Map baseline climate data

First, you'll import, symbolize, and explore baseline (historical) climate data in ArcGIS Pro. Baseline climate data is important because future climate projections are presented as differences from baseline climate conditions. While mapping, graphing, and exploring, you'll also learn key concepts and vocabulary related to climate and climate data.

Create a project

First, you'll download the climate data. Then, you'll create a project in ArcGIS Pro.

  1. Download the climate-data compressed folder.
  2. Locate the downloaded file on your computer and extract it to a location you can easily find, such as your Documents folder.
  3. Open the climate-data folder.

    The folder contains three subfolders. The mediterranean-precipitation-rasters and mediterranean-rcp-85 folders contain precipitation data that you'll use in a later lesson. The mme-netcdfs folder contains raw climate data in the form of NetCDF files. NetCDF is one of the most popular formats for archiving and distributing climate data, while MME stands for multimodel ensemble.

  4. Open the mme-netcdfs folder.

    The files inside the folder have long names that may be difficult to understand. Some of the file names begin with baseline, while others begin with cmip5. You'll create an ArcGIS Pro project to map and explore the baseline climate data.

    Note:

    To learn more about climate data, read about its terms, concepts, and common data structures.

  5. Start ArcGIS Pro. If prompted, sign in using your licensed ArcGIS account.
    Note:

    If you don't have ArcGIS Pro or an ArcGIS account, you can sign up for an ArcGIS free trial.

  6. Under New, click the Map template.
  7. In the Create a New Project window, for Name, type World Climate Data Explorer. For Location, browse to and choose your climate-data folder.
  8. Uncheck Create a new folder for this project and click OK.

    The project opens and displays a default map. You'll eventually add a second map to display projected climate data, so you'll rename this map so its purpose is clear.

  9. In the Contents pane, double-click Map.

    Map in Contents pane

    The Map Properties window appears.

  10. On the General tab, for Name, type Baseline.

    Map name

  11. Click OK.
  12. On the Quick Access Toolbar, click the Save button.

    Save button

    Tip:

    Save your project frequently.

Create a layer from NetCDF data

Next, you'll create a raster layer of climate data that shows mean annual temperature. Specifically, the data will represent a historical average for the years 1986 through 2005. This baseline dataset and the others you'll use include both observed and model data to generate a comprehensive record of climate.

The climate data you downloaded uses the NetCDF file format. To convert this file type into a raster layer, you'll use a geoprocessing tool.

To learn more about the NetCDF files, you can read the data documentation.

  1. On the ribbon, click the Analysis tab. In the Geoprocessing group, click the Tools button.

    Tools button

    The Geoprocessing pane appears.

  2. In the Geoprocessing pane search box, type NetCDF. In the list of results, click Make NetCDF Raster Layer.

    Search for NetCDF

  3. For Input netCDF File, browse to the mme-netcdfs folder and choose baseline_tas_annual_mean_1986_2005.nc.

    NetCDF data can have multiple variables, so you'll make sure to map the variable for mean annual temperature.

  4. For Variable, choose climatology_tas_annual_mean_of_monthly_means.

    This variable contains baseline values for mean annual temperature. The units are degrees Celsius (°C). In the file and variable name, tas means temperature 2 meters above the surface, which represents the temperature people feel. The temperature at or of the ground is more difficult to model given the many types of ground cover.

    The X Dimension and Y Dimension parameters are automatically set as longitude and latitude, respectively. You'll leave these parameters unchanged.

  5. For Output Raster Layer, type Temperature - Mean Annual Baseline. Leave the other parameters unchanged.

    Make NetCDF Raster Layer tool parameters

  6. Click Run.

    The tool adds a new layer to the map. The layer depicts mean annual temperature around the world.

    Make NetCDF Raster Layer tool output

    The cells in this raster layer are 1 degree latitude by 1 degree longitude, or about 70 square miles at the equator. While the height of each cell remains about 70 meters, the width decreases the farther the cell is from the equator.

    In the Contents pane, the legend for the layer indicates that the highest temperature value is 30.35°C (or 86.63°F) and the lowest value is -56.3°C (or -69.34°F).

Symbolize the layer

The default symbology of the layer uses a full-spectrum color ramp. On maps, changes of color usually imply changes in meaning. To show changes in magnitude for the same kind of information, the best option is to change lightness and value. You'll symbolize the layer with a color ramp that is more intuitive for visualizing temperature data.

You'll also adjust the legend to include temperature units.

  1. In the Contents pane, right-click Temperature - Mean Annual Baseline and choose Symbology.

    The Symbology pane appears. The Primary symbology parameter is set to Stretch by default. Stretch symbology applies a color ramp in 255 equal intervals over the range of values in a raster layer. You won't change the symbology type, only the colors used.

    The orange-red color scheme is recommended as being intuitive for visualizing temperature data.[1]

  2. For Color scheme, open the drop-down menu and check Show all and Show names. Choose the Orange-Red (continuous) color scheme.

    Orange-Red (Continuous) color scheme

    The symbology is automatically applied to the map. However, the stretch type is defined by standard deviations, rather than the minimum and maximum values. Also, the low temperatures have the darkest colors, so the color scheme needs to be reversed.

    Note:

    If a warning message appears, click Yes.

  3. For Stretch type, choose Minimum Maximum.

    Stretch type parameter

  4. In the Contents pane, right-click the color scheme for the Temperature - Mean Annual Baseline layer and click the Reverse color scheme button.

