Search Results
method that has been particularly successful, perhaps because of its simplicity and therefore computational efficiency, is the bulk mass flux approach ( Ooyama 1971 ; Yanai et al. 1973 ; Betts 1973 ). In this method, the vertical transport of a thermodynamic state variable ϕ , conserved for moist adiabatic motions, by a specific group of updrafts u is parameterized through a vertical advection model, where M u is the mass flux divided by density (hereafter referred to simply as mass flux), ϕ
method that has been particularly successful, perhaps because of its simplicity and therefore computational efficiency, is the bulk mass flux approach ( Ooyama 1971 ; Yanai et al. 1973 ; Betts 1973 ). In this method, the vertical transport of a thermodynamic state variable ϕ , conserved for moist adiabatic motions, by a specific group of updrafts u is parameterized through a vertical advection model, where M u is the mass flux divided by density (hereafter referred to simply as mass flux), ϕ
Lagrangian mass transport to second order in wave steepness. The vertically averaged drift current is obtained by dividing the volume flux by the local depth. The total Lagrangian drift current can be written as the sum of the Stokes drift ( Stokes 1847 ) and a mean Eulerian current (see, e.g., Longuet-Higgins 1953 ). In the present paper the Eulerian current arises as a balance between the radiation stresses, the Coriolis force, and bottom friction. Earlier, Kenyon (1969) considered the pure Stokes
Lagrangian mass transport to second order in wave steepness. The vertically averaged drift current is obtained by dividing the volume flux by the local depth. The total Lagrangian drift current can be written as the sum of the Stokes drift ( Stokes 1847 ) and a mean Eulerian current (see, e.g., Longuet-Higgins 1953 ). In the present paper the Eulerian current arises as a balance between the radiation stresses, the Coriolis force, and bottom friction. Earlier, Kenyon (1969) considered the pure Stokes
; Grant 2001 ; Grant and Lock 2004 ; Grant 2006 ). Fig . 2. Conceptual representation of both the clear and cloud-topped CBL. Compared to the clear CBL, cumulus convection introduces the mass flux M and its associated moisture transport M q to the system. The blue box denotes the main area that we are studying. The primary influence of cumulus convection on the mixed-layer development takes place at the top of this layer, where the convective mass flux transports air from the subcloud to the
; Grant 2001 ; Grant and Lock 2004 ; Grant 2006 ). Fig . 2. Conceptual representation of both the clear and cloud-topped CBL. Compared to the clear CBL, cumulus convection introduces the mass flux M and its associated moisture transport M q to the system. The blue box denotes the main area that we are studying. The primary influence of cumulus convection on the mixed-layer development takes place at the top of this layer, where the convective mass flux transports air from the subcloud to the
that a zonal symmetric transport equation using mass-weighted isentropic zonal means can accurately separate meridional transports of chemical constituents into mean and eddy flux terms and can express the conservative nature of minor constituents ( Iwasaki 1989 ; Miyazaki et al. 2005a ; Miyazaki and Iwasaki 2005 ; hereinafter, we refer to this method as “MI diagnosis”). In particular, the eddy transport term can be exactly and simply estimated in the MI diagnosis. Such a diagnosis considers mass
that a zonal symmetric transport equation using mass-weighted isentropic zonal means can accurately separate meridional transports of chemical constituents into mean and eddy flux terms and can express the conservative nature of minor constituents ( Iwasaki 1989 ; Miyazaki et al. 2005a ; Miyazaki and Iwasaki 2005 ; hereinafter, we refer to this method as “MI diagnosis”). In particular, the eddy transport term can be exactly and simply estimated in the MI diagnosis. Such a diagnosis considers mass
at subgrid model integration. It presents the extension of the eddy diffusivity mass flux (EDMF) framework for turbulent transport ( Siebesma and Teixeira 2000 ; Siebesma et al. 2007 ) into the statistical modeling of boundary layer clouds (e.g., Sommeria and Deardorff 1977 ; Mellor 1977 ; Bougeault 1981 ). Such close coupling between the modeling of boundary layer transport and clouds is motivated by the idea that both are in principle generated by the same turbulent eddies. The associated
at subgrid model integration. It presents the extension of the eddy diffusivity mass flux (EDMF) framework for turbulent transport ( Siebesma and Teixeira 2000 ; Siebesma et al. 2007 ) into the statistical modeling of boundary layer clouds (e.g., Sommeria and Deardorff 1977 ; Mellor 1977 ; Bougeault 1981 ). Such close coupling between the modeling of boundary layer transport and clouds is motivated by the idea that both are in principle generated by the same turbulent eddies. The associated
