In this assignment, I use NetCDF data from Unidata to explore future climate conditions based on climate scenario RCP 2.6.

Process

Converting NetCDF Data to Raster Data

In order to compare future projections to historical annual temperature data, I first added NetCDF data of the historical average global temperatures from 1986-2005. In order to display the NetCDF data in Arc Pro, I used the NetCDF to Raster Layer tool in the geoprocessing toolbox, setting the X dimension and Y dimension to latitude and longitude.

Adding the Anomaly Data

After adding the baseline data, I added the climate anomaly data. I used the climate scenario RCP 2.6, which refers to the level of greenhouse gases being added to the earth's atmosphere. I used two datasets; one for the near future (2020-2039), and one for the far future (2080-2099). After converting the NetCDF data to raster data, I was left with two layers that showed the values (degrees Celsius) that represented the change in temperature compared to the baseline historical data.

Adding Anomalies to Baseline Data

Next, I added the baseline data to the RCP 2.6 anomaly data to create two layers representing the projected mean annual temperatures for the two future time periods.  I used the Plus tool in raster functions, which sums the values of two rasters on a cell by cell basis. Doing this created two new layers which represented the mean annual temperatures in each of the future climate scenarios. 

Applying a Consistent Symbology

Once the two temperature layers had been created, I needed to apply a consistent symbology to both so that both layers represented the same range of temperatures for comparison. Using the Create Random Raster tool, I set the minimum and maximum values to encompass both extremes from the layers. I set the symbology to the stretch type 'minimum maximum' so that the darkest color would align with the highest temperature. Eventually, I switched the color ramp from one continuous color to two to represent colder and warmer regions. Finally, I saved the random raster layer as layer file, and imported the symbology to both future climate scenario layers.

Projecting the Data

In order to display the data at a global extent, I chose the global Robinson projection. I chose this projection because while some distortion is unavoidable on a 2D map, this was the best compromise at a global scale.