Towards a Chemical Inverse Modeling System Experiment: First Results on CO Inter-comparison
Abstract
In recent years, five independent chemical reanalyses spanning across the recent decade have been reported from leading data assimilation and inverse modeling groups. These represent a range of modeling and assimilation approaches, assimilating different sets of chemical observations, and in several cases updating emission estimates from various bottom-up inventories. This provides an unprecedented dataset to understand and characterize the uncertainties on the inferred estimates and their trends. It allows identifying biases related to representativeness and model errors, that cannot be fully address with a single modeling system and traditional evaluation diagnostics.
Here, we present an assessment of the state of CO in the past decade (2005-2015) as inferred from these reanalyses using available ground-, aircraft- and satellite-based observations of CO. In particular, we present the ensemble statistics (e.g., mean and spread) on the spatial and temporal patterns of CO abundance and associated emissions at megacity to global scales. This serves as a step towards an inter-comparison project on current chemical data assimilation/inverse modeling systems (CDAs) for reactive gases similar to what has been done for greenhouse gases within the TransCom experiments for CO2 and CH4. The relevance of this activity will be discussed within the context of the new IGAC project (Analysis of eMIssions usinG Observations or AMIGO) which is geared towards an assessment of current and past research using observation-based analysis techniques to better quantify emissions.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A11F2280A
- Keywords:
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- 0322 Constituent sources and sinks;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 1910 Data assimilation;
- integration and fusion;
- INFORMATICSDE: 1986 Statistical methods: Inferential;
- INFORMATICSDE: 3260 Inverse theory;
- MATHEMATICAL GEOPHYSICS