Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 16, 12005-12038, 2016
https://doi.org/10.5194/acp-16-12005-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
26 Sep 2016
Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2
Saroja M. Polavarapu1, Michael Neish1, Monique Tanguay2, Claude Girard2, Jean de Grandpré3, Kirill Semeniuk3, Sylvie Gravel3, Shuzhan Ren4, Sébastien Roche5, Douglas Chan1, and Kimberly Strong5 1Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, M3H 5T4, Canada
2Meteorological Research Division, Environment and Climate Change Canada, Dorval, Québec, H9P 1J3, Canada
3Air Quality Research Division, Environment and Climate Change Canada, Dorval, Québec, H9P 1J3, Canada
4Air Quality Research Division, Environment and Climate Change Canada, Toronto, Ontario, M3H 5T4, Canada
5Dept. of Physics, University of Toronto, Toronto, Ontario, M5S 1A7, Canada
Abstract. A new model for greenhouse gas transport has been developed based on Environment and Climate Change Canada's operational weather and environmental prediction models. When provided with realistic posterior fluxes for CO2, the CO2 simulations compare well to NOAA's CarbonTracker fields and to near-surface continuous measurements, columns from the Total Carbon Column Observing Network (TCCON) and NOAA aircraft profiles. This coupled meteorological and tracer transport model is used to study the predictability of CO2. Predictability concerns the quantification of model forecast errors and thus of transport model errors. CO2 predictions are used to compute model–data mismatches when solving flux inversion problems and the quality of such predictions is a major concern. Here, the loss of meteorological predictability due to uncertain meteorological initial conditions is shown to impact CO2 predictability. The predictability of CO2 is shorter than that of the temperature field and increases near the surface and in the lower stratosphere. When broken down into spatial scales, CO2 predictability at the very largest scales is mainly due to surface fluxes but there is also some sensitivity to the land and ocean surface forcing of meteorological fields. The predictability due to the land and ocean surface is most evident in boreal summer when biospheric uptake produces large spatial gradients in the CO2 field. This is a newly identified source of uncertainty in CO2 predictions but it is expected to be much less significant than uncertainties in fluxes. However, it serves as an upper limit for the more important source of transport error and loss of predictability, which is due to uncertain meteorological analyses. By isolating this component of transport error, it is demonstrated that CO2 can only be defined on large spatial scales due to the presence of meteorological uncertainty. Thus, for a given model, there is a spatial scale below which fluxes cannot be inferred simply due to the fact that meteorological analyses are imperfect. These unresolved spatial scales correspond to small scales near the surface but increase with altitude. By isolating other components of transport error, the largest or limiting error can be identified. For example, a model error due to the lack of convective tracer transport was found to impact transport error on the very largest (wavenumbers less than 5) spatial scales. Thus for wavenumbers greater than 5, transport model error due to meteorological analysis uncertainty is more important for our model than the lack of convective tracer transport.

Citation: Polavarapu, S. M., Neish, M., Tanguay, M., Girard, C., de Grandpré, J., Semeniuk, K., Gravel, S., Ren, S., Roche, S., Chan, D., and Strong, K.: Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2, Atmos. Chem. Phys., 16, 12005-12038, https://doi.org/10.5194/acp-16-12005-2016, 2016.
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Short summary
CO2 predictions are used to compute model–data mismatches when estimating surfaces fluxes using atmospheric observations together with an atmospheric transport model. By isolating the component of transport error which is due to uncertain meteorological analyses, it is demonstrated that CO2 can only be defined on large spatial scales. Thus, there is a spatial scale below which we cannot infer fluxes simply due to the fact that meteorological analyes are imperfect.
CO2 predictions are used to compute model–data mismatches when estimating surfaces fluxes using...
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