Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO<sub>2</sub>
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 CO<sub>2</sub>, the CO<sub>2</sub> simulations compa...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-09-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/16/12005/2016/acp-16-12005-2016.pdf |
| Summary: | 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
CO<sub>2</sub>, the CO<sub>2</sub> 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 CO<sub>2</sub>. Predictability concerns the quantification of
model forecast errors and thus of transport model errors. CO<sub>2</sub>
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 CO<sub>2</sub> predictability.
The predictability of CO<sub>2</sub> is shorter than that of the temperature field
and increases near the surface and in the lower stratosphere. When broken
down into spatial scales, CO<sub>2</sub> 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 CO<sub>2</sub> field.
This is a newly identified source of uncertainty in CO<sub>2</sub> 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 CO<sub>2</sub> 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. |
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| ISSN: | 1680-7316 1680-7324 |