Access the full text.
Sign up today, get DeepDyve free for 14 days.
J. Ward (1963)
Hierarchical Grouping to Optimize an Objective FunctionJournal of the American Statistical Association, 58
(1996)
Clouds and the Earth’s
R. Pincus, Crispian Batstone, R. Hofmann, K. Taylor, Peter Glecker (2008)
Evaluating the present‐day simulation of clouds, precipitation, and radiation in climate modelsJournal of Geophysical Research, 113
W. Rossow, R. Schiffer (1999)
Advances in understanding clouds from ISCCPBulletin of the American Meteorological Society, 80
R. Fovell, Mei-Ying Fovell (1993)
Climate zones of the conterminous United States defined using cluster analysisJournal of Climate, 6
K. Williams, G. Tselioudis (2007)
GCM intercomparison of global cloud regimes: present-day evaluation and climate change responseClimate Dynamics, 29
N. Rayner, P. Brohan, D. Parker, C. Folland, J. Kennedy, Michael Vanicek, T. Ansell, S. Tett (2006)
Improved Analyses of Changes and Uncertainties in Sea Surface Temperature Measured In Situ since the Mid-Nineteenth Century: The HadSST2 DatasetJournal of Climate, 19
M. Watanabe, Tatsuo Suzuki, Ryouta O’ishi, Y. Komuro, S. Watanabe, S. Emori, T. Takemura, M. Chikira, T. Ogura, M. Sekiguchi, K. Takata, Dai Yamazaki, T. Yokohata, T. Nozawa, H. Hasumi, H. Tatebe, M. Kimoto (2010)
Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate SensitivityJournal of Climate, 23
P. Gleckler, K. Taylor, C. Doutriaux (2008)
Performance metrics for climate modelsJournal of Geophysical Research, 113
B. Santera, K. Taylora, P. Glecklera, C. Bonfilsa, T. Barnettb, D. Pierceb, T. Wigleyc, C. Mearsd, F. Wentzd, W. Brüggemanne, N. Gillettf, S. Kleina, S. Solomong, P. Stotth, M. Wehneri (2009)
Incorporating model quality information in climate change detection and attribution studies
S. Uppala, P. Kållberg, A. Simmons, U. Andrae, V. Bechtold, M. Fiorino, J. Gibson, J. Haseler, A. Hernandez, G. Kelly, Xiao‐Ming Li, K. Onogi, S. Saarinen, N. Sokka, R. Allan, E. Andersson, K. Arpe, M. Balmaseda, A. Beljaars, L. Berg, J. Bidlot, N. Bormann, S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes, S. Hagemann, E. Holm, B. Hoskins, L. Isaksen, P. Janssen, R. Jenne, A. Mcnally, J. Mahfouf, J. Morcrette, N. Rayner, R. Saunders, P. Simon, A. Sterl, K. Trenberth, A. Untch, D. Vasiljevic, P. Viterbo, J. Woollen (2005)
The ERA‐40 re‐analysisQuarterly Journal of the Royal Meteorological Society, 131
K-1 Model Developers (2004)
K-1 coupled model (MIROC) description
K. Williams, M. Webb (2009)
A quantitative performance assessment of cloud regimes in climate modelsClimate Dynamics, 33
B. Wielicki, B. Barkstrom, E. Harrison, R. Lee, G. Smith, J. Cooper (1996)
Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System ExperimentBulletin of the American Meteorological Society, 77
R. Knutti, R. Furrer, C. Tebaldi, J. Cermak, G. Meehl (2010)
Challenges in Combining Projections from Multiple Climate ModelsJournal of Climate, 23
T. Iizumi, M. Nishimori, M. Yokozawa (2010)
Diagnostics of Climate Model Biases in Summer Temperature and Warm-Season Insolation for the Simulation of Regional Paddy Rice Yield in JapanJournal of Applied Meteorology and Climatology, 49
G. Meehl, C. Covey, T. Delworth, M. Latif, B. Mcavaney, J. Mitchell, R. Stouffer, K. Taylor (2007)
THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change ResearchBulletin of the American Meteorological Society, 88
S. Josey, Elizabeth Kent, P. Taylor (1999)
New Insights into the Ocean Heat Budget Closure Problem from Analysis of the SOC Air–Sea Flux ClimatologyJournal of Climate, 12
H. Kawase, T. Yoshikane, M. Hara, F. Kimura, T. Yasunari, B. Ailikun, H. Ueda, Tomoshige Inoue (2009)
Intermodel variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling methodJournal of Geophysical Research, 114
T. Reichler, J. Kim (2008)
How Well Do Coupled Models Simulate Today's Climate?Bulletin of the American Meteorological Society, 89
J. Murphy, D. Sexton, David Barnett, G. Jones, M. Webb, M. Collins, D. Stainforth (2004)
Quantification of modelling uncertainties in a large ensemble of climate change simulationsNature, 430
(2009)
Intermodel variability
G. Meehl, T. Stocker, W. Collins, P. Friedlingstein, T. Gaye, J. Gregory, A. Kitoh, R. Knutti, J. Murphy, A. Noda, S. Raper, I. Watterson, A. Weaver, Zong-ci Zhao (2007)
Global climate projections
R. Adler, G. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, E. Nelkin (2003)
The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present)Journal of Hydrometeorology, 4
E. Forgy (1965)
Cluster analysis of multivariate data : efficiency versus interpretability of classificationsBiometrics, 21
The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K -means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.
Journal of Applied Meteorology and Climatology – American Meteorological Society
Published: Aug 31, 2010
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.