    Reverse color scheme button

  5. Click any blank area in the pane to apply the changes.

    Symbolized baseline climate data

    Next, you'll add the units of measurement to the layer's legend.

  6. In the Symbology pane, for Label, add C to the end of each label.

    Raster layer legend labels

Add reference data of continents

In the map, the world's continents appear vaguely, but clearer continental boundaries would be useful reference information. You'll add a layer of continents from the ArcGIS Living Atlas of the World, a collection of curated, authoritative geographic information available in ArcGIS Online.

  1. On the Map tab, in the Layer group, click the Add Data button.

    Add Data button

  2. In the Add Data window, under Portal, click Living Atlas.

    Living Atlas option

  3. In the search box, type World Continents and press Enter. In the list of results, click the World Continents feature layer owned by esri or esri_dm.

    Add World Continents from Living Atlas

  4. Click OK.

    The layer is added to the map. The continents have a solid fill symbol that hides the temperature data, so you'll change the symbol to have no fill.

  5. In the Contents pane, click the symbol for the World_Continents layer.
  6. If necessary, in the Symbology pane, click Gallery. In the list of symbols, choose the Black Outline (1pt) symbol.

    Black Outline (1pt) symbol

    Tip:

    You can check the full name of a symbol in the gallery by pointing to it.

    The symbology is applied.

    Map symbolized with continents

Explore cell values in a local region

Your mean annual baseline temperature layer is appropriately symbolized and you've added proper reference data. Next, you'll explore the temperature values. You'll start by looking at the cells in and around the Red Sea to learn how geographic context can provide a stronger understanding of how climate varies from region to region.

  1. Zoom to the Red Sea, between the northeast of Africa and the Arabian peninsula.

    Red Sea and surrounding region

    The Red Sea is a narrow seawater inlet that runs into the Gulf of Aden and ultimately the Indian Ocean. It extends northwest to the Suez Canal, which provides access to the Mediterranean Sea. You'll create a bookmark to quickly navigate back to this region.

  2. On the Map tab, in the Navigate group, click the Bookmarks button and choose New Bookmark.

    Bookmarks button

  3. In the Create Bookmark window, for Name, type Red Sea. Click OK.

    Next, you'll inspect some of the mean annual temperature values in this region. When the Explore tool is active, clicking a cell will open a pop-up that contains the cell's value. But depending on where you click, you might open a pop-up for the World_Continents layer instead. You'll adjust the tool's setting to make sure you only open pop-ups for the correct layer.

  4. On the Map tab, in the Navigate group, click the Explore button and choose Selected in Contents.

    Selected in Contents option for Explore tool

    Now, the Explore tool will only show the pop-up of the layer selected in the Contents pane.

  5. In the Contents pane, click the Temperature - Mean Annual Baseline layer to select it.
  6. Click one of the warmest (darkest red) grid cells in the Red Sea.
    Note:

    The warmest cells in the Red Sea are generally near its southern end, around the two islands. These islands are actually small archipelagos known as Dahlak (near the African coast) and Farasan (near the Arabian coast).

    Pop-up of the warmest cell in the Red Sea

    The pop-up displays the Stretch.Pixel.Value which is the mean annual temperature value (in degrees C). In the example image, the pop-up belongs to a cell with the darkest red, and has a mean annual temperature of 29.534271.

  7. Click some of the other cells in and around the Red Sea.

    The values are hotter within the Red Sea than over adjacent land areas. This may seem counterintuitive, as one might expect the air over a large body of water to be cooler.

    The Red Sea is in a warm equatorial region and therefore has a high level of evaporation. This results in high salinity levels, which give the waters high heat capacity (4.18 J/g degrees C). Higher heat capacity means it takes longer to heat and cool the water. The surrounding land has a much lower heat capacity (just under 1 J/g degrees C), causing it to heat and cool quickly.

    The water's high heat capacity makes it difficult for the air immediately above the Red Sea to cool down at night and even seasonally, leading to higher average temperatures.

  8. Close the pop-up and save the project.

Create a table from NetCDF data

The cells above the Red Sea have temperature values close to 30°C, which seems very warm. But how warm, exactly? How do these values compare to elsewhere in the world?

To answer these questions, you'll create a table of the temperature values in the NetCDF file using a geoprocessing tool. With a table, you can sort the data to see the highest values and compare them to the values of the Red Sea.

  1. In the Geoprocessing pane, click the Back button. Search for and open the Make NetCDF Table View tool.
  2. For Input netCDF File, browse to the mme-netcdfs folder and choose baseline_tas_annual_mean_1986_2005.nc.

    Like when you created the raster layer from the same dataset, you'll make sure the temperature variable is included in the output.

  3. For Variables, choose climatology_tas_annual_mean_of_monthly_means.
  4. For Output Table View, type Mean Annual Temperature Values.

    You also need to set the table's row dimensions. Your raster layer's cells are based on latitude and longitude, so your table will be, too.

  5. For Row Dimensions, choose latitude and longitude.

    Make NetCDF Table View tool parameters

    By these parameters, the table will show the baseline mean annual air temperature for every combination of latitude and longitude. The remaining parameters don't need to be changed.

  6. Click Run.

    The table is added to the Contents pane.

