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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. [ABSTRACT FROM AUTHOR]

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 atmospheric modulation of CO2 transport. The predictability of CO2 due to initial state sensitivity is shorter than that for the temperature field but is consistent with the predictability of the wind fields. However, when broken down into spatial scales, CO2 has predictability at the very largest scales due to long time scale memory in surface CO2 fluxes as well as in 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. Predictability errors provide an upper limit for errors arising solely from the use of uncertain meteorological analyses. When considering meteorological analysis errors, CO2 can be defined only on large scales. Thus, there is a spatial scale below which information cannot be obtained simply due to the fact that meteorological analyses are imperfect. Compared to the spatial scales resolvable in the context of imperfect atmospheric analyses, the differences between two sets of posterior fluxes are resolvable only for very large scales. Similarly, the impact of convective tracer transport exceeds that due to atmospheric analysis errors for only the largest spatial scales. [ABSTRACT FROM AUTHOR]

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 atmospheric modulation of CO2 transport. The predictability of CO2 due to initial state sensitivity is shorter than that for the temperature field but is consistent with the predictability of the wind fields. However, when broken down into spatial scales, CO2 has predictability at the very largest scales due to long time scale memory in surface CO2 fluxes as well as in 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. Predictability errors provide an upper limit for errors arising solely from the use of uncertain meteorological analyses. When considering meteorological analysis errors, CO2 can be defined only on large scales. Thus, there is a spatial scale below which information cannot be obtained simply due to the fact that meteorological analyses are imperfect. Compared to the spatial scales resolvable in the context of imperfect atmospheric analyses, the differences between two sets of posterior fluxes are resolvable only for very large scales. Similarly, the impact of convective tracer transport exceeds that due to atmospheric analysis errors for only the largest spatial scales. [ABSTRACT FROM AUTHOR]