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Arctic climate change in winter is tightly linked to changes in the strength of surface temperature inversions, which occur frequently in the present climate as Arctic air masses form during polar night. Recent work proposed that, in a warmer climate, increasing low-cloud optical thickness of maritime air advected over high-latitude landmasses during polar night could suppress the formation of Arctic air masses, amplifying winter warming over continents and sea ice. But this mechanism was based on single-column simulations that could not assess the role of fractional cloud cover change. This paper presents two-dimensional cloud-resolving model simulations that support the single-column model results: low-cloud optical thickness and duration increase strongly with initial air temperature, slowing the surface cooling rate as the climate is warmed. The cloud-resolving model cools less at the surface than the single-column model, and the sensitivity of its cooling to warmer initial temperatures is also higher, because it produces cloudier atmospheres with stronger lower-tropospheric mixing and distributes cloud-top cooling over a deeper atmospheric layer with larger heat capacity. Resolving larger-scale cloud turbulence has the greatest impact on the microphysics schemes that best represent general observed features of mixed-phase clouds, increasing their sensitivity to climate warming. These findings support the hypothesis that increasing insulation of the high-latitude land surface by low clouds in a warmer world could act as a strong positive feedback in future climate change and suggest studying Arctic air formation in a three-dimensional climate model. [ABSTRACT FROM AUTHOR]

This paper introduces the average predictability time (APT) for characterizing the overall predictability of a system. APT is the integral of a predictability measure over all lead times. The underlying predictability measure is based on the Mahalanobis metric, which is invariant to linear transformation of the prediction variables and hence gives results that are independent of the (arbitrary) basis set used to represent the state. The APT is superior to some integral time scales used to characterize the time scale of a random process because the latter vanishes in situations when it should not, whereas the APT converges to reasonable values. The APT also can be written in terms of the power spectrum, thereby clarifying the connection between predictability and the power spectrum. In essence, predictability is related to the width of spectral peaks, with strong, narrow peaks associated with high predictability and nearly flat spectra associated with low predictability. Closed form expressions for the APT for linear stochastic models are derived. For a given dynamical operator, the stochastic forcing that minimizes APT is one that allows transformation of the original stochastic model into a set of uncoupled, independent stochastic models. Loosely speaking, coupling enhances predictability. A rigorous upper bound on the predictability of linear stochastic models is derived, which clarifies the connection between predictability at short and long lead times, as well as the choice of norm for measuring error growth. Surprisingly, APT can itself be interpreted as the “total variance” of an alternative stochastic model, which means that generalized stability theory and dynamical systems theory can be used to understand APT. The APT can be decomposed into an uncorrelated set of components that maximize predictability time, analogous to the way principle component analysis decomposes variance. Part II of this paper develops a practical method for performing this decomposition and applies it to meteorological data. [ABSTRACT FROM AUTHOR]

Studies have suggested that the persistence in the meridional vacillation of the midlatitude jet (i.e., annular mode time scale) in comprehensive climate models is related to the model biases in climatological jet latitude, with important implications for projections of future climates and midlatitude weather events. Through the use of the recently developed finite-amplitude wave activity formalism and feedback quantifying techniques, this paper has quantified the role of barotropic and baroclinic eddy feedbacks in annular mode time scales using an idealized dry atmospheric model. The eddy-mean flow interaction that characterizes the persistent anomalous state of the midlatitude jet depends on processes associated with the lower-tropospheric source of vertically propagating Rossby waves, baroclinic mechanisms, and processes associated with upper-tropospheric wave propagation and breaking, barotropic mechanisms. A variety of climate change-like thermal forcings are used to generate a range of meridional shifts in the midlatitude eddy-driven jet. The idealized model shows a reduction in annular mode time scale associated with an increase in jet latitude, similar to comprehensive climate models. This decrease in time scale can be attributed to a similar decrease in the strength of the barotropic eddy feedback, which, in the positive phase of the annular mode, is characterized by anomalous potential vorticity (PV) mixing on the equatorward flank of the climatological jet. The decrease in subtropical PV mixing is, in turn, associated with a stronger subtropical jet as the eddy-driven jet is more distant from the subtropics. These results highlight the importance of subtropical eddy-mean flow interactions for the persistence of an eddy-driven jet. [ABSTRACT FROM AUTHOR]