  7. In the Contents pane, right-click Mean Annual Temperature Values and choose Open.

    The table opens. It contains the NetCDF data, organized into four columns:

    • OID: Short for Object ID, a unique integer value for each row.
    • Latitude: The degrees north or south of the equator, ranging from -90 to 90.
    • Longitude: The degrees east or west of the Greenwich meridian, ranging from -180 to 180.
    • climatology_tas_annual_mean_of_monthly_means: Baseline mean annual air temperature value in degrees Celsius.
  8. If necessary, resize the table so you can see 15 to 20 rows of data.
  9. Right-click the climatology_tas_annual_mean_of_monthly_means column heading and choose Sort Descending.

    Sort Descending option

    The values are sorted from highest temperature to lowest. The hottest temperature values are upwards of 30°C, but only 16 entries are within this extreme temperature range. That makes the southern portion of the Red Sea one of the hottest places on the earth.

  10. Close the table.

Visualize high-temperature locations

Where are the other extreme high mean annual temperature values located? The table contains the latitude and longitude for all entries, but it's difficult to imagine exactly where they are.

Using the Make XY Event Layer geoprocessing tool, you'll create a point layer based on the table to visualize the location of each table entry. Then, you'll select features with values of over 30°C to visualize them on the map.

  1. In the Geoprocessing pane, click the Back button. Search for and open the Make XY Event Layer tool.
  2. For XY Table, choose Mean Annual Temperature Values.

    The longitude and latitude fields are automatically chosen for the X Field and Y Field parameters, respectively.

  3. For Layer Name, type Mean Annual Temperature XY Layer.

    Make XY Event Layer tool parameters

    The default spatial reference matches your map, so you'll leave that parameter unchanged.

  4. Click Run.

    The new point feature layer is added to the map. Because both the raster layer and the table were created from the same NetCDF file using the same X and Y field parameters, each point is centered on top of a raster grid cell.

    Note:

    The default symbology of the point layer is random and may differ from the example image.

    Map of Make XY Event Layer tool output

    Because the data overlaps, you don't need to see both layers. You'll hide the points below the raster layer.

  5. In the Contents pane, drag Mean Annual Temperature XY Layer below Temperature - Mean Annual Baseline.

    Contents pane reordered

    The points no longer appear on the map. However, you can still highlight data points by selecting their entry in the layer's attribute table. You'll do this to see the points with the highest temperature values.

  6. Right-click Mean Annual Temperature XY Layer and choose Zoom To Layer.

    You return to a global extent.

  7. Right-click Mean Annual Temperature XY Layer and choose Attribute Table.

    The table is almost identical to the table you used to create the layer, with an added field named Shape.

  8. Right-click the climatology_tas_annual_mean_of_monthly_means column heading and choose Sort Descending.
  9. Select all rows with temperature values above 30°C.
    Tip:

    To select a row, click the empty square on the row's left. To select multiple rows at once, drag the pointer.

    The points with those values are highlighted on the map.

    Extreme temperature values selected on map

    The hottest baseline mean annual air temperature values are in Africa and the Red Sea, just north of the equator.

  10. Close the table and save the project.

Graph temperatures across latitude

The hottest temperatures you encountered were all close to the equator. It's generally expected that mean annual temperatures are highest in equatorial regions and decrease toward the poles. To verify this assumption about climate, you'll graph the north-to-south geographic distribution of temperature values on a scatter plot.

To ensure all the data is included in the scatter plot, you'll clear the selected data points.

  1. On the Map tab, in the Selection group, click the Clear button.

    Clear button

  2. In the Contents pane, right-click Mean Annual Temperature XY Layer, point to Create Chart, and choose Scatter Plot.

    Scatter Plot option

    The Chart Properties and Mean Annual Temperature XY Layer Scatter Plot 1 panes open.

  3. If necessary, in the Chart Properties pane, click the Data tab.

    You'll set the parameters for the x-axis and y-axis. Because you're graphing the distribution of temperatures across latitude (north to south), you'll choose the appropriate fields.

  4. For X-axis Number, choose latitude. For Y-axis Number, choose climatology_tas_annual_mean_of_monthly_means.

    Variables for x-axis and y-axis

    The scatter plot is created. Before you examine it more closely, you'll remove the linear trend line, which isn't useful for the question you want to answer.

  5. Under Statistics, uncheck Show linear trend.

    Show linear trend option

    You'll also change the chart title and axis labels. The current title and labels use the exact names of the variables, which aren't easy to read.

  6. Click the General tab. For Chart title, type Mean Annual Temperature 1986 - 2005 by Latitude.
  7. For X axis title, type Latitude. For Y axis title, type Temperature (deg. C).

    General tab chart and axis title parameters

    The chart updates. Depending on your window size, it may have different proportions than the example image. It may also have different colored points.

    Scatter plot of temperature values by latitude

    As expected, the warmest values are centered around the equator (0 degrees latitude). Antarctica, which corresponds to the most negative latitude values, is much colder than the north pole. By selecting features in the chart, you can see where they appear on the map.

  8. Draw a box around the points with the warmest temperatures.

    Scatter plot with box being drawn around the warmest temperatures

    The points in the box are selected in both the chart and the map.

    Map showing the warmest temperatures selected

  9. Select the coldest points (those at about -20°C and lower).

    Map showing the coldest temperatures selected

    As predicted, latitude seems to have a significant effect on temperature.

