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Eq. (2) into the instantaneous tracer budget and averaging the result leads to the equation for the mean tracer b given by An immediate difficulty is presented by the eddy tracer flux 1 . These fluxes couple the mean tracer budget to that of the perturbations, such that the evolution of the perturbations has to be known to predict the mean tracer. Of course, the solution to this problem is thought to be given by parameterizing the perturbation quantities in terms of the mean quantities
Eq. (2) into the instantaneous tracer budget and averaging the result leads to the equation for the mean tracer b given by An immediate difficulty is presented by the eddy tracer flux 1 . These fluxes couple the mean tracer budget to that of the perturbations, such that the evolution of the perturbations has to be known to predict the mean tracer. Of course, the solution to this problem is thought to be given by parameterizing the perturbation quantities in terms of the mean quantities
1. Introduction Photochemical processes in the atmosphere are driven by solar radiation, which dissociates certain molecules into reactive atoms or free radicals. Models that simulate the chemistry of the atmosphere must accurately simulate the radiation processes that initiate photodissociation. The photodissociation rate is proportional to the actinic flux. In addition, for some biological applications such as exposure of small “bodies” suspended in air or in water (e.g., phytoplankton in the
1. Introduction Photochemical processes in the atmosphere are driven by solar radiation, which dissociates certain molecules into reactive atoms or free radicals. Models that simulate the chemistry of the atmosphere must accurately simulate the radiation processes that initiate photodissociation. The photodissociation rate is proportional to the actinic flux. In addition, for some biological applications such as exposure of small “bodies” suspended in air or in water (e.g., phytoplankton in the
1. Introduction The turbulent exchange process plays an important role in land–atmosphere interaction. Accurate representation of the eddy fluxes is essential for understanding the energy transfer in the land–atmosphere system and for successful numerical simulations of the atmosphere processes ( Lee 1997 ). Generally, the momentum and heat fluxes are estimated by the Bowen ratio energy balance (BREB) method ( Fritschen and Simpson 1989 ) and the profile method based on the Monin
1. Introduction The turbulent exchange process plays an important role in land–atmosphere interaction. Accurate representation of the eddy fluxes is essential for understanding the energy transfer in the land–atmosphere system and for successful numerical simulations of the atmosphere processes ( Lee 1997 ). Generally, the momentum and heat fluxes are estimated by the Bowen ratio energy balance (BREB) method ( Fritschen and Simpson 1989 ) and the profile method based on the Monin
1. Introduction The eddy correlation method is a standard method to determine turbulent scalar fluxes. The method uses correlation of the high-frequency signal of the vertical velocity and the scalar of interest. If the two signals are measured by separate instruments (which is the case for many scalars), the sensors must be separated to avoid flow distortion. This displacement unfortunately attenuates the flux estimate. The loss of flux occurs because of an unavoidable decorrelation between
1. Introduction The eddy correlation method is a standard method to determine turbulent scalar fluxes. The method uses correlation of the high-frequency signal of the vertical velocity and the scalar of interest. If the two signals are measured by separate instruments (which is the case for many scalars), the sensors must be separated to avoid flow distortion. This displacement unfortunately attenuates the flux estimate. The loss of flux occurs because of an unavoidable decorrelation between
1. Introduction More than 70% of the earth’s surface is covered with oceans. To understand the physical processes related to atmosphere–ocean interaction, flux measurements over the ocean are necessary. Because of insufficient direct observations of fluxes over the sea surface, air–sea fluxes in models have often been parameterized in terms of mean parameters and the bulk exchange coefficient. The parameterizations are based on the results of field experiments conducted over land and sea. While
1. Introduction More than 70% of the earth’s surface is covered with oceans. To understand the physical processes related to atmosphere–ocean interaction, flux measurements over the ocean are necessary. Because of insufficient direct observations of fluxes over the sea surface, air–sea fluxes in models have often been parameterized in terms of mean parameters and the bulk exchange coefficient. The parameterizations are based on the results of field experiments conducted over land and sea. While
