Skip to main content
Log in

Spatial analysis of future East Asian seasonal temperature using two regional climate model simulations

  • Published:
Asia-Pacific Journal of Atmospheric Sciences Aims and scope Submit manuscript

Abstract

CORDEX-East Asia, a branch of the coordinated regional climate downscaling experiment (CORDEX) initiative, provides high-resolution climate simulations for the domain covering East Asia. This study analyzes temperature data from regional climate models (RCMs) participating in the CORDEX - East Asia region, accounting for the spatial dependence structure of the data. In particular, we assess similarities and dissimilarities of the outputs from two RCMs, HadGEM3-RA and RegCM4, over the region and over time. A Bayesian functional analysis of variance (ANOVA) approach is used to simultaneously model the temperature patterns from the two RCMs for the current and future climate. We exploit nonstationary spatial models to handle the spatial dependence structure of the temperature variable, which depends heavily on latitude and altitude. For a seasonal comparison, we examine changes in the winter temperature in addition to the summer temperature data. We find that the temperature increase projected by RegCM4 tends to be smaller than the projection of HadGEM3-RA for summers, and that the future warming projected by HadGEM3-RA tends to be weaker for winters. Also, the results show that there will be a warming of 1-3°C over the region in 45 years. More specifically, the warming pattern clearly depends on the latitude, with greater temperature increases in higher latitude areas, which implies that warming may be more severe in the northern part of the domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Baek, H.-J., and Coauthors, 2013: Climate change in the 21st century simulated by hadgen2ao under representative concentration pathways. Asia-Pac. J. Atmos. Sci., 49, 603–618.

    Article  Google Scholar 

  • Christensen, J. H., and Coauthors, 2013: Climate phenomena and their relevance for future regional climate change. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. S. Stocker et al. Eds., Cambridge University Press, 1217–1308.

    Google Scholar 

  • Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005: A new dynamical core for the met office’s global and regional modeling of the atmosphere. Quart. J. Roy. Meteorol. Soc., 131, 1759–1782.

    Article  Google Scholar 

  • Furrer, R., M. Genton, and D. Nychka, 2006: Covariance tapering for interpolation of large spatial datasets. J. Comput. Graph. Stat., 15, 502–523.

    Article  Google Scholar 

  • Giorgi, F., C. Jones, and A. G. R., 2009: Addressing climate information needs at the regional level: the cordex framework. WMO Bull., 58, 175–183.

    Google Scholar 

  • Giorgi, F., and Coauthors, 2012: Regcm4: model description and preliminary tests over multiple CORDEX domains. Clim. Res., 52, 7–29.

    Article  Google Scholar 

  • Greasby, T. A., and S. R. Sain, 2011: Multivariate spatial analysis of climate change projections. J. Agr. Biol. Environ. Stat., 16, 571–585.

    Article  Google Scholar 

  • Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 1095–1107.

    Article  Google Scholar 

  • Kang, E., N. Cressie, and S. Sain, 2011: Combining outputs from the NARCCAP regional climate models using a Bayesian hierarchical model. Appl. Stat., 61, 291–313.

    Google Scholar 

  • Kaufman, C., and S. R. Sain, 2010: Bayesian functional ANOVA modeling using Gaussian process prior distributions. Bayesian Anal., 5, 123–150.

    Article  Google Scholar 

  • Kaufman, C., M. Schervish, and D. Nychka, 2008: Covariance tapering for likelihood-based estimation in large spatial data sets. J. Am. Stat. Assoc., 103, 1545–1555.

    Article  Google Scholar 

  • Min, S.-K., E.-H. Park, and W.-T. Kwon, 2004: Future projections of East Asian climate change from multi-AOGCM ensembles of IPCC SRES scenario simulations. J. Meteor. Soc. Japan, 82, 1187–1211.

    Article  Google Scholar 

  • Min, S.-K., and Coauthors, 2015: Changes in weather and climate extremes over Korea and possible causes: A review. Asia-Pac. J. Atmos. Sci., 51, 103–121.

    Article  Google Scholar 

  • Moss, R. H., and Coauthors, 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756.

    Article  Google Scholar 

  • Park, C., and Coauthors, 2016: Evaluation of multiple regional climate models for summer climate extremes over East Asia. Clim. Dynam., 46, 2469–2486.

    Article  Google Scholar 

  • Park, J.-H., S.-G. Oh, and M.-S. Suh, 2013: Impacts of boundary conditions on the precipitation simulation of regcm4 in the CORDEX East Asia domain. J. Geophys. Res., 118, 1652–1667.

    Google Scholar 

  • Roberts, G., and S. Sahu, 1997: Updating schemes, correlation structure, blocking and parameterization for Gibbs sampler. J. Roy. Stat. Soc., 59, 291–317.

    Article  Google Scholar 

  • Sain, S. R., D. Nychka, and L. Mearns, 2011: Functional ANOVA and regional climate experiments: a statistical analysis of dynamic downscaling. Environmetrics, 22, 700–711.

    Article  Google Scholar 

  • Salazar, E., B. Sanò, A. O. Finley, D. Hammerling, I. Steinsland, X. Wang, and P. Delamater, 2011: Comparing and blending regional climate model predictions for the American southwest. J. Agr. Biol. Environ. Stat., 16, 586–605.

    Article  Google Scholar 

  • Sang, H., and J. Z. Huang, 2012: A full-scale approximation of covariance functions for large spatial data sets. J. Roy. Stat. Soc. Series B, 74, 111–132.

    Article  Google Scholar 

  • Sang, H., M. Jun, and J. Z. Huang, 2011: Covariance approximation for large multivariate spatial datasets with an application to multiple climate model errors. Ann. Appl. Stat., 5, 2519–2548.

    Article  Google Scholar 

  • Stein, M. L., 1999: Interpolation of Spatial Data: Some Theory for Kriging. Springer, 247 pp.

    Book  Google Scholar 

  • Stein, M. L., Z. Chi, and L. Welty, 2004: Approximating likelihoods for large spatial data sets. J. Roy. Stat. Soc. Series B, 275–296.

    Google Scholar 

  • Suh, M.-S., and S.-G. Oh, 2012: Development of new ensemble methods based on the performance skills of regional climate models over South Korea. J. Climate, 25, 7067–7082.

    Article  Google Scholar 

  • Yasutomi, N., A. Hamada, and A. Yatagai, 2011: Development of a longterm daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation. Global Environ. Res., 15, 165–172.

    Google Scholar 

  • Yatagai, A., K. Kaminguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: Aphrodite: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Am. Meteorol. Soc., 93, 1401–1415.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mikyoung Jun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, Y., Jun, M., Min, SK. et al. Spatial analysis of future East Asian seasonal temperature using two regional climate model simulations. Asia-Pacific J Atmos Sci 52, 237–249 (2016). https://doi.org/10.1007/s13143-016-0022-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13143-016-0022-z

Key words

Navigation