deletions | additions       diff --git a/Everything.tex b/Everything.tex  index 6258d22..3c649a0 100644  --- a/Everything.tex  +++ b/Everything.tex 
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\includegraphics[width=\textwidth]{N60Nraw}  \end{figure}  \end{center}  Immediately it is evident that the data is very dense and unfiltered. All of the records for each latitude band go back into the 1800's, with a point for every month of the year, adding up to a very detailed, large dataset with many small spikes and fluctuations. In order to make the graphs appear less confusing and dense, reduce  and simplify the datain order  to focus on the bigger trends, the data was smoothed out. This was accomplished by using a while loop for every data point, and replacing it with average of the point and the two points before and after it. This averaging procedure helped to bring the more radical spikes and dips down to a more reasonable slope that made more sense when looking at such a large scale. \par  The fire data was very good and required very little processing. It was a small dataset and did not need reducing. The only processing required was flipping the data so it went from 1960 to 2014 instead of the other way around and separating the years, acres, and fires, into three different vectors. The number of fires and acres burned were first plotted separately and lines were fitted to them to show the overall trend to the data. Equations to the lines are shown on the figures below. 
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\end{center}  \par  Overall, the carbon dioxide trends helps out the research objective because the ocean temperature anomaly data is increasing as well. This means that there is a correlation which exists between the increasing carbon dioxide concentration and warmer temperature anomalies.  \par After smoothing the temperature anomaly  graphs, linear trends and higher degree trend lines were implemented to show how the patterns shape over the past 100 years. Insert VERY NICE GRAPH  \begin{center}  Figure 15  \begin{figure}[h]  \includegraphics[width=\textwidth]{VERY NICE GRAPH}% is this the file name?  \end{figure}  \end{center}  Insert VERY NICE GRAPH 2  \begin{center}  Figure 16  \begin{figure}[h]  \includegraphics[width=\textwidth]{VERY NICE GRAPH 2} % Is this the file name?  \end{figure}  \end{center}  \par Interesting things to note are how the 7th degree trend line reveals how the temperature anomalies are cyclical in nature, but the linear trend line shows how that there is an over arching upward trend, where in the 1980's there is a major departure from the zero line and almost all years after that have been warmer than the expected average.  \par The cyclical pattern of the sst has a period of approximately 60 years, with even smaller fluctuations within these over-arching cycles with amplitudes of approximately half of a degree. Has the years go by, the The  fluctuations tend to go warmer and warmer, warmer through time  and when the temperature anomaly cools down in the regular cycle, it doesn't does not  cool off as much, making the cooling downtrend unable to compensate for the complimentarywarming  upward warming  trends. In the end this creates the positive linear trend shown, where even though the cycle still has a 30 year cooling period, the warming trend overpowers it. Though these are the trends in the more medial latitudes, there have been different trends elsewhere. Insert *polar graph \begin{center}  Figure 17  \begin{figure}[h]  \includegraphics[width=\textwidth]{polar  disproving everything* everything}% Is this the file name?  \end{figure}  \end{center}  \par In this plot, there actually appears to be a cooling trend that contradicts all the other data sets, yet this seemingly contradictory data actually coincides with the warming trends of the medial latitudes. The atmospheric cells all interact with each other and effect temperatures and winds in the neighboring cells. An increase in temperature in the tropics leads to more heat rising up into the cycle, and eventually this creates air with lower temperatures and higher wind speeds in the polar cell. These cold, strong winds push out to the coast of Antarctica, carrying lower temperatures to the oceans and subsequently creating more ice to also be pushed out into the ocean. This combination of new ice and cold winds decreases the temperature of the oceans, which actually all draws back to the rising temperatures at the equator.  \par Shown above in figure 8 is the plot that shows the number of fires every year and it has some interesting anomalies. The red trend line for the graph has a negative slope which means that overall the numbers of fires have been decreasing since 1960. This is intriguing considering that in figure 9, the amount of acres burned every year has been increasing. Another anomaly in figure 8 is the sudden drop around 1983. Before this drop the figure shows that the number of fires was consistently very high for about 10 years. After this low, the number of fires slightly increases but the number stays below any of the previous years.  \par To better compare the Figure 8 and 9, they were combined without the trend lines in figure 15. 18.  This plot clearly shows similar spikes in the data around 1975 to 1982 and what appears to be a divergence between the plots starting in about 2000. This divergence indicates an increasing severity of fires as shown in the next figure. figure 19.  \par As analyzed above, global temperature is increasing and in areas that are hot and dry, increasing temperatures makes the areas even hotter and drier. With drier conditions there is more fuel for the fires to burn. This accounts for the increasing severity of the fires.  \par     \begin{center}  Figure 15 18  \begin{figure}[h]  \includegraphics[width=\textwidth]{FiresVSAcres}  \end{figure}  \end{center}  \begin{center}  Figure 16 19  \begin{figure}[h]  \includegraphics[width=\textwidth]{Severity}  \end{figure} 
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\par  The last error that could have existed in the carbon dioxide data came from trying to create the time lines on the bottoms of the graphs. Matlab did not work well when the data sets would consistently use the same numbers, like a year for example. The data would have ten or twelve measurements for a given year. To fix this a method called linspace() in Matlab was used. This takes in an initial value, an ending value, and how many points you want in between those two values. For example, if a data has 400 points and dates from 1979 to 2014. It would then create 400 value incremented from 1979 to 2014. This worked really well in plotting the data and getting the years on the bottom line. The error comes from the fact that all of the years do not have the same amount of points, some have less than others. So, evenly spacing the distance between each point is not one hundred percent accurate. However, this error would not be very significant because it would not affect the slopes of the trend-lines by more than .1, which was the main purpose of all the data graphs. In total there was not a significant amount of error within the carbon dioxide data to make a real difference.  \par Next, a look at the fire data was made. Since the data for the fires in the U.S. was very clean and needed little processing, there is no error that is accompanied with cleaning up data. However, the data only came from one site and there could be some discrepancies if this data was compared to other data sets from different sources. These differences should be small and would not impact the results found if different datasets were used.  \subsection{Results}    The results are clear for this project. There has been a steady increase in CO2 levels in the Earth's atmosphere since 1980. There has been a similar increase in ocean temperatures around the world. This indicates that there is a correlation between CO2 levels in the atmosphere and ocean temperatures. In addition, increasing global temperatures make hot dry climates hotter and drier, which increases the risk for fires. As shown in figure 16, fire severity has been increasing since the 1980's. The connection between increasing CO2 levels, increasing global temperatures and increasing fire severity is confirmed by the similar increasing trend in all of the data.