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In a recent article published in this journal, Ting et al. (1996) documented the three-dimensional structure of the stationary wave response to a distinctive mode of variability of the zonally averaged basic state: a meridional “see-saw” in zonal momentum represented by the algebraic difference between the zonal-mean, zonal (geostrophic) wind at 55° and 35°N. This particular choice of index was motivated by statistics derived from a suite of experiments with a linear baroclinic stationary
In a recent article published in this journal, Ting et al. (1996) documented the three-dimensional structure of the stationary wave response to a distinctive mode of variability of the zonally averaged basic state: a meridional “see-saw” in zonal momentum represented by the algebraic difference between the zonal-mean, zonal (geostrophic) wind at 55° and 35°N. This particular choice of index was motivated by statistics derived from a suite of experiments with a linear baroclinic stationary
-look metrics. 3. Guidelines for metrics Figure 1 presents the diurnal harmonic amplitude and phase from the observations. Since warm seasons have similar dynamics, Fig. 1 combines Northern Hemisphere July and Southern Hemisphere January in both maps. The resulting discontinuity at the equator is small. (Corresponding cold season maps provide little additional information because coherent diurnal variation outside the tropics is small; cf. Figs. S2–S5 in the supplemental material.) The diurnal
-look metrics. 3. Guidelines for metrics Figure 1 presents the diurnal harmonic amplitude and phase from the observations. Since warm seasons have similar dynamics, Fig. 1 combines Northern Hemisphere July and Southern Hemisphere January in both maps. The resulting discontinuity at the equator is small. (Corresponding cold season maps provide little additional information because coherent diurnal variation outside the tropics is small; cf. Figs. S2–S5 in the supplemental material.) The diurnal
comparison of sea level rise estimates made from an ocean running with today’s climate with others made from transient fully coupled climate experiments in which the surface forcing is different. Maps of the spatial pattern of sea level rise can be made from the output of coupled GCMs using a simple inverse method. It is shown that the solution obtained using this inverse method is more accurate than the solution obtained by using a simpler technique for finding the spatial map of pressure at a certain
comparison of sea level rise estimates made from an ocean running with today’s climate with others made from transient fully coupled climate experiments in which the surface forcing is different. Maps of the spatial pattern of sea level rise can be made from the output of coupled GCMs using a simple inverse method. It is shown that the solution obtained using this inverse method is more accurate than the solution obtained by using a simpler technique for finding the spatial map of pressure at a certain
relatively small number of spatial patterns that account for large fractions of variance of thefield. Similar analysis techniques such as canonical correlation analysis (CCA) and singular value decomposition (SVD) analysis that identify pairs of spatial patterns from two data fields are also becoming popular.Singular value decomposition, in general, is a basic matrix operation in linear algebra, whereas the SVD analysis discussed in the present study refers to the technique that isolates pairs of spatial
relatively small number of spatial patterns that account for large fractions of variance of thefield. Similar analysis techniques such as canonical correlation analysis (CCA) and singular value decomposition (SVD) analysis that identify pairs of spatial patterns from two data fields are also becoming popular.Singular value decomposition, in general, is a basic matrix operation in linear algebra, whereas the SVD analysis discussed in the present study refers to the technique that isolates pairs of spatial
reviewed current knowledge of the diffusivity of turbulence diffusivities throughout the oceans. Estimates of this parameter come from several sources, including direct measurements, parameterizations of oceanic finestructure, and indirect estimates based on algebraic and statistical inversions of hydrography. While each of these methods require assumptions with potential limitations, results are generally consistent among water mass–based comparisons. Diffusivities in the ventilated waters of the
reviewed current knowledge of the diffusivity of turbulence diffusivities throughout the oceans. Estimates of this parameter come from several sources, including direct measurements, parameterizations of oceanic finestructure, and indirect estimates based on algebraic and statistical inversions of hydrography. While each of these methods require assumptions with potential limitations, results are generally consistent among water mass–based comparisons. Diffusivities in the ventilated waters of the
