Global Modeling Initiative assessment model: Model description, integration, and testing of the transport shell

Rotman, D.; Tannahill, J.; Kinnison, D. E.; Connell, Peter S.; Bergmann, Dan; Proctor, Deanne D.; Rodríguez, José M.; Lin, Shian‐Jiann; Rood, Richard B.; Prather, Michael J.; Rasch, Philip J.; Considine, David B.; Ramaroson, R.; Kawa, S. R.

doi:10.1029/2000jd900463

Cited by 86 publications

(68 citation statements)

References 40 publications

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“…(Figs. 3b, 4b) which is consistent with Rissman et al (2004), who also found that the sensitivity ratio…”

Section: Sensitivity Of Global Cdnc Distribution To Input Parameterssupporting

confidence: 80%

“…For example, Rissman et al (2004) used a droplet formation parameterization to derive analytical expressions of the sensitivity of CDNC to aerosol and dynamical parameters. These expressions were then used to compare the sensitivity of droplet number to aerosol chemical composition, updraft velocity and size distribution parameters.…”

Section: A Karydis Et Al: Adjoint Sensitivity Of Cdncmentioning

confidence: 99%

“…The GMI 3-D global CTM (http://gmi.gsfc.nasa.gov) was originally described by Rotman et al (2001) and Considine et al (2005). The aerosol module used in this study includes primary emissions, gas phase and aqueous phase chemistry with one hour time step, wet and dry deposition, and hygroscopic growth .…”

Section: The Nasa Global Modeling Initiative (Gmi)mentioning

confidence: 99%

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Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

et al. 2012

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Abstract. We present the development of the adjoint of a comprehensive cloud droplet formation parameterization for use in aerosol-cloud-climate interaction studies. The adjoint efficiently and accurately calculates the sensitivity of cloud droplet number concentration (CDNC) to all parameterization inputs (e.g., updraft velocity, water uptake coefficient, aerosol number and hygroscopicity) with a single execution. The adjoint is then integrated within three dimensional (3-D) aerosol modeling frameworks to quantify the sensitivity of CDNC formation globally to each parameter. Sensitivities are computed for year-long executions of the NASA Global Modeling Initiative (GMI) Chemical Transport Model (CTM), using wind fields computed with the Goddard Institute for Space Studies (GISS) Global Circulation Model (GCM) II , and the GEOS-Chem CTM, driven by meteorological input from the Goddard Earth Observing System (GEOS) of the NASA Global Modeling and Assimilation Office (GMAO). We find that over polluted (pristine) areas, CDNC is more sensitive to updraft velocity and uptake coefficient (aerosol number and hygroscopicity). Over the oceans of the Northern Hemisphere, addition of anthropogenic or biomass burning aerosol is predicted to increase CDNC in contrast to coarse-mode sea salt which tends to decrease CDNC. Over the Southern Oceans, CDNC is most sensitive to sea salt, which is the main aerosol component of the region. Globally, CDNC is predicted to be less sensitive to changes in the hygroscopicity of the aerosols than in their concentration with the exception of dust where CDNC is very sensitive to particle hydrophilicity over arid areas. Regionally, the sensitivities differ considerably between the two frameworks and quantitatively reveal why the models differ considerably in their indirect forcing estimates.

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“…(Figs. 3b, 4b) which is consistent with Rissman et al (2004), who also found that the sensitivity ratio…”

Section: Sensitivity Of Global Cdnc Distribution To Input Parameterssupporting

confidence: 80%

Section: A Karydis Et Al: Adjoint Sensitivity Of Cdncmentioning

confidence: 99%

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Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

et al. 2012

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“…Better performance of the P86 algorithm comes at a cost because this scheme requires higher memory and computational resources than the other three. Additional moments needed by the P86 scheme increase the number of variables that need to be stored by a factor of 3 for onedimensional advection and factors of 6 and 10 for twoand three-dimensional problems, respectively (P86; Rotman et al 2001). The amount of additional computations also varies depending on the application, but for a two-dimensional rotation test the algorithm was shown to be about 2 times slower than the W00 or B92 algorithms (W00).…”

Section: A One-dimensional Advection Testsmentioning

confidence: 99%

Nonlinear Advection Algorithms Applied to Interrelated Tracers: Errors and Implications for Modeling Aerosol–Cloud Interactions

