Search Results
You are looking at 11 - 20 of 33 items for
- Author or Editor: J. S. Hall x
- Refine by Access: All Content x
Abstract
Detailed information on the four-channel Airborne Chromatograph for Atmospheric Trace Species (ACATS-IV), used to measure long-lived atmospheric trace gases, is presented. Since ACATS-IV was last described in the literature, the temporal resolution of some measurements was tripled during 1997–99, chromatography was significantly changed, and data processing improved. ACATS-IV presently measures CCl3F [chlorofluorocarbon (CFC)-11], CCl2FCClF2 (CFC-113), CH3CCl3 (methyl chloroform), CCl4 (carbon tetrachloride), CH4 (methane), H2 (hydrogen), and CHCl3 (chloroform) every 140 s, and N2O (nitrous oxide), CCl2F2 (CFC-12), CBrClF2 (halon-1211), and SF6 (sulfur hexafluoride) every 70 s. An in-depth description of the instrument operation, standardization, calibration, and data processing is provided, along with a discussion of precision and uncertainties of ambient air measurements for several airborne missions.
Abstract
Detailed information on the four-channel Airborne Chromatograph for Atmospheric Trace Species (ACATS-IV), used to measure long-lived atmospheric trace gases, is presented. Since ACATS-IV was last described in the literature, the temporal resolution of some measurements was tripled during 1997–99, chromatography was significantly changed, and data processing improved. ACATS-IV presently measures CCl3F [chlorofluorocarbon (CFC)-11], CCl2FCClF2 (CFC-113), CH3CCl3 (methyl chloroform), CCl4 (carbon tetrachloride), CH4 (methane), H2 (hydrogen), and CHCl3 (chloroform) every 140 s, and N2O (nitrous oxide), CCl2F2 (CFC-12), CBrClF2 (halon-1211), and SF6 (sulfur hexafluoride) every 70 s. An in-depth description of the instrument operation, standardization, calibration, and data processing is provided, along with a discussion of precision and uncertainties of ambient air measurements for several airborne missions.
Abstract
The Convective Transport of Active Species in the Tropics (CONTRAST) experiment was conducted from Guam (13.5°N, 144.8°E) during January–February 2014. Using the NSF/NCAR Gulfstream V research aircraft, the experiment investigated the photochemical environment over the tropical western Pacific (TWP) warm pool, a region of massive deep convection and the major pathway for air to enter the stratosphere during Northern Hemisphere (NH) winter. The new observations provide a wealth of information for quantifying the influence of convection on the vertical distributions of active species. The airborne in situ measurements up to 15-km altitude fill a significant gap by characterizing the abundance and altitude variation of a wide suite of trace gases. These measurements, together with observations of dynamical and microphysical parameters, provide significant new data for constraining and evaluating global chemistry–climate models. Measurements include precursor and product gas species of reactive halogen compounds that impact ozone in the upper troposphere/lower stratosphere. High-accuracy, in situ measurements of ozone obtained during CONTRAST quantify ozone concentration profiles in the upper troposphere, where previous observations from balloonborne ozonesondes were often near or below the limit of detection. CONTRAST was one of the three coordinated experiments to observe the TWP during January–February 2014. Together, CONTRAST, Airborne Tropical Tropopause Experiment (ATTREX), and Coordinated Airborne Studies in the Tropics (CAST), using complementary capabilities of the three aircraft platforms as well as ground-based instrumentation, provide a comprehensive quantification of the regional distribution and vertical structure of natural and pollutant trace gases in the TWP during NH winter, from the oceanic boundary to the lower stratosphere.
Abstract
The Convective Transport of Active Species in the Tropics (CONTRAST) experiment was conducted from Guam (13.5°N, 144.8°E) during January–February 2014. Using the NSF/NCAR Gulfstream V research aircraft, the experiment investigated the photochemical environment over the tropical western Pacific (TWP) warm pool, a region of massive deep convection and the major pathway for air to enter the stratosphere during Northern Hemisphere (NH) winter. The new observations provide a wealth of information for quantifying the influence of convection on the vertical distributions of active species. The airborne in situ measurements up to 15-km altitude fill a significant gap by characterizing the abundance and altitude variation of a wide suite of trace gases. These measurements, together with observations of dynamical and microphysical parameters, provide significant new data for constraining and evaluating global chemistry–climate models. Measurements include precursor and product gas species of reactive halogen compounds that impact ozone in the upper troposphere/lower stratosphere. High-accuracy, in situ measurements of ozone obtained during CONTRAST quantify ozone concentration profiles in the upper troposphere, where previous observations from balloonborne ozonesondes were often near or below the limit of detection. CONTRAST was one of the three coordinated experiments to observe the TWP during January–February 2014. Together, CONTRAST, Airborne Tropical Tropopause Experiment (ATTREX), and Coordinated Airborne Studies in the Tropics (CAST), using complementary capabilities of the three aircraft platforms as well as ground-based instrumentation, provide a comprehensive quantification of the regional distribution and vertical structure of natural and pollutant trace gases in the TWP during NH winter, from the oceanic boundary to the lower stratosphere.
