Resolved: Human Activity Is a Significant Factor in Global Warming on Planet Earth.

Resolved: Human Activity Is a Significant Factor in Global Warming on Planet Earth.I don't intend on reposting any of my opponents graphs, or data. However it is important that I enumerate his graphs.Figure 1: Atmospheric CO2 for 650,000 yearsFigure 2: Global Land-Ocean Temperature IndexFigure 3: Greenhouse ExplanationFigure 4: Average Monthly Arctic Sea Ice August 1979 to 2013Figure 5: Sea Level ChangePart 1: DoubtIt's telling that my opponent makes the claim that it is irrefutable, and then uses Figure 2. Even NASA, and NOAA would refute that graph since in June of this year they published serious corrections in an attempt to remove the hiatus from the argument.Figure 6: Corrected Figure 2 Now this claim that the pause doesn't exist poses a problem because a few years ago the claim was that the pause was caused by about 17 volcanoes back as little as 2014[2]. So which is it? Does the pause exist, and was caused by Volcanoes, or does the pause not exist, and was caused by weighting issues in the data? Any reasonable person should now have some doubt in the basic data claims of global warming for a basic contradiction.Part 2: Methodology BiasUnderstanding instrument, and methodology bias is needed to understand what was done in Figure 6, and understand if we should accept the graph at all.The data that is being corrected is the Ocean temperature measurements done by volunteer ships in the early half of the data. By itself this isn't a problem. The ship data has a higher inaccuracy, but all the ship data would have similar errors, and when compared to the same methodology there wouldn't be an issue. When you mix in other methodologies this error can become a big problem. What caused these differences isn't hard to understand. Think of a time where we collected temperature data without the aid of a machine. If someone has to go out, and check the temperature, and record it there is always the possibility they missed the true high, and missed the true low. When we get to machine aided recording missing that true high, and true low is drastically reduced. Furthermore the error is unidirectional. Missing the true high will only result in a lower value. Just as missing the true low only results in a higher value.We can actually see this type of bias in weather station dataset call the GHCN Data: [3]If we didn't accept the methodology bias Figure 7 would present an interesting problem since the average high increase about 4 degrees C, and the average Low decreases by about 6 degrees C. If that was global warming the decrease in the low would seriously outweigh the increase in the high.Figure 7: All GHCN High and Low Temperature Yearly Averages Part 3: Addressing BiasTo address bias the bias must remain as constant as possible. Part of the reason for only debating the last 115 years is that the bias for weather station methodology hasn't radically changed unlike in the seventeen, and eighteen hundreds where changes in methodology were much more common. By just comparing a station to itself, and only looking at the stations delta from hits own average we can show that the abrupt change around 1900 was actually an illusion caused by a large number of stations following a similar methodology, and not an actual change in temperature.We can see in Figure 8 that the change is more gradual like the low temperature in Figure 7. The disagreement between the two graphs implies that the change is due to method changes, and not due to real changes.Figure 8: All GHCN Station High Temperature Average Delta Figure 9: All GHCN Station Low Temperature Average Delta It would be an error to accept either Figure 7 or 8 as the truth. Both still suffer from a change in methodology bias, but they reflect that change differently. They both also high the noise in the data by utilizing a yearly, and 5 year mean. However, if you do accept Figure 7 as the truth you'd still have to reconcile the issue that Figure 9 shows that the Low temperature being higher than our modern time.We also have an issue with sample size. At the earliest part of the data there is only 1 station. It's not until the 1870's that we start to get a decent sample size to start to remove statistical sampling errors, but even then it's only at a minimum. At the peak of the data in 2005 we have nearly 14 thousand station, but by 2015 we only have 10 thousand stations. This radical change in sample size can cause. Just as in 1893 we only had 563 stations but in 1894 we had 1353 which is responsible for the abrupt change in Figure 7.The methodology below is what I will use to address each of these issues.Methodology GHCN Data Analysis: 1. To process this data the first step is to convert each