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Bryan C. Weare, Alfredo R. Navato, and Reginald E. Newell

Abstract

An empirical orthogonal function analysis has been performed on monthly mean sea surface temperatures for the greater part of the Pacific Ocean between 55°N and 20°S. The analysis identifies the most important modes of seasonal and non-seasonal variability during the period 1949–73. A mode is defined spatially in terms of an empirical orthogonal function which describes the degree of coherence of variation. The function's corresponding coefficient portray the evolution of the mode in time. The seasonal variation is dominated by a mode having a 12-month periodicity and greatest coherence in the higher latitudes. A second important seasonal mode has a period of approximately 6 months and is dominated by deviations in the North Pacific. The most important non-seasonal variation is identified with the, long-recognized El Niño. The spatial pattern of this mode demonstrates the large-scale nature of the El Niño phenomenon. Other important non-seasonal modes are discussed.

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Alfredo R. Navato, Reginald E. Newell, Jane C. Hsiung, Clare B. Billing Jr., and Bryan C. Weare

Abstract

Multiple-regression analyses of changes in tropospheric mean temperature as predictands and Pacific, Atlantic and Indian Ocean sea surface temperatures and atmospheric aerosol concentrations as predictors show that large fractions of the variances of the tropical, Northern Hemispheric and Southern Hemispheric extratropical tropospheric temperatures may be explained by fluctuations in ocean surface temperatures and atmospheric aerosols. The sensitivity of the tropical, Northern Hemisphere and Southern Hemisphere extratropical tropospheric temperatures to the various predictors are estimated.

To improve the precision of the estimates in the presence of serial correlations in the variables we used a generalized least-squares procedure to obtain the regression models.

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R. David Pyles, Bryan C. Weare, Kyaw Tha Paw U, and William Gustafson

Abstract

The University of California, Davis, Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) is coupled to the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) as a land surface scheme. Simulations for July 1998 over western North America show that this coupling, the first between a mesoscale model and a land surface model of this complexity, is successful. Comparisons among model output, National Centers for Environmental Prediction–NCAR reanalysis fields, and station data show that MM5–ACASA generally reproduces near-surface temperature in a realistic fashion, but with a stronger diurnal cycle than observations suggest. A control run using the existing Louis/European Centre for Medium-Range Weather Forecasts land surface formulation produces unrealistically low temperatures associated with high latent heating and precipitation amounts over much of the model domain. Simulations of heat and moisture fluxes using the Biosphere–Atmosphere Transfer Scheme (BATS) are generally comparable to ACASA, but near-surface air temperatures reveal excessively warm conditions. Low specific-humidity values over land in both MM5–ACASA and MM5–BATS simulations and low oceanic values in all three simulations suggest a possible dry bias in MM5. Comparison statistics between modeled near-surface climatological behavior and associated fluxes at three sites show that MM5–ACASA, out of the three simulations, agrees most with observations. Sensitivity tests show that MM5 is generally more sensitive to the choice of surface scheme than it is to soil moisture initialization. Comparisons of mean carbon dioxide fluxes reveal that ACASA can be a useful tool in examining the terrestrial carbon cycle.

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