Investigations of meltwater refreezing and density variations in the snowpack and firn within the percolation zone of the Greenland Ice Sheet

The mass balance of polythermal ice masses is critically dependent on the proportion of surface-generated meltwater that subsequently refreezes in the snowpack and firn. In order to quantify this effect and to characterize its spatial variability, we measured near-surface (26%, resulting in a 32% increase in net accumulation. This 'seasonal densification' increased at lower elevations, rising to 47% 10 km closer to the ice-sheet margin at 1860 m a. s. l. Density/depth profiles from nine sites within 1 km2 at ∼1945 m a.s.l. reveal complex stratigraphies that change over short spatial scales and seasonally. We conclude that estimates of mass-balance change cannot be calculated solely from observed changes in surface elevation, but that near-surface densification must also be considered. However, predicting spatial and temporal variations in densification may not be straightforward. Further, the development of complex firn-density profiles both masks discernible annual layers in the near-surface firn and ice stratigraphy and is likely to introduce error into radar-derived estimates of surface elevation

Understanding jumping to conclusions in patients with persecutory delusions: working memory and intolerance of uncertainty

Background. Persecutory delusions are a key psychotic experience. A reasoning style known as ‘jumping to conclusions’ (JTC) – limited information gathering before reaching certainty in decision making – has been identified as a contributory factor in the occurrence of delusions. The cognitive processes that underpin JTC need to be determined in order to develop effective interventions for delusions. In the current study two alternative perspectives were tested: that JTC partially results from impairment in information-processing capabilities and that JTC is a motivated strategy to avoid uncertainty.Method. A group of 123 patients with persistent persecutory delusions completed assessments of JTC (the 60:40 beads task), IQ, working memory, intolerance of uncertainty, and psychiatric symptoms. Patients showing JTC were compared with patients not showing JTC.Results. A total of 30 (24%) patients with delusions showed JTC. There were no differences between patients who did and did not jump to conclusions in overall psychopathology. Patients who jumped to conclusions had poorer working memory performance, lower IQ, lower intolerance of uncertainty and lower levels of worry.Working memory and worry independently predicted the presence of JTC.Conclusions. Hasty decision making in patients with delusions may partly arise from difficulties in keeping information in mind. Interventions for JTC are likely to benefit from addressing working memory performance, while in vivo techniques for patients with delusions will benefit from limiting the demands on working memory. The study provides little evidence for a contribution to JTC from top down motivational beliefs about uncertainty

Recent loss of floating ice and the consequent sea level contribution

We combine new and published satellite observations and the results of a coupled ice-ocean model to provide the first estimate of changes in the quantity of ice floating in the global oceans and the consequent sea level contribution. Rapid losses of Arctic sea ice and small Antarctic ice shelves are partially offset by thickening of Antarctic sea ice and large Antarctic ice shelves. Altogether, 746 +/- 127 km(3) yr(-1) of floating ice was lost between 1994 and 2004, a value that exceeds considerably the reduction in grounded ice over the same period. Although the losses are equivalent to a small (49 +/- 8 μm yr(-1)) rise in mean sea level, there may be large regional variations in the degree of ocean freshening and mixing. Ice shelves at the Antarctic Peninsula and in the Amundsen Sea, for example, have lost 481 +/- 38 km(3) yr(-1)

On the recent elevation changes at the Flade Isblink Ice Cap, northern Greenland

This is the final version of the article. Available from AGU via the DOI in this record.We have used Radar Altimeter 2 (RA-2) onboard ESA's EnviSAT and Geosciences Laser Altimeter System (GLAS) onboard NASA's ICESat to map the elevation change of the Flade Isblink Ice Cap (FIIC) in northern Greenland. Based on RA-2 data we show that the mean surface elevation change of the FIIC has been near zero (0.03±0.03 m/a) between fall 2002 and fall 2009. We present the elevation change rate maps and assess the elevation change rates of areas above the late summer snow line (0.09±0.04 m/a) and below it (-0.16±0.05 m/a). The GLAS elevation change rate maps show that some outlet glaciers, previously reported to have been in a surge state, are thickening rapidly. Using the RA-2 measured average elevation change rates for different parts of the ice cap we present a mass change rate estimate of 0.0±0.5 Gt/a for the FIIC. We compare the annual elevation changes with surface mass balance (SMB) estimates from a regional atmospheric climate model RACMO2. We find a strong correlation between the two (R = 0.94 and P < 0.002), suggesting that the surface elevation changes of the FIIC are mainly driven by net SMB. The correlation of modeled net SMB and measured elevation change is strong in the southern areas of the FIIC (R = 0.97 and P < 0.0005), but insignificant in the northern areas (R = 0.38 and P = 0.40). This is likely due to higher variability of glacier flow in the north relative to the south. Copyright 2011 by the American Geophysical Union

