Search Results
You are looking at 1 - 4 of 4 items for
- Author or Editor: Randall V. Martin x
- Refine by Access: All Content x
Abstract
Convective clouds in the Tropics that penetrate the boundary layer inversion preferentially detrain into a shallow outflow layer (2–5 km) or a deep outflow layer (10–17 km). The properties of these layers are diagnosed from a one-dimensional model of the Tropics constrained by observed mean temperature and water vapor profiles. The mass flux divergence of the shallow cumuli (2–5 km) is balanced by a mass flux convergence of evaporatively forced descent (downdrafts), while the mass flux divergence of deep cumulonimbus clouds (10–17 km) is balanced by a mass flux convergence of clear-sky radiative descent. The pseudoadiabatic temperature stratification of the midtroposphere (5–10 km) suppresses cloud outflow in this interval. The detrainment profile in the deep outflow layer is shifted downward by about 1.5 km from the profile one would anticipate based on undilute pseudoadiabatic ascent of air from the boundary layer. The main source of water vapor to most of the tropical troposphere is evaporative moistening. Below 12 km, evaporatively forced descent plays an important role in the vertical mass flux budget of the Tropics. This gives rise to a coupling between the water vapor and mass flux budgets, which, between 5 and 10 km, provides a constraint on the variation of relative humidity with height. Between 12 and 15 km, the observed relative humidity profile can be reproduced by assuming a simple first-order balance between detrainment moistening and subsidence drying. The mean ozone profile of the Tropics can be reproduced using a simple one-dimensional model constrained by the cloud mass flux divergence profile of the diagnostic model.
Abstract
Convective clouds in the Tropics that penetrate the boundary layer inversion preferentially detrain into a shallow outflow layer (2–5 km) or a deep outflow layer (10–17 km). The properties of these layers are diagnosed from a one-dimensional model of the Tropics constrained by observed mean temperature and water vapor profiles. The mass flux divergence of the shallow cumuli (2–5 km) is balanced by a mass flux convergence of evaporatively forced descent (downdrafts), while the mass flux divergence of deep cumulonimbus clouds (10–17 km) is balanced by a mass flux convergence of clear-sky radiative descent. The pseudoadiabatic temperature stratification of the midtroposphere (5–10 km) suppresses cloud outflow in this interval. The detrainment profile in the deep outflow layer is shifted downward by about 1.5 km from the profile one would anticipate based on undilute pseudoadiabatic ascent of air from the boundary layer. The main source of water vapor to most of the tropical troposphere is evaporative moistening. Below 12 km, evaporatively forced descent plays an important role in the vertical mass flux budget of the Tropics. This gives rise to a coupling between the water vapor and mass flux budgets, which, between 5 and 10 km, provides a constraint on the variation of relative humidity with height. Between 12 and 15 km, the observed relative humidity profile can be reproduced by assuming a simple first-order balance between detrainment moistening and subsidence drying. The mean ozone profile of the Tropics can be reproduced using a simple one-dimensional model constrained by the cloud mass flux divergence profile of the diagnostic model.
Abstract
Seasonal maize water-stress forecasts were derived for area averages of the primary maize-growing regions of South Africa and Zimbabwe. An agroclimatological model was used to create a historical record of maize water stress as a function of evapotranspiration for 1961–94. Water stress, the primary determinant of yield in water-limited environments such as southern Africa, was correlated with two well-known indices of the El Niño– Southern Oscillation: the Southern Oscillation index (SOI) and the Niño-3 region of the equatorial Pacific. Forecasts for South Africa using only the SOI at a 4-month lead yielded a hindcast correlation of 0.67 over 17 seasons (1961–78) and a forecast correlation of 0.69 over 16 seasons (1978–94). Forecasts for Zimbabwe were less remarkable.
Abstract
Seasonal maize water-stress forecasts were derived for area averages of the primary maize-growing regions of South Africa and Zimbabwe. An agroclimatological model was used to create a historical record of maize water stress as a function of evapotranspiration for 1961–94. Water stress, the primary determinant of yield in water-limited environments such as southern Africa, was correlated with two well-known indices of the El Niño– Southern Oscillation: the Southern Oscillation index (SOI) and the Niño-3 region of the equatorial Pacific. Forecasts for South Africa using only the SOI at a 4-month lead yielded a hindcast correlation of 0.67 over 17 seasons (1961–78) and a forecast correlation of 0.69 over 16 seasons (1978–94). Forecasts for Zimbabwe were less remarkable.
