Search Results
You are looking at 1 - 10 of 15 items for
- Author or Editor: Bing Lin x
- Refine by Access: All Content x
Abstract
Land surface microwave emissivities are important geophysical parameters for atmospheric, hydrological, and biospheric studies. This study estimates land surface microwave emissivity using an atmospheric microwave radiative transfer model and a combination of the Special Sensor Microwave Imager (SSM/I) satellite observations and data from the Atmospheric Radiation Measurement Program southern Great Plains (SGP) site during October of 1995. Emissivities are retrieved for both clear and cloudy conditions. Emissivity standard deviations of ∼0.035 were found at the SGP site. Much of the variability is produced by a distinct diurnal cycle. The emissivity variability at each SSM/I overpass time (0630, 1100, 1730, and 1000 local time) is about half that for all four times combined. Early morning emissivities are ∼0.06 less than those at other times, and the polarization differences at the four times are similar. This behavior is likely the result of dew and surface rewetting effects. Ground observations of dewpoint and temperature difference between air and skin support this theory. The surface emissivities have a significant negative correlation with soil moisture, which can explain about 60%–80% of the emissivity variance when pentad running means are used. Strong correlations among all seven SSM/I channels indicate that the emissivities need to be determined directly for only two or three channels.
Abstract
Land surface microwave emissivities are important geophysical parameters for atmospheric, hydrological, and biospheric studies. This study estimates land surface microwave emissivity using an atmospheric microwave radiative transfer model and a combination of the Special Sensor Microwave Imager (SSM/I) satellite observations and data from the Atmospheric Radiation Measurement Program southern Great Plains (SGP) site during October of 1995. Emissivities are retrieved for both clear and cloudy conditions. Emissivity standard deviations of ∼0.035 were found at the SGP site. Much of the variability is produced by a distinct diurnal cycle. The emissivity variability at each SSM/I overpass time (0630, 1100, 1730, and 1000 local time) is about half that for all four times combined. Early morning emissivities are ∼0.06 less than those at other times, and the polarization differences at the four times are similar. This behavior is likely the result of dew and surface rewetting effects. Ground observations of dewpoint and temperature difference between air and skin support this theory. The surface emissivities have a significant negative correlation with soil moisture, which can explain about 60%–80% of the emissivity variance when pentad running means are used. Strong correlations among all seven SSM/I channels indicate that the emissivities need to be determined directly for only two or three channels.
Abstract
Seasonal variations of liquid and ice water paths (LWP and IWP) in nonprecipitating clouds over oceans are estimated for 4 months by combining the International Satellite Cloud Climatology Project (ISCCP) and Special Sensor Microwave/Imager (SSM/I) data. The ISCCP data are used to separate clear/cloudy skies and warm/cold clouds and to determine cloud optical thickness, cloud-top temperature, and sea surface temperature. SSM/I data are used to separate precipitating and nonprecipitating clouds and to determine LWP. About 93% of all clouds are nonprecipitating clouds, and about half of nonprecipitating clouds are warm (cloud-top temperature > 0°C). The average LWP for warm nonprecipitating clouds is about 6 mg cm−2. The values of total water path obtained from the ISCCP values of optical thickness for cold nonprecipitating clouds are larger than the LWP values from SSM/I, which the authors explain in terms of IWP. The average IWP for cold nonprecipitating clouds is about 7 mg cm−2, with LWP being about 5 Mg cm−2. Tropical and cold hemisphere clouds have higher IWP values (around 10 mg cm−2) than those in warm hemispheres; where LWP values for warm nonprecipitating clouds vary little with latitude or season. Ice fractions, IWP/(LWP + IWP), in cold nonprecipitating clouds increase systematically with decreasing cloud-top temperatures, reaching 50% at about −15°C but ranging from about −5° to −10°C in the northern midlatitudes in autumn and the Tropics year-round to about −25°C in the southern midlatitudes in summer. The ratio of IWP to LWP in cold nonprecipitating clouds reaches almost 3 in the northern midlatitudes in autumn and falls as low as 0.6 in the southern midlatitudes in spring-summer. Combining warm and cold nonprecipitating clouds gives a global ratio of IWP to LWP that is about 0.7 over oceans.
