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time-specific reference frame common to the reanalyses and the ARM measurements. Specifically, we will address three central questions: Do the reanalysis models show any systematic errors in cloud cover and radiative fluxes? If so, how do these errors vary seasonally and across the reanalyses? Are biases in reanalysis radiation variables independent of the cloud fraction biases? Are the characteristics of the reanalyses’ clouds at Barrow, Alaska, representative of reanalyses’ clouds over the Arctic
time-specific reference frame common to the reanalyses and the ARM measurements. Specifically, we will address three central questions: Do the reanalysis models show any systematic errors in cloud cover and radiative fluxes? If so, how do these errors vary seasonally and across the reanalyses? Are biases in reanalysis radiation variables independent of the cloud fraction biases? Are the characteristics of the reanalyses’ clouds at Barrow, Alaska, representative of reanalyses’ clouds over the Arctic
1. Introduction The divergence of the longwave radiative fluxes is an important component of the thermodynamics of the atmospheric boundary layer ( Kondratyev 1969 ; Garratt and Brost 1981 ). The cooling associated with the divergence of longwave radiation is understood to be essential for the establishment and maintenance of persistent surface inversion layers close to the surface during the polar night ( Cerni and Parish 1984 ). Over large ice sheets, the strong radiative cooling has been
1. Introduction The divergence of the longwave radiative fluxes is an important component of the thermodynamics of the atmospheric boundary layer ( Kondratyev 1969 ; Garratt and Brost 1981 ). The cooling associated with the divergence of longwave radiation is understood to be essential for the establishment and maintenance of persistent surface inversion layers close to the surface during the polar night ( Cerni and Parish 1984 ). Over large ice sheets, the strong radiative cooling has been
( Gray and Prowse 1992 ; Pomeroy et al. 2003 ). The net radiation generally makes up about 80% of the energy balance ( Male and Granger 1981 ; Marks and Dozier 1992 ; Cline 1997 ). Therefore, the greatest potential sources of error in simulating snowmelt rates and timing are errors in radiation inputs. Complex terrain poses a great challenge for obtaining needed information on radiative fluxes from satellites because of elevation issues, spatially variable cloud cover, rapidly changing surface
( Gray and Prowse 1992 ; Pomeroy et al. 2003 ). The net radiation generally makes up about 80% of the energy balance ( Male and Granger 1981 ; Marks and Dozier 1992 ; Cline 1997 ). Therefore, the greatest potential sources of error in simulating snowmelt rates and timing are errors in radiation inputs. Complex terrain poses a great challenge for obtaining needed information on radiative fluxes from satellites because of elevation issues, spatially variable cloud cover, rapidly changing surface
active and passive afternoon (around 1330 LT) polar-orbiting satellite cloud retrievals is used to study the radiation flux sensitivity to ice cloud radiative property parameterization over the equatorial western Pacific Ocean region. Three ice cloud schemes are examined. The first is a widely used scheme that assumes hexagonal column ice particles ( Fu 1996 ). The second scheme is designed for cloud remote sensing applications; in this scheme, ice particles are assumed to be aggregates of surface
active and passive afternoon (around 1330 LT) polar-orbiting satellite cloud retrievals is used to study the radiation flux sensitivity to ice cloud radiative property parameterization over the equatorial western Pacific Ocean region. Three ice cloud schemes are examined. The first is a widely used scheme that assumes hexagonal column ice particles ( Fu 1996 ). The second scheme is designed for cloud remote sensing applications; in this scheme, ice particles are assumed to be aggregates of surface
1. Introduction Cloud radiative effect (CRE), defined as the difference between the radiative flux over a region in the presence of clouds and that under cloud-free conditions, is one of the highest priority quantities used in climate model evaluation efforts ( Burrows et al. 2018 ). Its use in climate model evaluation was first proposed in Ramanathan (1987) and Cess and Potter (1987) , and first demonstrated in Ramanathan et al. (1989) . Since then, many studies have used satellite
1. Introduction Cloud radiative effect (CRE), defined as the difference between the radiative flux over a region in the presence of clouds and that under cloud-free conditions, is one of the highest priority quantities used in climate model evaluation efforts ( Burrows et al. 2018 ). Its use in climate model evaluation was first proposed in Ramanathan (1987) and Cess and Potter (1987) , and first demonstrated in Ramanathan et al. (1989) . Since then, many studies have used satellite
