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Koch Brothers pay to show global warming is real
Topic Started: Oct 31 2011, 08:01 PM (4,895 Views)
Chris
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BTW, can I get a link to the published source of these graphs?
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No, you are posting an INTERPRETATION by a political organization with no credentials in the field, not the actual data from the study itself by qualified scientists. There is a huge difference.
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Quote:
 
BTW, can I get a link to the published source of these graphs?


You most certainly can. You will notice its the website of the organization doing the studies this whole thread is about.

Berkley Earth Group
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Chris
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telcoman
Nov 13 2011, 08:13 AM
No, you are posting an INTERPRETATION by a political organization with no credentials in the field, not the actual data from the study itself by qualified scientists. There is a huge difference.
I'm posting that because it shows how your graph omits data.

The graph itself is not important for the very reason it is an interpretation--just as your graph is.

What's important is the missing data. Why do you and bruce post graphs omitting that data?
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Chris
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telcoman
Nov 13 2011, 08:15 AM
Quote:
 
BTW, can I get a link to the published source of these graphs?


You most certainly can. You will notice its the website of the organization doing the studies this whole thread is about.

Berkley Earth Group
Thanks. The reason I asked for publication is I was looking for the date it was published. That might account for the missing data. Clearly the graphs omit data they have continued to collect.

To me the missing data is the elephant in the room here that no one wants to address.
Edited by Chris, Nov 13 2011, 08:25 AM.
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What data, The Berkeley group created graphs based on their data. There was tons of it and it was an amalgam and average of several data & statistical sources.

I quote:

The most important indicator of global warming, by far, is the land and sea surface temperature record. This has been criticized in several ways, including the choice of stations and the methods for correcting systematic errors. The Berkeley Earth Surface Temperature study sets out to to do a new analysis of the surface temperature record in a rigorous manner that addresses this criticism. We are using over 39,000 unique stations, which is more than five times the 7,280 stations found in the Global Historical Climatology Network Monthly data set (GHCN-M) that has served as the focus of many climate studies.

Our aim is to resolve current criticism of the former temperature analyses, and to prepare an open record that will allow rapid response to further criticism or suggestions. Our results include not only our best estimate for the global temperature change, but estimates of the uncertainties in the record.


They stated that was what they were doing from the very start. Are you accusing them of cooking the books? If they were doing that they would not have published the data to start with. Your graphs appear to be produced by people with political interests, cherry picking the bits they want, & ignoring the rest. It seems to be that your source is the very thing, and contributing to the very problem, that this study was trying to address
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BTW the study was this year, it is a preliminary release.

The Berkeley Earth Surface Temperature Study has created a preliminary merged data set by combining 1.6 billion temperature reports from 15 preexisting data archives.
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Chris
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telcoman
Nov 13 2011, 08:25 AM
What data, The Berkeley group created graphs based on their data. There was tons of it and it was an amalgam and average of several data & statistical sources.

I quote:

The most important indicator of global warming, by far, is the land and sea surface temperature record. This has been criticized in several ways, including the choice of stations and the methods for correcting systematic errors. The Berkeley Earth Surface Temperature study sets out to to do a new analysis of the surface temperature record in a rigorous manner that addresses this criticism. We are using over 39,000 unique stations, which is more than five times the 7,280 stations found in the Global Historical Climatology Network Monthly data set (GHCN-M) that has served as the focus of many climate studies.

Our aim is to resolve current criticism of the former temperature analyses, and to prepare an open record that will allow rapid response to further criticism or suggestions. Our results include not only our best estimate for the global temperature change, but estimates of the uncertainties in the record.


They stated that was what they were doing from the very start. Are you accusing them of cooking the books? If they were doing that they would not have published the data to start with. Your graphs appear to be produced by people with political interests, cherry picking the bits they want, & ignoring the rest. It seems to be that your source is the very thing, and contributing to the very problem, that this study was trying to address
"What data, The Berkeley group created graphs based on their data"

But the graphs show no data from a little after 2000 to now while BEST data indicates no temp rise during that period. Again, the elephant in the room.

"There was tons of it and it was an amalgam and average of several data & statistical sources."

I am not now nor have I in this thread questioned the data.

What I question, initially, was Muller's omission of the last decade or so's data, then bruce's, now yours. Why do none of you want to show data showing no rise in temps for well over a decade now? If bruce's hypothesis is correct, that CO2 drive temps, then, given the recent dramatic rise in CO2*, we should be seeing a corresponding rise in temps. We're not.


*Biggest Jump Ever in Global Warming Gases

The last decade or so:

Posted Image

How do you account for this?
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Chris
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telcoman
Nov 13 2011, 08:28 AM
BTW the study was this year, it is a preliminary release.

