Climate Temperature Anomalies- 02/06/22

This week in lab we worked on collecting our own data through online sources and building maps based on anomaly data that communicated how the average temperature is changing based on chosen variables.

Lab Objectives

• Importing data and verifying it against the source

  1. After selecting my map parameters and downloading two sets of NetCDF data I imported them into Arc. Because the data was being projected as a map on NASA GISS's site I was able to quickly verify the information was imported correctly. The earlier map (1990-2000) showed less significant anomalies with a smaller range of values, while the latter map (2010-2020) highlighted more extreme temperature changes.

  2. I also evaluated the raster size ( 2° lat x 2° long) and compared the missing data to the online source, this all appeared consistent

• Create map symbology

  1. I utilized the tool we learned last week to create a random raster layer and applied this to both maps to create a consistent layer. At first I was having issues importing the layers but after adjusting the mix/max in symbology statistics of the random raster layer I was provided with the correct color/shading. To corroborate I clicked back and forth between the layers to look at coloring and used the select by contents options to compare the actual anomaly values.

  2. When generating the layer I adjusted the 0 values and extremes to equally darken, as well as shrinking the proportion of lighter color at the 0 value to more clearly define where there were negative and positive values

  3. A few things I wish I would have taken notice of were the classification of colors option and using this tool to communicate the data. While stretch is more appeasing to the eye, by classifying the information into categories I think it would be easier to understand where the most dramatic warming is occurring and if there is any cooling.

• Generating a meaningful map

  1. Exporting information for a legend was the tallest task and I accomplished this by creating a layout, adding a legend, exporting a pdf and cropping the scale to a smaller image that I could manipulate. It took a lot of eyeballing to estimate where the lowest values were, and I believe I overestimated but in my legend I included a longer range of a gray gradient to indicate what the reader should be looking for to find cooling. I attempted to pull up the symbology color distribution and use the assigned percentage values to estimate where the lowest values fell, clicking around on the map where cooling appeared to be occurring also helped.

  2. Somethings that in hindsight I wish I would have adjusted are; the coloring of the background, labeling and coloring the missing data, and a different color way to symbolize negative values. I liked the way in which gray appeared as I thought it highlighted how cooling is not something that is significantly occurring, but I needed a different color background in the very least. Also, differentiating between what is "no-change" and what is the white background of my layout is difficult for a map reader. Lastly, I think a big goal of mine is getting better at exporting my map images, I am having difficulties generating an image with good resolution due to using a screen-shot method.

• https://drive.google.com/file/d/1Oo6pidW7guWSZ_PcxwusnvE7HxB77pO8/view