radiation and thus modulate the energy balance of the earth and the atmosphere as estimated from satellites ( Ramanathan 1987 ; Ramanathan et al. 1989 ) and from numerical models ( Ramanathan et al. 1983 ; Cess et al. 1989 ). The largest uncertainties in surface shortwave (SW) flux estimates from satellites are due to inadequate information on cloud properties. There have been many attempts at both regional and global scales to estimate surface radiative fluxes from satellite-observed radiances
radiation and thus modulate the energy balance of the earth and the atmosphere as estimated from satellites ( Ramanathan 1987 ; Ramanathan et al. 1989 ) and from numerical models ( Ramanathan et al. 1983 ; Cess et al. 1989 ). The largest uncertainties in surface shortwave (SW) flux estimates from satellites are due to inadequate information on cloud properties. There have been many attempts at both regional and global scales to estimate surface radiative fluxes from satellite-observed radiances
1. Introduction a. At issue To advance the understanding of the water cycle and land–atmosphere interactions on the global scale, information on radiative fluxes is needed at similar scales and, in principle, can be obtained from satellite observations. Only recently have global-scale satellite observations at climatic time scales become available. Issues related to paucity of data have been addressed in the past, but each climatic parameter poses a unique challenge for obtaining homogeneous
1. Introduction a. At issue To advance the understanding of the water cycle and land–atmosphere interactions on the global scale, information on radiative fluxes is needed at similar scales and, in principle, can be obtained from satellite observations. Only recently have global-scale satellite observations at climatic time scales become available. Issues related to paucity of data have been addressed in the past, but each climatic parameter poses a unique challenge for obtaining homogeneous
available energy. The empirical responses of latent heat flux to changes in available energy are generally greater than the theoretical sensitivities of latent heat flux to available energy derived from the Penman–Monteith framework. Both models underestimate the response of latent heat flux to changes in available energy when compared to FLUXNET using the empirical method. This damped response of latent heat flux to an increase in the radiative forcing will have a substantial effect on the energy
available energy. The empirical responses of latent heat flux to changes in available energy are generally greater than the theoretical sensitivities of latent heat flux to available energy derived from the Penman–Monteith framework. Both models underestimate the response of latent heat flux to changes in available energy when compared to FLUXNET using the empirical method. This damped response of latent heat flux to an increase in the radiative forcing will have a substantial effect on the energy
Ramanathan 1985 ) in GCMs is an ongoing line of research that ultimately addresses both the structural properties of clouds, ranging from particle size distributions to cloud fraction parameterizations, and radiative transport solvers that use these properties to compute fluxes and heating rate profiles ( Barker et al. 2003 ). Given the global nature of the problem, it is essential that GCM CREs be compared to global observations such as those provided by satellite-based instruments. Typically, GCM top
Ramanathan 1985 ) in GCMs is an ongoing line of research that ultimately addresses both the structural properties of clouds, ranging from particle size distributions to cloud fraction parameterizations, and radiative transport solvers that use these properties to compute fluxes and heating rate profiles ( Barker et al. 2003 ). Given the global nature of the problem, it is essential that GCM CREs be compared to global observations such as those provided by satellite-based instruments. Typically, GCM top
section 3 . Here, p cld corresponds to a radiatively active altitude that is about the midlevel pressure of the clouds, as has been demonstrated in a study with quasi-simultaneous data from the Lidar In-Space Technology Experiment (LITE) onboard the space shuttle Discovery ( Stubenrauch et al. 2005 ). The radiative flux computations are performed for large-scale semitransparent cirrus: clouds with a horizontal extent of at least 1° latitude × 1° longitude, p cld < 440 hPa and 0.50 < ε cld < 0
section 3 . Here, p cld corresponds to a radiatively active altitude that is about the midlevel pressure of the clouds, as has been demonstrated in a study with quasi-simultaneous data from the Lidar In-Space Technology Experiment (LITE) onboard the space shuttle Discovery ( Stubenrauch et al. 2005 ). The radiative flux computations are performed for large-scale semitransparent cirrus: clouds with a horizontal extent of at least 1° latitude × 1° longitude, p cld < 440 hPa and 0.50 < ε cld < 0
