Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ...
Aim: Microclimate information is often crucial for understanding ecological patterns and processes, including under climate change, but is typically absent from ecological and biogeographic studies owing to difficulties in obtaining microclimate data. Recent advances in microclimate modelling, howev...
| Main Authors: | , , , , , |
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| Format: | Text |
| Language: | unknown |
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La Trobe
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.26181/61a6cd8c8e4f7 https://opal.latrobe.edu.au/articles/journal_contribution/Evaluating_models_for_predicting_microclimates_across_sparsely_vegetated_and_topographically_diverse_ecosystems/17102942/1 |
| Summary: | Aim: Microclimate information is often crucial for understanding ecological patterns and processes, including under climate change, but is typically absent from ecological and biogeographic studies owing to difficulties in obtaining microclimate data. Recent advances in microclimate modelling, however, suggest that microclimate conditions can now be predicted anywhere at any time using hybrid physically and empirically based models. Here, we test these methods across a sparsely vegetated and topographically diverse sub-Antarctic island ecosystem (Macquarie Island). Innovation: Microclimate predictions were generated at a height of 4 cm above the surface on a 100 × 100 m elevation grid across the island for the snow-free season (Oct–Mar), with models driven by either climate reanalysis data (CRA) or CRA data augmented with meteorological observations from the island's automatic weather station (AWS+CRA). These models were compared with predictions from a simple lapse rate model (LR), where an elevational ... |
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