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Register a new userA Model of Intra-seasonal Oscillations in the Earth atmosphere
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We suggest a way of rationalizing an intra-seasonal oscillations (IOs) of the Earth atmospheric flow as four meteorological relevant triads of interacting planetary waves, isolated from the system of all the rest planetary waves. Our model is independent of the topography (mountains, etc.) and gives a natural explanation of IOs both in the North and South Hemispheres. Spherical planetary waves are an example of a wave mesoscopic system obeying discrete resonances that also appears in other areas of physics.
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We formulate and study a low-order nonlinear coupled ocean-atmosphere model with an emphasis on the impact of radiative and heat fluxes and of the frictional coupling between the two components. This model version extends a previous 24-variable version by adding a dynamical equation for the passive advection of temperature in the ocean, together with an energy balance model. The bifurcation analysis and the numerical integration of the model reveal the presence of low-frequency variability (LFV) concentrated on and near a long-periodic, attracting orbit. This orbit combines atmospheric and oceanic modes, and it arises for large values of the meridional gradient of radiative input and of frictional coupling. Chaotic behavior develops around this orbit as it loses its stability; this behavior is still dominated by the LFV on decadal and multi-decadal time scales that is typical of oceanic processes. Atmospheric diagnostics also reveals the presence of predominant low- and high-pressure zones, as well as of a subtropical jet; these features recall realistic climatological properties of the oceanic atmosphere. Finally, a predictability analysis is performed. Once the decadal-scale periodic orbits develop, the coupled systems short-term instabilities --- as measured by its Lyapunov exponents --- are drastically reduced, indicating the oceans stabilizing role on the atmospheric dynamics. On decadal time scales, the recurrence of the solution in a certain region of the invariant subspace associated with slow modes displays some extended predictability, as reflected by the oscillatory behavior of the error for the atmospheric variables at long lead times.
The understanding of the fundamental properties of the climate system has long benefitted from the use of simple numerical models able to parsimoniously represent the essential ingredients of its processes. Here we introduce a new model for the atmosphere that is constructed by supplementing the now-classic Lorenz 96 one-dimensional lattice model with temperature-like variables. The model features an energy cycle that allows for conversion between the kinetic and potential forms and for introducing a notion of efficiency. The models evolution is controlled by two contributions - a quasi-symplectic and a gradient one, which resemble (yet not conforming to) a metriplectic structure. After investigating the linear stability of the symmetric fixed point, we perform a systematic parametric investigation that allows us to define regions in the parameters space where at steady state stationary, quasi-periodic, and chaotic motions are realised, and study how the terms responsible for defining the energy budget of the system depend on the external forcing injecting energy in the kinetic and in the potential energy reservoirs. Finally, we find preliminary evidence that the model features extensive chaos. We also introduce a more complex version of the model that is able to accommodate for multiscale dynamics and that features an energy cycle that more closely mimics the one of the Earths atmosphere.
Turbulent boundary layers exhibit a universal structure which nevertheless is rather complex, being composed of a viscous sub-layer, a buffer zone, and a turbulent log-law region. In this letter we present a simple analytic model of turbulent boundary layers which culminates in explicit formulae for the profiles of the mean velocity, the kinetic energy and the Reynolds stress as a function of the distance from the wall. The resulting profiles are in close quantitative agreement with measurements over the entire structure of the boundary layer, without any need of re-fitting in the different zones.
One significant difference between the atmospheres of stars and exoplanets is the presence of condensed particles (clouds or hazes) in the atmosphere of the latter. The main goal of this paper is to develop a self-consistent microphysical cloud model for 1D atmospheric codes, which can reproduce some observed properties of Earth, such as the average albedo, surface temperature, and global energy budget. The cloud model is designed to be computationally efficient, simple to implement, and applicable for a wide range of atmospheric parameters for planets in the habitable zone. We use a 1D, cloud-free, radiative-convective, and photochemical equilibrium code originally developed by Kasting, Pavlov, Segura, and collaborators as basis for our cloudy atmosphere model. The cloud model is based on models used by the meteorology community for Earths clouds. The free parameters of the model are the relative humidity and number density of condensation nuclei, and the precipitation efficiency. In a 1D model, the cloud coverage cannot be self-consistently determined, thus we treat it as a free parameter. We apply this model to Earth (aerosol number density 100 cm^-3, relative humidity 77 %, liquid cloud fraction 40 %, and ice cloud fraction 25 %) and find that a precipitation efficiency of 0.8 is needed to reproduce the albedo, average surface temperature and global energy budget of Earth. We perform simulations to determine how the albedo and the climate of a planet is influenced by the free parameters of the cloud model. We find that the planetary climate is most sensitive to changes in the liquid water cloud fraction and precipitation efficiency. The advantage of our cloud model is that the cloud height and the droplet sizes are self-consistently calculated, both of which influence the climate and albedo of exoplanets.
Existing methods for diagnosing predictability in climate indices often make a number of unjustified assumptions about the climate system that can lead to misleading conclusions. We present a flexible family of state-space models capable of separating the effects of external forcing on inter-annual time scales, from long-term trends and decadal variability, short term weather noise, observational errors and changes in autocorrelation. Standard potential predictability models only estimate the fraction of the total variance in the index attributable to external forcing. In addition, our methodology allows us to partition individual seasonal means into forced, slow, fast and error components. Changes in the predictable signal within the season can also be estimated. The model can also be used in forecast mode to assess both intra- and inter-seasonal predictability. We apply the proposed methodology to a North Atlantic Oscillation index for the years 1948-2017. Around 60% of the inter-annual variance in the December-January-February mean North Atlantic Oscillation is attributable to external forcing, and 8% to trends on longer time-scales. In some years, the external forcing remains relatively constant throughout the winter season, in others it changes during the season. Skillful statistical forecasts of the December-January-February mean North Atlantic Oscillation are possible from the end of November onward and predictability extends into March. Statistical forecasts of the December-January-February mean achieve a correlation with the observations of 0.48.
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