I'll read the article.
"Climate scientist" doesn't necessarily sway me, no. I've been around long enough to know that scientists can be bought, and are. But, yes, it does mean something to be a certified expert in a given field.
Certainty any fossil fuel funded studies should be approached warily.
Do you wholeheartedly trust the NOAA data?
I'm not suggesting that someone being a climate scientist means you should believe everything they say about the subject. But when a group of people who AREN'T climate scientists are suggesting that the entire field of climate science is based not just bad data sets, but that all climate scientists don't know about this bad data, you should probably pause and be highly skeptical of anything they have to say. You seem to wholeheartedly trust what these guys are saying, when their financial motive is clear - why are you only skeptical of the established scientists? And don't tell me you're just as skeptical of these guys as you are of real climate scientists, because your first post on the topic pretty clearly implies that you believe what these guys are saying.
As for whether or not I trust the data, that question doesn't really make sense. Trust it in what way? The article points out that some of the NOAA temperature stations aren't in ideal locations - this isn't a grand conspiracy, its well known that every sensor isn't in a perfect spot to measure temperature in a way that the physical surrounding doesn't influence it. The study your link referred to selected a small subset of stations to review, and then extrapolated what they found to all other substations, which is how they got that 96% number - 96% of the stations they looked at had issues, so according to them that means 96% of all stations have issues. But, how did they select those stations? Because they weren't conducting a real study, we don't know - in a scientific paper, the selection methodology would be known and thoroughly discussed. What is most likely is that they specifically selected those stations because they are the stations that are known to have issues. This cherry picking the data, and its a hallmark of junk science that would be immediately caught by the peer review process, which is why they didn't go through that process.
Further, the question of whether or not the bias is relevant and thus if the data can be trusted is highly dependent on what you're doing with it. Big buildings and the concrete of a city hold more heat than vegetation, so a temperature sensor placed downtown in a city will likely read higher than one placed 30 miles outside the city in a forest or grassy field. But if all you're looking at is the year to year change, the fact that the sensor is in the city doesn't really matter - the bias of being in the city is there every year. You're not comparing the temperature reading of the city sensor to one out in a field, you're comparing it to itself yesterday, last month, last year and so on. Not only that, but when actual scientists do reviews of these 'bad' sensors, what they have frequently found is that quite often they read colder, rather than warmer because the most common issue is being placed in the shade. When you're using the temperature data to get a better big picture understanding of the global average temperature or something else that isn't a year to year comparison, scientists have methods to account for these sensors in not perfect locations.
So, the question, do I trust the data doesn't really make sense. A better question would be, do I believe that thousands of scientists all over the world missed something so incredibly obvious? That none of these thousands of Phds have any idea how to account for biases in data recording? That the whole field is a fraud because some temperature sensors were placed too close to buildings, and no one noticed or thought about it before now? That is certainly one possibility. The other possibility is that the guy who didn't even graduate college doesn't really know what he's talking about, and is arriving at a conclusion he's being paid to make.
Hm. Real brain buster trying to figure out which possibility is the more likely one.
www.theepochtimes.com