  10. Clear the selection.

Create a histogram

Do high temperatures or low temperatures occur more frequently? To answer this question, you'll create a histogram of mean annual temperatures. A histogram shows the distribution of values, allowing you to see which temperature values occur more or less frequently.

  1. In the Contents pane, right-click Mean Annual Temperature XY Layer, point to Create Chart, and choose Histogram.

    A new empty histogram is added to the pane that contains the scatter plot.

  2. If necessary, in the Chart Properties pane, click the Data tab. For Number, choose climatology_tas_annual_mean_of_monthly_means.

    The histogram is created. You'll adjust the number of bins, or bars, and also remove the line that shows the average value.

  3. For Bins, type 50. Under Statistics, uncheck Mean.

    Data parameters for histogram

    You'll also change the chart and axis titles. Because the y-axis of a histogram displays the count of features with a certain value, only the x-axis title needs to be changed.

  4. In the Chart Properties pane, click the General tab. For Chart title, type Distribution of Mean Annual Temperature.
  5. For X axis title, type Temperature (deg. C).

    General parameters for histogram

    The histogram updates.

    Histogram

    The histogram's distribution is skewed, meaning that the count of bins is higher on the right of the graph. This pattern indicates a higher frequency of warmer temperature values. By selecting a bin, you'll see where those values are located on the map.

  6. On the histogram, click the tallest bar (the third from the right).

    The bar is selected and the points that the bar represents are highlighted.

    Map of largest histogram bin values highlighted

  7. Click the tab to view your scatter plot.

    The selected data is also selected in the scatter plot.

    Scatter plot with largest histogram bin values highlighted

    The bin with the highest count contains data points distributed within -22 degrees to 31 degrees latitude. The equatorial region occupies almost half of the earth's surface area, but this fact isn't obvious on your map because the WGS 1984 coordinate system stretches the polar regions.

    In fact, the area of the earth's surface represented by the uppermost row of cells in your map is smaller than the surface area represented by just one cell at the equator. If your data were based on equal-area cells, the skew in this histogram would be even stronger, with a significant majority of cells being warm.

    The two small spikes on the left side of the histogram are another artifact of the WGS 1984 coordinate system. These represent the extreme polar values, which are repeated many times when stretched onto a flat rectangle.

  8. On the Map tab, in the Navigate group, click Bookmarks and choose the Red Sea bookmark.
  9. In the histogram, click the bin on the farthest right of the graph.

    This view confirms that some of the hottest baseline mean annual temperatures are in and around the Red Sea.

    Warmest values in and around the Red Sea

  10. Clear the selection. Close the two charts and the Chart Properties pane.
    Note:

    To reopen a chart, in the Contents pane, click the List By Charts button. Right-click the chart and choose Open.

  11. Return to the full extent of the map.
  12. Save the project.

In this lesson, you learned some of the fundamental concepts of climate and climate data. You added baseline (historic) temperature data to a map and explored the data further with charts. You also learned about some of the regional variations in climate, particularly around the Red Sea. In the next lesson, you'll examine projected climates and compare two projections to see how climate might change in the future.


Compare projected climates

In the previous lesson, you explored baseline climate data, which is historical in nature. In this lesson, you'll explore climate anomaly data, or models of possible future climates.

The range of future climate change depends on the concentrations of greenhouse gases that will be emitted into the atmosphere over years and decades. Climate science organizations worldwide generate future climate models based on scenarios called representative concentration pathways (RCPs).

As stated in the climate terms, concepts, and common data structures page, there are four RCPs. Each represents a different level of greenhouse gases being added to the earth's atmosphere.

  • RCP 2.6: Radiative forcing levels not only peak prior to, but decline to 2.6 W/m2 (watts per square meter) by 2100. Many people no longer consider scenario 2.6 a realistic possibility.
  • RCP 4.5: Radiative forcing levels stabilize without exceeding 4.5 W/m2 by 2100. Scenario 4.5 is still considered a realistic possibility.
  • RCP 6.0: Radiative forcing levels stabilize without exceeding 6.0 W/m2 by 2100. Scenario 6.0 is also considered a realistic possibility.
  • RCP 8.5: Radiative forcing levels are modeled with different assumptions than 2.6, 4.5, and 6.0. In scenario 8.5, very high levels of radiative forcing (8.5 W/m2) are assumed due to high population growth and lower incomes in developing countries. Scenario 8.5 is an extreme scenario, but still considered possible.

For discussions and overviews of these scenarios, check out the following papers:

Projected future climates are expressed in terms of change from baseline climate conditions. Climate scientists refer to this kind of change as an anomaly because it represents a deviation from normal climate conditions.

In this lesson, you'll combine anomaly temperature values in a future climate scenario with baseline values to show projected future temperatures. You'll also compare a near-future (2020–2039) projection for RCP 8.5 with a distant-future (2080–2099) projection.

Create a map

You'll create a map within the same project to depict future climate projections. You'll copy the Baseline map so you can display its baseline temperature data in conjunction with anomaly data.

  1. If necessary, open your World Climate Data Explorer project.
  2. In the Contents pane, right-click Mean Annual Temperature XY Layer and choose Remove.
  3. On the ribbon, click the View tab. In the Windows group, click Catalog Pane.

    Catalog Pane button

  4. In the Catalog pane, expand the Maps folder. Right-click the Baseline map and choose Copy.

    Copy option

  5. Right-click the Maps folder and choose Paste.

    A new map named Baseline1 is added to the folder.

