Abstract
In this study, we analyzed changes in the predicted flowering date (PFD) for cherry blossom trees under changing climate conditions by simulating the PFDs for six sites on the Korean Peninsula between 1981 and 2010. The spatial downscaled climate data from the Representative Concentration Pathways (RCP) 8.5 scenarios of 30 global climate models (GCMs) were used in the analysis. Here, we present the range of uncertainty in the PFDs, which were calculated by comparing the simulated PFDs to the observed flowering dates. We determined that the root-mean-square errors (RMSEs) of PFDs from individual GCMs, at 7-15 days, were greater in range than those of the mean PFDs from multiple GCMs, at 7-8 days. During three future periods of 2011-2040, 2041-2070, and 2071-2100, the standard deviations (SD), the interquartile ranges (IQRs), and the relative changes in the mean predicted flowering dates (MPFDs) were calculated to quantify the uncertainty levels inherent from the climate scenarios of multiple GCMs. Distinctive changes in the SDs and IQRs of MPFD were found among the analyzed sites. The SDs increased with time between each future period in Seoul, Incheon, and Jeonju, whereas those in Daegu, Busan, and Mokpo decreased with time. In addition, the IQRs increased with time at Seoul, Incheon, Jeonju, and Daegu but not at Busan and Mokpo. The relative changes in the MPFDs at all six sites became greater with time toward the year 2100. Therefore, combining multiple GCM scenarios may not contribute largely to reduce the uncertainty in the PFDs under changing climate conditions, although it may be useful in quantifying the uncertainty in order to make better decisions based on more accurate information.
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References
Cassou, C., and J. Cattiaux, 2016: Disruption of the European climate seasonal clock in a warming world. Nat. Clim. Change, doi:10.1038/nclimate2969.
Cesaraccio, C., D. Spano, R. L. Snyder, and P. Duce, 2004: Chilling and forcing model to predict bud-burst of crop and forest species. Agric. Forest Meteor., 126, 1–13.
Chung, U., L. Mack, J. I. Yun, and S. H. Kim, 2011: Predicting the timing of cherry blossoms in Washington, DCand mid-Atlantic States in response to climate change. PLoS ONE, 6, e27439, doi:10.1371/journal. pone.0027439.
Chung, U., J. Cho, and E. J. Lee, 2015: Evaluation of agro-climatic index using multi-model ensemble downscaled climate prediction of CMIP5. Korean J. Agric. Forest Meteor., 17, 108–125, doi:10.5532/KJAFM.2015.17.2.108.
Gudmundsson, L., J. B. Bremnes, J. E. Haugen, and T. Engen-Skaugen, 2012: Technical note: downscaling RCM precipitation to the station scale using quantile mapping—a comparison of methods. Hydrol. Earth Syst. Sci., 16, 3383–3390.
IPCC, 2013: The Physical Science Basis: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1535 pp.
IPCC, 2014: Climate Change 2014: Synthesis Report Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1454 pp.
Jeong, S. J., D. Medvigy, E. Shevliakova, and S. Malyshev, 2013: Predicting changes in temperate forest budburst using continental-scale observations and models. Geophys. Res. Lett., 40, 359–364, doi:10. 1029/2012GL054431.
Jung, J. E., E. Y. Kwon, U. Chung, and J. I. Yun, 2005: Predicting cherry flowering date using a plant phenology model. Korean J. Agric. Forest Meteor., 7, 148–155.
Kim, J. H., J. H. Cheon, and J. I. Yun, 2013a: Outlook on blooming dates of spring flowerings in the Korean Peninsula under the RCP8.5 projected climate. Korean J. Agric. Forest Meteor., 15, 50–58, doi:10.5532/KJAFM.2013.15.1.050.
Kim, J. H., E. J. Lee, and J. I. Yun, 2013b: Prediction of blooming dates of spring flowerings by using digital temperature forecasts and phenology models. Korean J. Agric. Forest Meteor., 15, 40–49, doi:10.5532/KJAFM.2013.15.1.040.
Kim, J. H., S. O. Kim, D. J. Kim, K. H. Moon, and J. I. Yun, 2015: Using daily temperature to predict phenology trends in spring flowers. Asia-Pac. J. Atmos. Sci., 51, 167–172, doi:10.1007/s13143-015-0067-4.
Kim, S. O., D. J. Kim, J-H. Kim, and J. I. Yun, 2012: Freeze risk assessment for three major peach growing areas under the future climate projected by RCP8.5 emission scenario. Korean J. Agric. Forest Meteor., 14, 124–131, doi:10.5532/KJAFM.2012.14.3.124.
Kwon, Y. S., S. O. Kim, H. H. Seo, K. H. Moon, and J. I. Yun, 2012: Geographical shift in blooming date of kiwifruits in Jeju Island by global warming. Korean J. Agric. Forest Meteor., 14, 179–188, doi:10. 5532/KJAFM.2012.14.4.179.
Martre, P., and Coauthors, 2015: Multimodel ensembles of wheat growth. Many models are better than one. Glob. Chang. Biol., 21, 911–925, doi: 10.1111/gcb.12768.
McDermid, S. P., and Coauthors, 2015: The AgMIP coordinated climatecrop modeling project (C3MP): methods and protocols. Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP), C. Rosenzweig and D. Hillel, Eds., Imperial College Press, 191–220.
Murphy, J. M., D. M. Sexton, D. N. Barnett, G. S. Jones, M. J. Webb, M. Collins, and D. A. Stainforth, 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430, 768–772, doi:10.1038/nature02771.
KMA, 2012: Climate Outlook for Korean Peninsula. Korean Meteorological Administration, 151 pp (in Korean).
Semenov, M. A., and P. Stratonovitch, 2010: Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Res., 41, 1–12.
Park, J. H., M. S. Kang, and I. H. Song, 2012: Bias correction of RCPbased future extreme precipitation using a quantile mapping method; for 20-weather stations of South Korea. Korean J. Agric. Eng., 54, 133–142, doi:10.5389/KSAE.2012.54.6.133.
Taylor, K. E., 2000: Summarizing multiple aspects of model performance in a single diagram. PCMDI Report No 65. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, University of California, Livermore CA, 24 pp.
Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183–7192.
Yun, J. I., 2006: Climate change impact on the flowering season of Japanese cherry (prunus serrulata var. spontanea) in Korea during 1941-2100. Korean J. Agric. Forest Meteor., 8, 68–76.
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Chung, U., Kim, JH. & Kim, KH. Variation and uncertainty in the predicted flowering dates of cherry blossoms using the CMIP5 climate change scenario. Asia-Pacific J Atmos Sci 52, 509–518 (2016). https://doi.org/10.1007/s13143-016-0033-9
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DOI: https://doi.org/10.1007/s13143-016-0033-9