Tropical and Stratospheric Influences on Extratropical Short-Term Climate Variability

Abstract

The relative impacts of tropical diabatic heating and stratospheric circulation anomalies on wintertime extratropical tropospheric variability are investigated in a linear inverse model (LIM) derived from the observed zero lag and 5-day lag covariances of 7-day running mean departures from the annual cycle. The model predicts the covariances at all other lags. The predicted and observed lag covariances are generally found to be in excellent agreement, even at the much longer lag of 21 days. This validates the LIM's basic premise that the dynamics of weekly averages are effectively linear and stochastically driven, which justifies further linear diagnosis of the system.Analysis of interactions among the LIM's variables shows that tropical diabatic heating greatly enhances persistent variability over most of the Northern Hemisphere, especially over the Pacific Ocean and North America. Stratospheric effects are largely confined to the polar region, where they ensure that the dominant pattern of sea level pressure variability is the annular Arctic Oscillation rather than the more localized North Atlantic Oscillation. Over the North Atlantic, both effects are important, although some of the stratospheric influence is ultimately traceable to tropical forcing. In general, the tropically forced anomalies extend through the depth of the troposphere and into the stratosphere, whereas stratospherically generated anomalies tend to be largest at the surface and relatively weak at midtropospheric levels. Some persistent variability is, however, found even in the absence of these "external" forcings, especially near the amplitude maxima of the leading eigenmodes of the internal extratropical tropospheric evolution operator. One of these eigenmodes has a circumglobal zonal wavenumber-5 structure with maxima over the Arabian Sea and the central Pacific, and two others are associated with north-south dipole variations across the North Atlantic jet. Overall, tropical influences are generally found to be larger than stratospheric influences on extratropical tropospheric variability and have a pronounced impact on the persistent, and therefore the potentially predictable, portion of that variability.

Reconciling Non-Gaussian Climate Statistics with Linear Dynamics

“…In most cases, this nonnormality is associated with the ability of anomalies to draw energy from a background state, for which there is pervasive evidence and which is indeed one of the cornerstones of dynamical meteorology and oceanography. Most recently, Newman and Sardeshmukh (2008) have confirmed that the variance budget of observed extratropical weekly circulation anomalies is dominated by a local balance between stochastic forcing and local damping and that nonlocal dynamical effects increase the anomaly variance at all locations. Such enhancements of variance cause the K-S parabola to have a slight negative bias, as evident in Figs.…”

“…In climatic contexts, its applicability is mostly limited to departures from the annual cycle and does not extend to the annual cycle itself (Huang and Sardeshmukh 2000). Some role for deterministic nonlinear dynamics has also been argued, for instance, in tropical SST variability (e.g., Penland and Sardeshmukh 1995;Monahan and Dai 2004;An and Jin 2004), extratropical atmospheric circulation variability (e.g., Kravtsov et al 2005;Kondrashov et al 2006;Newman and Sardeshmukh 2008), and sea surface wind variability (e.g., Monahan 2004). Nonetheless, the LSF approximation is a powerful approximation for diagnostic and prediction purposes, whose utility has been demonstrated in numerous studies and also in this paper.…”

“…Evidence has steadily accumulated, however, to support even this latter stronger claim, especially for ''coarse-grained'' anomalies averaged over various time and space scales. This evidence has come from a wide range of studies demonstrating the approximate linearity of the global climate response to combinations of radiative forcings (e.g., Knutson et al 2006 and references therein), the approximate linearity of the global atmospheric response to tropical SST changes (e.g., Barsugli and Sardeshmukh 2002;Schneider et al 2003;Barsugli et al 2006), the approximately linear dynamics of seasonal tropical SST anomalies (Penland and Sardeshmukh 1995), the competitiveness of simple linear seasonal forecast models with global coupled climate models (Saha et al 2006), the approximately linear evolution of weeklyaveraged atmospheric circulation anomalies (Winkler et al 2001;Newman and Sardeshmukh 2008), and the competitiveness of week-2 and week-3 linear forecast models with comprehensive numerical weather prediction (NWP) models (Winkler et al 2001;Newman et al 2003). Even on the time scales of daily weather, linear stochastically forced (LSF) models of the form (1), although not as accurate as NWP models for daily predictions, are realistic enough to capture many features of the second-order statistics of observed synoptic variability, such as the geographical structures of eddy variances and covariances and momentum and heat fluxes (e.g., Farrell and Ioannou 1995;Hall and Sardeshmukh 1998;Whitaker and Sardeshmukh 1998;DelSole 2004).…”

Multiscale Impacts of Variable Heating in Climate

“…1 and 3, considered together with the success of stochastically driven damped linear models of extratropical variability on these time scales (Farrell and Ioannou 1995;WS98;Winkler et al 2001;Newman et al 2003;Delsole 2004;Newman and Sardeshmukh 2008), suggest that a portion of the transient adiabatic nonlinear and diabatic forcing terms [TЈ ϩ FЈ in Eq. (3)] may be treated as a modified linear dissipation plus stochastic forcing.…”

“…It is hard to imagine how such a linear model would be competitive with comprehensive nonlinear GCMs unless the TЈ terms could be approximated as in (5) and a significant fraction of the transient diabatic forcing (after explicitly accounting for coherent tropical effects as mentioned above) could be treated as stochastic. Recently, Newman and Sardeshmukh (2008) have demonstrated that the lead-lag correlation structures produced by this linear stochastically driven model are also highly realistic. Similar to the argument given above for the synoptic scales, one can then use this fact and our results in Fig.…”