  6. Right-click the Baseline1 map and choose Rename. Rename the map RCP 8.5.
  7. Right-click the RCP 8.5 map and choose Open.

    The map opens as a new tab in the map view. You can switch between it and the Baseline map by clicking the tabs. The new map has all the same data and symbology as the old one. You won't need to view the baseline data in this map.

  8. In the Contents pane, uncheck Temperature - Mean Annual Baseline to turn it off.

Create layers of anomaly data

Next, you'll create raster layers from the NetCDF files containing the mean annual temperature anomaly data for the RCP 8.5 scenario. You'll add data from both the 2020–2039 and 2080–2099 time periods.

  1. In the Geoprocessing pane, search for and open the Make NetCDF Raster Layer tool.

    You used this tool previously for the baseline data. You'll run it two more times.

  2. For Input netCDF File, browse to the mme-netcdfs folder and choose cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp85_2020-2039.nc.
    Tip:

    While browsing, you can expand the Name field to view the full name of each file. You can also sort file names alphabetically to make them easier to find.

    This dataset contains projected average temperatures from a 20-year (2020–2039) time frame. It is based on a model that produces daily temperature anomalies. Twenty-year averages smooth out the extreme events and are useful when exploring long-term climate trends. Real climate is not gradually and smoothly getting warmer every year in every location. The climate models account for many variables in order to produce daily values that behave like real weather.

  3. For Variable, choose tas.

    This variable contains a possible future anomaly for mean annual temperatures two meters above the surface.

    When you choose this variable, the X Dimension and Y Dimension parameters populate with longitude and latitude, respectively. You'll leave these values unchanged.

  4. For Output Raster Layer, type Temperature Anomalies - Mean Annual RCP 8.5 2039.

    Make NetCDF Raster Layer tool parameters

  5. Click Run.

    The tool runs and the output raster layer is added to the map. The layer shows mean annual temperature anomalies under the RCP 8.5 scenario for the 2020–2039 time period. The cells in this layer are the same size as the cells in the Temperature - Mean Annual Baseline layer: each cell in this raster layer is 1 degree by 1 degree.

    Map of anomaly temperature data for the 2020–2039 time frame

    In the legend for your new layer, the highest value is 4.17°C (or 7.51°F) and the lowest value is 0.21°C (or 0.38°F). These values represent the change in temperature compared to the baseline.

    The layer indicates that the annual mean air temperature will be warmer everywhere on the planet. The greatest increases will be near the north pole. In general, land areas will have higher increases than water areas.

  6. Run the Make NetCDF Raster Layer tool again, but change the following parameters:
    • Input netCDF file: cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp85_2080-2099.nc
    • Output Raster Layer: Temperature Anomalies - Mean Annual RCP 8.5 2099

    The tool runs and the layer is added to the map. It looks similar to the previous result layer. The legend's highest value is 14.16°C (or 26.28°F) and the lowest value is 1.34°C (or 2.41°F).

    The highest and lowest temperature values are greater than those of the Temperature Anomalies - Mean Annual RCP 8.5 2039 raster layer, indicating that mean annual temperatures are expected to increase even more across the globe by the 2080–2099 time period under the RCP 8.5 scenario. The distribution of where temperatures increase the most doesn't change much.

  7. Save the project.

Add anomalies to baseline data

Next, you'll add the baseline temperature data to the RCP 8.5 anomaly data to create two layers representing the projected mean annual temperatures for the future time periods (2020–2039 and 2080–2099). You'll accomplish this task with raster functions, operations that apply calculations directly to a raster's pixel values without requiring new data to be saved.

  1. On the Analysis tab, in the Raster group, click the Raster Functions button.

    Raster Functions button

    The Raster Functions pane appears. To add the values of two rasters together, you'll use the Plus function.

  2. In the search box, type Plus. In the list of results, under Math, click Plus.

    Plus raster function

    Like geoprocessing tools, raster functions require input parameters.

  3. For Raster, choose Temperature - Mean Annual Baseline. For Raster2, choose Temperature Anomalies - Mean Annual RCP 8.5 2039.

    For the other parameters, the default settings are fine.

    Plus raster function parameters

  4. Click Create new layer.

    The layer is added to the map.

    Plus raster function output for 2020–2039 time frame

    The legend indicates that the highest value is 31.8657°C (or 89.4°F) and the lowest value is -55.0024°C (or -131°F). The default layer name is long, so you'll rename it.

  5. In the Contents pane, double-click the Plus_Temperature - Mean Annual Baseline_Temperature Anomalies - Mean Annual RCP 8.5 2039 layer.

    The Layer Properties window appears.

  6. On the General tab, for Name, change the name to Temperature - Mean Annual RCP 8.5 2039 and click OK.

    You'll repeat the process for the 2080–2099 anomaly data.

  7. Run the Plus raster function again with the following parameters:
    • Raster: Temperature Anomalies - Mean Annual RCP 8.5 2099
    • Raster2: Temperature - Mean Annual Baseline

    The raster function adds a new layer to the map. It looks similar to the other layer you added and also has a long name.