Abstract
Seasonal variations of liquid and ice water paths (LWP and IWP) in nonprecipitating clouds over oceans are estimated for 4 months by combining the International Satellite Cloud Climatology Project (ISCCP) and Special Sensor Microwave/Imager (SSM/I) data. The ISCCP data are used to separate clear/cloudy skies and warm/cold clouds and to determine cloud optical thickness, cloud-top temperature, and sea surface temperature. SSM/I data are used to separate precipitating and nonprecipitating clouds and to determine LWP. About 93% of all clouds are nonprecipitating clouds, and about half of nonprecipitating clouds are warm (cloud-top temperature > 0°C). The average LWP for warm nonprecipitating clouds is about 6 mg cm−2. The values of total water path obtained from the ISCCP values of optical thickness for cold nonprecipitating clouds are larger than the LWP values from SSM/I, which the authors explain in terms of IWP. The average IWP for cold nonprecipitating clouds is about 7 mg cm−2, with LWP being about 5 Mg cm−2. Tropical and cold hemisphere clouds have higher IWP values (around 10 mg cm−2) than those in warm hemispheres; where LWP values for warm nonprecipitating clouds vary little with latitude or season. Ice fractions, IWP/(LWP + IWP), in cold nonprecipitating clouds increase systematically with decreasing cloud-top temperatures, reaching 50% at about −15°C but ranging from about −5° to −10°C in the northern midlatitudes in autumn and the Tropics year-round to about −25°C in the southern midlatitudes in summer. The ratio of IWP to LWP in cold nonprecipitating clouds reaches almost 3 in the northern midlatitudes in autumn and falls as low as 0.6 in the southern midlatitudes in spring-summer. Combining warm and cold nonprecipitating clouds gives a global ratio of IWP to LWP that is about 0.7 over oceans.
Abstract
New data products from the Clouds and the Earth's Radiant Energy System (CERES) instrument on the Tropical Rainfall Measuring Mission Satellite have been examined in the context of the recently proposed adaptive tropical infrared Iris hypothesis. The CERES Single Scanner Footprint data products combine radiative fluxes with cloud properties obtained from a co-orbiting imaging instrument. This enables the use of cloud property–based definitions of the various regions in the simple Iris climate model. Regardless of definition, the radiative properties are found to be different from those assigned in the original Iris hypothesis. As a result, the strength of the feedback effect is reduced by a factor of 10 or more. Contrary to the initial Iris hypothesis, most of the definitions tested in this paper result in a small positive feedback. Thus, the existence of an effective infrared iris to counter greenhouse warming is not supported by the CERES data.
Abstract
New data products from the Clouds and the Earth's Radiant Energy System (CERES) instrument on the Tropical Rainfall Measuring Mission Satellite have been examined in the context of the recently proposed adaptive tropical infrared Iris hypothesis. The CERES Single Scanner Footprint data products combine radiative fluxes with cloud properties obtained from a co-orbiting imaging instrument. This enables the use of cloud property–based definitions of the various regions in the simple Iris climate model. Regardless of definition, the radiative properties are found to be different from those assigned in the original Iris hypothesis. As a result, the strength of the feedback effect is reduced by a factor of 10 or more. Contrary to the initial Iris hypothesis, most of the definitions tested in this paper result in a small positive feedback. Thus, the existence of an effective infrared iris to counter greenhouse warming is not supported by the CERES data.
Abstract
Convective parameterizations in general circulation models (GCMs) generally only aim to simulate the mean or first-order moment of convection; higher moments associated with subgrid variability are not explicitly considered. In this study, an empirically based stochastic convective parameterization is developed that uses an assumed mixed lognormal distribution of rainfall, tuned with parameter values derived from observations, to control selected nonmean statistical properties of convection. Testing of this stochastic convective parameterization reveals that large-scale model dynamics interacts heavily with the convective parameterization, in ways such that the resulting output is fundamentally different from the input. This suggests stochastic parameterizations cannot be calibrated outside of a model's dynamical framework. Implications are discussed for the relative merits of the empirical approach versus another approach that introduces the stochastic process within the framework of the convective parameterization. Inclusion of the variance arising from unresolved scales by stochastic parameterization of convection is found to have a substantial impact upon atmospheric variability in the Tropics, including intraseasonal and longer timescales.
