Global modeling of ULF waves at Mercury
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Eun‐Hwa Kim↗
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Results for “global models”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A molecular perspective for global modeling of upper atmospheric NH 3 from freezing clouds
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Cui Ge↗
Global Atmospheric Aerosol Modeling
Global aerosol models are used to study the distribution and properties of atmospheric aerosol particles as well as their effects on clouds, atmospheric chemistry, radiation, and climate. The present article provides an overview of the basic concepts of global atmospheric aerosol modeling and shows some examples from a global aerosol simulation. Particular emphasis is placed on the simulation of aerosol particles and their effects within global climate models.
Hendricks, Johannes↗
Recent Updates to the GEOS-5 Linear Model
Global Modeling and Assimilation Office (GMAO) is close to having a working 4DVAR system and has developed a linearized version of GEOS-5.This talk outlines a series of improvements made to the linearized dynamics, physics and trajectory.Of particular interest is the development of linearized cloud microphysics, which provides the framework for 'all-sky' data assimilation.
Adjoint↗
Excessive Subsidence of the Southeast Atlantic Biomass Burning Aerosol Plume in the NASA Goddard Earth Observing System (GEOS) Model
Global models struggle to accurately represent the meteorological environment over the Southeast Atlantic Ocean, as evidenced by the poor representation of persistent marine stratocumulus decks off the west coast of southern Africa. This complex regime also experiences significant seasonal biomass burning aerosol plumes that are transported from southern Africa over the Atlantic Ocean, which gradually descend via large scale subsidence and interact radiatively and microphysically with the cloudy environment of the region. In comparisons to space borne lidar and aircraft observations we find that the NASA Goddard Earth Observing System (GEOS) model’s MERRA-2 aerosol and meteorology reanalysis shows that the aerosol plume descends too rapidly due to exaggerated large-scale downward vertical motion. This complicates the evaluation of aerosol properties utilizing aircraft observations collected throughout the ORACLES campaign as well as the model’s representation of the heating rate profiles due to the aerosol itself and its radiative interaction with the semi-permanent cloud deck typically located lower in altitude than the smoke. Two types of modeling experiments using GEOS will be presented to diagnose deficiencies in the model related to the excessive subsidence. Ensembles of free-running model simulations with and without radiatively active biomass burning aerosol will be used to quantify the impact of smoke on the atmospheric circulation. It will be demonstrated that heating within the aerosol layer results in self lofting through a reduction of the subsidence but did not alter the vertical placement or speed of the African Easterly jet. The amount of aerosol absorption required to eliminate the bias in the height of the aerosol plume through self-lofting will then be quantified. Finally, a second type of modeling experiment will be discussed in which the meteorology is constrained to observations. This will allow for biases in the height of the aerosol plume due to the general circulation model to be distinguished from biases that may be induced through the data assimilation process.
Allison Collow↗
Empirical model of global thermospheric temperature and composition based on data from the OGO-6 quadrupole mass spectrometer
An empirical global model for magnetically quiet conditions has been derived from longitudinally averaged N2, O, and He densities by means of an expansion in spherical harmonics. The data were obtained by the OGO-6 neutral mass spectrometer and cover the altitude range 400 to 600 km for the period 27 June 1969 to 13 May 1971. The accuracy of the analytical description is of the order of the experimental error for He and O and about three times experimental error for N2, thus providing a reasonable overall representation of the satellite observations. Two model schemes are used: one representing densities extrapolated to 450 km and one representing densities extrapolated to 120 km with exospheric temperatures inferred from N2 densities. Using the best fit model parameters the global thermospheric structure is presented in the form of a number of contour plots.
Hedin, A. E.↗
Using Multi-scale Modeling Systems to Study the Precipitation Processes
Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (CRM), (2) a regional scale model, (3) a coupled CRM and global model, and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer processes and the explicit cloud-radiation, and cloudland surface interactive processes are applied in this multi-scale modeling system. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the interactions between clouds, precipitation, and aerosols will be presented.
