scholarly journals Supplementary material to "An online emission module for atmospheric chemistry transport models: Implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1"

2020 ◽  
Author(s):  
Michael Jähn ◽  
Gerrit Kuhlmann ◽  
Qing Mu ◽  
Jean-Matthieu Haussaire ◽  
David Ochsner ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Models ◽  
Supplementary Material

scholarly journals On interpreting studies of tracer transport by deep cumulus convection and its effects on atmospheric chemistry

2008 ◽  
Vol 8 (20) ◽  
pp. 6037-6050 ◽  
Author(s):  
M. G. Lawrence ◽  
M. Salzmann
Keyword(s):  
Atmospheric Chemistry ◽  
Large Scale ◽  
Deep Convection ◽  
Convective Transport ◽  
Advective Transport ◽  
Tracer Transport ◽  
Transport Models ◽  
Mass Fluxes ◽  
Split Treatment ◽  
The Mean

Abstract. Global chemistry-transport models (CTMs) and chemistry-GCMs (CGCMs) generally simulate vertical tracer transport by deep convection separately from the advective transport by the mean winds, even though a component of the mean transport, for instance in the Hadley and Walker cells, occurs in deep convective updrafts. This split treatment of vertical transport has various implications for CTM simulations. In particular, it has led to a misinterpretation of several sensitivity simulations in previous studies in which the parameterized convective transport of one or more tracers is neglected. We describe this issue in terms of simulated fluxes and fractions of these fluxes representing various physical and non-physical processes. We then show that there is a significant overlap between the convective and large-scale mean advective vertical air mass fluxes in the CTM MATCH, and discuss the implications which this has for interpreting previous and future sensitivity simulations, as well as briefly noting other related implications such as numerical diffusion.

scholarly journals Model – TCCON comparisons of column-averaged methane with a focus on the stratosphere

2016 ◽  
Author(s):  
Andreas Ostler ◽  
Ralf Sussmann ◽  
Prabir K. Patra ◽  
Sander Houweling ◽  
Marko De Bruine ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Meteorological Data ◽  
Michelson Interferometer ◽  
Total Carbon ◽  
Data Sets ◽  
Transport Models ◽  
Improved Model ◽  
The Impact ◽  
Chemistry Experiment

Abstract. The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model-TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. In comparison to simulated model fields we find an improved model-TCCON XCH4 agreement for all models with MIPAS-based stratospheric CH4 fields. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 ppb to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 ppb to 4.3 ppb), and LMDz (Laboratoire de Météorologie Dynamique model with Zooming capability; from 6.8 ppb to 4.3 ppb), respectively. MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduce the average XCH4 bias for ACTM (3.3 ppb), but increase the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that the range of satellite-based stratospheric CH4 is insufficient to resolve a possible stratospheric contribution to differences in total column CH4 between TCCON and TM5 or LMDz. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. This implies that there is a need to better understand the impact of individual model transport components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) on modeled stratospheric CH4.

scholarly journals An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1

2020 ◽  
Vol 13 (5) ◽  
pp. 2379-2392 ◽  
Author(s):  
Michael Jähn ◽  
Gerrit Kuhlmann ◽  
Qing Mu ◽  
Jean-Matthieu Haussaire ◽  
David Ochsner ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Atmospheric Transport ◽  
Model Simulation ◽  
Vertical Scaling ◽  
Transport Models ◽  
Input Size ◽  
Modeling Systems ◽  
Fortran 90 ◽  
Processing Steps ◽  
Python Package

Abstract. Emission inventories serve as crucial input for atmospheric chemistry transport models. To make them usable for a model simulation, they have to be pre-processed and, traditionally, provided as input files at discrete model time steps. In this paper, we present an “online” approach, which produces a minimal number of input data read-in at the beginning of a simulation and which handles essential processing steps online during the simulation. For this purpose, a stand-alone Python package “emiproc” was developed, which projects the inventory data to the model grid and generates temporal and vertical scaling profiles for individual emission categories. The package is also able to produce “offline” emission files if desired. Furthermore, we outline the concept of the online emission module (written in Fortran 90) and demonstrate its implementation in two different atmospheric transport models: COSMO-GHG and COSMO-ART. Simulation results from both modeling systems show the equivalence of the online and offline procedure. While the model run time is very similar for both approaches, input size and pre-processing time are greatly reduced when online emissions are utilized.

Supplementary material to "Accelerating simulations using Direct Relation Graphs for atmospheric chemistry mechanism reduction"

2018 ◽  
Author(s):  
Zacharias Marinou Nikolaou ◽  
Jyh-Yuan Chen ◽  
Yiannis Proestos ◽  
Johannes Lelieveld ◽  
Rolph Sander
Keyword(s):  
Atmospheric Chemistry ◽  
Direct Relation ◽  
Mechanism Reduction ◽  
Supplementary Material

Supplementary material to "The atmospheric chemistry box model CAABA/MECCA-4.0gmdd"

2018 ◽  
Author(s):  
Rolf Sander ◽  
Andreas Baumgaertner ◽  
David Cabrera ◽  
Franziska Frank ◽  
Jens-Uwe Grooß ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Box Model ◽  
Supplementary Material

scholarly journals Supplementary material to "Atmospheric Chemistry, Sources, and Sinks of Carbon Suboxide, C<sub>3</sub>O<sub>2</sub>"

2017 ◽  
Author(s):  
Stephan Keßel ◽  
David Cabrera-Perez ◽  
Abraham Horowitz ◽  
Patrick R. Veres ◽  
Rolf Sander ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Carbon Suboxide ◽  
Sources And Sinks ◽  
Supplementary Material

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

2021 ◽  
Vol 14 (8) ◽  
pp. 5331-5354
Author(s):  
Antoine Berchet ◽  
Espen Sollum ◽  
Rona L. Thompson ◽  
Isabelle Pison ◽  
Joël Thanwerdas ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Critical Role ◽  
Optimization Methods ◽  
Reactive Species ◽  
Research Community ◽  
Surface Fluxes ◽  
Bayesian Optimization ◽  
Transport Models ◽  
Area Of Interest ◽  
Atmospheric Inversion

Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

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