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Global modeling; satellite data assimilation and initialization; simulation of future observing systems; model and observed energetics; dynamics of planetary waves; First Global Atmospheric Research Program Global Experiment (FGGE) diagnosis studies; and National Research Council Research Associateship Program are discussed.
Large-eddy simulation (LES) is used as a tool to understand physical processes such as turbulence, aerosols, clouds, precipitation, radiation, the interactions among all these, and their interactions with the underlying surface. Over the next 10 years, LES will drive fundamental progress in open scientific questions in these areas as LES is increasingly used to gain understanding of complex interacting physical processes involving atmospheric turbulence. This growth will be driven both by scientific demand and the expansion of computational resources needed to conduct LES, and the form that the growth takes will largely be determined by how computational resources are leveraged for scientific gain. In particular, we suggest that computational resources are likely to be leveraged in two separate but not necessarily distinct ways. On one hand, growth in computational resources will allow LES to be made more routine, that is, performed more frequently, while on the other hand, the computational expense (measured in total floating point operations) afforded to individual LES will expand dramatically, allowing simulations to increase in both domain size and resolution as well as physical detail. Current U.S. Department of Energy (DOE) projects such as LES ARM Symbiotic Simulation and Observation Activity (LASSO) are leading the way in conducting routine LES, building large, public databases that are accessible for data science, sensitivity studies, and training for machine learning. LES will also become more routine as it becomes more accessible for individual researchers to address their scientific questions of interest. Scientific questions addressed by LES over the next 10 years are likely to include cloud organization and aggregation; aerosol cloud interactions and atmospheric chemistry (including geo-engineering); urban-scale LES; atmospheric extreme events, ranging from small-scale severe weather to wildfires; and ocean-wave-atmosphere interactions. Further LES-related research will likely grow significantly in areas related to societal impact studies of air quality and extreme weather events, applications to renewable energy forecasting and resource assessment, and aid in decision-making processes.
Large-eddy simulation (LES) is used as a tool to understand physical processes such as turbulence, aerosols, clouds, precipitation, radiation, the interactions among all these, and their interactions with the underlying surface. Over the next 10 years, LES will drive fundamental progress in open scientific questions in these areas as LES is increasingly used to gain understanding of complex interacting physical processes involving atmospheric turbulence. This growth will be driven both by scientific demand and the expansion of computational resources needed to conduct LES, and the form that the growth takes will largely be determined by how computational resources are leveraged for scientific gain. In particular, we suggest that computational resources are likely to be leveraged in two separate but not necessarily distinct ways. On one hand, growth in computational resources will allow LES to be made more routine, that is, performed more frequently, while on the other hand, the computational expense (measured in total floating point operations) afforded to individual LES will expand dramatically, allowing simulations to increase in both domain size and resolution as well as physical detail.
This paper explores the potential for airborne remote sensing for atmospheric sciences research. Passive and active techniques from the microwave to visible bands are discussed. It is concluded that technology has progressed sufficiently in several areas that the time is right to develop and operate new remote sensing instruments for use by the community of atmospheric scientists as general purpose tools. Promising candidates include Doppler radar and lidar, infrared short range radiometry, and microwave radiometry.
Atmospheric abundances and the geochemical cycle of certain volatile compounds and elements may be largely influenced or entirely controlled by magmatic sources. However, better estimates of the magnitude and variability of volcanic emissions are required if the importance of this natural source of atmospheric constituents and the resulting effect on atmospheric chemistry are to be elucidated. The project 'Research on Atmospheric Volcanic Emissions' (RAVE) is concerned with the improvement of knowledge of both geological and chemical phenomena attending these emissions by means of comprehensive instrumentation on board a research aircraft making simultaneous measurements of plume constituents. A description is presented of the equipment and the procedures used in the RAVE field study of Mt. St. Helens' plume. An overview of the results is also provided.
Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four-class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool-International Cloud Experiment (TWP-ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Further, comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.
Global climate models (GCMs) have been used widely to study radiative forcing and health risks of aerosols. A recent study using two GCMs found that light rain plays a dominant role in controlling aerosol loading. However, “too much light rain and too little heavy rain” is a longstanding bias in GCMs. It is unclear how much light rain affects aerosol-cloud-radiation interactions and health risks from air pollution. Here, in this work, we show that, with the correction of the rainfall intensity spectrum in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 by introducing a stochastic deep convection scheme, the reduced frequency of light rain (1–20 mm d -1 ) results in changes of aerosol direct radiative effects (DRE) of up to -0.5 ± 0.03 W/m 2 and aerosol cloud radiative effects (CRE) of up to -0.9 ± 0.03 W/m 2 . The total (CRE + DRE) radiative effects of light rain-mediated aerosol changes exceed the present-day anthropogenic forcing of aerosols relative to preindustrial levels from the Coupled Model Intercomparison Project (CMIP5&6) models. However, the correction of the rainfall intensity spectrum has little effect on anthropogenic aerosol forcing (defined as the radiative perturbation due to changes in aerosol concentrations between the industrial era and preindustrial levels). Due to increased exposure to fine particulates (PM 2.5 ), the estimated global total premature mortality is much higher than previously estimated, by 300,000 ± 60,000 deaths per year, and is more severe in populous regions such as India and China. The findings in this study highlight the need to understand uncertainties in radiative effects and health risks of aerosols due to simulation biases of precipitation in GCMs.
