An example of fingerprint detection of greenhouse climate change

As an example of the technique of fingerprint detection of greenhouse climate change, a multivariate signal or fingerprint of the enhanced greenhouse effect is defined using the zonal mean atmospheric temperature change as a function of height and latitude between equilibrium climate model simulations with control and doubled CO₂ concentrations. This signal is compared with observed atmospheric temperature variations over the period 1963 to 1988 from radiosonde-based global analyses. There is a significant increase of this greenhouse signal in the observational data over this period.These results must be treated with caution. Upper air data are available for a short period only, possibly too short to be able to resolve any real greenhouse climate change. The greenhouse fingerprint used in this study may not be unique to the enhanced greenhouse effect and may be due to other forcing mechanisms. However, it is shown that the patterns of atmospheric temperature change associated with uniform global increases of sea surface temperature, with El NinoSouthern Oscillation events and with decreases of stratospheric ozone concentrations individually are different from the greenhouse fingerprint used here.     

Integrated Futures for Europe’s Mountain Regions: Reconciling Biodiversity Conservation and Human Livelihoods

Introduction Europe's mountains cover nearly half of the continent's area (Price et al. 2004) and land cover varies significantly (European Commission 2004). In most massifs, except for Sicily, southern Greece, and the British Isles, forest cover is dominant. In northern Europe, grassland is proportionately more important, and much of the mountains of the British Isles is covered by moorland. In central and southern Europe, arable land is of far greater importance than grassland, with Med…     

Integration of RADARSAT and GIS modelling for estimating future Red River flood risk

A new geomatics-based approach for flood prediction was developed and used to model the magnitude and spatial extent of a future Red River flood in southern Manitoba. This approach combines the statistical modelling capabilities of Markov (non-spatial) analysis and logistic regression (spatial) within a geographic information system (GIS) environment, utilizing modelling inputs derived from remotely sensed RADARSAT imagery and other digital geographic data. The 1997 Red River flood was the second largest in recorded history, and the largest for which accurate data are available. The results indicate: (i) a flood “one time interval-in terms of 3 days time unit measurement- larger in area” than the 1997 flood is expected to affect 17.6% more land (an additional 47.6 km²) within the study area compared to 1997 levels based on Markovian probability derived from observations from the 1997 event; and (ii) the majority of this excess flooding will take place on agricultural land; no additional communities are expected to be at risk. Quantitative assessment verified the capability of this modelling approach for producing statistically significant results. The methodology used in this research would be easily transferable to other areas, and may provide the basis for a viable alternative to conventional hydrologic-based flood prediction approaches This revised version was published online in August 2006 with corrections to the Cover Date.