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

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

References

[1] Daly C, Halbleib M, Smith J I, Gibson W P, Doggett M K, Taylor G H, Curtis J, Pasteris P P. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 2008, 28(15): 2031–2064 doi:10.1002/joc.1688

[2] Hijmans R J, Cameron S E, Parra J L, Jones P G, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 2005, 25(15): 1965–1978 doi:10.1002/joc.1276

[3] Harris I, Jones P D, Osborn T J, Lister D H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. International Journal of Climatology, 2014, 34(3): 623–642 doi:10.1002/joc.3711

[4] Thornton P E, Thornton M M, Mayer B W, Wei Y, Devarakonda R, Vose R S, Cook R B. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL Distributed Active Archive Center; 2016.

[5] Hamann A, Wang T L, Spittlehouse D L, Murdock T Q. A comprehensive, high-resolution database of historical and projected climate surfaces for western North America. Bulletin of the American Meteorological Society, 2013, 94(9): 1307–1309 doi:10.1175/BAMS-D-12-00145.1

[6] Wang T L, Campbell E M, O’Neill G A, Aitken S N. Projecting future distributions of ecosystem climate niches: uncertainties and management applications. Forest Ecology and Management, 2012, 279: 128–140 doi:10.1016/j.foreco.2012.05.034

[7] McKenney D W, Hutchinson M F, Papadopol P, Lawrence K, Pedlar J, Campbell K, Milewska E, Hopkinson R F, Price D, Owen T. Customized spatial climate models for North America. Bulletin of the American Meteorological Society, 2011, 92(12): 1611–1622 doi:10.1175/2011BAMS3132.1

[8] Wang T, O’Neill G A, Aitken S N. Integrating environmental and genetic effects to predict responses of tree populations to climate. Ecological Applications, 2010, 20(1): 153–163 doi:10.1890/08-2257.1 PMID:20349837

[9] Rehfeldt G E, Ying C C, Spittlehouse D L, Hamilton D A Jr. Genetic responses to climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecological Monographs, 1999, 69(3): 375–407 doi:10.1890/0012-9615(1999)069[0375:GRTCIP]2.0.CO;2

[10] Hamann A, Wang T. Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology, 2006, 87(11): 2773–2786 doi:10.1890/0012-9658(2006)87[2773:PEOCCO]2.0.CO;2 PMID:17168022

[11] Rehfeldt G E, Crookston N L, Sáenz-Romero C, Campbell E M. North American vegetation model for land-use planning in a changing climate: a solution to large classification problems. Ecological Applications, 2012, 22(1): 119–141 doi:10.1890/11-0495.1 PMID:22471079

[12] Daly C, Smith J W, Smith J I, McKane R B. High-resolution spatial modeling of daily weather elements for a catchment in the Oregon Cascade Mountains, United States. Journal of Applied Meteorology and Climatology, 2007, 46(10): 1565–1586 doi:10.1175/JAM2548.1

[13] Daly C, Gibson W P, Taylor G H, Johnson G L, Pasteris P. A knowledge-based approach to the statistical mapping of climate. Climate Research, 2002, 22: 99–113 doi:10.3354/cr022099

[14] Rehfeldt G E, Crookston N L, Sáenz-Romero C, Campbell E M. North American vegetation model for land-use planning in a changing climate: a solution to large classification problems. Ecological Applications, 2012, 22(1): 119–141 doi:10.1890/11-0495.1 PMID:22471079

[15] Girvetz E H, Zganjar C, Raber G T, Maurer E P, Kareiva P, Lawler J J. Applied climate-change analysis: the climate wizard tool. PLoS One, 2009, 4(12): e8320 doi:10.1371/journal.pone.0008320 PMID:20016827

[16] Wang T, Hamann A, Spittlehouse D L, Aitken S N. Development of scale-free climate data for western Canada for use in resource management. International Journal of Climatology, 2006, 26(3): 383–397 doi:10.1002/joc.1247

