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\subsection{Timeline}
\begin{center}
Figure 1
\begin{figure}[h]
\includegraphics[height=7cm,width=18cm]{Gannt}
\end{figure}
...
\par
The last parameter used when gathering the carbon dioxide data was to make sure the data was up to date. This was not extremely difficult to do thanks to NOAA's filtering options. When looking for data an option was chosen that the data sets had to have been updated within the last 365 days. This means that all of the data sets have measurements within the last year, or slightly longer. It appeared that updating the data did not always mean adding new points in. The worst data set, in terms of new data, had its newest data point as the end of 2012.\\
\begin{figure}[h]
Figure 2
\includegraphics[width=\textwidth]{co2all}
\end{figure}
\par
...
The fire data was collected from the National Interagency Fire Center website. This site has many different data sets and information on fires from around the United States. The data set used has the total number and acreage of wild fires in the U.S. from 1960 to 2014. The use of wildfires is important because these are fires that were strictly caused by natural forces, no human caused fires or prescribed fires were included in this report. The use of these fires would significantly alter the analysis of the data and they don't accurately reflect the effects of climate change on the environment. Only U.S. fire data was used so this project could narrow its focus and have a better analysis of fires in this country.
\subsection{Data Reduction and Processing}
\par
The carbon dioxide data was the first to be downloaded. To start, the data sets were
downloaded and placed into individual latex files in emacs. For this project's research purposes, only two of the columns of data were needed. These two columns consisted of the date and the concentration of carbon dioxide in parts per million. There quite a few columns of straight zeros that had to be dealt with. To get rid of these systematically, a macro keyboard command was used in Emacs. This was done to all of the data sets to get them down to just the date and the concentration.
\par
The next step in the process was to place these data sets in Matlab for analysis. To do this, all of the data was inserted as cell arrays because the dates had symbols that Matlab could not use. So the data of the concentrations was converted into a Matlab array. When this was finished up, all of the data sets were plotted. All of the graphs looked great, except two of them had a lot of sharp peaks and values associated with them. To fix this, the two data sets were looked through with a fine tooth comb to find outlier points, and adjust them to reasonable values. After the major spikes and values were adjust, a smoothing function was used over each data set.
\par
The first data set which required smoothing was the Baring Head data. An averaging function was used across all of the data points. This smoothing takes the values around a certain point and averages them, and then replaces the point in question that average value. The details of this script are in the appendix under \'script 1\'. Both the original version and the smoothed version are shown below.
\begin{center}
Figure 3
\begin{figure}[h]
\includegraphics[width=\textwidth]{baringhead}
\end{figure}
\end{center}\\
\begin{center}
Figure 4
\begin{figure}[h]
\includegraphics[width=\textwidth]{baringheadsmooth}
\end{figure}
...
\begin{center}
\begin{figure}[h]
Figure 5
\includegraphics[width=\textwidth]{blacksea}
\end{figure}
\end{center}\\
\begin{center}
\begin{figure}[h]
Figure 6
\includegraphics[width=\textwidth]{blackseasmooth}
\end{figure}
\end{center}\\
...
\par The carbon dioxide data was located and downloaded from NOAA's(????) climate change website. The data sets contained very little errors which required adjustment.
\par After downloading the temperature anomaly data from NOAA, the raw data was cleaned up and formated in a text editor to easily be integrated into MATLAB, where it was plotted in reference to time in years.
\begin{figure}[h]
Figure 7
\includegraphics[width=\textwidth]{raw}
\end{figure}
The data goes all the way back into the 1800's, and is very dense with spikes and fluctuations, so the data was smoothed out by averaging neighboring values, creating less complicated graphs.
\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.
\begin{figure}[h!]
Figure 8
\includegraphics[width=\textwidth]{Fires.eps}
\end{figure}
\begin{figure}[h!]
Figure 9
\includegraphics[width=\textwidth]{Acres.eps}
\end{figure}
...
\subsection{Data Analysis, Interpretation and Integration}
\par
The analysis of the carbon dioxide started in a very simple manner. The graph of all of the data was looked at first. It is very easy to see the trend that all of the data shares (Figure 2). All of the general trends follow each other near exactly. One thing which made it slightly harder to analyze the data was the differing dates for all of the data sets. Some of the sets had their first recording as late as 1999, and some as early as 1979. This was solved using the linspace() command in Matlab. It takes in the start and end values, as well as the number of points inbetween. This allowed the data sets to all be plotted on the same graph because of the consistent numbers associated with them. As far as the trends go, it is very easy to see the upwards trend as time goes on. The rest of the data graphs are shown below, excluding the Black Sea and Baring Head sets because they are shown prior as Figures 3-6.
\begin{figure}[h]
\begin{center}
Figure 10
\includegraphics[width=\textwidth]{ascensionisland}
\end{center}
\end{figure}
\begin{figure}[h]
\begin{center}
Figure 11
\includegraphics[width=\textwidth]{barrow}
\end{center}
\end{figure}
\begin{figure}[h]
\begin{center}
Figure 12
\includegraphics[width=\textwidth]{coldbay}
\end{center}
\end{figure}
\begin{figure}[h]
\begin{center}
Figure 13
\includegraphics[width=\textwidth]{guam}
\end{center}
\end{figure}
\begin{figure}[h]
\begin{center}
Figure 14
\includegraphics[width=\textwidth]{po30s}
\end{center}
\end{figure}
\par
All of the graphs of the carbon dioxide above include a red trend-line. Evaluating these is the easiest way to see the upward trend in the concentration of carbon dioxide in the atmosphere. There was also a second major pattern recognized across all of the carbon dioxide data as well. This secondary pattern is the sinusoidal look that most of the data sets have. The barrow data (Figure 11) really shows this well. However, there is a big question what could be creating this pattern. By looking at the years on the bottom of the figure, one can see that there is almost exactly one per cycle on the carbon dioxide. This could most likely mean that it is a seasonal occurrence through a given year. The important thing to notice with this pattern is that it gets higher and higher each year. It looks like an x*sin(x) style function. However, there were a couple of exceptions in the sinusoidal pattern. For example, the Pacific Ocean 30 South data did not show much of a pattern except that it was increasing with time.
\par
Next a slight analysis of all the trend-lines was done to see how close the slopes truly are. This was done through Matlab's graphing system. These values are depicted in the table below.\\
*The original data sets, not smoothed versions.
\begin{center}
\begin{tabular}{|c|c|c|}
\hline
Data Location & Figure number & Slope of trend-line\\
\hline \hline
Ascension Island & 10 & 1.6\\
\hline
Baring Head* & 3 & 2.0\\
\hline
Barrow & 11 & 1.6\\
\hline
Black Sea* & 5 & 2.6\\
\hline
Cold Bay & 12 & 1.7\\
\hline
Guam & 13 & 1.7\\
\hline
Pacific Ocean 30 South & 14 & 1.8\\
\hline
Average & XXX & 1.85\\
\end{tabular}
\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 To further analyze the fire data, the number of fires and number of acres burned were plotted together using two separate y-axes. This plot clearly shows similar spikes in the data around 1975 to 1982 and what appears to be a divergence between the plots. This divergence indicates an increasing severity of fires as shown in the next figure. % Not done with this section yet.
\begin{figure}[h!]
\begin{center}
Figure 15
\includegraphics[width=\textwidth]{FiresVSAcres.eps}
\end{center}
\end{figure}
\begin{figure}[h!]
\begin{center}
Figure 16
\includegraphics[width=\textwidth]{Severity.eps}
\end{center}
\end{figure}