The Berkeley Earth Surface Temperature Study has created a preliminary merged data set by combining 1.6 billion temperature reports from 15 preexisting data archives.
OK, then they must have set a cut off date. Perhaps they start analysis in 2000/2001.

It still doesn't answer the elephant, why you all refuse to post the last decade or so's data and account for it.
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You have still not stated where your graphs came from, and just what data they are based upon to come up with this elephant allegation you keep talking about. If it is the Global Warming Policy Foundation, then please provide the names & references of the qualified climate scientists they used in producing their interpretation of the data and what portion of the data they used. The information Berkeley itself used, is laid right out their on their web site and I have pasted it below.

I am sorry, with science, hearsay is simply not acceptable. Let's see your data, and the source of it, so we can confirm or debunk it. Otherwise as you are fond of saying, it is nothing but a straw man.

If you want data for the last decade it is freely available on several other reputable websites. For example from NASA

Posted Image

Now there are flat spots, they can be due to volcanic activity or any other host of causes, but the trend is consistently there, regardless.




The Berkeley Earth Surface Temperature Study has created a preliminary merged data set by combining 1.6 billion temperature reports from 15 preexisting data archives. Whenever possible, we have used raw data rather than previously homogenized or edited data. After eliminating duplicate records, the current archive contains 39,390 unique stations. This is more than five times the 7,280 stations found in the Global Historical Climatology Network Monthly data set (GHCN-M) that has served as the focus of many climate studies. The GHCN-M is limited by strict requirements for record length, completeness, and the need for nearly complete reference intervals used to define baselines. We have developed new algorithms that reduce the need to impose these requirements (see methodology), and as such we have intentionally created a more expansive data set.

We performed a series of tests to identify dubious data and merge identical data coming from multiple archives. In general, our process was to flag dubious data rather than simply eliminating it. Flagged values were generally excluded from further analysis, but their content is preserved for future consideration.

We filtered and merged the data archives using the following steps:

Duplicate filter: We first separately searched each archive for multiple copies of the same record and eliminated the duplicates.
Data split: Each unique record was broken up into fragments having no gaps longer than 1 year. Each fragment was then treated as a separate record for filtering and merging. Note however that the number of stations is based on the number of unique locations, and not the number of record fragments.
Bad values filter: We flagged and excluded from further study values that had pre-existing indicators of data quality problems associated with instrumental error, in-filling of missing data, and/or post-hoc manipulations. We further removed values that exceeded global climate extremes (e.g. +5000 F).
Repetition filter: We tested for runs of repeated values, a common sign of in-filling missing days, and flagged repeated values exceeding an empirical 99.9% threshold for non-randomness.
Local outlier filter: We tested for and flagged values that exceeded a locally determined empirical 99.9% threshold for normal climate variation in each record.
Temperature consistency filter: We required that the minimum temperature (Tmin) be strictly less than the maximum temperature (Tmax) for each measurement. We further required that any reported average or instantaneous temperature (Tavg and Tobs) be between the reported max and min, inclusive.
Initial merge: Using nearby locations and matching station ID codes, we tested for the presence of identical data in multiple archives. Records that had identical content for at least 90% of values were then merged. Small segments of non-identical content within otherwise equivalent records were flagged and also carried forward.
Regional filter: For each record, the 21 nearest neighbors having at least 5 years of record were located. These were used to estimate a normal pattern of seasonal climate variation. After adjusting for changes in latitude and altitude, each record was compared to its local normal pattern and 99.9% outliers were flagged. Simultaneously, a test was conducted to detect long runs of data that had apparently been miscoded as Fahrenheit when reporting Celsius. Such values, which might include entire records, would be expected to match regional norms after the appropriate unit conversion but not before.
Second merge: Monthly time series were constructed from daily values with both a version using all values and a version using only non-flagged values. These monthly synthesis records were then compared to the values in data archives that reported only monthly data. Duplicates were found as before and merged.
Site reduction: Though a majority of all station repetitions are identified by the presence of duplicated data, in a significant number of cases the presence of pre-existing data manipulations inhibited our tests for data duplication. We designed several tests based on location, name, and id codes to identify matching sites with somewhat dissimilar data. These were then consolidated as single stations having multiple data series.
Best value series: “Best value” time series were formed by averaging across multiple records when they existed at the same site. In addition, flagged values were dropped and previously manipulated GHCN-M and Hadley Centre data was ignored in favor of other data sources when possible. These series are expected to be the primary records for most future studies, but the fully-flagged and multi-valued records will also be preserved and made available for more detailed analyses.
Seasonality removed series: Finally, non-seasonal series were created by determining the mean seasonal cycle at each location and subtracting this from the best value data.

We are currently preparing a detailed write up of the process used for the data filtering and merge.
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