A thermodynamic analysis is presented of an overturning circulation simulated by two cloud-resolving models, coupled by a weak temperature gradient parameterization. Taken together, they represent two separated regions over different sea surface temperatures, and the coupling represents an idealized large-scale circulation such as the Walker circulation. It is demonstrated that a thermodynamic budget linking net heat input to the generation of mechanical energy can be partitioned into contributions from the large-scale interaction between the two regions, as represented by the weak temperature gradient approximation, and from convective motions in the active warm region and the suppressed cool region. Model results imply that such thermodynamic diagnostics for the aggregate system are barely affected by the strength of the coupling, even its introduction, or by the SST contrast between the regions. This indicates that the weak temperature gradient parameterization does not introduce anomalous thermodynamic behavior. We find that the vertical kinetic energy associated with the large-scale circulation is more than three orders of magnitude smaller than the typical vertical kinetic energy in each region. However, even with very weak coupling circulations, the contrast between the thermodynamic budget terms for the suppressed and active regions is strong and is relatively insensitive to the degree of the coupling. Additionally, scaling arguments are developed for the relative values of the terms in the mechanical energy budget. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for diagnosing predictability on multiple time scales without time averaging. The method finds components that maximize the average predictability time (APT) of a system, where APT is defined as the integral of the average predictability over all lead times. Basing the predictability measure on the Mahalanobis metric leads to a complete, uncorrelated set of components that can be ordered by their contribution to APT, analogous to the way principal components decompose variance. The components and associated APTs are invariant to nonsingular linear transformations, allowing variables with different units and natural variability to be considered in a single state vector without normalization. For prediction models derived from linear regression, maximizing APT is equivalent to maximizing the sum of squared multiple correlations between the component and the time-lagged state vector. The new method is used to diagnose predictability of 1000-hPa zonal velocity on time scales from 6 h to decades. The leading predictable component is dominated by a linear trend and presumably identifies a climate change signal. The next component is strongly correlated with ENSO indices and hence is identified with seasonal-to-interannual predictability. The third component is related to annular modes and presents decadal variability as well as a trend. The next few components have APTs exceeding 10 days. A reconstruction of the tropical zonal wind field based on the leading seven components reveals eastward propagation of anomalies with time scales consistent with the Madden–Julian oscillation. The remaining components have time scales less than a week and hence are identified with weather predictability. The detection of predictability on these time scales without time averaging is possible by virtue of the fact that predictability on different time scales is characterized by different spatial structures, which can be optimally extracted by suitable projections. [ABSTRACT FROM AUTHOR]

The low-frequency response to changes in external forcing for the climate system is a fundamental issue. In two recent papers the authors developed a new blended response algorithm for predicting the response of a nonlinear chaotic forced-dissipative system to small changes in external forcing. This new algorithm is based on the fluctuation–dissipation theorem and combines the geometrically exact general response formula using integration of a linear tangent model at short response times and the classical quasi-Gaussian response algorithm at longer response times. This algorithm overcomes the inherent numerical instability in the geometric formula arising because of positive Lyapunov exponents at longer times while removing potentially large systematic errors from the classical quasi-Gaussian approximation at moderate times. Here the new blended method is tested on the low-frequency response for a T21 barotropic truncation on the sphere with realistic topography in two dynamical regimes corresponding to the mean climate behavior at 300- and 500-hPa geopotential height. The 300-hPa regime has robust strongly mixing behavior with a nearly Gaussian equilibrium state distribution, whereas the 500-hPa regime is weakly chaotic with slowly decaying time autocorrelation functions and strongly non-Gaussian climatology. It is found that the blended response algorithm has significant skill beyond the classical quasi-Gaussian algorithm for the response of the climate mean state and its variance. Additionally, the predicted response of the T21 barotropic model in the lowfrequency regime for these functionals does not seem to be affected by the model's structural instability. Thus, the results here suggest the use of the fluctuation–dissipation theorem–based blended response algorithm for more complex nonlinear geophysical models. [ABSTRACT FROM AUTHOR]

