In recent years, scientists and the general public have become increasingly aware of climate change. Scientists have begun and continue to study historical and geologic records in order to determine natural patterns versus human-induced changes in climate. By studying historical data and recent impacts, scientists may be able to determine consequences people will face now and in the future.
One of the potential consequences of the changing climate is an increase in anomalous weather events. By definition, an anomaly is an irregularity, or something that varies from the normal pattern or range. For the purpose of this study, anomalous weather refers to measured temperature and precipitation ...view middle of the document...
Though this study will focus on more extreme weather occurrences, it is important to know what normal conditions for the area are like. This area’s average temperatures are near the country’s averages. However, this area experiences higher amounts of precipitation than the national average. Flash floods are not uncommon in this area, and flooding accounts for the highest amount of natural disasters in Murray.
Weather data, such as temperature and precipitation, have been measured continually since the early 1900s at a monitoring station at this site. The station is part of the Midwestern Regional Climate Center (MRCC), a cooperative program of the Illinois State Water Survey (a division of the Prairie Research Institute at the University of Illinois, Urbana-Champaign), and the National Climatic Data Center (National Oceanic and Atmospheric Administration, U.S, Department of Commerce).
Data was collected at the MRCC station. This data is made available online, which is how it was obtained for this study. Temperature and precipitation data from 1960 to 2013 was used for this analysis. Although there were earlier data available, which began to be collected in 1926, there were some gaps in the records due to missing measurements. Also, the large amount of data was not feasible for analysis in this study. Too much information was distracting rather than useful, particularly on graphs.
Data acquired and used in this study include values for temperatures and precipitation. Mean data was collected and graphed, but was not particularly valuable for discerning anomalous weather occurrences. More essential to this study were values for monthly extremes, such as monthly high maximum and low minimum temperature values, as well as high precipitation events. Pertinent data was transferred into Microsoft Excel in order to be viewed, separated, graphed, and analyzed.
In Excel, much work was done in order to make the data ready for use and analysis. Graphs, one for each category (mean precipitation, high precipitation, etc.), were made to get an idea of what the data looked like, in general. Some graphs were made to observe high versus low precipitations and temperatures, as well. The graphs did not convey the information well because some remaining missing records created gaps or skewed the graphs by showing certain missing measurements as zeroes (see Figure 1).
Missing values were then deleted from the spreadsheets. When rechecked, the graphs were still distracting due to the large amount of information being plotted. Although general trends could be observed, it was difficult to discern the more irregular events from the graphs. Anomalous values needed to be separated from the occurrences which fell within the normal range for each category.
In order to separate values outside of the normal range, means and standard deviations were calculated for each series of data. Means, or averages, of a dataset can be used to analyze certain types of...