Atmospheric aerosols in global climate models (GCMs) can be removed by both large-scale and convective precipitation. As the horizontal resolution of GCMs increases, it is expected that large-scale precipitation increases and convective precipitation decreases if scale-aware convection schemes are well developed. Here, to explore its impact on aerosol burdens, this study develops a novel method and applies it to the National Center for Atmospheric Research Community Atmosphere Model version 5.3 to mimic the behavior of scale-aware convective schemes. Surprisingly, it is found that aerosol burden increases globally as convective precipitation decreases and large-scale precipitation increases, which can be attributed to the reduced light rain. In the light-rain intensity category, the decreased frequency of convective precipitation is not offset by the increased frequency of large-scale precipitation. Therefore, for aerosol simulations in high-resolution GCMs using scale-aware convection schemes, subgrid convection and large-scale condensation should be coordinated to assure light rain stays the same.
Abstract. The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.
Developments in upper atmosphere research noting atmospheric density, temperature, diurnal variations, solar and geomagnetic activity effects, etc
Developments in upper atmosphere research noting atmospheric density, temperature, diurnal variations, solar and geomagnetic activity effects
Ice clouds influence the climate system by changing the radiation budget and large-scale circulation. Therefore, climate models need to have an accurate representation of ice clouds and their radiative effects. In this paper, new broadband parameterizations for ice cloud bulk scattering properties are developed for severely roughened ice particles. The parameterizations are based on a general habit mixture that includes nine habits (droxtals, hollow/solid columns, plates, solid/hollow bullet rosettes, aggregate of solid columns, and small/large aggregates of plates). The scattering properties for these individual habits incorporate recent advances in light-scattering computations. The influence of ice particle surface roughness on the ice cloud radiative effect is determined through simulations with the Fu-Liou and the GCM version of the Rapid Radiative Transfer Model (RRTMG) codes and the National Center for Atmospheric Research Community Atmosphere Model (CAM, version 5.1). The differences in shortwave (SW) and longwave (LW) radiative effect at both the top of the atmosphere and the surface are determined for smooth and severely roughened ice particles. While the influence of particle roughening on the single-scattering properties is negligible in the LW, the results indicate that ice crystal roughness can change the SW forcing locally by more than 10 W m(exp −2) over a range of effective diameters. The global-averaged SW cloud radiative effect due to ice particle surface roughness is estimated to be roughly 1-2 W m(exp −2). The CAM results indicate that ice particle roughening can result in a large regional SW radiative effect and a small but nonnegligible increase in the global LW cloud radiative effect.
Understanding how dynamical and aerosol inputs affect the temporal variability of hydrometeor formation in climate models will help to explain sources of model diversity in cloud forcing, to provide robust comparisons with data, and, ultimately, to reduce the uncertainty in estimates of the aerosol indirect effect. This variability attribution can be done at various spatial and temporal resolutions with metrics derived from online adjoint sensitivities of droplet and crystal number to relevant inputs. Such metrics are defined and calculated from simulations using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) and the National Center for Atmospheric Research Community Atmosphere Model Version 5.1 (CAM5.1). Input updraft velocity fluctuations can explain as much as 48% of temporal variability in output ice crystal number and 61% in droplet number in GEOS-5 and up to 89% of temporal variability in output ice crystal number in CAM5.1. In both models, this vertical velocity attribution depends strongly on altitude. Despite its importance for hydrometeor formation, simulated vertical velocity distributions are rarely evaluated against observations due to the sparsity of relevant data. Coordinated effort by the atmospheric community to develop more consistent, observationally based updraft treatments will help to close this knowledge gap.
Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four-class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool-International Cloud Experiment (TWP-ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.
Research performed in the U.S. from October 1982 to May 1986 on the middle-atmosphere dynamics is reviewed. The topics discussed include extratropical large- and medium-scale waves and instabilities, gravity waves, tropical motions, and the general circulation. In addition, consideration is given to the studies of atmosphere constituent transport. A number of studies in the latter area have shed light on the specific seasonal behavior of dynamically controlled important constituents in the stratosphere, such as ozone, water vapor, and aerosols.
Atmospheric model development relating temperature, density, moisture and energy measurements from satellite observations for long term weather forecasting
Research work is discussed for the following: (1) the exploration of new H and Cl chemistry in Io's atmosphere using the already developed two-dimensional multi-species hydrodynamic model of Wong and Smyth; and (2) for the development of a new three-dimensional multi-species hydrodynamic model for Io's atmosphere.
Research projects for the period ending September 15, 1973 are reported as follows: (1) the abundances of carbon dioxide in the atmosphere, and the processes by which it is released from carbonate deposits in the earth and then transferred to organic material by photosynthesis; the pathways for movement of carbon and oxygen through the atmosphere; (2) space science computation assistance by PDP computer; the performance characteristics and user instances; (3) OGO-6 data analysis studies of the variations of nighttime ion temperature in the upper atmosphere.