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: | , , , , , |
|---|---|
| Format: | Other Non-Article Part of Journal/Newspaper |
| Language: | unknown |
| Published: |
La Trobe
2021
|
| 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 |
| 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 adjustment was applied to hourly temperature measurements from the AWS. Prediction errors tended to be lower for AWS+CRA-driven models, particularly when compared to the CRA-driven models. The AWS+CRA and LR models had similar prediction errors averaged across the season for Tmin and Tmean, but prediction errors for Tmax were much smaller for the former. The within-site correlation between observed and predicted daily Tmean was on average >0.8 in all months for AWS+CRA predictions and >0.7 in all months for LR predictions, but consistently lower for CRA predictions. Main conclusions: Prediction of microclimate conditions at ecologically relevant spatial and temporal scales is now possible using hybrid models, and these often provide added value over lapse rate models, particularly for daily extremes and when driven by in situ meteorological observations. These advances will help add the microclimate dimension to ecological and biogeographic studies and aid delivery of climate change-resilient conservation planning in climate change-exposed ecosystems. |
|---|