G1.10

G1.10 Insufficiently traceable uncertainty estimates

Gap detailed description

Limited availability of traceable uncertainty estimates propagates to applications that use model or reanalysis fields. While a vast amount of data are available, the uncertainty of such data is - in a metrological sense - often only insufficiently specified, estimated or even unknown, which frequently limits the accuracy and thus the strict interpretation and use of atmospheric measurements. This concern has been raised also by the NMIs participating in atmospheric networks (e.g. METEOMET). In order to achieve progress it is critical to have data records that are stable over time, insensitive to the method of measurement, uniformly processed worldwide, and based on traceable references. This will allow us to establish the robust scientific basis for using such fields as a transfer standard in satellite dataset characterization and other activities, and for assessing the cost-effectiveness of potential observing system enhancements.
Benefits will be logical rigour, reduction in ambiguity and better communication. A more informed use of data generated might allow large improvement in the accuracy of climate data records and might also allow to use a few satellites as reference data for calibration of models and re-analysis systems but, at present, potential users have low knowledge about the relative qualities of alternative datasets.

Activities within GAIA-CLIM related to this gap

GAIA-CLIM WP2, starting from the Posited system of systems approach to observing system maturity arising from Task 1.1, will define reference quality measurement capabilities for instruments in reference quality networks and sub-orbital (sonde and airborne) measurement capabilities currently lacking full traceability.

Gap remedy(s)

Remedy

Specific remedy proposed

This gap requires improvements in the operational and research observing systems, addressed by GAIA-CLIM for several techniques (e.g. lidar, FTIR, microwave radiometer) in WP2, but also a better characterization of model-based & assimilation-based uncertainty, initiated by GAIA-CLIM in WP4.

Measurable outcome of success

Application of the GAIA-CLIM recommendations and its operational implementation in the networks will be the obvious measurable outcome of success.

Achievable outcomes

Technological / organizational viability: medium.
Indicative cost estimate: low (<1 million).

Relevance

GAIA-CLIM work will establish the premises to solve this gap, but will not be able to address it operationally because this is a task that each network must undertake by fully exploiting the recommendations provided within GAIA-CLIM WP2.

Timebound

A long term strategy, with a moderately low cost, is needed and likely more studies need to performed in future to improve the model performance through the data assimilation.

Gap risks to non-resolution