Step 8: Compare and Contrast Results

1. Now, from the data shown, we are able to begin to compare the data sets. We can begin to look for similarities and differences in the datasets and draw inferences and conclusions accordingly.
   1. a. Remember, what is shown in Figure 22 are the yearly data points (not monthly), so we can't think about seasonal-scale trends using this dataset, but we could look for decadal-scale and longer-term trends. Thus, you might want to use the "moving average" technique as well.
   2. b. When you do this, and increase the period averaged, you will see that the amplitude of the annual data becomes subdued (less volatile), and as you approach 7 or 8 periods, it is clear that there is likely a decadal-scale pattern of sea level rise/fall superimposed on the longer-term sea level rise pattern.
   3. c. There is the obvious longer-term trend in the data shown by the linear regression equations and R2 value. In this case, the R2 value for the Virginia dataset is 0.7865. This is pretty close to 1.0 which means the data are pretty well constrained. This gives us the ability, when we forecast sea level positions in the future, to be in the ballpark, as long as the existing controls on sea level remain in place (i.e., no additional changes in sea level forcing occurs).
   4. d. As we all have learned, scientists are debating changing baselines and changes in forcing mechanisms (i.e., anthropogenic vs natural effects), but, again, that isn't the purpose of this assignment.
2. As you analyze and interpret your data, think about quantitative and qualitative measurements. What can you definitively say the data is showing, unequivocally?
   1. What have the actual sea level changes been? Give some numbers from your data plots.
   2. What inferences can you make and deductive conclusions might you draw from this?
   3. Does your data show, seasonal, decadal, or longer-term trends? Do all sites agree? Why or Why not?
   4. In this case, Virginia shows the greatest slope of the regression line (m in the equation y = mx + b) is 3.4761. Cuba has the lowest slope (1.4185) in the long-term trend; perhaps if data were collected beyond 1971 maybe this slope would be different.
   5. What do these factual observations mean?
   6. What is different about the geography/geology of Virginia, USA relative to Cuba? What are the similarities between Virginia and Cuba? Based on Unit 1 discussions, you might have some insights to bring to the discussion, but certainly a look at a map might help you infer some relationships.
   7. As you work, you might want to return to the PSMSL website and look at global trends or anomalies. PSMSL offers a great set of visual tools that might be of help to you as you work. There is a slider scale at the bottom and a few other buttons that you can toggle to look at the data in different ways using the 1960-1990 (30 year) interval average.
   8. You may also want to use Google Earth, or any of the other viewer tools used previously in the course, to help derive ideas and infer facts about geography, climate setting, recent geologic history, rates and proximity of tectonic activity, rates of sedimentation, etc.
   9. You should answer these types of questions at the minimum in your own data sets.
   10. As every student will likely use a different set of 3 sites, you may find different direct and indirect correlations. In the context of the entire module, you should have some practical, logical, well-thought ideas to help support your deductive reasoning.