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sensitive to soil moisture anomalies ( Koster and Suarez 2003 ). Thus, land–atmosphere coupling strength, intuitively defined as the extent to which rainfall generation and other atmospheric processes could be affected by anomalies in land surface state (e.g., soil moisture), has received increased attention in the last decade (e.g., Dirmeyer 2001 ; Koster et al. 2002 , 2004 ; Lawrence and Slingo 2005 ; Koster et al. 2006 ; Guo et al. 2006 ; Seneviratne et al. 2006 ; Wang et al. 2007 ; Wei and
sensitive to soil moisture anomalies ( Koster and Suarez 2003 ). Thus, land–atmosphere coupling strength, intuitively defined as the extent to which rainfall generation and other atmospheric processes could be affected by anomalies in land surface state (e.g., soil moisture), has received increased attention in the last decade (e.g., Dirmeyer 2001 ; Koster et al. 2002 , 2004 ; Lawrence and Slingo 2005 ; Koster et al. 2006 ; Guo et al. 2006 ; Seneviratne et al. 2006 ; Wang et al. 2007 ; Wei and
1. Introduction State-of-the-art level of development of acousto-optic devices allows for their wide application, particularly in the diagnosis of random media 1 by means of high-accuracy angle measurements ( Balakshii et al. 1985 ). These may be applied in the control of slowly changing transient processes in turbulent atmosphere, gaseous, and liquid media. In particular, it may be valuable as a sensing instrument in atmospheric optics for the study of intensity fluctuations during the
1. Introduction State-of-the-art level of development of acousto-optic devices allows for their wide application, particularly in the diagnosis of random media 1 by means of high-accuracy angle measurements ( Balakshii et al. 1985 ). These may be applied in the control of slowly changing transient processes in turbulent atmosphere, gaseous, and liquid media. In particular, it may be valuable as a sensing instrument in atmospheric optics for the study of intensity fluctuations during the
1. Introduction In efforts to describe the dynamics at the land–atmosphere boundary, a principal focus of the climate community has been the development and validation of land surface models for controlled experiments and forecasting. This endeavor has largely concentrated on modeling schemes that seek to incorporate a robust representation of processes operating at the land–atmosphere boundary in order to realistically capture the exchange of energy, mass, and momentum across the interface
1. Introduction In efforts to describe the dynamics at the land–atmosphere boundary, a principal focus of the climate community has been the development and validation of land surface models for controlled experiments and forecasting. This endeavor has largely concentrated on modeling schemes that seek to incorporate a robust representation of processes operating at the land–atmosphere boundary in order to realistically capture the exchange of energy, mass, and momentum across the interface
1. Introduction The importance of air–sea interaction to extratropical atmospheric variability has been the subject of research for over 50 years ( Namias 1959 ; Bjerknes 1964 ). The fundamental issue is that while sea surface temperature (SST) anomalies are largely forced by the atmosphere ( Cayan 1992 ), they can feed back onto the atmosphere ( Kushnir et al. 2002 ). This coupled system was expressed simply by Barsugli and Battisti (1998 , hereafter BB98) , as where T S and T A are
1. Introduction The importance of air–sea interaction to extratropical atmospheric variability has been the subject of research for over 50 years ( Namias 1959 ; Bjerknes 1964 ). The fundamental issue is that while sea surface temperature (SST) anomalies are largely forced by the atmosphere ( Cayan 1992 ), they can feed back onto the atmosphere ( Kushnir et al. 2002 ). This coupled system was expressed simply by Barsugli and Battisti (1998 , hereafter BB98) , as where T S and T A are
forecasting centers attests to the difficulty of the problem. Complicating the problem is the change in behavior, and often an increase in sensitivity, that occurs when component models of the climate system (e.g., atmosphere, land, and ocean) are coupled. A practical way to make progress is to reduce the systematic biases in a model by applying a statistical correction. Several strategies for empirically correcting models have been proposed, such as adjusting the fluxes between component models (e
forecasting centers attests to the difficulty of the problem. Complicating the problem is the change in behavior, and often an increase in sensitivity, that occurs when component models of the climate system (e.g., atmosphere, land, and ocean) are coupled. A practical way to make progress is to reduce the systematic biases in a model by applying a statistical correction. Several strategies for empirically correcting models have been proposed, such as adjusting the fluxes between component models (e
