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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]

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 effect of wind-induced surface heat exchange (WISHE) on axisymmetric, solstitial Hadley circulations is examined for forcings strong enough to produce meridional flow that nearly conserves absolute angular momentum in the free troposphere. Such forcings are known to produce an off-equatorial ascent zone in the summer hemisphere where the convergence of zonal momentum is balanced by drag on surface westerlies. Here, a convective quasi-equilibrium model with two vertical modes is used to show that enhanced surface entropy fluxes induced by these westerlies can intensify and shift both this ascent zone and the subcloud-layer entropy peak toward the equator. The equatorward shift of the subcloud entropy peak is associated with a reduction in the forcing amplitude needed to produce angular momentum–conserving (AMC) meridional flow. A previous theory of frontogenesis in tropical cyclones is adapted to axisymmetric Hadley circulations to show how WISHE shifts the peak subcloud entropy toward the equator. These effects also occur for forcings that vary in a seasonal cycle, with the precise effect of WISHE depending on the peak amplitude of the forcing. For weak seasonally varying forcings, WISHE can abruptly increase the intensity of a local, viscous circulation, whereas for forcings of intermediate strength WISHE produces a transition to AMC flow when such a transition would not otherwise occur. For the strongest forcings, WISHE shifts the transition to AMC flow to a time earlier in the seasonal cycle. The possible relevance of these results to monsoon dynamics is discussed, as are possible effects of processes not represented in these axisymmetric models. [ABSTRACT FROM AUTHOR]

An approach for convective parameterization is presented here, in which grid-scale budget equations of parameterization use separate microphysics and transport terms. This separation is used both as a way to introduce into the parameterization a more explicit causal link between all involved processes and as a vehicle for an easier representation of the memory of convective cells. The equations of parameterization become closer to those of convection-resolving models [cloud-system-resolving models (CSRMs) and large-eddy simulations (LESs)], facilitating parameterization development and validation processes versus a detailed budget of these high-resolution models. The new Microphysics and Transport Convective Scheme (MTCS) equations are presented and discussed. A first version of a convective scheme based on these equations is tested within a single-column framework. The results obtained with the new scheme are close to those of traditional ones in very moist convective cases [like the Global Atmospheric Research Programme (GARP) Atlantic Tropical Experiment (GATE) Phase III, 1974]. The simulation of more difficult drier situations [European Cloud Systems Study/Global Energy and Water Cycle Experiment (GEWEX) Cloud System Studies (EUROCS/GCSS)] is improved through more memory due to higher sensitivity of simulated convection to dry midtropospheric layers; a prognostic relation between cloudy entrainment and precipitation evaporation dramatically improves the prediction of the phase lag of the convective diurnal cycle over land with respect to surface heat forcing. The present proposal contains both a relatively general equation set, which can deal continuously with dry, moist, and deep precipitating convection, and separate—and still crude—explicit moist microphysics. In the future, when increasing the complexity of microphysical computations, such an approach may help to unify dry, moist, and deep precipitating convection inside a single parameterization, as well as facilitate global climate model (GCM) and limited-area model (LAM) parameterizations in sharing the same formulation of explicit microphysics with CSRMs. [ABSTRACT FROM AUTHOR]

Data from the Arctic Ocean Experiment 2001 (AOE-2001) are used to study the vertical structure and diurnal cycle of the summertime central Arctic cloud-capped boundary layer. Mean conditions show a shallow stratocumulus-capped boundary layer, with a nearly moist neutrally stratified cloud layer, although cloud tops often penetrated into the stable inversion. The subcloud layer was more often stably stratified. Conditions near the surface were relatively steady, with a strong control on temperature and moisture by the melting ice surface. A statistically significant diurnal cycle was found in many parameters, although weak in near-surface temperature and moisture. Near-surface wind speed and direction and friction velocity had a pronounced cycle, while turbulent kinetic energy showed no significant diurnal variability. The cloud layer had the most pronounced diurnal variability, with lowest cloud-base height midday followed by enhanced drizzle and temporarily higher cloud-top heights in the afternoon. This is opposite to the cycle found in midlatitude or subtropical marine stratocumulus. The cloud layer was warmest (coolest) and more (less) stably stratified midafternoon (midmorning), coinciding with the coolest (warmest) but least (most) stably stratified capping inversion layer. It is speculated that drizzle is important in regulating the diurnal variability in the cloud layer, facilitated by enhanced midday mixing due to a differential diurnal variability in cloud and subcloud layer stability. Changing the Arctic aerosol climate could change these clouds to a more typical “marine stratocumulus structure,” which could act as a negative feedback on Arctic warming. [ABSTRACT FROM AUTHOR]

During the second phase of the Arabian Sea Monsoon Experiment (ARMEX-II), extensive measurements of spectral aerosol optical depth, mass concentration, and mass size distribution of ambient aerosols as well as mass concentration of aerosol black carbon (BC) were made onboard a research vessel during the intermonsoon period (i.e., when the monsoon winds are in transition from northeasterlies to westerlies/southwesterlies) over the Arabian Sea (AS) adjoining the Indian Peninsula. Simultaneous measurements of spectral aerosol optical depths (AODs) were made at different regions over the adjoining Indian landmass. Mean AODs (at 500-nm wavelength) over the ocean (∼0.44) were comparable to those over the coastal land (∼0.47), but were lower than the values observed over the plateau regions of central Indian Peninsula (∼0.61). The aerosol properties were found to respond distinctly with respect to change in the trajectories, with higher optical depths and flatter AOD spectra associated with trajectories indicating advection from west Asia, and northwest and west-coastal India. On average, BC constituted only ∼2.2% to total aerosol mass compared to the climatological values of ∼6% over the coastal land during the same season. These data are used to characterize the physical properties of aerosols and to assess the resulting short-wave direct aerosol forcing. The mean values were –27 W m-2 at the surface and -12 W m-2 at the top of the atmosphere (TOA), resulting in a net atmospheric forcing of +15 W m-2. The forcing also depended on the region from where the advection predominates. The surface and atmospheric forcing were in the range -40 to -57 W m-2 and +27 to +39 W m-2, respectively, corresponding to advection from the west Asian and western coastal India where they were as low as -19 and +10 W m-2, respectively, when the advection was mainly from the Bay of Bengal and from central/peninsular India. In all these cases, the net atmospheric forcing (heating) efficiency was lower than the values reported for northern Indian Ocean during northern winter, which is attributed to the reduced BC mass fraction. [ABSTRACT FROM AUTHOR]