[17] Wang T, Hamann A, Spittlehouse D L, Murdock T. ClimateWNA—high-resolution spatial climate data for Western North America. Journal of Applied Meteorology and Climatology, 2012, 51(1): 16–29 doi:10.1175/JAMC-D-11-043.1

[18] Wang T, Hamann A, Spittlehouse D, Carroll C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One, 2016, 11(6): e0156720 doi:10.1371/journal.pone.0156720 PMID:27275583

[19] Mitchell T D, Jones P D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology, 2005, 25(6): 693–712 doi:10.1002/joc.1181

[20] IPCC. Climate Change 2014: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2014

[21] Taylor K E, Stouffer R J, Meehl G A. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 2012, 93(4): 485–498 doi:10.1175/BAMS-D-11-00094.1

[22] Allen R G, Pereira L S, Raes D, Smith M. Crop evapotranspiration—guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper FAO56, U.N. Food and Agriculture Organization, Rome, 1998

[23] Daly C, Gibson W P, Taylor G H, Johnson G L, Pasteris P. A knowledge-based approach to the statistical mapping of climate. Climate Research, 2002, 22(2): 99–113 doi:10.3354/cr022099

[24] Wang T L, Hamann A, Spittlehouse D L, Murdock T Q. ClimateWNA-high-resolution spatial climate data for Western North America. Journal of Applied Meteorology and Climatology, 2012, 51(1): 16–29 doi:10.1175/JAMC-D-11-043.1

[25] Daly C, Halbleib M, Smith J I, Gibson W P, Doggett M K, Taylor G H, Curtis J, Pasteris P A. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology, 2008, 28(15): 2031–2064 doi:10.1002/joc.1688

[26] Rehfeldt G E, Jaquish B C, Lopez-Upton J, Saenz-Romero C, St Clair J B, Leites L P, Joyce D G. Comparative genetic responses to climate for the varieties of Pinus ponderosa and Pseudotsuga menziesii: realized climate niches. Forest Ecology and Management, 2014, 324: 126–137 doi:10.1016/j.foreco.2014.02.035

[27] Wang T, Hamann A, Yanchuk A, O’Neill G A, Aitken S N. Use of response functions in selecting lodgepole pine populations for future climates. Global Change Biology, 2006, 12(12): 2404–2416 doi:10.1111/j.1365-2486.2006.01271.x

[28] Chakraborty D, Wang T, Andre K, Konnert M, Lexer M J, Matulla C, Weißenbacher L, Schueler S. Adapting Douglas-fir forestry in Central Europe: evaluation, application, and uncertainty analysis of a genetically based model. European Journal of Forest Research, 2016, 135(5): 919–936 doi:10.1007/s10342-016-0984-5

[29] Yeaman S, Hodgins K A, Lotterhos K E, Suren H, Nadeau S, Degner J C, Nurkowski K A, Smets P, Wang T, Gray L K, Liepe K J, Hamann A, Holliday J A, Whitlock M C, Rieseberg L H, Aitken S N. Convergent local adaptation to climate in distantly related conifers. Science, 2016, 353(6306): 1431–1433 doi:10.1126/science.aaf7812 PMID:27708038

[30] Fowler H J, Blenkinsop S, Tebaldi C. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 2007, 27(12): 1547–1578 doi:10.1002/joc.1556

[31] Wang T, Campbell E M, O’Neill G A, Aitken S N. Projecting future distributions of ecosystem climate niches: uncertainties and management applications. Forest Ecology and Management, 2012, 279: 128–140 doi:10.1016/j.foreco.2012.05.034

[32] Roberts D R, Hamann A. Predicting potential climate change impacts with bioclimate envelope models: a palaeoecological perspective. Global Ecology and Biogeography, 2012, 21(2): 121–133 doi:10.1111/j.1466-8238.2011.00657.x

[33] Wang T, Wang G, Innes J, Nitschke C, Kang H. Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia–Pacific region. Forest Ecology and Management, 2016, 360: 357–366 doi:10.1016/j.foreco.2015.08.004

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)