Preliminary Ice Shelf Changes Since the New Millennium (WEEK 3)

In the last blog post, I went into specifics on how I have measured the rifts. Here, I will talk about how I have observed the Amery Ice Shelf to be changing over time.

As a refresher, below is an image of the Amery Ice Shelf that was made by overlaying on Python an image from 2010 and another from 2018. The yellow portions of it indicate the parts of the Amery Ice Shelf that have been changing over time.

Python_image

Figure 1: changes in the Amery Ice Shelf from 2010 to 2018

Overall Trends

As is shown in figure 2.1, the rift is getting wider over time, and the midpoint of the width is shifting to the northeast. As is shown in figure 2.2, the rift is getting longer over time, and the endpoint of the length is generally shifting northwest. However, the rift appeared to lengthen significantly within the winter 2012 season, which

Rift_length_graphicRift_width_graphic

Figures 2.1 and 2.2: Changes in rift width and length, respectively, over time

Figure 3 shows four crucial rift measurements plotted over time. As can be seen, I have included various best-fit lines and curves in these graphs. The process of obtaining these best fits is explained in the following section.

Var_graphs_1

Figures 3.1-3.4: Plots of variables of interest over time

 

Regressions

After plotting all these points, I tried to examine overall trends and patterns in the graphs. To do so, I fitted on each graph a linear, exponential, logarithmic, and power function through Excel, and I recorded their accompanying correlation coefficients. The following values were obtained, with the highest correlation coefficient in each data set bolded and italicized.

Widths Width Midpoint Shifts Lengths Length Endpoint Shifts
Linear trend 0.9464 0.9986 0.9661 0.9767
Logarithmic trend 0.5155 0.7759 0.8219 0.596
Exponential trend 0.9593 0.8006 0.7142 0.9087
Power trend 0.6329 0.9978 0.9608 0.8091

As can be seen from the above table, most of the best-fit lines are linear. As such, the data and graphs suggest that the rates of change in rift length, length endpoint position, and width midpoint position have been roughly the same since at least 2001. The rift width is the measurement that’s rate of position change has most likely not remained constant since 2001. It is also evident that the width midpoint shifts and lengths over time could also be modeled well with the power function, as their correlation coefficients differ by less than 0.01. Nevertheless, the only data set I chose to model non-linearly was the widths over time.

In the equations below, note that “x” represents the number of days since 1/1/2001, and “y” represents the corresponding data set of interest in meters. Also note that these equations might change, as I might revise them to include data from the Landsat satellite and/or other sources.

Data set Best-fit equation
Widths y = 1.1992168110015 e0.000252156397473x
Width midpoint shifts y = 0.003531107969x – 0.064263624599
Lengths y = 0.004887396812x + 0.795232275191
Length endpoint shifts y = 0.002879858183x + 1.332537375363

Other Observations

Another thing I noticed through eyeballing the graphs of rift length and length endpoint is that while the overall 2-decade trend is most likely linear, there might be other trends embedded within the overall trends. For each of these graphs, I split the data points up into three time intervals: 2001-2007; 2007-2013 (first half of 2013); and 2013-2019 (second half of 2013). With each of these, I then repeated the process of testing out regression equations and finding the one with the highest correlation coefficient.

Three-part_length_analyses

Figures  4.1 and 4.2: Plots of length measurements split up into three intervals

Below are the correlation coefficients I obtained for the rift lengths in each time interval:

Lengths: 2001-2007 Lengths: 2008-2013 Lengths: 2013-2019
Linear trend 0.9472 0.8772 0.924
Logarithmic trend 0.5377 0.8307 0.9145
Exponential trend 0.9735 0.9048 0.9356
Power trend 0.6701 0.8705 0.9311

 

Endpoints: 2001-2007 Endpoints: 2008-2013 Endpoints: 2013-2019
Linear trend 0.9668 0.8309 0.9339
Logarithmic trend 0.7552 0.8017 0.9171
Exponential trend 0.8838 0.8529 0.9537
Power trend 0.9592 0.8313 0.9434

When split up into these three time chunks, it appears that the data can best be modeled with exponential, rather than linear, equations. Thus, while most overall two-decadal trends are likely linear, there could exist other trends embedded within the overall trend.

Similar to the previous best-fit equations table, note that “x” represents the number of days since 1/1/2001, and “y” represents the corresponding data set of interest in meters. Also note that these equations might change, as I may, in the future, revise them to include data from the Landsat satellite, and/or adding in data from other sources.

Data set Best-fit equation
Lengths: 2001-2007 y = 2.531246757670 e0.000760775188x
Lengths: 2008-2013 y = 4.613242316488 e0.000362826084x
Lengths: 2013-2019 y = 11.017525959676 e0.000169141280x
Midpoints: 2001-2007 y = 0.004481479544x – 0.603884594986
Midpoints: 2008-2013 y = 5.795148826366 e0.000199377559x
Midpoints: 2013-2019 y = 4.481513328281 e0.000234047062x

Seasonal Trends

While there are overall decadal trends that can be observed, there are also possible seasonal fluctuations that influence the rate of rift propagation. I had hypothesized that the rift propagates quicker in the summer and slower in the winter, due to how surrounding temperatures fluctuate within a year.

To explore seasonality, I calculated the rates of rift widening; rift midpoint position change; rift lengthening; and rift length endpoint position change between each spring and fall season that had seasonal data available.

I then performed an ANOVA test to determine whether or not a statistically significant difference between rates existed. However, because my F-critical value was always significantly lower than my F-statistic, I failed to find any statistically significant seasonal variability.

Seasonality_test

Figure 5: An example of ANOVA, with data of rift midpoint shift rates

Due to the limited sets of images and lack of statistically significant tests, seasonal variability is not something that I will continue investigating in this research project.

In the future, I might further investigate the dynamics of the AIS rift propagation by examining more satellite images and/or compiling others’ data sets. In the rest of my blog posts, however, I will move on from exploring the HOW to exploring the WHY behind this rift propagation.

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