Atmospheric flow regimes are usually defined as large-scale circulation patterns associated with statistical equilibria in phase space, in which the dynamical tendencies of the large-scale flow are balanced by tendencies due to non-linear interactions of high-frequency transients. The existence of states with such properties can be verified in a rigorous way in numerical simulations with simplified numerical models (as in the pioneering study of Reinhold and Pierrehumbert (1982), or in the experiments by Vautard and Legras (1988)). On the other hand, the existence of flow regimes in the real atmosphere has been strongly debated. The detection of regimes in the observational record of upper-air field is indeed a complex task, which has been approached by a number of research groups with a variety of sophisticated statistical methods (see Section 3). If regimes statistics are dependent on boundary conditions and other forcing parameters, the consequent inhomogeneity of the observed record makes the detection of atmospheric flow regimes more difficult. On the other hand, if we were able to determine the statistical properties of regimes as a function of the forcing anomalies, this would imply some degree of predictability of the atmospheric conditions on interannual to interdecadal timescales, which we can refer to as regime predictability. Given the limited size of the observed record, it is difficult to find statistically significant results on such an issue from observed data. In order to understand the dynamical meaning of interdecadal differences in regime distributions such as those shown by Corti et al. (1999), one has to resort to ensemble simulations made with general circulation models (GCMs), in which multiple realizations of the atmospheric flow for the same boundary (or GHG) forcing can be obtained. Is regime predictability a sound concept? Is it feasible to obtain reliable information about the structure and frequency of regimes from observations and GCM simulations? Is the level of significance of these results acceptable? In this paper, we will argue for positive answers to these questions, focussing on two specific issues. Firstly, we will address the issue of the statistical significance of regime estimates from the current upper-air observational record. Secondly, we will demonstrate the feasibility of regime predictions as a function of SST conditions, investigating the effects of ENSO on extratropical regime statistics in ensembles of GCM simulations, and their impact on the predictability of inter-decadal variations of the wintertime Northern-Hemisphere circulation
On the predictability of flow-regime properties on interannual to interdecadal timescales
S Corti
2006
Abstract
Atmospheric flow regimes are usually defined as large-scale circulation patterns associated with statistical equilibria in phase space, in which the dynamical tendencies of the large-scale flow are balanced by tendencies due to non-linear interactions of high-frequency transients. The existence of states with such properties can be verified in a rigorous way in numerical simulations with simplified numerical models (as in the pioneering study of Reinhold and Pierrehumbert (1982), or in the experiments by Vautard and Legras (1988)). On the other hand, the existence of flow regimes in the real atmosphere has been strongly debated. The detection of regimes in the observational record of upper-air field is indeed a complex task, which has been approached by a number of research groups with a variety of sophisticated statistical methods (see Section 3). If regimes statistics are dependent on boundary conditions and other forcing parameters, the consequent inhomogeneity of the observed record makes the detection of atmospheric flow regimes more difficult. On the other hand, if we were able to determine the statistical properties of regimes as a function of the forcing anomalies, this would imply some degree of predictability of the atmospheric conditions on interannual to interdecadal timescales, which we can refer to as regime predictability. Given the limited size of the observed record, it is difficult to find statistically significant results on such an issue from observed data. In order to understand the dynamical meaning of interdecadal differences in regime distributions such as those shown by Corti et al. (1999), one has to resort to ensemble simulations made with general circulation models (GCMs), in which multiple realizations of the atmospheric flow for the same boundary (or GHG) forcing can be obtained. Is regime predictability a sound concept? Is it feasible to obtain reliable information about the structure and frequency of regimes from observations and GCM simulations? Is the level of significance of these results acceptable? In this paper, we will argue for positive answers to these questions, focussing on two specific issues. Firstly, we will address the issue of the statistical significance of regime estimates from the current upper-air observational record. Secondly, we will demonstrate the feasibility of regime predictions as a function of SST conditions, investigating the effects of ENSO on extratropical regime statistics in ensembles of GCM simulations, and their impact on the predictability of inter-decadal variations of the wintertime Northern-Hemisphere circulationI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