Commissioning and Calibrating the CMS Silicon Strip Tracker

The data acquisition system for the CMS Silicon Strip Tracker (SST) is based around a custom analogue front-end ASIC, an analogue optical link system and an off-detector VME board that performs digitization, zero-suppression and data formatting. A complex procedure is required to optimally configure, calibrate and synchronize the 107 channels of the SST readout system. We present an overview of this procedure, which will be used to commission and calibrate the SST during the integration, Start-Up and operational phases of the experiment. Recent experiences from the CMS Magnet Test Cosmic Challenge and system tests at the Tracker Integration Facility are also reported

Monitoring the CMS strip tracker readout system

The CMS Silicon Strip Tracker at the LHC comprises a sensitive area of approximately 200 m2 and 10 million readout channels. Its data acquisition system is based around a custom analogue front-end chip. Both the control and the readout of the front-end electronics are performed by off-detector VME boards in the counting room, which digitise the raw event data and perform zero-suppression and formatting. The data acquisition system uses the CMS online software framework to configure, control and monitor the hardware components and steer the data acquisition. The first data analysis is performed online within the official CMS reconstruction framework, which provides many services, such as distributed analysis, access to geometry and conditions data, and a Data Quality Monitoring tool based on the online physics reconstruction. The data acquisition monitoring of the Strip Tracker uses both the data acquisition and the reconstruction software frameworks in order to provide real-time feedback to shifters on the operational state of the detector, archiving for later analysis and possibly trigger automatic recovery actions in case of errors. Here we review the proposed architecture of the monitoring system and we describe its software components, which are already in place, the various monitoring streams available, and our experiences of operating and monitoring a large-scale system

Data acquisition software for the CMS strip tracker

The CMS silicon strip tracker, providing a sensitive area of approximately 200 m2 and comprising 10 million readout channels, has recently been completed at the tracker integration facility at CERN. The strip tracker community is currently working to develop and integrate the online and offline software frameworks, known as XDAQ and CMSSW respectively, for the purposes of data acquisition and detector commissioning and monitoring. Recent developments have seen the integration of many new services and tools within the online data acquisition system, such as event building, online distributed analysis, an online monitoring framework, and data storage management. We review the various software components that comprise the strip tracker data acquisition system, the software architectures used for stand-alone and global data-taking modes. Our experiences in commissioning and operating one of the largest ever silicon micro-strip tracking systems are also reviewed

Slepian functions and their use in signal estimation and spectral analysis

It is a well-known fact that mathematical functions that are timelimited (or spacelimited) cannot be simultaneously bandlimited (in frequency). Yet the finite precision of measurement and computation unavoidably bandlimits our observation and modeling scientific data, and we often only have access to, or are only interested in, a study area that is temporally or spatially bounded. In the geosciences we may be interested in spectrally modeling a time series defined only on a certain interval, or we may want to characterize a specific geographical area observed using an effectively bandlimited measurement device. It is clear that analyzing and representing scientific data of this kind will be facilitated if a basis of functions can be found that are "spatiospectrally" concentrated, i.e. "localized" in both domains at the same time. Here, we give a theoretical overview of one particular approach to this "concentration" problem, as originally proposed for time series by Slepian and coworkers, in the 1960s. We show how this framework leads to practical algorithms and statistically performant methods for the analysis of signals and their power spectra in one and two dimensions, and on the surface of a sphere.Comment: Submitted to the Handbook of Geomathematics, edited by Willi Freeden, Zuhair M. Nashed and Thomas Sonar, and to be published by Springer Verla

Scalar and vector Slepian functions, spherical signal estimation and spectral analysis

It is a well-known fact that mathematical functions that are timelimited (or spacelimited) cannot be simultaneously bandlimited (in frequency). Yet the finite precision of measurement and computation unavoidably bandlimits our observation and modeling scientific data, and we often only have access to, or are only interested in, a study area that is temporally or spatially bounded. In the geosciences we may be interested in spectrally modeling a time series defined only on a certain interval, or we may want to characterize a specific geographical area observed using an effectively bandlimited measurement device. It is clear that analyzing and representing scientific data of this kind will be facilitated if a basis of functions can be found that are "spatiospectrally" concentrated, i.e. "localized" in both domains at the same time. Here, we give a theoretical overview of one particular approach to this "concentration" problem, as originally proposed for time series by Slepian and coworkers, in the 1960s. We show how this framework leads to practical algorithms and statistically performant methods for the analysis of signals and their power spectra in one and two dimensions, and, particularly for applications in the geosciences, for scalar and vectorial signals defined on the surface of a unit sphere.Comment: Submitted to the 2nd Edition of the Handbook of Geomathematics, edited by Willi Freeden, Zuhair M. Nashed and Thomas Sonar, and to be published by Springer Verlag. This is a slightly modified but expanded version of the paper arxiv:0909.5368 that appeared in the 1st Edition of the Handbook, when it was called: Slepian functions and their use in signal estimation and spectral analysi