Modelling Antarctic lake ice responses to meteorological variables
Inland Antarctic lakes are among the harshest environments in the world for life to inhabit. Ice cover causes low levels of light and temperature, and prevents mixing by wind, resulting in low nutrient levels and truncated food chains. Such ecosystems are widely regarded as sensitive indicators of c...
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| Format: | Thesis |
| Language: | English |
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2005
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| Online Access: | http://eprints.nottingham.ac.uk/10154/ https://eprints.nottingham.ac.uk/10154/1/TimReidThesis.pdf |
| Summary: | Inland Antarctic lakes are among the harshest environments in the world for life to inhabit. Ice cover causes low levels of light and temperature, and prevents mixing by wind, resulting in low nutrient levels and truncated food chains. Such ecosystems are widely regarded as sensitive indicators of climate change, and it is therefore useful to build up a strong physical and biological understanding of them. In 2003 an automatic probe (Palethorpe et al. 2004) was deployed on Crooked Lake, an ultra-oligotrophic freshwater lake in Eastern Antarctica which has been the subject of limnological studies since 1990. The probe measured several physical parameters in, above, and below the ice layer at temporal resolutions of up to one measurement every five minutes. A physics-based model was developed to simulate the growth and melt of the lake ice over time, considering all heat and radiation fluxes. Meteorological data were used as inputs to the model, with ice thickness the main output. The model fitted Crooked Lake ice thickness well, despite having narrow mechanistic constraints on parameter values. A number of simpler models were also developed which provided comparable goodness of fit, and illustrated that air temperature is the dominant variable in such systems. The issue of optimum complexity was addressed using model selection criteria, and some criteria selected a simple model over the physics-based model. However when both were subjected to long-term model runs with superimposed global warming scenarios, the simple model was shown to be unstable. In addition, a 1992-93 biological dataset was analysed. Populations were shown to exhibit a significant annual cycle, but no significant smaller-scale population oscillations, suggesting that higher sample rates were required to identify such phenomena. A prototype procedure was developed using simulated data to inform field sampling strategies, in the aim of identifying the population dynamics that are predicted by many plankton models. |
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