  8. Rename the new layer Temperature - Mean Annual RCP 8.5 2099.

    The highest value for this layer is 35.7°C (or 96.3°F) and the lowest value is -51.3°C (or -124.3°F). These temperatures, like those in the 2039 layer, are the annual averages of projected daily average temperatures. The temperatures for individual days may be hotter or colder. Additionally, the cells of these layers cover large areas (over 4,800 square miles or 12,000 square kilometers), so temperature values may also vary within each cell. Mountains may be colder, while basins may be hotter.

    Data of this cell size is a starting point for producing more refined data. Other climate variables, like barometric pressure, elevation, and land cover, can help refine this data. But datasets involving those variables vary in quality and may introduce bias and error.

    The process to create refined data is called downscaling. When climate data is downscaled, the errors within the data cannot be controlled for. For most locations, there is no way to know the accuracy of the model. This problem is particularly difficult when downscaling a global dataset because the data quality of ancillary data varies from country to country.

  9. Save the project.

Apply a consistent symbology

Now that you've created raster layers for projected climates in 2020–2039 and 2080–2099, you'll compare them to visualize the changes. Each layer has a different range of symbols, which means the same color does not represent the same temperature.

To visually compare the rasters, you'll change the symbology so that both layers have the same range. This range will include all temperature values in both layers. This way, specific colors will represent the same temperature in each layer.

First, you'll create a raster with the full range of temperatures in both layers. This raster is only needed for symbology purposes, so the location of its temperature values doesn't matter. You'll use the Create Random Raster geoprocessing tool, which creates a raster dataset of random values within a specified range.

  1. In the Geoprocessing pane, search for and open the Create Random Raster (Data Management tools) tool.
  2. If necessary, for Output Location, browse to World Climate Data Explorer.gdb.
  3. For Raster Dataset Name with Extension, type RCP_85_Desired_Symbology.

    Next, you'll set the minimum and maximum values of the dataset. They need to be inclusive of the highest and lowest values from your two RCP 8.5 layers.

  4. For Minimum, type -55.99. For Maximum, type 35.99.

    These values extend the range over the actual minimum and maximum values. If you used the exact minimum and maximum values from the RCP 8.5 layers, it's possible that the full range of values would not be represented in the output dataset.

    The Output extent and Cellsize parameters must be identical to those of the RCP 8.5 layers in order to apply the same symbology.

  5. For Output extent, choose Temperature - Mean Annual RCP 8.5 2099. For Cellsize, type 1.

    Create Random Raster tool parameters

  6. Click Run.

    The tool runs and adds the layer to the map. It looks like random grayscale noise, but it has the same extent and cell size as the other raster layers.

    The exact values in the legend are random, but the highest value should be higher than 35.7°C and the lowest should be lower than -55.0°C.

    Next, you'll apply the orange-red symbology to the layer and apply it to the RCP 8.5 layers.

  7. In the Contents pane, right-click RCP_85_Desired_Symbology and choose Symbology.
  8. For Color scheme, check Show All and Show Names. Choose Orange-Red (Continuous).
  9. For Label, add a C to the end of each label.

    Desired symbology color ramp parameters

    Note:

    Because the RCP_85_Desired_Symbology raster has random values, the values in your labels may differ from the example image.

  10. For Stretch type, choose Minimum Maximum.

    It's possible that the color scheme values are already reversed, so that the darkest reds correspond to the hottest temperatures. If not, you'll need to reverse the color scheme.

  11. If necessary, in the Contents pane, right-click the color scheme for the RCP_85_Desired_Symbology layer and click the Reverse color scheme button.

    Next, you'll save the RCP_85_Desired_Symbology layer as a layer file and import the layer file's symbology to your other RCP 8.5 layers.

  12. In the Contents pane, uncheck RCP_85_Desired_Symbology. Right-click RCP85_Desired_Symbology, point to Sharing, and choose Save As Layer File.

    The Save Layer(s) as LYRX File window appears.

  13. If necessary, browse to your climate-data folder. Save the layer as RCP_85_Desired_Symbology.lyrx (the default name).
  14. Open the Symbology pane for the Temperature - Mean Annual RCP 8.5 2099 layer. Click the menu button and choose Import.

    Import option

    The Import symbology window appears.

  15. Browse to your climate-data folder, click the RCP_85_Desired_Symbology.lyrx file, and click OK.

    The symbology is applied.

    Map with desired symbology

  16. Import the same symbology for the Temperature - Mean Annual RCP 8.5 2039 layer.

    The symbology for the Mean Annual RCP 8.5 2039 and Mean Annual RCP 8.5 2099 raster layers is now identical. The legends contain the same minimum and maximum values, which correspond to the values of the RCP_85_Desired_Symbology layer.

  17. In the Contents pane, right-click the RCP_85_Desired_Symbology layer and choose Remove.
  18. Save the project.

Evaluate the difference

Next, you'll compare the two projected climates using the Swipe tool. You'll also take a look at the projected future climates for the Red Sea.

  1. In the Contents pane, confirm that the Temperature - Mean Annual RCP 8.5 2099 layer is below the World Continents layer and above the Mean Annual RCP 8.5 2039 layer.
  2. Click the Temperature - Mean Annual RCP 8.5 2099 layer to select it.

    Contents pane with selected layer

    Selecting a layer in the Contents pane causes contextual tabs to appear on the ribbon. Contextual tabs generally involve appearance and sharing options for the selected layer.

  3. On the ribbon, click the Appearance tab. In the Effects group, click the Swipe button.

    Swipe button

    Note:

    You may need to activate the Explore tool before you can activate the Swipe tool.

