de Ars Technica 
In order to improve long-term predictions of global climate change, we need more information about the current and changing environment. Unfortunately, in the current era of government budget problems, expensive satellite climate studies are being cut, so it is important to identify the measurements we need the most, choosing among things like air temperature, pressure, humidity, radiance at various wavelengths, radiation transfer to and from the surface, etc.
One possible way of prioritizing is to figure out which of those measures would help us the most when it comes to projecting future climate change, and focus research funds there. A paper that recently appeared in the Proceedings of the National Academies of Science presents a statistical method for doing this and shows that surface temperature measurements may not be the most useful data to improve surface temperature predictions.
In order to improve long-term predictions of global climate change, we need more information about the current and changing environment. Unfortunately, in the current era of government budget problems, expensive satellite climate studies are being cut, so it is important to identify the measurements we need the most, choosing among things like air temperature, pressure, humidity, radiance at various wavelengths, radiation transfer to and from the surface, etc.
One possible way of prioritizing is to figure out which of those measures would help us the most when it comes to projecting future climate change, and focus research funds there. A paper that recently appeared in the Proceedings of the National Academies of Science presents a statistical method for doing this and shows that surface temperature measurements may not be the most useful data to improve surface temperature predictions.
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