1. Introduction Land–atmosphere coupling can be considered a two-legged process split into terrestrial and atmospheric components ( Guo et al. 2006 ). The terrestrial component is associated with the covariability between soil moisture and the surface turbulent energy fluxes (e.g., Dirmeyer 2011a ). The atmospheric component is associated with the covariability between these fluxes and surface heating, entrainment, or boundary layer growth (e.g., Santanello et al. 2011 ). Research on land–atmosphere
1. Introduction Land–atmosphere coupling can be considered a two-legged process split into terrestrial and atmospheric components ( Guo et al. 2006 ). The terrestrial component is associated with the covariability between soil moisture and the surface turbulent energy fluxes (e.g., Dirmeyer 2011a ). The atmospheric component is associated with the covariability between these fluxes and surface heating, entrainment, or boundary layer growth (e.g., Santanello et al. 2011 ). Research on land–atmosphere
1. Introduction Temperature and winds in the middle- to high-latitude winter stratosphere can undergo large variations on time scales ranging from a few days to interannual. Variations are largest in the Northern Hemisphere winter but are also seen in the Southern Hemisphere winter. Observations and numerical modeling show that the stratospheric variability is correlated with variations in the winter mesosphere and in the tropical and summer middle atmosphere. Both observations and
1. Introduction Temperature and winds in the middle- to high-latitude winter stratosphere can undergo large variations on time scales ranging from a few days to interannual. Variations are largest in the Northern Hemisphere winter but are also seen in the Southern Hemisphere winter. Observations and numerical modeling show that the stratospheric variability is correlated with variations in the winter mesosphere and in the tropical and summer middle atmosphere. Both observations and
distribution and conversion of different atmospheric energy components, offers a valuable perspective to characterize the general circulation and dynamics of planetary atmospheres ( Peixoto and Oort 1992 ). The characterization of atmospheric energetics in the El Niño and La Niña years, which is conducted in this study, has the potential application to explore the atmospheric dynamics and physics of ENSO events. The atmospheric energetics have been extensively discussed based on observations ( Wiin
distribution and conversion of different atmospheric energy components, offers a valuable perspective to characterize the general circulation and dynamics of planetary atmospheres ( Peixoto and Oort 1992 ). The characterization of atmospheric energetics in the El Niño and La Niña years, which is conducted in this study, has the potential application to explore the atmospheric dynamics and physics of ENSO events. The atmospheric energetics have been extensively discussed based on observations ( Wiin
1. Introduction The nonlinear equations describing the atmosphere are known to be a deterministic chaotic system, which has been extensively studied since Lorenz (1963) . A characteristic feature of such systems is that small errors introduced in the initial conditions grow exponentially with time until they “saturate.” This exponential error growth fundamentally limits the predictability of numerical models. This error growth has been thoroughly studied in the context of tropospheric model
1. Introduction The nonlinear equations describing the atmosphere are known to be a deterministic chaotic system, which has been extensively studied since Lorenz (1963) . A characteristic feature of such systems is that small errors introduced in the initial conditions grow exponentially with time until they “saturate.” This exponential error growth fundamentally limits the predictability of numerical models. This error growth has been thoroughly studied in the context of tropospheric model
1. Introduction The oceans provide a vast repository of both heat and water that are of critical importance to the earth’s hydrologic and energy cycles. Because of their inherent high heat capacity relative to the atmosphere, the global oceans integrate energy exchanges across the atmospheric interface, providing both “memory” of past fluxes and a potential source of predictability for the atmosphere. These exchanges of moisture and heat with the atmosphere vary richly on a wide range of space
1. Introduction The oceans provide a vast repository of both heat and water that are of critical importance to the earth’s hydrologic and energy cycles. Because of their inherent high heat capacity relative to the atmosphere, the global oceans integrate energy exchanges across the atmospheric interface, providing both “memory” of past fluxes and a potential source of predictability for the atmosphere. These exchanges of moisture and heat with the atmosphere vary richly on a wide range of space
