Professor Myles Allen CBE FRS

Abstract:

This paper reviews recent research that assesses evidence for the detection of anthropogenic and natural external influences on the climate. Externally driven climate change has been detected by a number of investigators in independent data covering many parts of the climate system, including surface temperature on global and large regional scales, ocean heat content, atmospheric circulation, and variables of the free atmosphere, such as atmospheric temperature and tropopause height. The influence of external forcing is also clearly discernible in reconstructions of hemispheric-scale temperature of the last millennium. These observed climate changes are very unlikely to be due only to natural internal climate variability, and they are consistent with the responses to anthropogenic and natural external forcing of the climate system that are simulated with climate models. The evidence indicates that natural drivers such as solar variability and volcanic activity are at most partially responsible for the large-scale temperature changes observed over the past century, and that a large fraction of the warming over the last 50 yr can be attributed to greenhouse gas increases. Thus, the recent research supports and strengthens the IPCC Third Assessment Report conclusion that "most of the global warming over the past 50 years is likely due to the increase in greenhouse gases." © 2005 American Meteorological Society.

Abstract:

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impracfical. In this paper, we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 × 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation. © 2005 IEEE.

Abstract:

Large-scale distributed computing projects have many security concerns due to their public and often "open" nature. Climateprediction.net (CPDN) is taking the concept of public-resource, high-throughput Grid computing a stage further, by using it for a major piece of modeling research. The aim is to harness the spare CPU cycles of potentially millions of individual users' PCs to run a massive ensemble of climate simulations. In doing so it has been faced with a range of security and integrity issues beyond those encountered by previous projects but likely also to confront similar initiatives in the future. This paper introduces the project and its software architecture, and outlines a threat model for such situations, including threats to participants, threats to the experiment and threats to other stakeholders. It goes on to discuss how these threats have been addressed, with the procedures presented expected to form a valuable foundation for an increasing number of similar projects.