Stochastic assessment of temperature-CO2 causal relationship in climate from the Phanerozoic through modern times

As a result of recent research, a new stochastic methodology of assessing causality was developed. Its application to instrumental measurements of temperature (T) and atmospheric carbon dioxide concentration ([CO2]) over the last seven decades provided evidence for a unidirectional, potentially causal link between T as the cause and [CO2] as the effect. Here, I refine and extend this methodology and apply it to both paleoclimatic proxy data and instrumental data of T and [CO2]. Several proxy series, extending over the Phanerozoic or parts of it, gradually improving in accuracy and temporal resolution up to the modern period of accurate records, are compiled, paired, and analyzed. The extensive analyses made converge to the single inference that change in temperature leads, and that in carbon dioxide concentration lags. This conclusion is valid for both proxy and instrumental data in all time scales and time spans. The time scales examined begin from annual and decadal for the modern period (instrumental data) and the last two millennia (proxy data), and reach one million years for the most sparse time series for the Phanerozoic. The type of causality appears to be unidirectional, T→[CO2], as in earlier studies. The time lags found depend on the time span and time scale and are of the same order of magnitude as the latter. These results contradict the conventional wisdom, according to which the temperature rise is caused by [CO2] increase.

Feeding the world: impacts of elevated [CO2] on nutrient content of greenhouse grown fruit crops and options for future yield gains

Several long-term studies have provided strong support demonstrating that growing crops under elevated [CO2] can increase photosynthesis and result in an increase in yield, flavour and nutritional content (including but not limited to Vitamins C, E and pro-vitamin A). In the case of tomato, increases in yield by as much as 80% are observed when plants are cultivated at 1000 ppm [CO2], which is consistent with current commercial greenhouse production methods in the tomato fruit industry. These results provide a clear demonstration of the potential for elevating [CO2] for improving yield and quality in greenhouse crops. The major focus of this review is to bring together 50 years of observations evaluating the impact of elevated [CO2] on fruit yield and fruit nutritional quality. In the final section, we consider the need to engineer improvements to photosynthesis and nitrogen assimilation to allow plants to take greater advantage of elevated CO2 growth conditions.

Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series

Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener–Granger’s idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC). We demonstrate that PCCC retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially unobserved variables which can be reconstructed using the embedding theorem. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples.

Revisiting causality using stochastics: 2. Applications

In a companion paper, we develop the theoretical background of a stochastic approach to causality with the objective of formulating necessary conditions that are operationally useful in identifying or falsifying causality claims. Starting from the idea of stochastic causal systems, the approach extends it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. The framework developed is applicable to large-scale open systems, which are neither controllable nor repeatable. In this paper, we illustrate and showcase the proposed framework in a number of case studies. Some of them are controlled synthetic examples and are conducted as a proof of applicability of the theoretical concept, to test the methodology with a priori known system properties. Others are real-world studies on interesting scientific problems in geophysics, and in particular hydrology and climatology.

Revisiting causality using stochastics: 1. Theory

Causality is a central concept in science, in philosophy and in life. However, reviewing various approaches to it over the entire knowledge tree, from philosophy to science and to scientific and technological applications, we locate several problems, which prevent these approaches from defining sufficient conditions for the existence of causal links. We thus choose to determine necessary conditions that are operationally useful in identifying or falsifying causality claims. Our proposed approach is based on stochastics, in which events are replaced by processes. Starting from the idea of stochastic causal systems, we extend it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. Theoretical considerations allow the development of an effective algorithm, applicable to large-scale open systems, which are neither controllable nor repeatable. The derivation and details of the algorithm are described in this paper, while in a companion paper we illustrate and showcase the proposed framework with a number of case studies, some of which are controlled synthetic examples and others real-world ones arising from interesting scientific problems.

Cumulative Emissions of CO2 for Electric and Combustion Cars: A Case Study on Specific Models

This work includes calculations of the cumulative CO2 emissions of two comparable cars—the VW Golf VII—one with a combustion engine and the other with an electric motor. Calculation of CO2 emissions was performed, taking into account the stages of production, utilization and use of the above-mentioned vehicles. For the use phase, it was assumed that the total mileage of the car will be 150,000 km over 10 years. For the electric vehicle, calculations were made assuming five different sources of electricity (from coal only, from natural gas only, from PV and wind turbines, an average mix of European power sources and an average mix of Polish power sources; W1–W5 designations, respectively). For individual sources of electricity, cumulative CO2 emissions were taken into account, that is, resulting both from the production of electricity and the use of the resources (for example, technical service per 1 kWh of electricity produced). The obtained results of the analysis show that for the adopted assumptions regarding operation, for variants W2–W5, the use of an electric car results in lower cumulative CO2 emission than a the use of a combustion car. For a combustion car, the value was 37,000 kg-CO2, and for an electric car, depending on the variant, the value was 43, 31, 16, 23 and 34 thousand kg-CO2 for variants W1 to W5, respectively. Based on the emissions results obtained for individual stages of the use of selected vehicles, a comparative analysis of cumulative CO2 emissions was performed. The purpose of this analysis was to determine whether the replacement of an existing combustion car (that has already been manufactured; therefore, this part of the analysis does not include CO2 emissions in the production stage) with a new electric car, which has to be manufactured, therefore associated with additional CO2 emissions, would reduce cumulative CO2 emissions. Considering three adopted average annual car mileages (3000, 7500 and 15,000 km) and the previously described power options (W1–W5), we sought an answer as to whether the use of a new electric car would be burdened with lower cumulative CO2 emissions. In this case we assumed an analysis time of 15 years. For the worst variant from the point of view of CO2 emissions (W1, electricity from coal power sources only), further use of a combustion car is associated with lower cumulative CO2 emissions than the purchase of a new electric car over the entire analyzed period of 15 years. In turn, for the most advantageous variant (W3, electricity from PV or wind power sources) with an annual mileage of 3000 km, the purchase of a new electric car results in higher cumulative CO2 emissions throughout the analyzed period, whereas for an annual milage of 7500 or 15,000 km, replacing the car with an electric car “pays back” in terms of cumulative CO2 emissions after 8.5 or 4 years, respectively.

Sci: An Inclusive, Multidisciplinary Scientific Journal

Sci (ISSN 2413-4155) is an international, open-access journal that covers most fields of scientific research. It has set out to challenge the conventional single- and double-blind peer review processes by adopting a post-publication public peer review (P4R) model. The model faced some difficulties with indexing and archiving services, prolongated the peer review process and its transparency received some opposition. It was therefore necessary to revisit the P4R model and modify it, resulting in the hybrid model (P4R hybrid) which is implemented in Sci today. Sci remains open to the whole scientific community as an inclusive and multidisciplinary scientific journal. In this context, we present you with six valuable contributions to the first Special Issue of Feature Papers Editors Collection 2020. The topics of the contributions address relevant and compelling issues ranging from data protection, material science, COVID-19 to the environment and climate change.

On Spurious Causality, CO2, and Global Temperature

Stips et al. (2016) use information flows (Liang (2008, 2014)) to establish causality from various forcings to global temperature. We show that the formulas being used hinge on a simplifying assumption that is nearly always rejected by the data. We propose the well-known forecast error variance decomposition based on a Vector Autoregression as an adequate measure of information flow, and find that most results in Stips et al. (2016) cannot be corroborated. Then, we discuss which modeling choices (e.g., the choice of CO2 series and assumptions about simultaneous relationships) may help in extracting credible estimates of causal flows and the transient climate response simply by looking at the joint dynamics of two climatic time series.

Rethinking Climate, Climate Change, and Their Relationship with Water

We revisit the notion of climate, along with its historical evolution, tracing the origin of the modern concerns about climate. The notion (and the scientific term) of climate was established during the Greek antiquity in a geographical context and it acquired its statistical content (average weather) in modern times after meteorological measurements had become common. Yet the modern definitions of climate are seriously affected by the wrong perception of the previous two centuries that climate should regularly be constant, unless an external agent acts upon it. Therefore, we attempt to give a more rigorous definition of climate, consistent with the modern body of stochastics. We illustrate the definition by real-world data, which also exemplify the large climatic variability. Given this variability, the term “climate change” turns out to be scientifically unjustified. Specifically, it is a pleonasm as climate, like weather, has been ever-changing. Indeed, a historical investigation reveals that the aim in using that term is not scientific but political. Within the political aims, water issues have been greatly promoted by projecting future catastrophes while reversing true roles and causality directions. For this reason, we provide arguments that water is the main element that drives climate, and not the opposite.