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...
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ftdatacite:10.26181/17102942.v2 2024-09-15T17:42:15+00:00 Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... Baker, DJ Dickson, Catherine Bergstrom, DM Whinam, J Maclean, IMD McGeoch, Melodie 2024 https://dx.doi.org/10.26181/17102942.v2 https://opal.latrobe.edu.au/articles/journal_contribution/Evaluating_models_for_predicting_microclimates_across_sparsely_vegetated_and_topographically_diverse_ecosystems/17102942/2 unknown La Trobe https://dx.doi.org/10.26181/17102942 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Biological sciences FOS Biological sciences Environmental sciences Climate change impacts and adaptation Text Journal contribution ScholarlyArticle article-journal 2024 ftdatacite https://doi.org/10.26181/17102942.v210.26181/17102942 2024-08-01T10:40:36Z 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 ... Text Antarc* Antarctic Macquarie Island DataCite |
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Biological sciences FOS Biological sciences Environmental sciences Climate change impacts and adaptation |
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Biological sciences FOS Biological sciences Environmental sciences Climate change impacts and adaptation Baker, DJ Dickson, Catherine Bergstrom, DM Whinam, J Maclean, IMD McGeoch, Melodie Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... |
topic_facet |
Biological sciences FOS Biological sciences Environmental sciences Climate change impacts and adaptation |
description |
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 ... |
format |
Text |
author |
Baker, DJ Dickson, Catherine Bergstrom, DM Whinam, J Maclean, IMD McGeoch, Melodie |
author_facet |
Baker, DJ Dickson, Catherine Bergstrom, DM Whinam, J Maclean, IMD McGeoch, Melodie |
author_sort |
Baker, DJ |
title |
Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... |
title_short |
Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... |
title_full |
Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... |
title_fullStr |
Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... |
title_full_unstemmed |
Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... |
title_sort |
evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems ... |
publisher |
La Trobe |
publishDate |
2024 |
url |
https://dx.doi.org/10.26181/17102942.v2 https://opal.latrobe.edu.au/articles/journal_contribution/Evaluating_models_for_predicting_microclimates_across_sparsely_vegetated_and_topographically_diverse_ecosystems/17102942/2 |
genre |
Antarc* Antarctic Macquarie Island |
genre_facet |
Antarc* Antarctic Macquarie Island |
op_relation |
https://dx.doi.org/10.26181/17102942 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.26181/17102942.v210.26181/17102942 |
_version_ |
1810488759761764352 |