19th International CODATA Conference
Category: Knowledge Discovery

Key Aspects Of Data And Information For Climate Change Science

Alexander Sterin, Ph.D. (STERIN@METEO.RU)
Russian Research Institute for Hydrometeorological Information – World Data Center, Russia

The paper mentions several aspects related to methodology of the observed data processing and analysis, when the final goal is to obtain new knowledge of climate changes. This subject is of key interest, in connection with the problem of global warming. It is not limited by purely research aspects, but is significant for the decisions in social, economical spheres, as well as for politics.

The possible strategies of constructing informational products based on observed data, will be considered.

The ways how to process and to analyze data, will be discussed, in particular:

1. which statistics to use, traditional or robust, for simple processing? The robust statistics is a good instrument to eliminate the effects of outliers in climate-related statistical calculations. On the other hand, one should be careful in using them, as soon as he studies climatology of extremal events.
2. how to generalize data, to obtain large-scale products for climate studies?
3. the data quality problems (flagging data values, chance to make “step back” in data quality check)
4. how to calculate trends in climate series, (Least Squares Method versus alternative techniques in trend calculations, trend sensitivity problems and examples, the phenomenon of trend differences in temperature for surface and in troposphere)
5. the inhomogeneities in climate data (are the “corrected” series really correct, and “uncorrected” series really incorrect? – possible approaches and some samples)
6. how to compare time series, obtained by various groups, and is it possible to eliminate the uncertainty in large-scale climate signals, using the ensembles of independent climate products? An ensemble approach to large scale climate signals estimation.

The ten plus one principles of climate monitoring, which must be taken as key strategy of empirical climate analysis and requirements to climate data, will be listed and commented.