possibility of two-way atmosphere–ocean interaction is very attractive because it implies that climate variations may to some extent be predictable. In this context surface heat fluxes play a crucial role, since it is through the fluxes at the sea surface that the atmosphere and ocean communicate. There are different ways to study the variability of air–sea interaction. Coupled models have been widely used since, among others, they can provide relatively long time series of the characteristics of the
possibility of two-way atmosphere–ocean interaction is very attractive because it implies that climate variations may to some extent be predictable. In this context surface heat fluxes play a crucial role, since it is through the fluxes at the sea surface that the atmosphere and ocean communicate. There are different ways to study the variability of air–sea interaction. Coupled models have been widely used since, among others, they can provide relatively long time series of the characteristics of the
) measurements of reflected and earth-emitted radiation in three broadband channels: a shortwave (SW) channel (0.2–5.0 μ m), a total channel (from 0.2 to >100 μ m), and a thermal infrared (IR) window channel (8–12 μ m). An extensive modeling effort is subsequently used with TOA measurements for deriving surface SW and longwave (LW) fluxes and corresponding flux profiles at multiple levels in the atmosphere. The three LW algorithms discussed in this study are part of the surface-only flux algorithms (SOFA
) measurements of reflected and earth-emitted radiation in three broadband channels: a shortwave (SW) channel (0.2–5.0 μ m), a total channel (from 0.2 to >100 μ m), and a thermal infrared (IR) window channel (8–12 μ m). An extensive modeling effort is subsequently used with TOA measurements for deriving surface SW and longwave (LW) fluxes and corresponding flux profiles at multiple levels in the atmosphere. The three LW algorithms discussed in this study are part of the surface-only flux algorithms (SOFA
intimately connected to irreversible processes such as lateral small-scale mixing and damping processes associated with air–sea fluxes ( Zhai and Greatbatch 2006a , b ; Greatbatch et al. 2007 ). It is a quantification of the latter process that is the focus of attention here. Figure 1 shows a wintertime instantaneous ( Fig. 1a ) and monthly-mean ( Fig. 1b ) net air–sea heat flux obtained from a global ⅛° eddy-resolving model driven by observed atmospheric fields through bulk formulas that allow the
intimately connected to irreversible processes such as lateral small-scale mixing and damping processes associated with air–sea fluxes ( Zhai and Greatbatch 2006a , b ; Greatbatch et al. 2007 ). It is a quantification of the latter process that is the focus of attention here. Figure 1 shows a wintertime instantaneous ( Fig. 1a ) and monthly-mean ( Fig. 1b ) net air–sea heat flux obtained from a global ⅛° eddy-resolving model driven by observed atmospheric fields through bulk formulas that allow the
diffusivity diagnosed in the equivalent latitude coordinate ( Nakamura 1996 ; Haynes and Shuckburgh 2000a ) and its relationship to the flux and the decay rate of the tracer, we will examine the effects of the spatiotemporal variations of the barrier properties on the global mixing. The next section introduces the 1D model and addresses the role of a barrier under various model configurations. Section 3 describes the results of the isentropic transport calculations in the UTLS region. The final section
diffusivity diagnosed in the equivalent latitude coordinate ( Nakamura 1996 ; Haynes and Shuckburgh 2000a ) and its relationship to the flux and the decay rate of the tracer, we will examine the effects of the spatiotemporal variations of the barrier properties on the global mixing. The next section introduces the 1D model and addresses the role of a barrier under various model configurations. Section 3 describes the results of the isentropic transport calculations in the UTLS region. The final section
1. Introduction For more than a half century, air–sea interaction experimentalists have employed the bulk aerodynamic method in compiling estimates of momentum, heat, and water vapor fluxes. Flux estimates are applied in turn to numerical models of oceanic and/or atmospheric circulations, wave state, waveguide prediction, and loads on engineered infrastructures. More recently, flux estimates have served to support major remote sensing programs: for example, where microwave signatures of the
1. Introduction For more than a half century, air–sea interaction experimentalists have employed the bulk aerodynamic method in compiling estimates of momentum, heat, and water vapor fluxes. Flux estimates are applied in turn to numerical models of oceanic and/or atmospheric circulations, wave state, waveguide prediction, and loads on engineered infrastructures. More recently, flux estimates have served to support major remote sensing programs: for example, where microwave signatures of the