A baroclinic model of intermediate complexity for the atmospheric jet at middle latitudes is used as a stochastic generator of atmosphere-like time series. In this case, time series of the total energy of the system are considered. Statistical inference of extreme values is applied to sequences of yearly maxima extracted from the time series in the rigorous setting provided by extreme value theory. The generalized extreme value (GEV) family of distributions is used here as a basic model, both for its qualities of simplicity and its generality. Several physically plausible values of the parameter TE, which represents the forced equator-to-pole temperature gradient and is responsible for setting the average baroclinicity in the atmospheric model, are used to generate stationary time series of the total energy. Estimates of the three GEV parameters—location, scale, and shape—are inferred by maximum likelihood methods. Standard statistical diagnostics, such as return level and quantile–quantile plots, are systematically applied to assess goodness-of-fit. The GEV parameters of location and scale are found to have a piecewise smooth, monotonically increasing dependence on TE. The shape parameter also increases with TE but is always negative, as is required a priori by the boundedness of the total energy. The sensitivity of the statistical inferences is studied with respect to the selection procedure of the maxima: the roles occupied by the length of the sequences of maxima and by the length of data blocks over which the maxima are computed are critically analyzed. Issues related to model sensitivity are also explored by varying the resolution of the system. The method used in this paper is put forward as a rigorous framework for the statistical analysis of extremes of observed data, to study the past and present climate and to characterize its variations. [ABSTRACT FROM AUTHOR]

The fluctuation–dissipation theorem (FDT) states that for systems with certain properties it is possible to generate a linear operator that gives the response of the system to weak external forcing simply by using covariances and lag-covariances of fluctuations of the undisturbed system. This paper points out that the theorem can be shown to hold for systems with properties very close to the properties of the earth’s atmosphere. As a test of the theorem’s applicability to the atmosphere, a three-dimensional operator for steady responses to external forcing is constructed for data from an atmospheric general circulation model (AGCM). The response of this operator is then compared to the response of the AGCM for various heating functions. In most cases, the FDT-based operator gives three-dimensional responses that are very similar in structure and amplitude to the corresponding GCM responses. The operator is also able to give accurate estimates for the inverse problem in which one derives the forcing that will produce a given response in the AGCM. In the few cases where the operator is not accurate, it appears that the fact that the operator was constructed in a reduced space is at least partly responsible. As an example of the potential utility of a response operator with the accuracy found here, the FDT-based operator is applied to a problem that is difficult to solve with an AGCM. It is used to generate an influence function that shows how well heating at each point on the globe excites the AGCM’s Northern Hemisphere annular mode (NAM). Most of the regions highlighted by this influence function, including the Arctic and tropical Indian Ocean, are verified by AGCM solutions as being effective locations for stimulating the NAM. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear dry model, forced by fixed radiative forcing alone, has been constructed to simulate the Northern Hemisphere winter storm tracks. A procedure has been devised to iterate the radiative equilibrium temperature profile such that at the end of the iterations the model climate closely resembles the desired target climate. This iterative approach is applied to simulate the climatological storm tracks in January. It is found that, when the three-dimensional temperature distribution in the model resembles the observed distribution, the model storm tracks are much too weak. It is hypothesized that this is due to the fact that eddy development is suppressed in a dry atmosphere, owing to the lack of latent heat release in the ascending warm air. To obtain storm tracks with realistic amplitudes, the static stability of the target climate is reduced to simulate the enhancement in baroclinic energy conversion due to latent heat release. With this modification, the storm tracks in the model simulation closely resemble those observed except that the strength of the Atlantic storm track is slightly weaker than observed. The model, when used as a forecast model, also gives high-quality forecasts of the evolution of observed eddies. The iterative approach is applied to force the model to simulate climate anomalies associated with ENSO and the interannual variations of the winter Pacific jet stream/storm tracks. The results show that the model not only succeeds in simulating the climatology of storm tracks, but also produces realistic simulations of storm track anomalies when the model climate is forced to resemble observed climate anomalies. An extended run of the control experiment is conducted to generate monthly mean flow and storm track statistics. These statistics are used to build a linear statistical model relating storm track anomalies to mean flow anomalies. This model performs well when used to hindcast observed storm track anomalies based on observed mean flow anomalies, showing that the storm track/mean flow covariability in the model is realistic and that storm track distribution is not sensitive to the exact form of the applied forcings. [ABSTRACT FROM AUTHOR]

