ClimateAP: an application for dynamic local downscaling of historical and future climate data in Asia Pacific

Tongli WANG, Guangyu WANG, John L. INNES, Brad SEELY, Baozhang CHEN

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Front. Agr. Sci. Eng. ›› 2017, Vol. 4 ›› Issue (4) : 448-458. DOI: 10.15302/J-FASE-2017172
RESEARCH ARTICLE

ClimateAP: an application for dynamic local downscaling of historical and future climate data in Asia Pacific

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Abstract

While low-to-moderate resolution gridded climate data are suitable for climate-impact modeling at global and ecosystems levels, spatial analyses conducted at local scales require climate data with increased spatial accuracy. This is particularly true for research focused on the evaluation of adaptive forest management strategies. In this study, we developed an application, ClimateAP, to generate scale-free (i.e., specific to point locations) climate data for historical (1901–2015) and future (2011–2100) years and periods. ClimateAP uses the best available interpolated climate data for the reference period 1961–1990 as baseline data. It downscales the baseline data from a moderate spatial resolution to scale-free point data through dynamic local elevation adjustments. It also integrates and downscales the historical and future climate data using a delta approach. In the case of future climate data, two greenhouse gas representative concentration pathways (RCP 4.5 and 8.5) and 15 general circulation models are included to allow for the assessment of alternative climate scenarios. In addition, ClimateAP generates a large number of biologically relevant climate variables derived from primary monthly variables. The effectiveness of the local downscaling was determined based on the strength of the local linear regression for the estimate of lapse rate. The accuracy of the ClimateAP output was evaluated through comparisons of ClimateAP output against observations from 1805 weather stations in the Asia Pacific region. The local linear regression explained 70%–80% and 0%–50% of the total variation in monthly temperatures and precipitation, respectively, in most cases. ClimateAP reduced prediction error by up to 27% and 60% for monthly temperature and precipitation, respectively, relative to the original baselines data. The improvements for baseline portions of historical and future were more substantial. Applications and limitations of the software are discussed.

Keywords

biologically relevant climate variables / downscaling / dynamic local regression / future climate / historical climate

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Tongli WANG, Guangyu WANG, John L. INNES, Brad SEELY, Baozhang CHEN. ClimateAP: an application for dynamic local downscaling of historical and future climate data in Asia Pacific. Front. Agr. Sci. Eng., 2017, 4(4): 448‒458 https://doi.org/10.15302/J-FASE-2017172

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Acknowledgements

This study was funded by a research grant “Adaptation of Asia-Pacific Forests to Climate Change” (APFNet/2010/PPF/001) funded by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation.

Compliance with ethics guidelines

Tongli Wang, Guangyu Wang, John L. Innes, Brad Seely, and Baozhao Chen declare that they have no conflicts of interest or financial conflicts to disclose.
This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
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