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Dispersion of pollutants in the urban atmosphere is a subject that is presently under much investigation. In this paper the variables used in turbulent dispersion and plume rise schemes of the Met Office Nuclear Accident Model (NAME) are discussed. Those parameters that can be measured by Doppler lidar are emphasized. Information derived from simultaneous measurements from two Doppler lidars are presented, using methodologies not tried previously, with the aim of improving the forecasting of urban pollution dispersion. The results demonstrate how Doppler lidars can be used as measuring tools for the specific parameters needed within urban dispersion models. A procedure used for carrying out the dual-lidar measurements is outlined. This research shows how dual-lidar measurements can be used to calculate the relevant dispersion parameters, and compares the dual-lidar measurements with model calculations in a case study. Differences between model parameters and lidar observations are discussed. Dual-Doppler lidar data are extremely useful for measuring turbulence profiles within the part of the atmospheric boundary layer that is inaccessible using traditional methods.
Dispersion of pollutants in the urban atmosphere is a subject that is presently under much investigation. In this paper the variables used in turbulent dispersion and plume rise schemes of the Met Office Nuclear Accident Model (NAME) are discussed. Those parameters that can be measured by Doppler lidar are emphasized. Information derived from simultaneous measurements from two Doppler lidars are presented, using methodologies not tried previously, with the aim of improving the forecasting of urban pollution dispersion. The results demonstrate how Doppler lidars can be used as measuring tools for the specific parameters needed within urban dispersion models. A procedure used for carrying out the dual-lidar measurements is outlined. This research shows how dual-lidar measurements can be used to calculate the relevant dispersion parameters, and compares the dual-lidar measurements with model calculations in a case study. Differences between model parameters and lidar observations are discussed. Dual-Doppler lidar data are extremely useful for measuring turbulence profiles within the part of the atmospheric boundary layer that is inaccessible using traditional methods.
By leveraging current technologies to manage distributed climate data in a unified virtual environment, the Earth System Grid (ESG) project is promoting data sharing between international research centers and diverse users. In transforming these data into a collaborative community resource, ESG is changing the way global climate research is conducted.
Since ESG's production beginnings in 2004, its most notable accomplishment was to efficiently store and distribute climate simulation data of some 20 global coupled ocean-atmosphere models to the scores of scientific contributors to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC); the IPCC collective scientific achievement was recognized by the award of a 2007 Nobel Peace Prize. Other international climate stakeholders such as the North American Regional Climate Change Assessment Program (NARCCAP) and the developers of the Community Climate System Model (CCSM) and of the Climate Science Computational End Station (CCES) also have endorsed ESG technologies for disseminating data to their respective user communities. In coming years, the recently created Earth System Grid Center for Enabling Technology (ESG-CET) will extend these methods to assist the international climate community in its efforts to better understand the global climate system.
By leveraging current technologies to manage distributed climate data in a unified virtual environment, the Earth System Grid (ESG) project is promoting data sharing between international research centers and diverse users. In transforming these data into a collaborative community resource, ESG is changing the way global climate research is conducted.
Since ESG's production beginnings in 2004, its most notable accomplishment was to efficiently store and distribute climate simulation data of some 20 global coupled ocean-atmosphere models to the scores of scientific contributors to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC); the IPCC collective scientific achievement was recognized by the award of a 2007 Nobel Peace Prize. Other international climate stakeholders such as the North American Regional Climate Change Assessment Program (NARCCAP) and the developers of the Community Climate System Model (CCSM) and of the Climate Science Computational End Station (CCES) also have endorsed ESG technologies for disseminating data to their respective user communities. In coming years, the recently created Earth System Grid Center for Enabling Technology (ESG-CET) will extend these methods to assist the international climate community in its efforts to better understand the global climate system.
Abstract
While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Early results demonstrate the substantial influence of internal climate variability on twentieth- to twenty-first-century climate trajectories. Global warming hiatus decades occur, similar to those recently observed. Internal climate variability alone can produce projection spread comparable to that in CMIP5. Scientists and stakeholders can use CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change.
Abstract
While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Early results demonstrate the substantial influence of internal climate variability on twentieth- to twenty-first-century climate trajectories. Global warming hiatus decades occur, similar to those recently observed. Internal climate variability alone can produce projection spread comparable to that in CMIP5. Scientists and stakeholders can use CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change.
