Characterizing a cyanobacterial bloom in western Lake Erie using satellite imagery and meteorological data

The distribution and intensity of a bloom of the toxic cyanobacterium, Microcystis aeruginosa, in western Lake Erie was characterized using a combination of satellite ocean-color imagery, field data, and meteorological observations. The bloom was first identified by satellite on 14 August 2008 and persisted for more than 2 months. The distribution and intensity of the bloom was estimated using a satellite algorithm that is sensitive to near-surface concentrations of M. aeruginosa. Increases in both area and intensity were most pronounced for wind stress less than 0.05 Pa. Area increased while intensity did not change for wind stresses of 0.05–0.1 Pa, and both decreased for wind stress greater than 0.1 Pa. The recovery in intensity at the surface after strong wind events indicated that high wind stress mixed the bloom through the water column and that it returned to the surface once mixing stopped. This interaction is consistent with the understanding of the buoyancy of these blooms. Cloud cover (reduced light) may have a weak influence on intensity during calm conditions. While water temperature remained greater than 15°C, the bloom intensified if there were calm conditions. For water temperature less than 15°C, the bloom subsided under similar conditions. As a result, wind stress needs to be considered when interpreting satellite imagery of these blooms.

The effects of climate change on ovine parasitic gastroenteritis determined using veterinary surveillance and meteorological data for Northern Ireland over the period 1999-2009

While the influence of temperature and moisture on the free-living stages of gastrointestinal nematodes have been described in detail, and evidence for global climate change is mounting, there have been only a few attempts to relate altered incidence or seasonal patterns of disease to climate change. Studies of this type have been completed for England Scotland and Wales, but not for Northern Ireland (NI). Here we present an analysis of veterinary diagnostic data that relates three categories of gastrointestinal nematode infection in sheep to historical meteorological data for NI. The infections are: trichostrongylosis/teladorsagiosis (Teladorsagia/Trichostrongylus), strongyloidosis and nematodirosis. This study aims to provide a baseline for future climate change analyses and to provide basic information for the development of nematode control programmes. After identifying and evaluating possible sources of bias, climate change was found to be the most likely explanation for the observed patterns of change in parasite epidemiology, although other hypotheses could not be refuted. Seasonal rates of diagnosis showed a uniform year-round distribution for Teladorsagia and Trichostrongylus infections, suggesting consistent levels of larval survival throughout the year and extension of the traditionally expected seasonal transmission windows. Nematodirosis showed a higher level of autumn than Spring infection, suggesting that suitable conditions for egg and larval development occurred after the Spring infection period. Differences between regions within the Province were shown for strongyloidosis, with peaks of infection falling in the period September-November. For all three-infection categories (trichostrongylosis/teladorsagiosis, strongyloidosis and nematodirosis), significant differences in the rates of diagnosis, and in the seasonality of disease, were identified between regions. (C) 2012 Elsevier B.V. All rights reserved.

A general-method for tracking analysis and its application to meteorological data

Recent interest in the validation of general circulation models (GCMs) has been devoted to objective methods. A small number of authors have used the direct synoptic identification of phenomena together with a statistical analysis to perform the objective comparison between various datasets. This paper describes a general method for performing the synoptic identification of phenomena that can be used for an objective analysis of atmospheric, or oceanographic, datasets obtained from numerical models and remote sensing. Methods usually associated with image processing have been used to segment the scene and to identify suitable feature points to represent the phenomena of interest. This is performed for each time level. A technique from dynamic scene analysis is then used to link the feature points to form trajectories. The method is fully automatic and should be applicable to a wide range of geophysical fields. An example will be shown of results obtained from this method using data obtained from a run of the Universities Global Atmospheric Modelling Project GCM.

Detection of recent changes in climate using meteorological data from south-eastern Bangladesh

Analysis of meteorological records from four stations (Chittagong, Cox’s Bazar, Rangamati, Sitakunda) in south-eastern Bangladesh show coherent changes in climate over the past three decades. Mean maximum daily temperatures have increased between 1980 and 2013 by ca. 0.4 to 0.6°C per decade, with changes of comparable magnitude in individual seasons. The increase in mean maximum daily temperature is associated with decreased cloud cover and wind speed, particularly in the pre- and post-monsoon seasons. During these two seasons, the correlation between changes in maximum temperature and clouds is between -0.5 and -0.7; the correlation with wind speed is weaker although similar values are obtained in some seasons. Changes in mean daily minimum (and hence mean) temperature differ between the northern and southern part of the basin: northern stations show a decrease in mean daily minimum temperature during the post-monsoon season of between 0.2 and 0.5°C per decade while southern stations show an increase of ca. 0.1 to 0.4°C per decade during the pre-monsoon and monsoon seasons. In contrast to the significant changes in temperature, there is no trend in mean or total precipitation at any station. However, there is a significant increase in the number of rain days at the northern sites during the monsoon season, with an increase per decade of 3 days in Sitakunda and 7 days at Rangamati. These climate changes could have a significant impact on the hydrology of the Halda Basin, which supplies water to Chittagong and is the major pisciculture centre in Bangladesh.

Modelling short-term temporal changes of bare soil CO2 emissions in a tropical agrosystem by using meteorological data

Determining the variability of carbon dioxide emission from soils is an important task as soils are among the largest sources of carbon in biosphere. In this work the temporal variability of bare soil CO2 emissions was measured over a 3-week period. Temporal changes in soil CO2 emission were modelled in terms of the changes that occurred in solar radiation (SR), air temperature (T-air), air humidity (AR), evaporation (EVAP) and atmospheric pressure (ATM) registered during the time period that the experiment was conducted. The multiple regression analysis (backward elimination procedure) includes almost all the meteorological variables and their interactions into the final model (R-2 = 0.98), but solar radiation showed to be the one of the most relevant variables. The present study indicates that meteorological data could be taken into account as the main forces driving the temporal variability of carbon dioxide emission from bare soils, where microbial activity is the sole source of carbon dioxide emitted. (C) 2003 Elsevier B.V. All rights reserved.

International Polar Year 1882-1883 the digitized meteorological data legacy

The first International Polar Year (IPY) was an international effort to perform continous meteorological and geophysical observations over a time period of two years (1882-1883). Eleven nations established twelve research stations in the Arctic along with thirteen auxilary stations. Two stations were operated on the southern hemisphere (South Georgia and Tierra del Fuego). The data were published in 26 volumes on 8700+ pages of reports, descriptions, tables and graphs in total. The list of meteorological parameters includes temperature, wind, pressure, clouds, precipitation, evaporation, humidity and radiation. In the light of Global Change and the intensification of observations and continous measurements in both polar regions, long-time series increase in importance. The observations of the first IPY from the 19th century enable us to extend the data from the 20th century even more back into the past. In the occasion of the fourth IPY (2007-2009) WDC-MARE decided to digitize the complete set of meteorological data in full hourly resolution and publish it in its reports and make it available in Open Access via the data library PANGAEA.