Large-scale tracer transport in the upper troposphere and lower stratosphere (UTLS) is investigated using simulations of the Whole Atmosphere Community Climate Model (WACCM) over the period 1955–2099. The analyses are based on e90, an artificial passive tracer with constant emissions and atmospheric loss rates. The separate contributions of advection by the residual circulation, eddy mixing, and subgrid convection to total transport are explicitly evaluated. The results highlight distinct large-scale transport regimes in the tropics, characterized by efficient vertical tracer transport, and the extratropics, dominated by isentropic mixing. One novel result is the important role of vertical eddy mixing in the tropical upper troposphere. It is shown that interannual variability in e90 is largely driven by El Niño–Southern Oscillation and the quasi-biennial oscillation. The long-term trends emphasize a strong impact of a rising tropopause with climate change on UTLS dynamics and tracer transport. The analyses directly attribute the e90 trends to changes in the different transport components. Stronger residual circulation in the future leads to increased tracer concentrations in the tropical lower stratosphere. Enhanced eddy mixing increases e90 in the extratropical lowermost stratosphere, linked to an upward shift of wave dissipation tied to the tropopause rise. In the troposphere, reduced concentrations in the future are due to weaker convective transport out of the boundary layer and weaker extratropical isentropic eddy mixing. [ABSTRACT FROM AUTHOR]

Large-scale tracer transport in the upper troposphere and lower stratosphere (UTLS) is investigated using simulations of the Whole Atmosphere Community Climate Model (WACCM) over the period 1955-2099. The analyses are based on e90, an artificial passive tracer with constant emissions and atmospheric loss rates. The separate contributions of advection by the residual circulation, eddy mixing, and subgrid convection to total transport are explicitly evaluated. The results highlight distinct large-scale transport regimes in the tropics, characterized by efficient vertical tracer transport, and the extratropics, dominated by isentropic mixing. One novel result is the important role of vertical eddy mixing in the tropical upper troposphere. It is shown that interannual variability in e90 is largely driven by El Niño-Southern Oscillation and the quasi-biennial oscillation. The long-term trends emphasize a strong impact of a rising tropopause with climate change on UTLS dynamics and tracer transport. The analyses directly attribute the e90 trends to changes in the different transport components. Stronger residual circulation in the future leads to increased tracer concentrations in the tropical lower stratosphere. Enhanced eddy mixing increases e90 in the extratropical lowermost stratosphere, linked to an upward shift of wave dissipation tied to the tropopause rise. In the troposphere, reduced concentrations in the future are due to weaker convective transport out of the boundary layer and weaker extratropical isentropic eddy mixing. [ABSTRACT FROM AUTHOR]

Zonal-mean storm-track shifts in response to perturbations in climate occur even in idealized simulations of dry atmospheres with axisymmetric forcing. Nonetheless, a generally accepted theory of the mechanisms controlling the storm-track shifts is still lacking. Here, mean available potential energy (MAPE), a general measure of baroclinicity that is proportional to the square of the Eady growth rate, is used to understand storm-track shifts. It is demonstrated that, in dry atmospheres, the eddy kinetic energy (EKE) in a storm track is linearly related to the mean available potential energy, relative to a local reference state, and that maxima of the two are generally collocated in latitude. Changes in MAPE with climate are then decomposed into components. It is shown that in simulations of dry atmospheres, changes in the latitude of maximum MAPE are dominated by changes in near-surface meridional temperature gradients. By contrast, changes in the magnitude of MAPE are primarily determined by changes in static stability and in the depth of the troposphere. A theory of storm-track shifts may build upon these findings and primarily needs to explain changes in near-surface meridional temperature gradients. The terminus of the Hadley circulation often shifts in tandem with storm tracks and is hypothesized to play an important role in triggering the storm-track shifts seen in this idealized dry context, especially in simulations where increases only in the convective static stability in the deep tropics suffice to shift storm tracks poleward. [ABSTRACT FROM AUTHOR]

This article introduces a new decomposition of climate feedback mechanisms based on their characteristic times. As the last of a series of three, it complements the first two parts by Lahellec and Dufresne to give a comprehensive review of climate feedbacks that will help to ensure consistency between practice and theory. In Parts I and II, analysis of the climate response to perturbations at the large spatial scales and time scales necessary to obtain linearity restricted the characterization to the slow components of the response. This part incorporates the fast mechanisms' impact on the climate feedbacks, bringing the seasonal cycle into the analysis. Thanks to the Floquet theory, the authors could extend the formal framework of Parts I and II to incorporate the fast mechanisms. An illustration of the formal results with a simple 1D model highlights a clear distinction between the role of fast (intraseasonal) and slow (decadal) feedbacks, with an application to the water-cycle feedback of Part II. The same implementation of the Gâteaux difference in LMDZ, the GCM of LMD, as in Part II is used for comparing results with the authors' toy model. This comparison essentially validates the Floquet decomposition obtained with the toy model: the fast component linked to precipitation is contributing negatively to the water-cycle feedback while the slow component is building up the positive feedback. The comparison also provides further insight on the sensitivity of models' precipitation to climate warming. [ABSTRACT FROM AUTHOR]

