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  • 1.
    Bengtsson, Lisa
    et al.
    SMHI, Research Department, Meteorology.
    Körnich, Heiner
    SMHI, Research Department, Meteorology.
    Kaellen, Erland
    Svensson, Gunilla
    Large-Scale Dynamical Response to Subgrid-Scale Organization Provided by Cellular Automata2011In: Journal of Atmospheric Sciences, ISSN 0022-4928, E-ISSN 1520-0469, Vol. 68, no 12, p. 3132-3144Article in journal (Refereed)
    Abstract [en]

    Because of the limited resolution of numerical weather prediction (NWP) models, subgrid-scale physical processes are parameterized and represented by gridbox means. However, some physical processes are better represented by a mean and its variance; a typical example is deep convection, with scales varying from individual updrafts to organized mesoscale systems. This study investigates, in an idealized setting, whether a cellular automaton (CA) can be used to enhance subgrid-scale organization by forming clusters representative of the convective scales and thus yield a stochastic representation of subgrid-scale variability. The authors study the transfer of energy from the convective to the larger atmospheric scales through nonlinear wave interactions. This is done using a shallow water (SW) model initialized with equatorial wave modes. By letting a CA act on a finer resolution than that of the SW model, it can be expected to mimic the effect of, for instance, gravity wave propagation on convective organization. Employing the CA scheme permits the reproduction of the observed behavior of slowing down equatorial Kelvin modes in convectively active regions, while random perturbations fail to feed back on the large-scale flow. The analysis of kinetic energy spectra demonstrates that the CA subgrid scheme introduces energy backscatter from the smallest model scales to medium scales. However, the amount of energy backscattered depends almost solely on the memory time scale introduced to the subgrid scheme, whereas any variation in spatial scales generated does not influence the energy spectra markedly.

  • 2.
    Johansson, Åke
    et al.
    SMHI, Research Department, Meteorology.
    Barnston, A
    Saha, S
    van den Dool, H
    On the level and origin of seasonal forecast skill in northern Europe1998In: Journal of Atmospheric Sciences, ISSN 0022-4928, E-ISSN 1520-0469, Vol. 55, no 1, p. 103-127Article in journal (Refereed)
    Abstract [en]

    This study examines the level and origin of seasonal forecast skill of surface air temperature in northern Europe. The forecasts are based on an empirical methodology, canonical correlation analysis (CCA), which is a method designed to find correlated patterns between predictor and predictand fields. A modified form of CCA is used where a prefiltering step precedes the CCA as proposed by T.P. Barnett and R. Preisendorfer. The predictive potential of four fields is investigated, namely, (a) surface air temperature (i.e., the predictand field itself), (b) local sea surface temperature (SST) in the northern European area on a dense grid, (c) Northern Hemisphere 700-hPa geopotential height, and (d) quasi-global SST on a coarse grid, The design is such that four contiguous predictor periods (of 3 months each) are followed by a lead time and then a single predictand period (3 months long). The shortest lead time is 1 month and the longest is 15 months. The skill of the CCA based forecasts is estimated for the 39-yr time period 1955-93, using cross-validated hindcasting. Skill estimates are expressed as the temporal correlation between the forecasts and the respective verifying observations. The forecasts are most skillful in the winter seasons with a secondary weaker skill maximum during summer. During winter the geopotential height field produces the highest skill scores of the four predictor fields. The dominant predictor pattern of the geopotential height field is confined to the predictor period that is closest to a preceding core winter season and resembles the North Atlantic Oscillation (NAG) teleconnection pattern. The time series of the expansion coefficients of this dominant predictor pattern correlates well with a low-pass filtered rime series of an NAO index. The obtained skill is similar to what is found in the United States, both with regard to seasonal distribution and level of skill. The origin of skill is however different. In the United States it is the El Nino-Southern Oscillation (ENSO) with its predominantly interannual character that is the main source of skill in winter. In northern Europe it is instead the NAO that contributes the most, and especially the lower frequency part of the NAO (periods between 4 and 10 yr). Spatially sparse station data of surface pressure extending back to the middle of the nineteenth century suggests a nonstationarity in the NAO behavior. The implications of this nonstationarity for the obtained results of this study is briefly discussed. Because finely resolved field data are not readily available for this earlier period, the level of skill realizable for that period using a pattern relationship technique such as CCA remains an open question.

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