upper ocean. So, there must be a convergent flow in the upper ocean that balances both the mass and vorticity fluxes induced by entrainment. b. Time variability associated with overflows Satellite altimetry revealed regions of high variability of the sea surface height about 50–100 km downstream from the marginal sea strait of the Mediterranean, Faroe Bank Channel, and Denmark Strait overflows ( Høyer and Quadfasel 2001 ; Høyer et al. 2002 ). Satellite infrared imagery and floats have also shown
upper ocean. So, there must be a convergent flow in the upper ocean that balances both the mass and vorticity fluxes induced by entrainment. b. Time variability associated with overflows Satellite altimetry revealed regions of high variability of the sea surface height about 50–100 km downstream from the marginal sea strait of the Mediterranean, Faroe Bank Channel, and Denmark Strait overflows ( Høyer and Quadfasel 2001 ; Høyer et al. 2002 ). Satellite infrared imagery and floats have also shown
et al. 2001 ; Walker and Schneider 2006 ; Merlis et al. 2013a ). The fluxes of energy and mass by the Hadley cells are linked via the gross moist stability (GMS). As the Hadley cells overturn, their upper and lower branches transport energy in opposite directions so that the net meridional energy flux depends on their degree of compensation ( Held and Hoskins 1985 ). This compensation depends on 1) the rate of mass circulation (known as the mass flux) and 2) the meridional energy flux per unit
et al. 2001 ; Walker and Schneider 2006 ; Merlis et al. 2013a ). The fluxes of energy and mass by the Hadley cells are linked via the gross moist stability (GMS). As the Hadley cells overturn, their upper and lower branches transport energy in opposite directions so that the net meridional energy flux depends on their degree of compensation ( Held and Hoskins 1985 ). This compensation depends on 1) the rate of mass circulation (known as the mass flux) and 2) the meridional energy flux per unit
and atmospheric volume. 2) Definition of the quantities Given the domain and associated volumes and surfaces, four flux processes and two storages are designated. All water stored in the atmospheric volume is referred to as atmospheric water storage (AWS), and all water stored in the terrestrial volume is referred to as terrestrial water storage (TWS). Flux is designated as a transport of water mass through an area over a given time. More generally, flux processes refer to the mechanisms by
and atmospheric volume. 2) Definition of the quantities Given the domain and associated volumes and surfaces, four flux processes and two storages are designated. All water stored in the atmospheric volume is referred to as atmospheric water storage (AWS), and all water stored in the terrestrial volume is referred to as terrestrial water storage (TWS). Flux is designated as a transport of water mass through an area over a given time. More generally, flux processes refer to the mechanisms by
1. Introduction How to appropriately represent the vertical transport of moist convection in large-scale models is a longstanding problem in weather forecasting and climate simulation and projection. The most common method to parameterize the convection-induced vertical transport is the mass-flux approach pioneered by Arakawa and Schubert (1974) . In this framework, the total kinematic vertical flux of a generic variable χ at an arbitrary height may be decomposed into three parts, where σ
1. Introduction How to appropriately represent the vertical transport of moist convection in large-scale models is a longstanding problem in weather forecasting and climate simulation and projection. The most common method to parameterize the convection-induced vertical transport is the mass-flux approach pioneered by Arakawa and Schubert (1974) . In this framework, the total kinematic vertical flux of a generic variable χ at an arbitrary height may be decomposed into three parts, where σ
, and humidity profiles are measured and no additional experimental configurations are available. In this work, on the basis of measurements of wind, temperature, and relative humidity, we intend to verify the application of the variational method with mass conservation constraint to estimate surface fluxes. The results have been compared against the direct measurements and against those estimated by the variational method without physical constraints and by the conventional flux-profile method
, and humidity profiles are measured and no additional experimental configurations are available. In this work, on the basis of measurements of wind, temperature, and relative humidity, we intend to verify the application of the variational method with mass conservation constraint to estimate surface fluxes. The results have been compared against the direct measurements and against those estimated by the variational method without physical constraints and by the conventional flux-profile method