Tao, Wei-Kuo↗
Global Cloud-Resolving Models
Global cloud-resolving models (GCRMs) are a new category of atmospheric global models designed to solve different flavors of the nonhydrostatic equations through the use of kilometer-scale global meshes. GCRMs make it possible to explicitly simulate deep convection, thereby avoiding the need for cumulus parameterization and allowing for clouds to be “resolved” by microphysical models responding to grid-scale forcing. GCRMs require high-resolution discretization over the globe, for which a variety of mesh structures have been proposed and employed. The first GCRM was constructed 15 years ago, and in recent years, other groups have also begun adopting this approach, enabling the first intercomparison studies of such models. Because conventional general circulation models (GCMs) suffer from large biases associated with cumulus parameterization, GCRMs are attractive tools for researchers studying global weather and climate. In this review, GCRMs are described, with some emphasis on their historical development and the associated literature documenting their use. The advantages of GCRMs are presented, and currently existing GCRMs are listed and described. Future prospects for GCRMs are also presented in the final section.
Satoh, Masaki↗
The Effects of a 1998 Observing System Change on MERRA-2-Based Ozone Profile Simulations
Model simulations of ozone (O3) driven by meteorological reanalyses are useful for filling observational gaps and interpreting observed O3 variability and trends. However, the transport circulation of reanalysis products is impacted by changes to the observing system (the data assimilated into the reanalyses). We examine the impacts of these changes on simulated O3 from two models, Global Modeling Initiative (GMI) Chemistry Transport Model (GMI CTM) and Modern‐Era Retrospective Analysis for Research Applications version 2 (MERRA‐2) GMI Replay (M2 GMI Replay) simulation, using observations from global ozonesondes (>50,000 profiles) and satellites from 1980 to 2016. Both models are constrained by meteorology from the NASA MERRA‐2 reanalysis, and both use versions of NASA's GMI chemical mechanism. We focus on an observing system change affecting simulated O3 after 1998, associated with the assimilation of temperature and humidity data from new microwave profiling satellites. A large post‐1998 O3 increase, mainly confined to 15–20‐km altitude, of ~10 Dobson units (DU) in midlatitudes occurs in the GMI CTM, worsening the bias compared to observations. In contrast, an increase in M2 GMI Replay simulation O3 of ~10 DU is observed only near −60° latitude, reducing the bias compared to observations. The GMI CTM O3 high biases display a Quasi‐Biennial Oscillation (QBO)‐like periodicity that result from excessive transport from the tropical stratosphere to the midlatitude lower stratosphere during the QBO westerly phase. We quantify O3 discontinuities caused by MERRA‐2 observing system changes and demonstrate how the MERRA‐2 Global Modeling Initiative Replay simulation dampens the effects of these changes and QBO‐driven artifacts on simulated lower stratospheric and total O3. We caution against using simulations driven by a reanalysis to derive multidecadal O3 trends, especially prior to 1998.
Replay↗
Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information
The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.
Zobler, L.↗
Machine Learning Emulators and Empirical Models Combining Climate and Global Crop Models for Seasonal Agricultural Production
We present results from several connected efforts to apply machine learning methods to estimates of seasonal agricultural production anomalies around the world. First, we apply the XGBoost Random Forest method to fit emulators that mimic global crop models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI). These are the same models used in the agricultural sector simulations of the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). These emulators use 8 climate variables split across 5 sub-seasonal representations of the growing season for each ½ degree grid cell around the world for maize, wheat, rice and soybeans. Emulators are useful for estimating conditions that have not already been simulated by GGCMI (e.g., in a seasonal prediction model) and also to diagnose model differences and capabilities. For example, emulators of the pDSSAT maize model tend to be more reliant on mean temperatures than the LPJmL model, and few models have strong responses to cold extremes. Second, we use a similar XGBoost approach to fit empirical models for national production data for the top 20 producing countries according to the United Nations Food and Agricultural Organization (FAO). Models utilize both climate observations and the GGCM models as predictors, resulting in skillful models for many (but not all) top producing-countries. The patterns of climate and crop model features selected indicate regions and systems that are better or worse simulated by the GGCMs. For example, information in cold extreme predictors is often combined with GGCM output predictors to provide sensitivity that models may underrepresent.
machine learning↗
A Martian global groundwater model
A global groundwater flow model was constructed for Mars to study hydrologic response under a variety of scenarios, improving and extending earlier simple cross sectional models. The model is capable of treating both steady state and transient flow as well as permeability that is anisotropic in the horizontal dimensions. A single near surface confining layer may be included (representing in these simulations a coherent permafrost layer). Furthermore, in unconfined flow, locations of complete saturation and seepage are determined. The flow model assumes that groundwater gradients are sufficiently low that DuPuit conditions are satisfied and the flow component perpendicular to the ground surface is negligible. The flow equations were solved using a finite difference method employing 10 deg spacing of latitude and longitude.
