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Evaluating models for predicting microclimates across sparsely vegetated and topographically diverse ecosystems

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journal contribution
posted on 01.12.2021, 01:19 authored by DJ Baker, CR Dickson, DM Bergstrom, J Whinam, IMD Maclean, Melodie McGeochMelodie McGeoch
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.

Funding

This research was funded by the Australian Antarctic Division and the Australian Government under an Australian Antarctic Science Program Grant #4312, and support to CRD from an Australian Government Research Training Program (RTP) Scholarship.

History

Publication Date

01/11/2021

Journal

Diversity and Distributions

Volume

27

Issue

11

Pagination

11p. (p. 2093-2103)

Publisher

Wiley

ISSN

1366-9516

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

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