Psychology of the normal and subnormal.

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Low-frequency noise reduction of spacecraft structures

Low frequency noise reduction of spacecraft structure

Ozone loss derived from balloon-borne tracer measurements in the 1999/2000 Arctic winter

Balloon-borne measurements of CFC11 (from the DIRAC in situ gas chromatograph and the DESCARTES grab sampler), ClO and O3 were made during the 1999/2000 Arctic winter as part of the SOLVE-THESEO 2000 campaign, based in Kiruna (Sweden). Here we present the CFC11 data from nine flights and compare them first with data from other instruments which flew during the campaign and then with the vertical distributions calculated by the SLIMCAT 3D CTM. We calculate ozone loss inside the Arctic vortex between late January and early March using the relation between CFC11 and O3 measured on the flights. The peak ozone loss (~1200ppbv) occurs in the 440-470K region in early March in reasonable agreement with other published empirical estimates. There is also a good agreement between ozone losses derived from three balloon tracer data sets used here. The magnitude and vertical distribution of the loss derived from the measurements is in good agreement with the loss calculated from SLIMCAT over Kiruna for the same days

CARBON BALANCE AND VEGETATION DYNAMICS IN AN OLD‐GROWTH AMAZONIAN FOREST

Amazon forests could be globally significant sinks or sources for atmospheric carbon dioxide, but carbon balance of these forests remains poorly quantified. We surveyed 19.75 ha along four 1‐km transects of well‐drained old‐growth upland forest in the Tapajós National Forest near Santarém, Pará, Brazil (2°51′ S, 54°58′ W) in order to assess carbon pool sizes, fluxes, and climatic controls on carbon balance. In 1999 there were, on average, 470 live trees per hectare with diameter at breast height (dbh) ≥10 cm. The mean (and 95% ci) aboveground live biomass was 143.7 ± 5.4 Mg C/ha, with an additional 48.0 ± 5.2 Mg C/ha of coarse woody debris (CWD). The increase of live wood biomass after two years was 1.40 ± 0.62 Mg C·ha−1·yr−1, the net result of growth (3.18 ± 0.20 Mg C·ha−1·yr−1 from mean bole increment of 0.36 cm/yr), recruitment of new trees (0.63 ± 0.09 Mg C·ha−1·yr−1, reflecting a notably high stem recruitment rate of 4.8 ± 0.9%), and mortality (−2.41 ± 0.53 Mg C·ha−1·yr−1 from stem death of 1.7% yr−1). The gain in live wood biomass was exceeded by respiration losses from CWD, resulting in an overall estimated net loss from total aboveground biomass of 1.9 ± 1.0 Mg C·ha−1·yr−1. The presence of large CWD pools, high recruitment rate, and net accumulation of small‐tree biomass, suggest that a period of high mortality preceded the initiation of this study, possibly triggered by the strong El Niño Southern Oscillation events of the 1990s. Transfer of carbon between live and dead biomass pools appears to have led to substantial increases in the pool of CWD, causing the observed net carbon release. The data show that biometric studies of tropical forests neglecting CWD are unlikely to accurately determine carbon balance. Furthermore, the hypothesized sequestration flux from CO2 fertilization (\u3c0.5 Mg C·ha−1·yr−1) would be comparatively small and masked for considerable periods by climate‐driven shifts in forest structure and associated carbon balance in tropical forests

Data mining: a tool for detecting cyclical disturbances in supply networks.

Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause

Using δ13C-CH4 and δD-CH4 to constrain Arctic methane emissions

We present a global methane modelling study assessing the sensitivity of Arctic atmospheric CH4 mole fractions, δ13C-CH4 and δD-CH4 to uncertainties in Arctic methane sources. Model simulations include methane tracers tagged by source and isotopic composition and are compared with atmospheric data at four northern high-latitude measurement sites. We find the model's ability to capture the magnitude and phase of observed seasonal cycles of CH4 mixing ratios, δ13C-CH4 and δD-CH4 at northern high latitudes is much improved using a later spring kick-off and autumn decline in northern high-latitude wetland emissions than predicted by most process models. Results from our model simulations indicate that recent predictions of large methane emissions from thawing submarine permafrost in the East Siberian Arctic Shelf region could only be reconciled with global-scale atmospheric observations by making large adjustments to high-latitude anthropogenic or wetland emission inventories

The potential to narrow uncertainty in projections of stratospheric ozone over the 21st century

Future stratospheric ozone concentrations will be determined both by changes in the concentration of ozone depleting substances (ODSs) and by changes in stratospheric and tropospheric climate, including those caused by changes in anthropogenic greenhouse gases (GHGs). Since future economic development pathways and resultant emissions of GHGs are uncertain, anthropogenic climate change could be a significant source of uncertainty for future projections of stratospheric ozone. In this pilot study, using an "ensemble of opportunity" of chemistry-climate model (CCM) simulations, the contribution of scenario uncertainty from different plausible emissions pathways for ODSs and GHGs to future ozone projections is quantified relative to the contribution from model uncertainty and internal variability of the chemistry-climate system. For both the global, annual mean ozone concentration and for ozone in specific geographical regions, differences between CCMs are the dominant source of uncertainty for the first two-thirds of the 21st century, up-to and after the time when ozone concentrations return to 1980 values. In the last third of the 21st century, dependent upon the set of greenhouse gas scenarios used, scenario uncertainty can be the dominant contributor. This result suggests that investment in chemistry-climate modelling is likely to continue to refine projections of stratospheric ozone and estimates of the return of stratospheric ozone concentrations to pre-1980 levels