associated time series by performing the eigenanalysis (so-called singular value decomposition in linear algebra) on the temporal covariance matrix between two data fields. To do so, MCA identifies linear functions of two variables that are most strongly related to each other. For example, the sea surface temperature (SST; T ) and zonal wind stress ( τ x ) anomalies are represented by linear combinations by applying MCA, where e n ( x, y ) and f m ( x, y ) are the spatial patterns (eigenvectors
associated time series by performing the eigenanalysis (so-called singular value decomposition in linear algebra) on the temporal covariance matrix between two data fields. To do so, MCA identifies linear functions of two variables that are most strongly related to each other. For example, the sea surface temperature (SST; T ) and zonal wind stress ( τ x ) anomalies are represented by linear combinations by applying MCA, where e n ( x, y ) and f m ( x, y ) are the spatial patterns (eigenvectors
observational data for the past several decades and then testing the GCMs’ output behavior against nature. Only those that pass this comparison are capable of delivering correct answers in experiments with external forcing, and this (we hope) will narrow the present uncertainty in climate change studies. In this paper, we focus on one of the major trouble spots, cloudiness, and its interaction within the climatic system where parameterizations are still not well determined. We will consider overall cloud
observational data for the past several decades and then testing the GCMs’ output behavior against nature. Only those that pass this comparison are capable of delivering correct answers in experiments with external forcing, and this (we hope) will narrow the present uncertainty in climate change studies. In this paper, we focus on one of the major trouble spots, cloudiness, and its interaction within the climatic system where parameterizations are still not well determined. We will consider overall cloud
3248JOURNAL OF CLIMATEVOLUME 9A Comparison of General Circulation Mode]l Predictions to Sand Drift and Dune Orientations DAN G. BLUMBERGRemote Sensing Laboratory, Institute for Desert Research, Arizona State University, Tempe, Arizona RONALD GREELEY Department of Geology, Arizona State University, Tempe, ArizonaDepartment of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Shera, Israel (Manuscript
3248JOURNAL OF CLIMATEVOLUME 9A Comparison of General Circulation Mode]l Predictions to Sand Drift and Dune Orientations DAN G. BLUMBERGRemote Sensing Laboratory, Institute for Desert Research, Arizona State University, Tempe, Arizona RONALD GREELEY Department of Geology, Arizona State University, Tempe, ArizonaDepartment of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Shera, Israel (Manuscript
anomaly field for any variable based on the reanalyses was constructed as the simple algebraic mean of the two 31-day running mean anomaly fields, one for the mid-January and the other for mid-February. 3. Interannual variability in the storm track and monsoon activities Our main analysis domain is limited within the Far East and the NW Pacific (20°–60°N, 100°E–180°), where both the northwesterly monsoonal flow ( υ * T *) and storm track activity ( υ h T h ) in the lower troposphere are particularly
anomaly field for any variable based on the reanalyses was constructed as the simple algebraic mean of the two 31-day running mean anomaly fields, one for the mid-January and the other for mid-February. 3. Interannual variability in the storm track and monsoon activities Our main analysis domain is limited within the Far East and the NW Pacific (20°–60°N, 100°E–180°), where both the northwesterly monsoonal flow ( υ * T *) and storm track activity ( υ h T h ) in the lower troposphere are particularly
important for the surface pressure than zonal winds ( North et al. 1982b ) so that comparison of models P 1 and P 2 to the atmosphere is more tenuous; the result may be the product of canceling errors. Also, the reanalysis winds and pressure EOFs appear to decay exponentially with wavenumber. The differences in the relative importance of EOFs between model M and P 2 is a result of low order algebraic decay. With exponential decay, the top EOFs of both pressure and winds can explain the same
important for the surface pressure than zonal winds ( North et al. 1982b ) so that comparison of models P 1 and P 2 to the atmosphere is more tenuous; the result may be the product of canceling errors. Also, the reanalysis winds and pressure EOFs appear to decay exponentially with wavenumber. The differences in the relative importance of EOFs between model M and P 2 is a result of low order algebraic decay. With exponential decay, the top EOFs of both pressure and winds can explain the same