Abstract
Convective parameterizations in general circulation models (GCMs) generally only aim to simulate the mean or first-order moment of convection; higher moments associated with subgrid variability are not explicitly considered. In this study, an empirically based stochastic convective parameterization is developed that uses an assumed mixed lognormal distribution of rainfall, tuned with parameter values derived from observations, to control selected nonmean statistical properties of convection. Testing of this stochastic convective parameterization reveals that large-scale model dynamics interacts heavily with the convective parameterization, in ways such that the resulting output is fundamentally different from the input. This suggests stochastic parameterizations cannot be calibrated outside of a model's dynamical framework. Implications are discussed for the relative merits of the empirical approach versus another approach that introduces the stochastic process within the framework of the convective parameterization. Inclusion of the variance arising from unresolved scales by stochastic parameterization of convection is found to have a substantial impact upon atmospheric variability in the Tropics, including intraseasonal and longer timescales.
Abstract
An intraseasonal tropical oscillation with a period of 20–80 days is simulated in the Neelin–Zeng Quasi-Equilibrium Tropical Circulation Model. This model is an intermediate-level atmospheric model that includes primitive equation nonlinearity, radiative– convective feedbacks, a simple land model with soil moisture, and a Betts–Miller-type moist convective adjustment parameterization. Vertical temperature and moisture structures in the model are based on quasi-equilibrium profiles taken from deep convective regions. The tropical intraseasonal variability is reasonably broadband. The eastward propagating 20–80-day variability is dominated by zonal wavenumber 1, shows features similar to an irregular Madden–Julian oscillation (MJO), and exhibits amplitude and phase speeds that vary both seasonally and between events. At higher wavenumbers, the model has a distinction between the low-frequency MJO-like band and the moist Kelvin wave band, similar to that found in observations. In the model, it is conjectured that this arises by interaction of the wavenumber-1 moist Kelvin wave with the zonally asymmetric basic state.
Experiments using climatological sea surface temperature forcing are conducted using this model to examine the effects of evaporation–wind feedback and extratropical excitation on the maintenance of intraseasonal variability, with particular attention paid to the low wavenumber mode in the 20–80-day band. These experiments indicate that evaporation–wind feedback partially organizes this intraseasonal variability by reducing damping, but it is not by itself sufficient to sustain this oscillation for the most realistic parameters. Excitation by extratropical variability is a major source of energy for the intraseasonal variability in this model. When midlatitude storms are suppressed, tropical intraseasonal variability is nearly eliminated. However, the eastward propagating intraseasonal signal appears most clearly when midlatitude excitation is aided by the evaporation–wind feedback.
Abstract
An intraseasonal tropical oscillation with a period of 20–80 days is simulated in the Neelin–Zeng Quasi-Equilibrium Tropical Circulation Model. This model is an intermediate-level atmospheric model that includes primitive equation nonlinearity, radiative– convective feedbacks, a simple land model with soil moisture, and a Betts–Miller-type moist convective adjustment parameterization. Vertical temperature and moisture structures in the model are based on quasi-equilibrium profiles taken from deep convective regions. The tropical intraseasonal variability is reasonably broadband. The eastward propagating 20–80-day variability is dominated by zonal wavenumber 1, shows features similar to an irregular Madden–Julian oscillation (MJO), and exhibits amplitude and phase speeds that vary both seasonally and between events. At higher wavenumbers, the model has a distinction between the low-frequency MJO-like band and the moist Kelvin wave band, similar to that found in observations. In the model, it is conjectured that this arises by interaction of the wavenumber-1 moist Kelvin wave with the zonally asymmetric basic state.
Experiments using climatological sea surface temperature forcing are conducted using this model to examine the effects of evaporation–wind feedback and extratropical excitation on the maintenance of intraseasonal variability, with particular attention paid to the low wavenumber mode in the 20–80-day band. These experiments indicate that evaporation–wind feedback partially organizes this intraseasonal variability by reducing damping, but it is not by itself sufficient to sustain this oscillation for the most realistic parameters. Excitation by extratropical variability is a major source of energy for the intraseasonal variability in this model. When midlatitude storms are suppressed, tropical intraseasonal variability is nearly eliminated. However, the eastward propagating intraseasonal signal appears most clearly when midlatitude excitation is aided by the evaporation–wind feedback.
