DATA RELIABILITY

Data reliability involves the determination of the extent to which the data sample can address the question. For data samples widely distributed over time, one can raise the issue of the reliability of data taken with older instruments.

Data reliability is an issue when you can not measure a phenomena directly but instead you have to use some tracer.

For example:

What is the best way to measure the extent of urban air pollution?

• Measure daily pollutants combined with some circulation model?
• Set up many measuring stations around the city? costly
• Use Lichen ?

What is the best way to measure acid rain?

• Direct Measure of ph in Rain at various sites? this depends on many factors, some natural
• Use the New Brunswick Data ?
• Measure SO_2 output directly; Global patterns ; More recent annual data

• Examine effect on certain kinds of trees -e.g. sugar maples in NE USA
• Measure soil acidity?
• Measure the rate of degradation of historic monuments?

Another good example of the issue of data reliability is evidence for Global Warming:

Issues to raise:

• Is the determination of the world's average annual temperature as a function of time the correct diagnostic for showing global warming?

• Is the Northern Hemisphere Data more reliable than the Southern Hemisphere Data? Is it a better diagnostic.

• Are measurements made 100 years ago as reliable as measurements made now?

• Are the thermometers used located in the correct areas?

• Are their enough individual measurements to reliably establish what the global mean temperature is?

Let's see the data:

Here is some Global Data whose reliability is high. There is no doubt that CO_2 in the atmosphere is increasing and is now at unprecedented levels

Methane concentration from ice core data!

This shows strong exponential growth in the last 100 years or so - consistent with growth in the world's population.

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