It is unclear how the width and strength of the Hadley circulation are controlled and how they respond to climate changes. Simulations of global warming scenarios with comprehensive climate models suggest the Hadley circulation may widen and weaken as the climate warms. But these changes are not quantitatively consistent among models, and how they come about is not understood. Here, a wide range of climates is simulated with an idealized moist general circulation model (GCM) coupled to a simple representation of ocean heat transport, in order to place past and possible future changes in the Hadley circulation into a broader context and to investigate the mechanisms responsible for them. By comparison of simulations with and without ocean heat transport, it is shown that it is essential to take low-latitude ocean heat transport and its coupling to wind stress into account to obtain Hadley circulations in a dynamical regime resembling Earth''s, particularly in climates resembling present-day Earth''s and colder. As the optical thickness of an idealized longwave absorber in the simulations is increased and the climate warms, the Hadley circulation strengthens in colder climates and weakens in warmer climates; it has maximum strength in a climate close to present-day Earth''s. In climates resembling present-day Earth''s and colder, the Hadley circulation strength is largely controlled by the divergence of angular momentum fluxes associated with eddies of midlatitude origin; the latter scale with the mean available potential energy in midlatitudes. The importance of these eddy momentum fluxes for the Hadley circulation strength gradually diminishes as the climate warms. The Hadley circulation generally widens as the climate warms, but at a modest rate that depends sensitively on how it is determined. [ABSTRACT FROM AUTHOR]

Climate models consistently predict a strengthened Brewer--Dobson circulation in response to greenhouse gas (GHG)-induced climate change. Although the predicted circulation changes are clearly the result of changes in stratospheric wave drag, the mechanism behind the wave-drag changes remains unclear. Here, simulations from a chemistry--climate model are analyzed to show that the changes in resolved wave drag are largely explainable in terms of a simple and robust dynamical mechanism, namely changes in the location of critical layers within the subtropical lower stratosphere, which are known from observations to control the spatial distribution of Rossby wave breaking. In particular, the strengthening of the upper flanks of the subtropical jets that is robustly expected from GHG-induced tropospheric warming pushes the critical layers (and the associated regions of wave drag) upward, allowing more wave activity to penetrate into the subtropical lower stratosphere. Because the subtropics represent the critical region for wave driving of the Brewer--Dobson circulation, the circulation is thereby strengthened. Transient planetary-scale waves and synoptic-scale waves generated by baroclinic instability are both found to play a crucial role in this process. Changes in stationary planetary wave drag are not so important because they largely occur away from subtropical latitudes. [ABSTRACT FROM AUTHOR]

The effects of anticipated twenty-first-century global climate change on the stratospheric quasi-biennial oscillation (QBO) have been studied using a high-resolution version of the Model for Interdisciplinary Research on Climate (MIROC) atmospheric GCM. This version of the model is notable for being able to simulate a fairly realistic QBO for present-day conditions including only explicitly resolved nonstationary waves. A long control integration of the model was run with observed climatological sea surface temperatures (SSTs) appropriate for the late twentieth century, followed by another integration with increased atmospheric CO2 concentration and SSTs incremented by the projected twenty-first-century warming in a multimodel ensemble of coupled ocean--atmosphere runs that were forced by the Special Report on Emissions Scenarios (SRES) A1B scenario of future atmospheric composition. In the experiment for late twenty-first-century conditions the QBO period becomes longer and QBO amplitude weaker than in the late twentieth-century simulation. The downward penetration of the QBO into the lowermost stratosphere is also curtailed in the late twenty-first-century run. These changes are driven by a significant (30%%--40%%) increase of the mean upwelling in the equatorial stratosphere, and the effect of this enhanced mean circulation overwhelms counteracting influences from strengthened wave fluxes in the warmer climate. The momentum fluxes associated with waves propagating upward into the equatorial stratosphere do strengthen overall by ∼∼(10%%--15%%) in the warm simulation, but the increases are almost entirely in zonal phase speed ranges that have little effect on the stratospheric QBO but that would be expected to have important influences in the mesosphere and lower thermosphere. [ABSTRACT FROM AUTHOR]

