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The Role of Data Reliability on Climate Change Studies

Authors:
[ Jahangiri ] -
[ Rahimzadeh ] -

Abstract:

In a usual climate atlas, the reader finds average charts of various climate elements (temperature, precipitation, etc) or charts of any climate classification that again represent average aspects. Thus the impression arises that climate, in contrast to weather, is more or loss constant, Even the reference period (time interval that is covered by the climatic data) is often not specified, In reality, climate varies on all scales of time and space. It is not easy to detect climate change statistically. The study of climate change detection is accomplished based on meteorological measurements. These measurements imply errors or uncertainly ranges, respectively, and even without errors the variation behavior of climate is so complicated that proper statistical methods are needed take into account the various aspects of climate variability and to separate the various components of climate variation. Change of instruments (with different systematic errors) and change of environment conditions (changing vegetation in the neighborhood of station, formation of buildings, etc) and also displacement of station (e. g. from the center to the surrounding of a city) is caused inhomogeneity at the observational climatic time series. Inhomogeneity means that any observational climatic time time series at any station imply variations that have nothing to do with climate itself Often such inhomogeneities appear in the form of sudden jumps of time series data values. In this case they are easily detectable, especially if neighbor stations do not show this jump. In other cases this detection is difficult. We needed long-term high quality and correct statistical data in meteorological stations. These stations should be standard necessarily; in other words, data used in climate change studies must be reliable. For these reasons World Meteorological Organization emphasizes to collect metadata WCAP, No. 3, 19881. From the climate change of review, if these problems are not considered, conclusions are not correct. For example displacement of Khoramabad and Kerman stations imply a false negative trend for temperature data. Change of environment conditions and formation of buildings in the neighborhood of Anzali synoptic station and construction of a highway around Tabriz synoptic station created inhomogeneity in time series of meteorological parameters. It is appropriate to eliminate some meteorological data produced some stations because it is possible to get results incorrectly.

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