Abstract
The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) and its embedded Atmospheric Dispersion Model is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state of the art in numerical weather prediction in order to improve the capability to predict the transport and diffusion of hazardous releases. OMEGA is based upon an unstructured grid that makes possible a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning because its unstructured grid permits the addition of grid elements at any point in space and time. In particular, unstructured grid cells in the horizontal dimension can increase local resolution to better capture topography or the important physical features of the atmospheric circulation and cloud dynamics. This means that OMEGA can readily adapt its grid to stationary surface or terrain features, or to dynamic features in the evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first model to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and hence real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with data.
Abstract
The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) and its embedded Atmospheric Dispersion Model is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state of the art in numerical weather prediction in order to improve the capability to predict the transport and diffusion of hazardous releases. OMEGA is based upon an unstructured grid that makes possible a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning because its unstructured grid permits the addition of grid elements at any point in space and time. In particular, unstructured grid cells in the horizontal dimension can increase local resolution to better capture topography or the important physical features of the atmospheric circulation and cloud dynamics. This means that OMEGA can readily adapt its grid to stationary surface or terrain features, or to dynamic features in the evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first model to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and hence real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with data.
Abstract
Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.
Abstract
Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.
ABSTRACT
Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.
ABSTRACT
Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
Abstract
How are rain forest photosynthesis and turbulent fluxes influenced by clouds? To what extent are clouds affected by local processes driven by rain forest energy, water, and carbon fluxes? These interrelated questions were the main drivers of the intensive field experiment CloudRoots-Amazon22 which took place at the Amazon Tall Tower Observatory (ATTO)/Campina supersites in the Amazon rain forest during the dry season, in August 2022. CloudRoots-Amazon22 collected observational data to derive cause–effect relationships between processes occurring at the leaf level up to canopy scales in relation to the diurnal evolution of the clear-to-cloudy transition. First, we studied the impact of cloud and canopy radiation perturbations on the subdiurnal variability of stomatal conductance. Stoma opening is larger in the morning, modulated by the cloud optical thickness. Second, we combined 1-Hz frequency measurements of the stable isotopologues of carbon dioxide and water vapor with measurements of turbulence to determine carbon dioxide and water vapor sources and sinks within the canopy. Using scintillometer observations, we inferred 1-min sensible heat flux that responded within minutes to the cloud passages. Third, collocated profiles of state variables and greenhouse gases enabled us to determine the role of clouds in vertical transport. We then inferred, using canopy and upper-atmospheric observations and a parameterization, the cloud cover and cloud mass flux to establish causality between canopy and cloud processes. This shows the need for a comprehensive observational set to improve weather and climate model representations. Our findings contribute to advance our knowledge of the coupling between cloudy boundary layers and primary carbon productivity of the Amazon rain forest.
Abstract
How are rain forest photosynthesis and turbulent fluxes influenced by clouds? To what extent are clouds affected by local processes driven by rain forest energy, water, and carbon fluxes? These interrelated questions were the main drivers of the intensive field experiment CloudRoots-Amazon22 which took place at the Amazon Tall Tower Observatory (ATTO)/Campina supersites in the Amazon rain forest during the dry season, in August 2022. CloudRoots-Amazon22 collected observational data to derive cause–effect relationships between processes occurring at the leaf level up to canopy scales in relation to the diurnal evolution of the clear-to-cloudy transition. First, we studied the impact of cloud and canopy radiation perturbations on the subdiurnal variability of stomatal conductance. Stoma opening is larger in the morning, modulated by the cloud optical thickness. Second, we combined 1-Hz frequency measurements of the stable isotopologues of carbon dioxide and water vapor with measurements of turbulence to determine carbon dioxide and water vapor sources and sinks within the canopy. Using scintillometer observations, we inferred 1-min sensible heat flux that responded within minutes to the cloud passages. Third, collocated profiles of state variables and greenhouse gases enabled us to determine the role of clouds in vertical transport. We then inferred, using canopy and upper-atmospheric observations and a parameterization, the cloud cover and cloud mass flux to establish causality between canopy and cloud processes. This shows the need for a comprehensive observational set to improve weather and climate model representations. Our findings contribute to advance our knowledge of the coupling between cloudy boundary layers and primary carbon productivity of the Amazon rain forest.