    When the Swipe tool is active, your pointer shows an arrow when you point to the map. The direction of the arrow changes depending on where the pointer is positioned.

  4. Drag the pointer across the map.

    Swipe tool dragged across the map

    Depending on the direction you drag the pointer, there will be a vertical or horizontal change in gradient. The difference between these layers is most evident over the northern landmasses. Generally, the 2099 layer has hotter temperatures than the 2039 layer.

    Next, you'll explore the changes in the Red Sea.

  5. Navigate to the Red Sea bookmark. On the Map tab, in the Navigate section, click the lower part of the Explore button and choose Selected in Contents.
  6. In the Contents pane, confirm that the Temperature - Mean Annual RCP 8.5 2099 layer is selected. Press the Ctrl key while clicking the Temperature - Mean Annual RCP 8.5 2039 layer to additionally select it.
  7. On the map, click one of the hotter cells in the southern portion of the Red Sea.

    A pop-up appears. Because two layers are selected, information for both RCP 8.5 layers is contained in the pop-up.

  8. If necessary, resize the pop-up so you can see all the information.
    Tip:

    You can also resize just the upper portion of the pop-up, where the cell values for both layers are located.

    Pop-up with information for both RCP 8.5 layers

    In the example image, the pop-up indicates that the value for the RCP 8.5 2099 scenario is 34.0658°C and 31.146338°C for the RCP 8.5 2039 scenario. The difference is 2.919462°C, or 5.255°F.

    You'll also examine the differences in temperature in the area where you live. You'll find out whether the projected future changes in temperature are more or less than those for the Red Sea.

  9. On the Map tab, in the Inquiry section, click the Locate button.

    Locate button

    The Locate pane appears.

  10. In the search box, type the name of your city and press Enter. In the list of results, right-click your city and choose Pan To.
    Note:

    The example images use Redlands, California, the home of Esri headquarters.

    Pan To option

    The map pans to your city. It's possible that the map is zoomed too far in to see any geographic context.

  11. If necessary, zoom out until you can see the area around your city. Close the Locate pane.
  12. In the Contents pane, turn on the Temperature - Mean Annual Baseline layer. Press Ctrl and click to select it in addition to the other layers.
  13. Click the map where your city is located. If necessary, resize the pop-up to see all the information.

    Pop-up for Redlands, California

    In the example image, the projected temperatures are much lower than those in the Red Sea. However, the temperature change from 2039 to 2099 is about 3.84°C, which is more than the change in the Red Sea.

  14. Close the pop-up. Navigate to the full extent of the data and save the project.
  15. Close ArcGIS Pro.

In this lesson, you created raster layers of anomaly data, showing projected temperature changes around the world for the RCP 8.5 scenario. You added the anomaly values to the baseline values and symbolized the result layers to show projected future climate. In the next lesson, you'll locate a specific type of climate based on a climate classification system.


Locate a type of climate

In the previous lesson, you explored future climate projections for the entire world. In this lesson, you'll map the location of a specific type of climate, the Mediterranean climate, as defined by the Köppen climate classification system.

The Mediterranean climate has summers that are dry with warm to hot temperatures, and winters that are moist with cool to moderate temperatures. The Köppen system divides the Mediterranean zone into two parts, based on whether the dry summers have temperatures that are considered warm versus hot. You'll locate an area that includes both temperature zones, encompassing the total area considered to be Mediterranean climate.

You'll use monthly climate data, including precipitation data, to account for the seasonal variations that define the Mediterranean climate. This data represents the mean of daily temperatures (°C) and the mean of the sums of daily precipitation (mm) levels for a given month from 1986 to 2005.

Create monthly temperature layers

You have over 100 NetCDF files and together they contain over 800 variables. That means you can potentially create over 800 raster layers in ArcGIS Pro. Although you only need 12 layers for this workflow (one for each month), repeating the same process to create each one can be tedious.

Instead, you'll use a project template that was supplied with your original data. This template contains custom geoprocessing tools to automate the creation of layers needed for the lesson.

Note:

It's a good idea to prepare geoprocessing models when performing tasks that need to be repeated frequently. This lesson won't go into detail about the specifics of creating models, but if you want to learn how to make them, check out Build a Model to Connect Mountain Lion Habitat or Estimate Storage Capacity with Drone Imagery.

  1. Open ArcGIS Pro. If necessary, sign in to your ArcGIS organizational account.
  2. Near the bottom of the window, click Select another project template.

    Select another project template option

    The Create New Project From Template window appears.

  3. Browse to your climate-data folder. Click the Mediterranean-Climate.aptx ArcGIS Pro project template and click OK.
  4. In the Create a New Project window, name the project Mediterranean Climate Explorer. For Location, browse to your climate-data folder. Uncheck Create a new folder for this project and click OK.

    A new project opens. It contains a map named Mediterranean with basemaps and other contextual data layers. It also contains a geoprocessing model that you'll run next to create layers of climate variables needed to determine the Mediterranean climate's location.

  5. In the Catalog pane, expand the Toolboxes folder and the Mediterranean Climate Explorer toolbox.

    The toolbox contains the Import NetCDF Baseline Temperature Layers model. This model creates 12 raster layers, one for each month, based on a NetCDF file with monthly mean data. You'll need to edit the model so it uses the data path of your NetCDF files.

