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OsloCTM3 is a global, 3D chemical transport model. It is maintained by CICERO Center for International Climate Research and is also used and developed at the University of Oslo.

OsloCTM3 is partly based on the University of California, Irvine (UCI) CTM and is an update of OsloCTM2. The development of OsloCTM began already in the 1990s at the University of Oslo Department of Geoscience and the model has since evolved in complexity. Today it represents the chemistry of the troposphere and stratosphere, as well as the main climate-relevant aerosols, of both anthropogenic and natural origin.

The model has been used in a large number of studies and peer-reviewed publications, and has participated in several multi-model intercomparisons, including AeroCom and the Task Force on Hemispheric Transport of Air Pollution (HTAP2) conducted under the auspices of the UNECE Convention on Long-range Transboundary Air Pollution.

See menu for details about the model code, required input data, and how to become a user of OsloCTM3.

What is a chemical transport model?

Knowledge about the abundance and distributions of chemical and particles in the atmosphere is essential for a number of applications – from assessing air quality to understanding climate change.

Concentrations of chemicals and particles in the atmosphere are affected by four general types of processes: emissions, chemistry, transport, and deposition. Chemical transport models (CTMs) are a class of numerical models that simulate the composition of the atmosphere in time and space based on mathematical descriptions of our current understanding of the processes involved.

Within atmospheric chemistry and climate research, CTMs have a wide range of applications. They are tools to interpret atmospheric observations and understand the underlying processes of changes in the atmospheric composition. They provide source – receptor relationships to understand how pollution in one region can be affected by emissions in another. They can be used to quantify how emissions from different human activities contribute to local pollution levels, and hence where and which mitigation measures should be implemented. They enable researchers to study the possible effects of future emission changes using different scenarios. They can help identify knowledge gaps and prior knowledge for remote sensing and measurements, and give a consistency check between different types of observations of the atmosphere. They can also be used to constrain surface fluxes using concentration measurements (inverse modeling) or integrate many observations to improve the description of the atmosphere (data assimilation).

CTMs differ from general circulation models, Earth System models and regional climate models in that they do not simulate the atmospheric dynamics. Instead, they use meteorological data as input to drive them. Commonly, meteorological data from reanalysis products is used. Reanalysis gives a description of past weather and climate variables, such as temperature, wind, pressure and clouds, and is produced by combining models with observations. CTMs can also be driven by meteorological data from a climate model, for instance to study how the atmospheric chemistry functions in a warmer climate.

A CTM gives the user output of atmospheric concentrations. Because it is not a free running, coupled model, it cannot be used to study the climate impact, such as changes in temperature due to a change in concentration of a chemical. However, concentrations are often used as input to so-called radiative transfer models to quantify the effect of changes in the atmosphere on the Earth’s energy balance, as a first-order estimate of the climate impact.

 

Flight  tracks of various flight campaigns overlaid on annual mean BC burden in 2010 simulated by OsloCTM3. Figure 1 of Lund, M. T., Samset, B. H., Skeie, R. B., Watson-Parris, D., Katich, J. M., Schwarz, J. P., and Weinzierl, B.: Short Black Carbon lifetime inferred from a global set of aircraft observations, npj Climate and Atmospheric Science, 1,31, 10.1038/s41612-018-0040-x, 2018.