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J. Christensen, K. Kanikicharla, G. Marshall, J. Turner (2013)
Climate phenomena and their relevance for future regional climate change, 9781107057999
C. Kaufman, S. Sain (2010)
Bayesian functional ANOVA modeling using Gaussian process prior distributionsBayesian Analysis, 5
E. Hawkins, R. Sutton (2009)
The Potential to Narrow Uncertainty in Regional Climate PredictionsBulletin of the American Meteorological Society, 90
M. Stein (1999)
Interpolation of spatial data
Bjorn Lambrigtsen, Van Dang, Eric Fetzer (1992)
Overview and summarySustainability in the Twenty-First Century
T. Davies, M. Cullen, A. Malcolm, M. Mawson, A. Staniforth, A. White, N. Wood (2005)
A new dynamical core for the Met Office's global and regional modelling of the atmosphereQuarterly Journal of the Royal Meteorological Society, 131
M. Stein, Zhiyi Chi, Leah Welty (2004)
Approximating likelihoods for large spatial data setsJournal of the Royal Statistical Society: Series B (Statistical Methodology), 66
Ju‐Hee Park, Seok-Geun Oh, M. Suh (2013)
Impacts of boundary conditions on the precipitation simulation of RegCM4 in the CORDEX East Asia domainJournal of Geophysical Research: Atmospheres, 118
S. Min, E. Park, W. Kwon (2004)
Future Projections of East Asian Climate Change from Multi-AOGCM Ensembles of IPCC SRES Scenario SimulationsJournal of the Meteorological Society of Japan, 82
E. Kang (2011)
Combining Outputs from the NARCCAP Regional Climate Models Using a Bayesian Hierarchical Model
F. Giorgi, Colin Jones, G. Asrar (2009)
Addressing climate information needs at the regional level: the CORDEX framework, 58
R. Woodard (1999)
Interpolation of Spatial Data: Some Theory for KrigingTechnometrics, 42
E. Salazar, B. Sansó, A. Finley, D. Hammerling, I. Steinsland, Xia Wang, P. Delamater (2011)
Comparing and Blending Regional Climate Model Predictions for the American SouthwestJournal of Agricultural, Biological, and Environmental Statistics, 16
S. Sain, D. Nychka, L. Mearns (2011)
Functional ANOVA and regional climate experiments: a statistical analysis of dynamic downscalingEnvironmetrics, 22
H. Baek, Johan Lee, Hyo-Shin Lee, Y. Hyun, C. Cho, W. Kwon, C. Marzin, Sun Gan, Min-Ji Kim, D. Choi, Jonghwa Lee, Jae-Hee Lee, K. Boo, Hyun‐Suk Kang, Y. Byun (2013)
Climate change in the 21st century simulated by HadGEM2-AO under representative concentration pathwaysAsia-Pacific Journal of Atmospheric Sciences, 49
(2011)
Development of a longterm daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation
A. Yatagai, K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, A. Kitoh (2012)
APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain GaugesBulletin of the American Meteorological Society, 93
S. Jun, Jianhua Huang (2011)
COVARIANCE APPROXIMATION FOR LARGE MULTIVARIATE SPATIAL DATASETS WITH AN APPLICATION TO MULTIPLE CLIMATE MODEL ERRORS By Huiyan
M. Suh, Seok-Geun Oh, Dong‐Kyou Lee, D. Cha, Suk‐Jin Choi, C. Jin, Song‐You Hong (2012)
Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South KoreaJournal of Climate, 25
C. Kaufman, M. Schervish, D. Nychka (2008)
Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data SetsJournal of the American Statistical Association, 103
H. Sang, Jianhua Huang (2012)
A full scale approximation of covariance functions for large spatial data setsJournal of the Royal Statistical Society: Series B (Statistical Methodology), 74
Changyong Park, S. Min, Donghyun Lee, D. Cha, M. Suh, Hyun‐Suk Kang, Song‐You Hong, Dong‐Kyou Lee, H. Baek, K. Boo, W. Kwon (2016)
Evaluation of multiple regional climate models for summer climate extremes over East AsiaClimate Dynamics, 46
F. Giorgi, E. Coppola, F. Solmon, L. Mariotti, M. Sylla, X. Bi, N. Elguindi, G. Diro, V. Nair, G. Giuliani, U. Turuncoglu, S. Cozzini, I. Güttler, T. O’Brien, A. Tawfik, A. Shalaby, A. Zakey, A. Steiner, F. Stordal, L. Sloan, Č. Branković (2012)
RegCM4 : model description and preliminary tests over multiple CORDEX domainsClimate Research, 52
E. Kang, N. Cressie, S. Sain (2012)
Combining outputs from the North American Regional Climate Change Assessment Program by using a Bayesian hierarchical modelJournal of the Royal Statistical Society: Series C (Applied Statistics), 61
R. Furrer, M. Genton, D. Nychka (2006)
Covariance Tapering for Interpolation of Large Spatial DatasetsJournal of Computational and Graphical Statistics, 15
T. Greasby, S. Sain (2011)
Multivariate Spatial Analysis of Climate Change ProjectionsJournal of Agricultural, Biological, and Environmental Statistics, 16
S. Sahu G. Roberts (1997)
Updating schemes, correlation structure, blocking and parameterization for Gibbs samplerJ. Roy. Stat. Soc., 59
R. Moss, J. Edmonds, K. Hibbard, M. Manning, S. Rose, D. Vuuren, T. Carter, S. Emori, M. Kainuma, T. Kram, G. Meehl, J. Mitchell, N. Nakicenovic, K. Riahi, Steven Smith, R. Stouffer, A. Thomson, J. Weyant, T. Wilbanks (2010)
The next generation of scenarios for climate change research and assessmentNature, 463
S. Min, S. Son, K. Seo, J. Kug, S. An, Yong-Sang Choi, Jee-Hoon Jeong, Baek‐Min Kim, Ji‐Won Kim, Yeonhee Kim, June‐Yi Lee, Myong-in Lee (2015)
Changes in weather and climate extremes over Korea and possible causes: A reviewAsia-Pacific Journal of Atmospheric Sciences, 51
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.
"Asia-Pacific Journal of Atmospheric Sciences" – Springer Journals
Published: May 1, 2016
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