stations monthly 31 day values into a single monthly median value for each station.2. Then each stations monthly median values are averages to create the average median for the life of the station.3. The station average median is then subtracted from the station median to create a delta that represents that stations change away from the average median.4. Stations are then selected based on the date of operation. For analysis starting in 1900 all weather stations that were operational in 1900 and operational at the end of the analysis must be used. Weather stations that were not operational at the start of the analysis cannot be used because they will induce a change in the region bias.5. From there the data can be graphed, or each months delta for the year can be averaged to create a yearly change for a simpler graph.Part 4: GHCN Data AnalysisFigure 10 below is the above analysis applied to the Global Historical Climatology Network Data. The blue dots represent the monthly noise that Figure 2, and 6 hides. We can now see that the average temperature varies by +- 4 degrees from normal, and not the few tenths of a degree that the yearly mean implies. As a note the two largest positive deviations from the mean are March 2010, and 2012 by 6.1, and 6.6 degrees respectively. They are separated by nearly a century, and show that the high temperatures of 2012 are hardly abnormal.If you notice in the graph between 1930, and 1940 you'll notice a clear bulge in the 5 year mean about zero. This is of note because it correlates with the known heat waves of the 30's that attributed to an climaxed with the Dust Bowl in 1936 as seen in the EPA Heat Wave Index[5]. Anyone looking at this data in the 50's and 60's might actually believe that we were heading towards a global cooling event since in the 70's the median clearly drops below zero. Conversely anyone starting their analysis in the 70's is starting at a low, and thus only see a movement upwards. Which may also explain my opponent's Figures 4 and 5 which start with the low 70's era, and lack any data for the early half of the 115 year period this debate is over.We can even apply a trend line to see if there is a trend upwards over the time period. There is a slight trend of 0.0009 degrees celsius per year, but this is with an R Squared of 0.0004 which means the significance of that upward trend is almost nothing, and can't be used for any real future predictions. For those unfamiliar with R Squared we really want to see a value as close to 1 as possible. A value of 0.5 is ok for some things like predicting erratic human behavior, but the more significant the claim the closer to 1 it needs to be. 0.0004 is so close to zero that it may as well be zero.Figure 10: GHCN High Monthly Average Delta 1900 - 2015 I'd present A figure on the Low, but it would be redundant with Figure 10, and given the time and text constraints I will cut it from my argument.Part 5: Is Temperature the Appropriate Measure?When we say that CO2 causes the Earth's temperature to rise what we really mean is that the increase in CO2 causes the atmosphere to hold more energy. The Pan Evaporation Rate can be used to measure the energy in the atmosphere. If you take a look at Figure 11 you'll notice it significantly deviates from a rather consistent trend. I've reduced the time range to 1980 to 2015 to give the largest possible sample of 156 pans in the GHCN dataset.Figure 11: Pan Evaporation to Sun Spots If we accept the increase is due to an abnormal warming due to solar storms that track with sun spots then we have a problem from the lack of any significant change during the man made ramp up of CO2 over the last 3, and a half decades. Since the sun's solar output varies by about 0.1 percent[6] it could be that even adding more CO2 has little effect because we've long since been able to absorb nearly all of the available solar energy.So why is there a spontaneous increase after 2005? In 2008 NASA announced a discovery of an odd phenomena. They observed a Northern polarity Coronal Mass Ejection rip open our Northern Magnetic pole, and thus exposed the entire day side of the planet to solar storms that it doesn't normally get[7]. This resulted in the prediction that solar cycle 24 which would send more northern polarity CME's towards the Earth would, and subject us to an unusually high volume of Solar Storms that our Magnetic field usually protects us from. This would also explain why the shape with many of the same peaks and valleys visible in the data.[1] https://www.ncdc.noaa.gov...[2] http://time.com...[3] ftp.ncdc.noaa.gov/pub/data/ghcn/daily/[4] https://en.wikipedia.org... (Only as a reference to a historical fact)[5] http://www3.epa.gov...[6] http://www.dailykos.com...[7] http://science.nasa.gov...