Characterizing and comparing control-run variability of eight coupled AOGCMs and of observations. Part 1: temperature View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003-11-15

AUTHORS

L. D. D. Harvey, T. M. L. Wigley

ABSTRACT

We examine the spatial patterns of variability of annual-mean temperature in the control runs of eight coupled atmosphere–ocean general circulation models (AOGCMs) and of observations. We characterize the patterns of variability using empirical orthogonal functions (EOFs) and using a new technique based on what we call quasi-EOFs. The quasi-EOFs are computed based on the spatial pattern of the correlation between the temperature variation at a given grid point and the temperature defined over a pre-determined reference region, with a different region used for each quasi-EOF. For the first four quasi-EOFs, the reference regions are: the entire globe, the Niño3 region, Western Europe, and Siberia. Since the latter three regions are the centers of strong anomalies associated with the El Niño, North Atlantic, and Siberian oscillations, respectively, the spatial pattern of the covariance with temperature in these regions gives the structure of the model or observed El Niño, North Atlantic, and Siberian components of variability. When EOF analysis is applied to the model control runs, the patterns produced generally have no similarity to the EOF patterns produced from observational data. This is due in some cases to large NAO-like variability appearing as part of EOF1 along with ENSO-like variability, rather than as separate EOF modes. This is a disadvantage of EOF analysis. The fraction of the model time-space variation explained by these unrealistic modes of variability is generally greater than the fraction explained by the principal observed modes of variability. When qEOF analysis is applied to the model data, all three natural modes of variability are seen to a much greater extent. However, the fraction of global time-space variability that is accounted for by the model ENSO variability is, in our analysis, less than observed for all models except the HadCM2 model, but within 20% for another three models. The space-time variation accounted for by the other modes is comparable to or somewhat larger than that observed in all models. As another teleconnection indicator, we examined both Southern Oscillation Index (SOI) and its relation to tropical Pacific Ocean temperature variations (the qEOF2 amplitude), and the North Atlantic Oscillation Index (NAOI) and its relation to North Atlantic region temperatures (the qEOF3 amplitude). All models exhibit a relationship between these indices, and the qEOF amplitudes are comparable to those observed. Furthermore, the models show realistic spatial patterns in the correlation between local temperature variations and these indices. More... »

PAGES

619-646

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    54 Western Europe
    55 amplitude
    56 analysis
    57 annual mean temperature
    58 anomalies
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    60 cases
    61 center
    62 circulation model
    63 components
    64 control
    65 correlation
    66 covariance
    67 data
    68 different regions
    69 disadvantages
    70 empirical orthogonal functions
    71 entire globe
    72 extent
    73 fraction
    74 function
    75 general circulation model
    76 globe
    77 greater extent
    78 grid points
    79 index
    80 indicators
    81 local temperature variations
    82 mode
    83 model
    84 model control
    85 model data
    86 modes of variability
    87 natural modes
    88 new technique
    89 observational data
    90 observations
    91 ocean temperature variations
    92 orthogonal functions
    93 oscillations
    94 part
    95 patterns
    96 patterns of variability
    97 point
    98 principals
    99 realistic spatial patterns
    100 reference region
    101 region
    102 region temperature
    103 relation
    104 relationship
    105 similarity
    106 space-time variation
    107 spatial patterns
    108 strong anomalies
    109 structure
    110 technique
    111 temperature
    112 temperature variation
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