Multiscale Impacts of Variable Heating in Climate

Abstract

While it is obvious that the mean diabatic forcing of the atmosphere is crucial for maintaining the mean climate, the importance of diabatic forcing fluctuations is less evident in this regard. Such fluctuations do not appear directly in the equations of the mean climate but affect the mean indirectly through their effects on the time-mean transient-eddy fluxes of heat, momentum, and moisture. How large are these effects? What are the effects of tropical phenomena associated with substantial heating variations such as ENSO and the MJO? To what extent do variations of the extratropical surface heat fluxes and precipitation affect the mean climate? What are the effects of the rapid "stochastic" components of the heating fluctuations? Most current climate models misrepresent ENSO and the MJO and ignore stochastic forcing; they therefore also misrepresent their mean effects. To what extent does this contribute to climate model biases and to projections of climate change?This paper provides an assessment of such impacts by comparing with observations a long simulation of the northern winter climate by a dry adiabatic general circulation model forced only with the observed time-mean diabatic forcing as a constant forcing. Remarkably, despite the total neglect of all forcing variations, the model reproduces most features of the observed circulation variability and the mean climate, with biases similar to those of some state-of-the-art general circulation models. In particular, the spatial structures of the circulation variability are remarkably well reproduced. Their amplitudes, however, are progressively underestimated from the synoptic to the subseasonal to interannual and longer time scales. This underestimation is attributed to the neglect of the variable forcing. The model also excites significant tropical variability from the extratropics on interannual scales, which is overwhelmed in reality by the response to tropical heating variability. It is argued that the results of this study suggest a role for the stochastic, and not only the coherent, components of transient diabatic forcing in the dynamics of climate variability and the mean climate.

Reconciling Non-Gaussian Climate Statistics with Linear Dynamics

“…Despite the complete neglect of forcing variations F 0 i , the model reproduces many second-order statistics of the observed atmospheric circulation variability as described fully in Sardeshmukh and Sura (2007). Figure 4, shown in an identical format to Fig.…”

“…1 due to CAM noise, and if so, are they associated primarily with turbulent adiabatic or diabatic forcing fluctuations? To clarify this, we examine a long 1200-winter simulation of the northern winter climate generated by Sardeshmukh and Sura (2007) using a dry adiabatic atmospheric general circulation model forced only with the observed longterm winter-mean diabatic forcing as a constant forcing.…”

“…We are especially interested in determining if it is associated primarily with adiabatic or diabatic noise (i.e., with turbulent adiabatic fluxes or rapid diabatic forcing variations). To this end we will examine a long 108 000-day perpetual winter simulation (equivalent to 1200 90-day winters) generated by Sardeshmukh and Sura (2007) using a dry adiabatic GCM forced only with the observed time-mean diabatic forcing as a constant forcing. We will assess to what extent this constant-forcing simulation captures the nonGaussian statistics shown in Figs.…”

A Theory of Baroclinic Turbulence

“…It follows that the key physical mechanism sustaining turbulence is that process maintaining the nonnormal subspace of growing perturbations against continual depletion under shearing by the mean flow. In statistically steady unforced turbulence, this process is the energetically conservative scattering by nonlinear wave-wave interaction; because this scattering process is chaotic and has short time and space scales, it can be parameterized as stochastic (DelSole 2004b;Sardeshmukh and Sura 2007).…”

Formation of Jets by Baroclinic Turbulence

“…A characteristic property of mean jets in strong synoptic-scale turbulence is marginal stability coexisting with robust transient growth (Farrell 1985;Hall and Sardeshmukh 1998;Sardeshmukh and Sura 2007). In the case of the midlatitude jet, this marginally stable state is traditionally referred to as a baroclinically adjusted state after its original description by Stone (1978), who drew attention to the proximity of mean jet states to the classical two-layer model stability boundary.…”