Lab Objectives

• Utilize NetCDF climate data to create raster layers in ArcGIS

NetCDF files require special steps in order to create raster layers, using the 'NetCDF raster layer' you are able to convert the climate data (ArcGIS) into a visual layer thus taking the data that is at a scale of one degree latitude by one degree longitude visualizing it. Upon converting the data to a raster layer symbology(colors) was assigned to the different temperature values with a range of values in the content tab. I able to observe the temperature values in relation to the coloring on the world map which all helped me infer that the data was imported correctly.

• Use Arc's statistical capabilities to make NetCDF tables and visualize data in the form of graphs

I described in the previous step one way that NetCDF data can be conveyed but NetCDF data can also be imported in tabular form which more easily allows for statistical computing. Using a similar tool as the raster layer, 'NetCDF Table View' allows for the same climate data which uses a 1x1 degree resolution and puts it in an attribute type table. I created a visual data set from the table which displayed a similar point in each raster point and by using the same data from my raster layer I could cross references data points, e.g. selecting the most extreme point in my table and having them highlighted on my map. By looking at known extreme temperature points worldwide, like the Red Sea, the data was verified. This was all based on current global climate temperature averages.

• Standardize symbology in raster data to better convey the information

This step was by far my favorite and taught me a way to easily create symbology for raster data in ArcGIS. Using a fresh map I imported the climate anomaly data and used a raster function to add the values to the base map values using 'Math', this turned the raster anomaly data from two separate timeframes (2020-2039 and 2080-2099) into a raster layer that highlighted the differences in the temperature. Next, to communicate the anomaly data I overlayed a random raster set to the map and then adjusted the settings to include the temperature differences from the two anomaly layers and set the resolution and cell size to be identical to that of the anomaly data set. Then changed the symbology to a color of choice which accurately represented the information. Lastly, by saving the symbology as a layer I could import it into the two anomaly timestamps and save the characteristic settings this created a uniform basis for which the two maps were based off of and as shown in the two maps above it is easy to observe how temperature continues to greatly vary both spatially and temporally under these high emission scenarios.