Nonparametric analysis of high wind speed data

In this paper, nonparametric curve estimation methods are applied to analyze time series of wind speeds, focusing on the extreme events exceeding a chosen threshold. Classical parametric statistical approaches in this context consist in fitting a generalized Pareto distribution (GPD) to the tail of the empirical cumulative distribution, using maximum likelihood or the method of the moments to estimate the parameters of this distribution. Additionally, confidence intervals are usually computed to assess the uncertainty of the estimates. Nonparametric methods to estimate directly some quantities of interest, such as the probability of exceedance, the quantiles or return levels, or the return periods, are proposed. Moreover, bootstrap techniques are used to develop pointwise and simultaneous confidence intervals for these functions. The proposed models are applied to wind speed data in the Gulf Coast of US, comparing the results with those using the GPD approach, by means of a split-sample test. Results show that nonparametric methods are competitive with respect to the standard GPD approximations. The study is completed generating synthetic data sets and comparing the behavior of the parametric and the nonparametric estimates in this framework.     

Analysis of uncertainties in future climate projections for South America: comparison of WCRP-CMIP3 and WCRP-CMIP5 models

This paper identifies two sources of uncertainties in model projections of temperature and precipitation: internal and inter-model variability. Eight models of WCRP-CMIP3 and WCRP-CMIP5 were compared to identify improvements in the reliability of projections from new generation models. While no significant differences are observed between both datasets, some improvements were found in the new generation models. For example, in summer CMIP5 inter-model variability of temperature was lower over northeastern Argentina, Paraguay and northern Brazil, in the last decades of the 21st century. Reliability of temperature projections from both sets of models is high, with signal to noise ratio greater than 1 over most of the study region. Although no major differences were observed in both precipitation datasets, CMIP5 inter-model variability was lower over northern and eastern Brazil in summer (especially at the end of the 21st century). Reliability of precipitation projections was low in both datasets. However, the signal to noise ratio in new generation models was close to 1, and even greater than 1 over eastern Argentina, Uruguay and southern Brazil in some seasons.     

Winter-to-spring temperature dynamics in Turkey derived from tree rings since AD 1125

In the eastern Mediterranean in general and in Turkey in particular, temperature reconstructions based on tree rings have not been achieved so far. Furthermore, centennial-long chronologies of stable isotopes are generally also missing. Recent studies have identified the tree species Juniperus excelsa as one of the most promising tree species in Turkey for developing long climate sensitive stable carbon isotope chronologies because this species is long-living and thus has the ability to capture low-frequency climate signals. We were able to develop a statistically robust, precisely dated and annually resolved chronology back to AD 1125. We proved that variability of δ¹³C in tree rings of J. excelsa is mainly dependent on winter-to-spring temperatures (January–May). Low-frequency trends, which were associated with the medieval warm period and the little ice age, were identified in the winter-to-spring temperature reconstruction, however, the twentieth century warming trend found elsewhere could not be identified in our proxy record, nor was it found in the corresponding meteorological data used for our study. Comparisons with other northern-hemispherical proxy data showed that similar low-frequency signals are present until the beginning of the twentieth century when the other proxies derived from further north indicate a significant warming while the winter-to-spring temperature proxy from SW-Turkey does not. Correlation analyses including our temperature reconstruction and seven well-known climate indices suggest that various atmospheric oscillation patterns are capable of influencing the temperature variations in SW-Turkey.     

The challenge to detect and attribute effects of climate change on human and natural systems

Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation.     

Do we know each other? Bilateral ties and the location of clean development mechanism projects

This paper examines how bilateral ties between developed (home) countries and developing (host) countries influence the location of Clean Development Mechanism projects (CDMs). With the home-host country pair as the unit of analysis (2,058 country-pairs), we employ a logistic regression model to analyze decisions of home countries in selecting the location for their CDMs. We are most interested in examining how home countries’ familiarity with the host country influences CDM location decisions. The familiarity factors are: (1) colonial history; (2) bilateral trade; and (3) bilateral aid. Using a binary logistical model, we find that that bilateral familiarity factors strongly influence CDM location decisions. Further, with respect to host country characteristics, we find that total carbon dioxide emissions and UNFCCC specific domestic institutions influence CDM location decisions, but not general investment institutions or high carbon intensity of host country economies.     

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.     

Statistical Analysis of Large-Scale EUV Waves Observed by STEREO/EUVI

We statistically analyzed the kinematical evolution and wave pulse characteristics of 60 strong large-scale EUV wave events that occurred during January 2007 to February 2011 with the STEREO twin spacecraft. For the start velocity, the arithmetic mean is 312±115 km?s^?1 (within a range of 100?–?630 km?s^?1). For the mean (linear) velocity, the arithmetic mean is 254±76 km?s^?1 (within a range of 130?–?470 km?s^?1). 52 % of all waves under study show a distinct deceleration during their propagation (a≤?50 m?s^?2), the other 48 % are consistent with a constant speed within the uncertainties (?50≤a≤50 m?s^?2). The start velocity and the acceleration are strongly anticorrelated with c≈?0.8, i.e. initially faster events undergo stronger deceleration than slower events. The (smooth) transition between constant propagation for slow events and deceleration in faster events occurs at an EUV wave start-velocity of v≈230 km?s^?1, which corresponds well to the fast-mode speed in the quiet corona. These findings provide strong evidence that the EUV waves under study are indeed large-amplitude fast-mode MHD waves. This interpretation is also supported by the correlations obtained between the peak velocity and the peak amplitude, impulsiveness, and build-up time of the disturbance. We obtained the following association rates of EUV wave events with other solar phenomena: 95 % are associated with a coronal mass ejection (CME), 74 % to a solar flare, 15 % to interplanetary type II bursts, and 22 % to coronal type II bursts. These findings are consistent with the interpretation that the associated CMEs are the driving agents of the EUV waves.