Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2
| Autor: | Jean de Grandpré, Saroja Polavarapu, Sébastien Roche, Claude Girard, Kimberly Strong, Monique Tanguay, Douglas Chan, K. Semeniuk, Michael Neish, Sylvie Gravel, Shuzhan Ren |
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| Rok vydání: | 2016 |
| Předmět: |
Convection
Atmospheric Science 010504 meteorology & atmospheric sciences Climate change Forcing (mathematics) 010502 geochemistry & geophysics 01 natural sciences Climatology Greenhouse gas Spatial ecology Environmental science Errors-in-variables models Predictability Stratosphere Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences |
| Zdroj: | Atmospheric Chemistry and Physics. 16:12005-12038 |
| ISSN: | 1680-7324 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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