role on recent global warming. [ABSTRACT FROM AUTHOR]

This paper designs a comprehensive approach based on the engineering machine/ system concept, to model, analyze, and assess the level of carbon dioxide (CO) exchange between the atmosphere and terrestrial ecosystems, which is an important factor in understanding changes in global climate. The focus of this article is on spatial patterns and on the correlation between levels of CO fluxes and a variety of influencing factors in eco-environments. The engineering/machine concept used is a system protocol that includes the sequential activities of design, test, observe, and model. This concept is applied to explicitly include various influencing factors and interactions associated with CO fluxes. To formulate effective models of a large and complex climate system, this article introduces a modeling technique that will be referred to as stochastic filtering analysis of variance (SF-ANOVA). The CO flux data observed from some sites of AmeriFlux are used to illustrate and validate the analysis, prediction, and globalization capabilities of the proposed engineering approach and the SF-ANOVA technique. The SF-ANOVA modeling approach was compared to stepwise regression, ridge regression, and neural networks. The comparison indicated that the proposed approach is a valid and effective tool with similar accuracy and less complexity than the other procedures. [ABSTRACT FROM AUTHOR]

Detection and attribution methodologies have been developed over the years to delineate anthropogenic from natural drivers of climate change and impacts. A majority of prior attribution studies, which have used climate model simulations and observations or reanalysis datasets, have found evidence for human-induced climate change. This papers tests the hypothesis that Granger causality can be extracted from the bivariate series of globally averaged land surface temperature (GT) observations and observed CO in the atmosphere using a reverse cumulative Granger causality test. This proposed extension of the classic Granger causality test is better suited to handle the multisource nature of the data and provides further statistical rigor. The results from this modified test show evidence for Granger causality from a proxy of total radiative forcing (RC), which in this case is a transformation of atmospheric CO, to GT. Prior literature failed to extract these results via the standard Granger causality test. A forecasting test shows that a holdout set of GT can be better predicted with the addition of lagged RC as a predictor, lending further credibility to the Granger test results. However, since second-order-differenced RC is neither normally distributed nor variance stationary, caution should be exercised in the interpretation of our results. [ABSTRACT FROM AUTHOR]

Nocturnal eddy-covariance carbon dioxide fluxes have uncertainties arising from non-stationary atmospheric processes. Low-level jets (LLJ) are one of the prominent nocturnal boundary-layer phenomena observed over non-mountainous terrain, and are capable of generating shear and turbulence close to the ground. The influence of intermittent LLJ activity on nocturnal carbon dioxide exchange measurements is investigated using wind profile observations and eddy-covariance flux measurements over a tall forest canopy. Results suggest that the buildup and venting of CO2 are closely associated with LLJ activity during the night. Of significance in quantifying nocturnal fluxes, this paper demonstrates how low-level jet activity introduces sporadic coupling between the canopy and the atmosphere. [ABSTRACT FROM AUTHOR]