GMI Applications

 

GMI Chemistry Transport Model Applications

The GMI Model and its simulations are used by many groups for different applications, from testing model components to understanding observations. Nearly all GMI simulations use reanalysis meteorology, e.g., MERRA2. These simulations (“Hindcasts”) credibly reproduce observations, making them ideal tools for understanding the processes that influence observations. Recent activities and a few examples are described below. See the Publications page for complete references.

  1. Attributing observed trends and variability

This is the most frequently used model application. In the past decade numerous studies have quantified stratospheric polar ozone depletion by comparing simulations with and without heterogeneous chemical reactions that occur on ice particles (i.e., PSCs). Examples are Strahan et al. (2013), Strahan et al. (2016), and Strahan et al. (2019). These studies separate the roles of chemistry and dynamics to understand processes that influencs trends in observations.

Several tropospheric studies explained factors (e.g., emissions, jet location) that control surface O3 variability and trends. See Kerr et al. (2019, 2020) and Strode et al. (2015).  Other studies used Hindcast simulations to explain trends in observed tropospheric carbon monoxide (Strode et al., 2016) and in upper tropospheric O3 sonde measurements in the southern hemisphere (Liu et al., 2016).

  1. Interpreting sparse observations (ground-based and aircraft)

Customized GMI model outputs are routinely provided to the NDACC Lidar, FTIR, Dobson, and Sonde Working Groups (WG).  These files provide chemical and meteorological fields requested by the Working Groups at the temporal and spatial resolution of their choosing.  A GMI Hindcast played a supporting role in the analysis of global column HNO3 and HCl measurements taken by the FTIR WG. This study identified transport trends in the lower stratospheric circulation (Strahan et al., 2020).

The GMI model supported the ATom DC-8 aircraft campaign by simulating trace gas distributions and reactivities [Prather et al., 2017]. Customized outputs using the spatial constraints of individual flight tracks were provided for flights during all 4 field campaigns. Specialized simulations with various processes removed (e.g., transport, clouds) were integrated to investigate the roles of transport, clouds, and photochemistry in observed distributions of reactive species. See Prather et al. (2018) and Hall et al. (2018). GMI simulations were used to interpret tropospheric chemistry and transport during the ARCTAS mission [e.g., Liang et al., 2011; Bian et al., 2013].

  1. Testing new chemistry, inputs, and modules.

While testing new inputs and modules was the original purpose of the GMI project (see ‘About GMI’ for examples and references), today testing is generally limited to updates to the chemistry mechanism, including tests of new kinetic or photolytic reaction rates, and of new reactions. This testing serves the GEOS model family, which uses the GMI stratosphere-troposphere mechanism (known as the ‘GMI mechanism’ in GEOS). GMI chemical mechanism changes are tested in the GMI CTM before implementation in GEOS. 

  1. Participating in Assessments and Model Intercomparisons

Since the time of GMI’s first assessment of supersonic aircraft impacts on composition and chemistry (Douglass et al. (1999) and Kinnison et al. (2001)), GMI models have participated in the IPCC-Assessment Report 4 (e.g., Stevenson et al. (2006) and Dentener et al. (2006)), the POLARCAT Model Intercomparison Project (POLMIP) (e.g., Emmons et al. (2015) and Arnold et al. (2015)), AeroCom (e.g., Jiao et al. (2014), Koffi et al. (2016), and Bian et al. (2017)), and most recently the Chemistry-Climate Modeling Initiative (CCMI) (e.g. Orbe et al. (2018), Nicely et al. (2017)).

  1. Providing Input for Satellite Data Retrievals

Members of the NASA Aura Ozone Monitoring Instrument (OMI) team have used NO2 profiles from GMI simulations driven with MERRA meteorology for a prioris to improve NO2 retrievals, e.g., Krotkov et al. (2017), Bucsela et al. (2013), Lamsal et al. (2014 & 2017); Choi et al., 2014].

 

Please visit the Publications page for a detailed reference list that includes GMI science from the project’s inception to the present.