Abstract
Recent studies of the Earth Radiation Budget Satellite (ERBS) nonscanner radiation data indicate decadal changes in tropical cloudiness and unexpected radiative anomalies between the 1980s and 1990s. In this study, the ERBS decadal observations are compared with the predictions of the Iris hypothesis using 3.5-box model. To further understand the predictions, the tropical radiative properties observed from recent Clouds and the Earth's Radiant Energy System (CERES) radiation budget experiment [the NASA Langley Research Center (LaRC) parameters] are used to replace the modeled values in the Iris hypothesis. The predicted variations of the radiation fields strongly depend on the relationship (−22% K−1) of tropical high cloud and sea surface temperature (SST) assumed by the Iris hypothesis.
On the decadal time scale, the predicted tropical mean radiative flux anomalies are generally significantly different from those of the ERBS measurements, suggesting that the decadal ERBS nonscanner radiative energy budget measurements do not support the strong negative feedback of the Iris effect. Poor agreements between the satellite data and model predictions even when the tropical radiative properties from CERES observations (LaRC parameters) are used imply that besides the Iris-modeled tropical radiative properties, the unrealistic variations of tropical high cloud generated from the detrainment of deep convection with SST assumed by the Iris hypothesis are likely to be another major factor for causing the deviation between the predictions and observations.
Abstract
Recent studies of the Earth Radiation Budget Satellite (ERBS) nonscanner radiation data indicate decadal changes in tropical cloudiness and unexpected radiative anomalies between the 1980s and 1990s. In this study, the ERBS decadal observations are compared with the predictions of the Iris hypothesis using 3.5-box model. To further understand the predictions, the tropical radiative properties observed from recent Clouds and the Earth's Radiant Energy System (CERES) radiation budget experiment [the NASA Langley Research Center (LaRC) parameters] are used to replace the modeled values in the Iris hypothesis. The predicted variations of the radiation fields strongly depend on the relationship (−22% K−1) of tropical high cloud and sea surface temperature (SST) assumed by the Iris hypothesis.
On the decadal time scale, the predicted tropical mean radiative flux anomalies are generally significantly different from those of the ERBS measurements, suggesting that the decadal ERBS nonscanner radiative energy budget measurements do not support the strong negative feedback of the Iris effect. Poor agreements between the satellite data and model predictions even when the tropical radiative properties from CERES observations (LaRC parameters) are used imply that besides the Iris-modeled tropical radiative properties, the unrealistic variations of tropical high cloud generated from the detrainment of deep convection with SST assumed by the Iris hypothesis are likely to be another major factor for causing the deviation between the predictions and observations.
Abstract
Using the Tropical Rainfall Measuring Mission (TRMM) satellite measurements over tropical oceans, this study evaluates the iris hypothesis recently proposed by Lindzen et al. that tropical upper-tropospheric anvils act as a strong negative feedback in the global climate system. The modeled radiative fluxes of Lindzen et al. are replaced by the Clouds and the Earth's Radiant Energy System (CERES) directly observed broadband radiation fields. The observations show that the clouds have much higher albedos and moderately larger longwave fluxes than those assumed by Lindzen et al. As a result, decreases in these clouds would cause a significant but weak positive feedback to the climate system, instead of providing a strong negative feedback.
Abstract
Using the Tropical Rainfall Measuring Mission (TRMM) satellite measurements over tropical oceans, this study evaluates the iris hypothesis recently proposed by Lindzen et al. that tropical upper-tropospheric anvils act as a strong negative feedback in the global climate system. The modeled radiative fluxes of Lindzen et al. are replaced by the Clouds and the Earth's Radiant Energy System (CERES) directly observed broadband radiation fields. The observations show that the clouds have much higher albedos and moderately larger longwave fluxes than those assumed by Lindzen et al. As a result, decreases in these clouds would cause a significant but weak positive feedback to the climate system, instead of providing a strong negative feedback.
