Volcanic ash layer depth: processes and mechanisms

The long duration of the 2010 Eyjafjallajökull eruption provided a unique opportunity to measure a widely dispersed volcanic ash cloud. Layers of volcanic ash were observed by the European Aerosol Research Lidar Network with a mean depth of 1.2 km and standard deviation of 0.9 km. In this paper we evaluate the ability of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) to simulate the observed ash layers and examine the processes controlling their depth. NAME simulates distal ash layer depths exceptionally well with a mean depth of 1.2 km and standard deviation of 0.7 km. The dominant process determining the depth of ash layers over Europe is the balance between the vertical wind shear (which acts to reduce the depth of the ash layers) and vertical turbulent mixing (which acts to deepen the layers). Interestingly, differential sedimentation of ash particles and the volcano vertical emission profile play relatively minor roles.

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10–20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately 5–7 (14–16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m−3 is used for the MLD estimation. Using the larger criterion (0.125 kg m−3) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual variability by the ensemble mean of the reanalyses indicates a great potential of the ensemble mean MLD field for investigating and monitoring upper ocean processes.

The role of local atmospheric forcing on the modulation of the ocean mixed layer depth in reanalysis and a coupled single column ocean model

The role of the local atmospheric forcing on the ocean mixed layer depth (MLD) over the global oceans is studied using ocean reanalysis data products and a single-column ocean model coupled to an atmospheric general circulation model. The focus of this study is on how the annual mean and the seasonal cycle of the MLD relate to various forcing characteristics in different parts of the world's ocean, and how anomalous variations in the monthly mean MLD relate to anomalous atmospheric forcings. By analysing both ocean reanalysis data and the single-column ocean model, regions with different dominant forcings and different mean and variability characteristics of the MLD can be identified. Many of the global oceans' MLD characteristics appear to be directly linked to different atmospheric forcing characteristics at different locations. Here, heating and wind-stress are identified as the main drivers; in some, mostly coastal, regions the atmospheric salinity forcing also contributes. The annual mean MLD is more closely related to the annual mean wind-stress and the MLD seasonality is more closely to the seasonality in heating. The single-column ocean model, however, also points out that the MLD characteristics over most global ocean regions, and in particular the tropics and subtropics, cannot be maintained by local atmospheric forcings only, but are also a result of ocean dynamics that are not simulated in a single-column ocean model. Thus, lateral ocean dynamics are essentially in correctly simulating observed MLD.

Optical chemical sensors using polythiophene derivatives as active layer for detection of volatile organic compounds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Melanin as an active layer in biosensors

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optical chemical sensors using polythiophene derivatives as active layer for detection of volatile organic compounds

Several studies on polythiophene gas sensors, based mainly on electrochemical and gravimetric principles can be found in the literature. However, other principles of gas detection, such as optical and thermal, are still little studied. Optical sensing is suitable for remote detection and offers great versatility at low cost. Here,we report on the use of thin films of seven polythiophene derivatives as active layer in optical sensors for the detection of six volatile organic compounds (n-hexane, toluene, tetrahydrofuran, chloroform, dichloromethane and methanol) and water vapor, in concentration range of 500-30,000 ppm. The results showed that it is possible to use different polythiophene derivatives to differentiate VOCs by optical sensing. Differentiation can be performed based on the presence or not of response to an analyte and the sensitivity value of the sensors for the analytes. Another important feature is the lack of the effect of humidity on the response of most films, which could be a major drawback in the application of these sensors. (C) 2011 Elsevier B.V. All rights reserved.

Decadal Variability in the Northeast Pacific in a Physical-Ecosystem Model: Role of Mixed Layer Depth and Trophic Interactions

A basin-wide interdecadal change in both the physical state and the ecology of the North Pacific occurred near the end of 1976. Here we use a physical-ecosystem model to examine whether changes in the physical environment associated with the 1976-1977 transition influenced the lower trophic levels of the food web and if so by what means. The physical component is an ocean general circulation model, while the biological component contains 10 compartments: two phytoplankton, two zooplankton, two detritus pools, nitrate, ammonium, silicate, and carbon dioxide. The model is forced with observed atmospheric fields during 1960-1999. During spring, there is a similar to 40% reduction in plankton biomass in all four plankton groups during 1977-1988 relative to 1970-1976 in the central Gulf of Alaska (GOA). The epoch difference in plankton appears to be controlled by the mixed layer depth. Enhanced Ekman pumping after 1976 caused the halocline to shoal, and thus the mixed layer depth, which extends to the top of the halocline in late winter, did not penetrate as deep in the central GOA. As a result, more phytoplankton remained in the euphotic zone, and phytoplankton biomass began to increase earlier in the year after the 1976 transition. Zooplankton biomass also increased, but then grazing pressure led to a strong decrease in phytoplankton by April followed by a drop in zooplankton by May: Essentially, the mean seasonal cycle of plankton biomass was shifted earlier in the year. As the seasonal cycle progressed, the difference in plankton concentrations between epochs reversed sign again, leading to slightly greater zooplankton biomass during summer in the later epoch.

Using ground-penetrating radar, topography and classification of vegetation to model the sediment and active layer thickness in a periglacial lake catchment, Western Greenland, link to shapefiles

The geometries of a catchment constitute the basis for distributed physically based numerical modeling of different geoscientific disciplines. In this paper results from ground-penetrating radar (GPR) measurements, in terms of a 3D model of total sediment thickness and active layer thickness in a periglacial catchment in western Greenland, is presented. Using the topography, thickness and distribution of sediments is calculated. Vegetation classification and GPR measurements are used to scale active layer thickness from local measurements to catchment scale models. Annual maximum active layer thickness varies from 0.3 m in wetlands to 2.0 m in barren areas and areas of exposed bedrock. Maximum sediment thickness is estimated to be 12.3 m in the major valleys of the catchment. A method to correlate surface vegetation with active layer thickness is also presented. By using relatively simple methods, such as probing and vegetation classification, it is possible to upscale local point measurements to catchment scale models, in areas where the upper subsurface is relatively homogenous. The resulting spatial model of active layer thickness can be used in combination with the sediment model as a geometrical input to further studies of subsurface mass-transport and hydrological flow paths in the periglacial catchment through numerical modelling.