Howard, Alan D.↗
Updates on Modeling the Water Cycle with the NASA Ames Mars Global Climate Model
Global Circulation Models (GCMs) have made steady progress in simulating the current Mars water cycle. It is now widely recognized that clouds are a critical component that can significantly affect the nature of the simulated water cycle. Two processes in particular are key to implementing clouds in a GCM: the microphysical processes of formation and dissipation, and their radiative effects on heating/ cooling rates. Together, these processes alter the thermal structure, change the dynamics, and regulate inter-hemispheric transport. We have made considerable progress representing these processes in the NASA Ames GCM, particularly in the presence of radiatively active water ice clouds. We present the current state of our group's water cycle modeling efforts, show results from selected simulations, highlight some of the issues, and discuss avenues for further investigation.
Global Circulation Models (GCMs)↗
A new global ionospheric model
A new global ionospheric model has been successfully implemented. The daytime portion of this model provides one-way ionospheric range corrections that compare favorably with those derived from the Mariner Venus Mercury (MVM'73) S- and X-band dual frequency doppler data. For elevation angles, γ, higher than 30 deg and solar zenith angle, X, less than 80 deg, this model provides calibrations accurate to a few centimeters. The calibrations provided by the nighttime model are also very reasonable. These, however, will have to be compared with the S/X data from Viking ‘75 for final confirmation. It is interesting to note that the daytime ionospheric calibrations derived from the current calibration scheme, DIEN/TIEN, are fairly close to those given by the new global model, especially in the temporal variations and thus the doppler effects. In the daytime and for γ ≳ 30 deg and X < 80 deg, the calibrations from the two models agree almost exactly. However, for situations when γ is below 30 deg and X is larger than 80 deg simultaneously, as much as 12% (~70 cm, typically) improvement of the new model over DIEN/TIEN results. The comparison between the nighttime model and DIEN/TIEN has been based on the one-way ionospheric range corrections for three passes near the Mariner 9 encounter with Mars in 1971 tracked at Goldstone. It is found that they can differ by over 30% (typically 20 cm). As mentioned, the confirmation of these nighttime calibrations awaits the availability of the Viking S/X dual frequency doppler data. Finally, the ionospheric calibration effects on orbit determination provided by DIEN/TIEN, the global model, and the S/X data will be intercompared during the Viking Mission.
K. W. Yip↗
Global Imaging Mission
Recent correlative observations have advanced our understanding of the solar-terrestrial environment. Among the suite of useful measurements, global auroral images have proven to be useful in guiding global models. Global auroral images provide an irreplaceable contextual measure that is required for understanding the momentum and mass transfer between the magnetosphere and ionosphere, and required for understanding their dynamic response to the solar wind. Global auroral images can provide much more than contextual information. Quantitative data, such as hemispheric power input distribution on a global scale provides needed information with regards to the dynamic nature of the magnetosphere. However, truly global images (both hemispheres simultaneously) with appropriate temporal, spectral and spatial resolution are needed in order to fully comprehend the dynamic nature of the magnetosphere. Differences in response of the two hemispheres as a function of time, space, and energy, provide needed boundary conditions for models as well as a gateway to understand the mechanism relevant to the provide a quantitative measure of the magnetosphere, A mission concept based on the need to provide a quantitative measure of the global distribution of the power input and average energy including in-situ particle measurement will be described.
Lumnierzheim, D.↗
A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols
Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-kilometer scales are resolved in horizontal domains as large as 10,000 kilometers in two dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.
Tao, Wei-Kuo↗
A New Approach to Using a Cloud-resolving Model to Study the Interactions Between Clouds, Precipitation and Aerosols
Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as l0,OOO km in two-dimensions, and 1,OOO x 1,OOO km2 in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.
Tao, Wei-Kuo↗
A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols
Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.
Tao, Wei-Kuo↗