Cosmic Rays and Climate

Among the most puzzling questions in climate change is that of solar-climate variability, which has attracted the attention of scientists for more than two centuries. Until recently, even the existence of solar-climate variability has been controversial - perhaps because the observations had largely involved temporary correlations between climate and the sunspot cycle. Over the last few years, however, diverse reconstructions of past climate change have revealed clear associations with cosmic ray variations recorded in cosmogenic isotope archives, providing persuasive evidence for solar or cosmic ray forcing of the climate. However, despite the increasing evidence of its importance, solar climate variability is likely to remain controversial until a physical mechanism is established. Although this remains a mystery, observations suggest that cloud cover may be influenced by cosmic rays, which are modulated by the solar wind and, on longer time scales, by the geomagnetic field and by the galactic environment of Earth. Two different classes of microphysical mechanisms have been proposed to connect cosmic rays with clouds: firstly, an influence of cosmic rays on the production of cloud condensation nuclei and, secondly, an influence of cosmic rays on the global electrical circuit in the atmosphere and, in turn, on ice nucleation and other cloud microphysical processes. Considerable progress on understanding ion-aerosol-cloud processes has been made in recent years, and the results are suggestive of a physically- plausible link between cosmic rays, clouds and climate. However, a concerted effort is now required to carry out definitive laboratory measurements of the fundamental physical and chemical processes involved, and to evaluate their climatic significance with dedicated field observations and modelling studies.Comment: 42 pages, 19 figure

Beam Measurements of a CLOUD (Cosmics Leaving OUtdoor Droplets) Chamber

A striking correlation has recently been observed between global cloud cover and the flux of incident cosmic rays. The effect of natural variations in the cosmic ray flux is large, causing estimated changes in the Earth's energy radiation balance that are comparable to those attributed to greenhouse gases from the burning of fossil fuels since the Industrial Revolution. However a direct link between cosmic rays and cloud formation has not been unambiguously established. We therefore propose to experimentally measure cloud (water droplet) formation under controlled conditions in a test beam at CERN with a CLOUD chamber, duplicating the conditions prevailing in the troposphere. These data, which have never been previously obtained, will allow a detailed understanding of the possible effects of cosmic rays on clouds and confirm, or otherwise, a direct link between cosmic rays, global cloud cover and the Earth's climate. The measurements will, in turn, allow more reliable calculations to be made of the residual effect on global temperatures of the burning of fossil fuels, an issue of profound importance to society. Furthermore, light radio-isotope records indicate a correlation has existed between global climate and the cosmic ray flux extending back over the present inter-glacial and perhaps earlier. This suggests it may eventually become possible to make long-term (10-1,000 year) predictions of changes in the Earth's climate, provided a deeper understanding can be achieved of the ``geomagnetic climate'' of the Sun and Earth that modulates the cosmic-ray flux.Comment: More information and higher resolution drawings at http://cern.ch/Cloud Improved figure qualit

A Study of Hadronic Backgrounds to Isolated Hard Photon Production with L3

I describe two methods for studying hadronic backgrounds to prompt photon production with L3, and compare the observed background rates with Monte Carlo predictions. I find that the Monte Carlo models JETSET and HERWIG underestimate the production of isolated neutral hadrons in hadronic Z decays at LEP. By extrapolating results obtained with L3, I estimate that the rate of prompt-photon + jet background to a H -> gamma gamma search at the LHC will be larger than Monte Carlo predictions by a factor of 1.5-2.5.Comment: 4 page

The numerical solution of certain differential equations occurring in Crocco's theory of the laminar boundary layer

A numerical method is described for the solution of certain differential equations which result from the application of Crocco’s transformation to the laminar boundary layer equations appropriate to high supersonic Mach numbers. (i.e. at hypersonic speeds). Continues

