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Ethane is the most abundant non-methane hydrocarbon in the Earth’s atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns. As with many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity, and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. The goal of this paper is therefore to analyze trends in atmospheric ethane with statistical tools that correctly address these data features. We present selected methods designed for the analysis of time trends and trend reversals. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model, we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above (we provide R code for all proposed methods on https://www.stephansmeekes.nl/code.)., Horizon 2020 https://doi.org/10.13039/501100007601, Fonds De La Recherche Scientifique - FNRS https://doi.org/10.13039/501100002661

This paper investigates the correlation between monthly average temperatures and tithe leasing dates in the Low Countries between 1600 and 1810. The information was obtained from manorial tithe leasing records distributed across the Netherlands and Belgium. Similar research in France and in Switzerland relied on annual dates of grape harvest as a temperature proxy and provided a strong correlation between harvest dates and average monthly temperatures. However, the analysis of our data indicates a low correlation between average monthly temperatures, using the Labrijn temperature series, and tithe leasing dates. Possible reasons for this behaviour include soil variety, diversity in crop growth patterns, historical factors, and variety of crop types. The complexity of variables, therefore, does not permit for the usage of tithe leasing dates for ancient temperature reconstruction. Nonetheless, useful information on crop behaviour under extreme climatic conditions was obtained. © 2013 Springer Science+Business Media Dordrecht.

Existing methods for the assessment of the potential impacts of climate change in productive activities and sectors are usually limited to point estimates that do not consider the inherent variability and uncertainty of climatic and socioeconomic variables. This is a major drawback given that only a limited and potentially misleading estimation of risk can be expected when ignoring such determinant factors. In this paper, a new methodology is introduced that is capable of integrating the agent's beliefs and expert judgment into the assessment of the potential impacts of climate change in a quantitative manner by means of an objective procedure. The goal is to produce tailor-made information to assist decision-making under uncertainty in a way that is consistent with the current state of knowledge and the available subjective "expert" information. Time-charts of the evolution of different risk measures, that can be relevant for assisting decision-making and planning, can be constructed using this new methodology. This methodology is illustrated with a case study of coffee production in Mexico. Time-dependent probabilistic scenarios for coffee production and income, conditional on the agent's beliefs and expert judgment, are developed for the average producer under uncertain future conditions. It is shown that variability in production and income, generated by introducing climate variability and uncertainty are important factors affecting decision-making and the assessment of economic viability that are frequently ignored. The concept of Value at Risk, commonly applied in financial risk management, is introduced as a means for estimating the maximum expected loss for a previously chosen confidence level. Results are tailor-made for agents that have incomplete information and different beliefs. In this case study, the costs of climate change for coffee production in Veracruz are estimated to have a present value representing from 3 to 14 times the current annual value of coffee production in the state. © 2011 The Author(s).

There is a widespread sense that a sufficiently stringent climate mitigation policy, that is, a considerable reduction of greenhouse gas emissions to avoid extreme climate change, will come with very high economic costs for society. This is supported by many cost-benefit analyses (CBA) and policy cost assessments of climate policy. All of these, nevertheless, are based on debatable assumptions. This paper will argue instead that safe climate policy is not excessively expensive and is indeed cheaper than suggested by most current studies. To this end, climate CBA and policy cost assessments are critically evaluated, and as a replacement twelve complementary perspectives on the cost of climate policy are offered. © 2009 The Author(s).

After reading the reply of Kaufmann et al. (2009) to our paper Global and hemispheric temperatures revisited (Gay et al. 2009), we believe that there are several ideas that should be clarified regarding the trend-stationary description of global and hemispheric temperatures and our arguments on why the cointegration methodology may not be adequate for modeling them. We believe that the strategy chosen by Kaufmann et al. (2009) of comparing insample forecasts is not appropriate for evaluating whether global and hemispheric temperature series are best described as integrated or trend stationary processes. It is important to notice that the issue that is being discussed is not forecast accuracy but the adequacy of the probabilistic models that have been proposed for describing global and hemispheric temperatures. This issue cannot be solved by evaluating forecast performance but by using the relevant tests, such as the ones presented in Gay et al. (2009). Moreover, it is a well known result in time series and forecast literature that the best forecasts are not necessarily produced by the model that best describes the underlying data generating process (Hendry and Clements 2002). For example, it is of common knowledge that the accuracy of forecasts may be improved by using a model in first differences instead of a model in levels, even if the series are I(0) (see, for example, Box and Jenkins 1976; Hendry and Clements 2002). Consequently, this is not an appropriate way for inferring what the data generating process is and therefore, the forecast evaluation presented in Kaufmann et al. (2009) cannot help defining what type of process best describes global and hemispheric temperatures.

We are always happy when someone scrutinizes our work (Tavoni and Tol 2010). We are happier still when such scrutiny leaves our results unaffected, as is the case with (Barker and CrawfordBrown 2013; BCB henceforth). BCB launch a lengthy attack on the statistical foundations of our analysis. We concede on all points. From a frequentist point of view, what we did does not make any sense whatsoever. From a Bayesian perspective, however, our work is perfectly valid. We are puzzled that anyone would approach our analysis from a frequentist angle. Model results are not observations of some unknown data generating process about which we seek to test hypotheses. The frequentist approach to statistics has nothing to say about a situation like ours, in which wemake inferences about model results and, particularly, about results models would have reported if experiments had been run. BCB downplay the context of our paper. We respond to the IPCC, who compared incomparable sets of results and drew conclusions that may easily mislead. We suggest an imputation method for making the results comparable. BCB raise three main criticisms to our analysis.

The Stern Review of the Economics of Climate Change (Stern et al. 2006a, b; Stern 2007)has been the catalyst of an enormous amount of discussion since its release in the fall of2006. Policy makers see it as an authoritative report that makes an economic case for rapidreduction of greenhouse gas emissions. Most economists have a different opinion of thequality of the analysis, even though there is a virtual consensus among economists thatclimate change is a serious externality which calls for the immediate implementation of acarbon tax or some other type of policy intervention. Economists who have glanced at theStern Review typically argue, for example, that its choice of ethical parameters (rate of puretime preference, rate of risk aversion) is peculiar, biased, and potentially misleading to bepoint of being counterproductive (Arrow 2007; Dasgupta 2007; Varian 2006; Yohe and Tol2007a, b). Analysts who took a closer look at the Stern Review tend to notice a range ofother assumptions that are questionable, and many have lamented what they perceive to beinadequate documentation (Jensen and Webster 2007; Mendelsohn 2006; Nordhaus 2007a;Pielke, 2007;Tol2006; Tol and Yohe 2006; Yohe 2006; Yohe and Tol 2007b). Some papersclaim that the Stern Review underestimated the impacts of climate change (Neumayer 2007;Spash 2007; Sterner and Persson 2007), while other papers argue that human-inducedclimate change is not real (Byatt et al. 2006; Carter et al. 2006). Neither position has anyempirical support. The Stern Review team has published a number of rebuttals to our