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Kevin McHugh
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I've read in the popular media that there is a difference between the two. Is this true, and if so, why?
Could you supply a link to the source of the data you plotted? The rules for discussion of Global Warning in the Earth forum are fairly restrictive -- we can discuss the science behind the measurements, but not the politics involved. (See the stickie thread at the top of the Earth forum for details) Thank you.FrankMlinar said:I am in a discussion with someone concerning AGW, and he likes to use "the last two decades!" to prove there is no AGW or AGW is in the "noise". I generated a 20 year moving trend plot for HADCRUT (surface) and three troposphere (satellite) data sets. I think this clearly shows the strong similarities between the data sets. (The center of the moving trend is the plot point)
"Smoothing over weather noise" when comparing observed and model-output trends is well-justified because they look at different "weather noise," but aren't two given observation methods supposed to be measuring the same (real) "weather noise"?FrankMlinar said:...because of the relatively large variations in the data, it is good to have a lot of data points when determining a trend. Climate scientists like to have 30 or more years of data before declaring a trend. Figure 1 has 18 years of data, and the trend has a lot of uncertainty associated with it. A larger database, 30 years in Figure 2) reduces the uncertainty in the trend and increases the confidence in the trend value. As a result, the trends are more similar between satellite and ground meaning both are tracking each other.
The high-frequency part that gets filtered out when people look for climate trends is often called "weather noise" because it's assumed to be due to "weather" along with other higher frequency stuff superposed on the underlying long-term trend.FrankMlinar said:"Smoothing over weather noise"... Actually this is not "noise", but high frequency information. That is, real, measured data with variations that happen over short time periods. Smoothing is done to look at the long term variations and to determine their underlying causes. Plus, I don't believe I ever said "weather noise".
My point is that time-filtering to remove differences between observations obtained by different instruments, sampling at different places and times, etc., isn't as trivial to justify as time-filtering to remove "weather noise."Now, "two given observation methods", by the very definition, will result in two different measurements simply because of uncertainties in the measurement methods; the instruments themselves, the locations of the measurements, the time of measurement, etc. or this discussion, we are looking at satellite based measurements and surface based measurements, that is, different locations. For satellite measurements, we are looking at different heights in the atmosphere (TLT, TTT, TMT), and differences will appear. The surface based measurement is a different location from the satellites, using different instruments at different times, and will show differences in the measurements.
Satellite temperature data refers to measurements of the Earth's temperature from sensors on satellites orbiting the planet, while ground based data refers to measurements from weather stations and other instruments located on the Earth's surface.
Both types of data have their own advantages and limitations, so it's difficult to say which one is more accurate overall. Generally, satellite data provides a more comprehensive view of the Earth's temperature, but ground based data can be more precise for specific locations.
By using both types of data, scientists are able to get a more complete understanding of the Earth's temperature and how it is changing over time. Satellite data can provide a global perspective, while ground based data can provide more detailed and localized information.
In general, satellite and ground based data do show similar trends in terms of global temperature changes. However, there can be slight differences due to the different methods of measurement and potential biases in the data.
Both satellite and ground based data are important for studying climate change. Satellite data can provide a larger scale perspective, while ground based data can provide more detailed information about specific regions. Therefore, it is important for scientists to use both types of data in order to fully understand the impacts of climate change on our planet.