Ovtchinnikov

Easter

2009

Monthly Weather Review

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Monotonicity constraints and gradient-preserving flux corrections employed by many advection algorithms used in atmospheric models make these algorithms nonlinear. Consequently, any relations among model variables transported separately are not necessarily preserved in such models. These errors cannot be revealed by traditional algorithm testing based on advection of a single tracer. New types of tests are developed and conducted to evaluate the monotonicity of a sum of several number mixing ratios advected independently of each other-as is the case, for example, in models using bin or sectional representations of aerosol or cloud particle size distributions. The tests show that when three tracers with an initially constant sum are advected separately in one-dimensional constant velocity flow, local errors in their sum can be on the order of 10%. When cloudlike interactions are allowed among the tracers in the idealized ''cloud base'' test, errors in the sum of three mixing ratios can reach 30%. Several approaches to eliminate the error are suggested, all based on advecting the sum as a separate variable and then using it to normalize the sum of the individual tracers' mixing ratios or fluxes. A simple scalar normalization ensures the monotonicity of the total number mixing ratio and positive definiteness of the variables, but the monotonicity of individual tracers is no longer maintained. More involved flux normalization procedures are developed for the flux-based advection algorithms to maintain the monotonicity for individual scalars and their sum.

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“…The OMI NO 2 SCDs are converted to vertical column densities (VCDs) by using the 10 concept of air mass factor (AMF) (Solomon et al, 1987). The AMFs are calculated based on NO 2 and temperature profiles derived from the Global Modeling Initiative (GMI) chemistry transport model simulations with a horizontal resolution of 1 (Rotman et al, 2001). Separation of stratospheric and tropospheric columns is achieved by the local analysis of the stratospheric field over unpolluted areas (Bucsela et al, 2013).…”

mentioning

confidence: 99%

Analysis of spatial and temporal patterns of on-road NO<sub>2</sub> concentrations in Hong Kong

Zhu¹,

Chan²,

Lam³

et al. 2018

Preprint

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Abstract. In this paper we present an investigation of the spatial and temporal variability of street level concentrations of NO 2 in Hong Kong as an example for a densely populated megacity with heavy traffic. For the study we use a combination of open path remote sensing and in-situ measurement techniques that allows us to separate temporal changes and spatial patterns and analyse them separately. Two measurement campaigns have been conducted, one in December 2010 and one in March 2017. Each campaign lasted for a week which allowed us to examine diurnal cycles, weekly patterns as well as spatially 5 resolved long term changes. We combined a long-path Differential Optical Absorption Spectroscopy (DOAS) instrument with a cavity enhanced DOAS and applied several normalizations to the data sets in order to make the different measurement routes comparable. For the analysis of long term changes we used the entire unfiltered data set, for the comparison of spatial patterns we filtered out the accumulation of NO 2 when stopping at traffic lights for focusing on the changes of NO 2 spatial distribution instead of comparing traffic flow patterns, and for the generation of composite maps the diurnal cycle has been normalized by 10 scaling the mobile data with coinciding citywide path-averaged measurement results.An overall descending trend from 2010 to 2017 could be observed, consistent with the observations of the ozone monitoring instrument (OMI) and the Environment Protection Department (EPD) air quality monitoring network data. However, long term difference maps show pronounced spatial structures with some areas, e.g. around subway stations, revealing an increasing trend. We could also show, that the weekend effect, which for the most part of Hong Kong shows reduced NO 2 concentrations 15 on Sundays and to a lesser degree on Saturdays, is reversed around shopping malls. Our study shows that the spatial differences have to be considered when discussing city-wide trends and can be used to put local point measurements into perspective. The resulting data set provides a better insight into on-road NO 2 characteristics in Hong Kong which helps to identify heavily polluted areas and represents a useful database for urban planning and the design of pollution control measures.

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Global Modeling Initiative assessment model: Model description, integration, and testing of the transport shell

Cited by 86 publications

References 40 publications

Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

Nonlinear Advection Algorithms Applied to Interrelated Tracers: Errors and Implications for Modeling Aerosol–Cloud Interactions

Analysis of spatial and temporal patterns of on-road NO<sub>2</sub> concentrations in Hong Kong

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Global Modeling Initiative assessment model: Model description, integration, and testing of the transport shell

Cited by 86 publications

References 40 publications

Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

Nonlinear Advection Algorithms Applied to Interrelated Tracers: Errors and Implications for Modeling Aerosol–Cloud Interactions

Analysis of spatial and temporal patterns of on-road NO&lt;sub&gt;2&lt;/sub&gt; concentrations in Hong Kong

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Analysis of spatial and temporal patterns of on-road NO<sub>2</sub> concentrations in Hong Kong