Identification and analysis of temporal trends and low-frequency variability in discrete time series is an important practical topic in the understanding and prediction of many atmospheric processes, for example, in analysis of climate change. Widely used numerical techniques of trend identification (like local Gaussian kernel smoothing) impose some strong mathematical assumptions on the analyzed data and are not robust to model sensitivity. The latter issue becomes crucial when analyzing historical observation data with a short record. Two global robust numerical methods for the trend estimation in discrete nonstationary Markovian data based on different sets of implicit mathematical assumptions are introduced and compared here. The methods are first compared on a simple model example; then the importance of mathematical assumptions on the data is explained and numerical problems of local Gaussian kernel smoothing are demonstrated. Presented methods are applied to analysis of the historical sequence of atmospheric circulation patterns over the United Kingdom between 1946 and 2007. It is demonstrated that the influence of the seasonal pattern variability on transition processes is dominated by the long-term effects revealed by the introduced methods. Despite the differences in the mathematical assumptions implied by both presented methods, almost identical symmetrical changes of the cyclonic and anticyclonic pattern probabilities are identified in the analyzed data, with the confidence intervals being smaller than in the case of the local Gaussian kernel smoothing algorithm. Analysis results are investigated with respect to model sensitivity and compared to a standard analysis technique based on a local Gaussian kernel smoothing. Finally, the implications of the discussed strategies on long-range predictability of the data-fitted Markovian models are discussed. [ABSTRACT FROM AUTHOR]

Aerosol radiative forcing in the Persian Gulf region is derived from data collected during the United Arab Emirates (UAE) Unified Aerosol Experiment (UAE2). This campaign took place in August and September of 2004. The land–sea-breeze circulation modulates the diurnal variability of the aerosol properties and aerosol radiative forcing at the surface. Larger aerosol radiative forcing is observed during the land breeze in comparison to the sea breeze. The aerosol optical properties change as the onshore wind brings slightly cleaner air. The mean diurnal value of the surface aerosol forcing during the UAE2 campaign is about -20 W m-2, which corresponds to large aerosol optical thickness (0.45 at 500 nm). The aerosol forcing efficiency [i.e., broadband shortwave forcing per unit optical depth at 550 nm, W m-2 (τ500)-1] is -53 W m-2 (τ500)-1 and the average single scattering albedo is 0.93 at 550 nm. [ABSTRACT FROM AUTHOR]

General circulation models are unable to resolve subgrid-scale moisture variability and associated cloudiness and so must parameterize grid-scale cloud properties. This typically involves various empirical assumptions and a failure to capture the full range (synoptic, geographic, diurnal) of the subgrid-scale variability. A variational parameter estimation technique is employed to adjust empirical model cloud parameters in both space and time, in order to better represent assimilated International Satellite Cloud Climatology Project (ISCCP) cloud fraction and optical depth and Special Sensor Microwave Imager (SSM/I) liquid water path. The value of these adjustments is verified by much improved cloud radiative forcing and persistent improvement in cloud fraction forecasts. [ABSTRACT FROM AUTHOR]

The concept of feedback has been used by several authors in the field of climate science to describe the behavior of models and to assess the importance of the different mechanisms at stake. Here, a simple 1D model of climate has been built to analyze the water vapor feedback. Beyond a static quantification of the water feedback, a more general formal definition of feedback gain based on the tangent linear system is introduced. This definition reintroduces the dynamical aspect of the system response to perturbation from Bode's original concept. In the model here, it is found that, even though the water vapor static gain proves consistent with results from GCMs, it turns out to be negative for time scales below 4 yr and positive only for longer time scales. These results suggest two conclusions: (i) that the water vapor feedback may be fully active only in response to long-lived perturbations; and (ii) that the water vapor feedback could reduce the natural variability due to tropospheric temperature perturbations over short time scales, while enhancing it over longer time scales. This second conclusion would be consistent with studies investigating the influence of air–sea coupling on variability on different time scales. [ABSTRACT FROM AUTHOR]