Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO<sub>2</sub>

Abstract

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.

Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)

“…The estimated NEE from CTEM-CRUNCEP and CTEM-GEM was used as a surface boundary condition in GEM-MACH-GHG, which transports the signal from the surface fluxes throughout the atmosphere, to validate the resulting modeled concentrations against observations of atmospheric CO 2 . The other fluxes are kept the same as in Polavarapu et al (2016). Specifically, the anthropogenic emissions from fossil fuel burning and cement manufacturing, biomass burning, ocean-atmosphere carbon exchange, and initial atmospheric concentration (1 January 2009) are based on CT2013B (Peters et al, 2007).…”

“…For comparisons, GEM-MACH-GHG was also run using the posterior NEE fluxes from CT2013B as described in Polavarapu et al (2016). In these forward simulations, the anthropogenic emissions from fossil fuel burning and cement manufacturing, biomass burning, and ocean-atmosphere car- bon exchange are based on CT2013B (Peters et al, 2007), so that the only difference between the three runs is the terrestrial NEE fluxes.…”

“…In this study, the meteorological fields required to drive CLASS-CTEM are produced from GEM-MACH-GHG (v.107-glb) following the same approach as in Polavarapu et al (2016) for the 2009-2010 period. Prior to 22 June 2009, the operational analyses were produced using a model with a lid at 10 hPa.…”

“…GEM is used for operational weather forecasting in both global and regional (North America) domains, whereas GEM-MACH includes an online chemical model that is fully integrated into the meteorological model to provide air quality forecasts over North America. GEM-MACH-GHG (v.107-glb) (Polavarapu et al, 2016) is a variant of GEM-MACH that removes the reactive chemistry and replaces it with climate-chemistry (e.g., OH climatology). In addition, a number of modifications to GEM-MACH were made, including the implementation of a mass conservation scheme, and modifying the vertical mixing in the boundary layer.…”

“…At Environment and Climate Change Canada (ECCC), a carbon assimilation system (EC-CAS) (Polavarapu et al, 2016) is being developed by implementing an ensemble Kalman filter (EnKF) for greenhouse gas state and flux estimation. EC-CAS will assimilate satellite and in situ data to generate hindcasts of atmospheric CO 2 and estimates of regional fluxes of CO 2 .…”