Abstract
The Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the aerosol optical thickness τ for major types of tropospheric aerosols including sulfate, dust, organic carbon (OC), black carbon (BC), and sea salt. The GOCART model uses a dust emission algorithm that quantifies the dust source as a function of the degree of topographic depression, and a biomass burning emission source that includes seasonal and interannual variability based on satellite observations. Results presented here show that on global average, dust aerosol has the highest τ at 500 nm (0.051), followed by sulfate (0.040), sea salt (0.027), OC (0.017), and BC (0.007). There are large geographical and seasonal variations of τ, controlled mainly by emission, transport, and hygroscopic properties of aerosols. The model calculated total τs at 500 nm have been compared with the satellite retrieval products from the Total Ozone Mapping Spectrometer (TOMS) over both land and ocean and from the Advanced Very High Resolution Radiometer (AVHRR) over the ocean. The model reproduces most of the prominent features in the satellite data, with an overall agreement within a factor of 2 over the aerosol source areas and outflow regions. While there are clear differences among the satellite products, a major discrepancy between the model and the satellite data is that the model shows a stronger variation of τ from source to remote regions. Quantitative comparison of model and satellite data is still difficult, due to the large uncertainties involved in deriving the τ values by both the model and satellite retrieval, and by the inconsistency in physical and optical parameters used between the model and the satellite retrieval. The comparison of monthly averaged model results with the sun photometer network [Aerosol Robotics Network (AERONET)] measurements shows that the model reproduces the seasonal variations at most of the sites, especially the places where biomass burning or dust aerosol dominates.
Abstract
The Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the aerosol optical thickness τ for major types of tropospheric aerosols including sulfate, dust, organic carbon (OC), black carbon (BC), and sea salt. The GOCART model uses a dust emission algorithm that quantifies the dust source as a function of the degree of topographic depression, and a biomass burning emission source that includes seasonal and interannual variability based on satellite observations. Results presented here show that on global average, dust aerosol has the highest τ at 500 nm (0.051), followed by sulfate (0.040), sea salt (0.027), OC (0.017), and BC (0.007). There are large geographical and seasonal variations of τ, controlled mainly by emission, transport, and hygroscopic properties of aerosols. The model calculated total τs at 500 nm have been compared with the satellite retrieval products from the Total Ozone Mapping Spectrometer (TOMS) over both land and ocean and from the Advanced Very High Resolution Radiometer (AVHRR) over the ocean. The model reproduces most of the prominent features in the satellite data, with an overall agreement within a factor of 2 over the aerosol source areas and outflow regions. While there are clear differences among the satellite products, a major discrepancy between the model and the satellite data is that the model shows a stronger variation of τ from source to remote regions. Quantitative comparison of model and satellite data is still difficult, due to the large uncertainties involved in deriving the τ values by both the model and satellite retrieval, and by the inconsistency in physical and optical parameters used between the model and the satellite retrieval. The comparison of monthly averaged model results with the sun photometer network [Aerosol Robotics Network (AERONET)] measurements shows that the model reproduces the seasonal variations at most of the sites, especially the places where biomass burning or dust aerosol dominates.
We review the progress of tropospheric trace gas observations and address the need for additional measurement capabilities as recommended by the National Research Council. Tropospheric measurements show pollution in the Northern Hemisphere as a result of fossil fuel burning and a strong seasonal dependence with the largest amounts of carbon monoxide and nitrogen dioxide in the winter and spring. In the summer, when photochemistry is most intense, photochemically generated ozone is found in large concentrations over and downwind from where anthropogenic sources are largest, such as the eastern United States and eastern China. In the tropics and the subtropics, where photon flux is strong throughout the year, trace gas concentrations are driven by the abundance of the emissions. The largest single tropical source of pollution is biomass burning, as can be seen readily in carbon monoxide measurements, but lightning and biogenic trace gases may also contribute to trace gas variability. Although substantive progress has been achieved in seasonal and global mapping of a few tropospheric trace gases, satellite trace gas observations with considerably better temporal and spatial resolution are essential to forecasting air quality at the spatial and temporal scales required by policy makers. The concurrent use of atmospheric composition measurements for both scientific and operational purposes is a new paradigm for the atmospheric chemistry community. The examples presented illustrate both the promise and challenge of merging satellite information with in situ observations in state-of-the-art data assimilation models.
We review the progress of tropospheric trace gas observations and address the need for additional measurement capabilities as recommended by the National Research Council. Tropospheric measurements show pollution in the Northern Hemisphere as a result of fossil fuel burning and a strong seasonal dependence with the largest amounts of carbon monoxide and nitrogen dioxide in the winter and spring. In the summer, when photochemistry is most intense, photochemically generated ozone is found in large concentrations over and downwind from where anthropogenic sources are largest, such as the eastern United States and eastern China. In the tropics and the subtropics, where photon flux is strong throughout the year, trace gas concentrations are driven by the abundance of the emissions. The largest single tropical source of pollution is biomass burning, as can be seen readily in carbon monoxide measurements, but lightning and biogenic trace gases may also contribute to trace gas variability. Although substantive progress has been achieved in seasonal and global mapping of a few tropospheric trace gases, satellite trace gas observations with considerably better temporal and spatial resolution are essential to forecasting air quality at the spatial and temporal scales required by policy makers. The concurrent use of atmospheric composition measurements for both scientific and operational purposes is a new paradigm for the atmospheric chemistry community. The examples presented illustrate both the promise and challenge of merging satellite information with in situ observations in state-of-the-art data assimilation models.