Role of magnesium in carbon partitioning and alleviating photooxidative damage

Magnesium (Mg) deficiency exerts a major influence on the partitioning of drymatter and carbohydrates between shoots and roots. One of the very early reactions of plants to Mg deficiency stress is themarked increase in the shootto- root dry weight ratio, which is associated with a massive accumulation of carbohydrates in source leaves, especially of sucrose and starch. These higher concentrations of carbohydrates in Mg-deficient leaves together with the accompanying increase in shoot-to-root dry weight ratio are indicative of a severe impairment in phloem export of photoassimilates from source leaves. Studies with common bean and sugar beet plants have shown that Mg plays a fundamental role in phloem loading of sucrose. At a very early stage of Mg deficiency, phloem export of sucrose is severely impaired, an effect that occurs before any noticeable changes in shoot growth, Chl concentration or photosynthetic activity. These findings suggest that accumulation of carbohydrates in Mg-deficient leaves is caused directly by Mg deficiency stress and not as a consequence of reduced sink activity. The role of Mg in the phloem-loading process seems to be specific; resupplying Mg for 12 or 24 h to Mg-deficient plants resulted in a very rapid recovery of sucrose export. It appears that the massive accumulation of carbohydrates and related impairment in photosynthetic CO2 fixation in Mg-deficient leaves cause an over-reduction in the photosynthetic electron transport chain that potentiates the generation of highly reactive O2 species (ROS). Plants respond to Mg deficiency stress by marked increases in antioxidative capacity of leaves, especially under high light intensity, suggesting that ROS generation is stimulated by Mg deficiency in chloroplasts. Accordingly, it has been found that Mg-deficient plants are very susceptible to high light intensity. Exposure of Mg-deficient plants to high light intensity rapidly induced leaf chlorosis and necrosis, an outcome that was effectively delayed by partial shading of the leaf blade, although the Mg concentrations in different parts of the leaf blade were unaffected by shading. The results indicate that photooxidative damage contributes to development of leaf chlorosis under Mg deficiency, suggesting that plants under high-light conditions have a higher physiological requirement for Mg. Maintenance of a high Mg nutritional status of plants is, thus, essential in the avoidance of ROS generation, which occurs at the expense of inhibited phloem export of sugars and impairment of CO2 fixation, particularly under high-light conditions

Tie-breaking in Hoeffding trees

A thorough examination of the performance of Hoeffding trees, state-of-the-art in classification for data streams, on a range of datasets reveals that tie breaking, an essential but supposedly rare procedure, is employed much more than expected. Testing with a lightweight method for handling continuous attributes, we find that the excessive invocation of tie breaking causes performance to degrade significantly on complex and noisy data. Investigating ways to reduce the number of tie breaks, we propose an adaptive method that overcomes the problem while not significantly affecting performance on simpler datasets

Batch-Incremental Learning for Mining Data Streams

The data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival. Third, a model may only use a constant amount of memory, and must be ready for prediction at any point in time. We attempt to overcome these restrictions by presenting a data stream classification algorithm where the data is split into a stream of disjoint batches. Single batches of data can be processed one after the other by any standard non-incremental learning algorithm. Our approach uses ensembles of decision trees. These tree ensembles are iteratively merged into a single interpretable model of constant maximal size. Using benchmark datasets the algorithm is evaluated for accuracy against state-of-the-art algorithms that make use of the entire dataset

The glacial cycles and cosmic rays

The cause of the glacial cycles remains a mystery. The origin is widely accepted to be astronomical since paleoclimatic archives contain strong spectral components that match the frequencies of Earth's orbital modulation. Milankovitch insolation theory contains similar frequencies and has become established as the standard model of the glacial cycles. However, high precision paleoclimatic data have revealed serious discrepancies with the Milankovitch model that fundamentally challenge its validity and re-open the question of what causes the glacial cycles. We propose here that the ice ages are initially driven not by insolation cycles but by cosmic ray changes, probably through their effect on clouds. This conclusion is based on a wide range of evidence, including results presented here on speleothem growth in caves in Austria and Oman, and on a record of cosmic ray flux over the past 220 kyr obtained from the 10Be composition of deep-ocean sediments

Mining data streams using option trees (revised edition, 2004)

The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time. This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset