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Volumn 287, Issue 5456, 2000, Pages 1242-1245

Multidecadal changes in the vertical temperature structure of the tropical troposphere

Author keywords

[No Author keywords available]

Indexed keywords

AIR-SEA INTERACTION; TEMPERATURE; TEMPORAL VARIATION; TROPOSPHERE;

EID: 0034681553     PISSN: 00368075     EISSN: None     Source Type: Journal    
DOI: 10.1126/science.287.5456.1242     Document Type: Article
Times cited : (107)

References (40)
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    • -1 (MSU 2LT) for version c. The two versions differ in that d includes adjustments for satellite orbital decay (6), diurnal sampling drift, and instrument-body temperature effects, as discussed by J. R. Christy, R. W. Spencer, and W. D. Braswell [J. Atmos. Oceanic Tech., in press]. The studies cited in (6), (7), (14), and (16) use version c, or an earlier version, of the MSU data.
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    • Christy, J.R.1    Spencer, R.W.2    Braswell, W.D.3
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    • The adjustments identified by Wentz and Schabel (6) were applied to globally averaged time series of MSU data, version c. As explained in (2), the discrepancy remains in the newer version, d.
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    • Satellite observations, surface meteorological observations, and radiosonde observations are the three primary sources of long-term atmospheric temperature data, and they are virtually independent. MSU data are compared with, but not calibrated to, radiosonde data. The surface temperature observations in radiosonde reports may be included in surface temperature data sets (3, 4), but because the surface network is much denser than the radiosonde network, radiosonde observations would have a minor impact on trends derived from surface temperature data sets. Radiosonde data used in this study are from the core network of the Comprehensive Aerological Research Data Set (CARDS). Station records were used if observations were available for at least 7 days of at least 85% of all months in the 19- or 38-year period under investigation. The CARDS product is described by R. E. Eskridge et al. [Bull. Am. Meteorol. Soc. 76, 1759 (1995)] and T. W. R. Wallis [J. Clim. 11, 272 (1998)].
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    • Satellite observations, surface meteorological observations, and radiosonde observations are the three primary sources of long-term atmospheric temperature data, and they are virtually independent. MSU data are compared with, but not calibrated to, radiosonde data. The surface temperature observations in radiosonde reports may be included in surface temperature data sets (3, 4), but because the surface network is much denser than the radiosonde network, radiosonde observations would have a minor impact on trends derived from surface temperature data sets. Radiosonde data used in this study are from the core network of the Comprehensive Aerological Research Data Set (CARDS). Station records were used if observations were available for at least 7 days of at least 85% of all months in the 19- or 38-year period under investigation. The CARDS product is described by R. E. Eskridge et al. [Bull. Am. Meteorol. Soc. 76, 1759 (1995)] and T. W. R. Wallis [J. Clim. 11, 272 (1998)].
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    • Trends are least squares linear regression estimates. Confidence intervals am tZ SD of the trend estimate, with the number of degrees of freedom adjusted for lag-one autocorrelation in the monthly anomaly time series.
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    • 700). Monthly means and quartiles were computed separately from 0000 and 1200 UTC soundings. Temporal increases in lapse rates mean a steepening of the rate of decrease of T with Z and a tendency toward more unstable conditions.
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    • note
    • Empirical orthogonal function analysis of the data reveals strong spatial consistency of the lapse-rate trends. The dominant mode of variability, which explains 21% of the total variance, has a spatial pattern that is positive throughout the domain and a temporal structure showing an increase from 1979 to 1997.
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    • The three coupled ocean-atmosphere models are the Parallel Climate Model (PCM), the Climate System Model (CSM), and Max-Planck-Institut für Meteorologie ECHAM4/OPYC model. Based on the distributions of lapse-rate trend values in each model run, Fig. 2C shows the ranges, encompassing 95% of the distribution. Monthly layer mean lapse rates were computed in the same manner as the observations, but with monthly mean temperatures and heights at 700 hPa, 2-m (surface) air temperature, and the models' surface elevation. L. Bengtsson, E. Roeckner, and M. Stendel (15) discuss the ECHAM4 model; B. A. Boville and P. R. Gent [J. Clim. 11, 1115 (1998)] describe the CSM; and the PCM is discussed by W. M. Washington et al. (Clim. Dyn., in press).
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    • in press
    • The three coupled ocean-atmosphere models are the Parallel Climate Model (PCM), the Climate System Model (CSM), and Max-Planck-Institut für Meteorologie ECHAM4/OPYC model. Based on the distributions of lapse-rate trend values in each model run, Fig. 2C shows the ranges, encompassing 95% of the distribution. Monthly layer mean lapse rates were computed in the same manner as the observations, but with monthly mean temperatures and heights at 700 hPa, 2-m (surface) air temperature, and the models' surface elevation. L. Bengtsson, E. Roeckner, and M. Stendel (15) discuss the ECHAM4 model; B. A. Boville and P. R. Gent [J. Clim. 11, 1115 (1998)] describe the CSM; and the PCM is discussed by W. M. Washington et al. (Clim. Dyn., in press).
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    • note
    • We are grateful to L. Bengtsson, E. Roeckner, and M. Esch (Max-Planck-Institut für Meteorologie) for supplying the ECHAM3 model and the ECHAM4/OPYC simulations; T. Wigley [National Center for Atmospheric Research (NCAR)] for the CSM simulations; G. Meehl (NCAR) for the PCM simulations; M. Tyree (Scripps Institution of Oceanography) for performing ECHAM3 model runs; and J. Angell and M. Free (NOAA) for beneficial discussions.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.