  6. Right-click the Import NetCDF Baseline Temperature Layers model and choose Edit.

    Edit Import NetCDF Baseline Temperature Layers model

    A model view appears, showing the model. The model runs the Make NetCDF Raster Layer 12 times on a single input layer.

  7. On the ModelBuilder tab, in the Run group, click Validate.

    Validate button

    The model checks to see if it can run. All the variables in the model turn white because the current path for the input layer is incorrect.

  8. Click the Baseline Monthly Temperature NetCDF File variable to select it. Right-click the variable and choose Open.

    Open option

    The Baseline Monthly Temperature NetCDF File window appears.

  9. For Baseline Monthly Temperature NetCDF File, browse to the mme-netcdfs folder and double-click the baseline_tas_monthly_mean_1986_2005.nc file.
  10. Click OK.

    The model automatically validates and the variables return to their original colors (blue for inputs, yellow for tools, and green for outputs).

  11. On the ModelBuilder tab, in the Run group, click Run.

    Run button

    The tool runs and adds 12 layers to the Contents pane.

  12. Close the Import NetCDF Baseline Temperature Layers model results window and the Import NetCDF Baseline Temperature Layers model view. If prompted to save the model, click Yes.

    Because of the large number of layers, it may not be possible to see all layers in the Contents pane without scrolling. You'll collapse the legends of the new layers to reduce space.

  13. In the Contents pane, press Ctrl and click the button to collapse the legend for the Temperature Dec, Baseline layer.

    Collapse button

    The legends for all layers collapse.

Identify lowest winter temperatures

According to the Köppen system, three conditions define Mediterranean climate. The first condition is a wintertime temperature ranging between -3 and 18°C (26.6 to 64.4°F).

To determine winter temperatures based on your monthly data, you'll create a custom raster function that runs multiple raster functions in a specific order. Ultimately, you'll create three custom raster functions, one for each of the conditions that define Mediterranean climate. To store these raster functions, you'll create a raster function subcategory.

  1. Click the Analysis tab. In the Raster group, click the Raster Functions button.
  2. In the Raster Functions pane, click Project. Click the menu button and choose Add New Sub-Category.

    Add New Sub-Category option

    The Add New Sub-Category window appears.

  3. For Name, type Mediterranean-Climate. Click OK.
    Note:

    A notification may explain that changes will be lost if the project is not saved. You can save the project and close the notification at any time.

    Next, you'll edit a custom raster function.

  4. On the Analysis tab, in the Raster group, click Function Editor.

    Function Editor button

    The Raster Function Template 1 view appears. This view is also known as the function editor. The view is currently empty. You'll add raster functions and input layers to it, similar to the model you previously ran. First, you'll add input layers that contain temperature data for winter months.

    Tip:

    You can move or resize the function editor any way you like.

  5. In the Contents pane, drag the layers of the three winter months in the Northern Hemisphere (December, January, and February) and the three in the Southern Hemisphere (June, July, and August) into the function editor.

    You now have six input layers in total.

    Input layers for winter months

    Tip:

    To rearrange raster function elements after you've added them to the view, click the element once to select it. Point to the center of the element until the pointer changes and drag the element where you want.

    Next, you'll add the Cell Statistics raster function. This raster function will calculate the temperature values for each cell based on a statistical operation of your choice. You'll use it to identify the minimum temperatures of each cell, which will reflect the coldest winter temperatures.

  6. In the Raster Functions pane, click System. Search for Cell Statistics and drag the Cell Statistics raster function into the function editor.

    You'll connect the inputs to the raster function.

  7. Without selecting it, point to the December (Dec) element. Drag a line from it to the Rasters input position of the Cell Statistics raster function.

    Line drawn from input to raster function

    The layer is designated as an input for the raster function.

  8. Connect the remaining five raster layers to the Raster input position of the Cell Statistics raster function.

    You'll also adjust the raster function's properties to make sure the tool finds the minimum values.

  9. Double-click the Cell Statistics raster function. In the Cell Statistics Properties window, for Operation, choose Minimum.

    Operation parameter set to Minimum

  10. Click OK.
  11. On the Function Editor toolbar, click the Auto Layout button.

    Auto Layout button

    The layout of the elements is automatically organized.

    Cell Statistics raster function layout for winter months

Find areas with specific winter temperatures

The current configuration will produce an output that contains the coldest winter temperature values in each grid cell. You'll add another raster function, Remap, to the configuration to identify locations where those temperatures are within the Mediterranean winter temperature range: -3 to 18°C (26.6 to 64.4°F).

  1. In the Raster Functions pane, search for Remap and drag the Remap raster function into the function editor.

    The output of the Cell Statistics raster function will be the input of the Remap raster function.

  2. Drag a line from the Out position of the Cell Statistics raster function to the Raster input position of the Remap raster function.

    Connection between Cell Statistics and Remap raster functions

  3. Double-click Remap. In the Remap Properties window, change Minimum to -3 and Maximum to 19.

    You'll use 19 for the maximum instead of 18 because the Remap raster function's maximum value is not inclusive.

    The Output parameter determines the new value to which cells within the minimum and maximum value will be remapped. You'll specify values to be remapped to a new value of 1. You'll have all values outside the range remapped to NoData, so they won't appear on the map.

  4. Change Output to 1. Check Change Missing Values to NoData.

    Remap raster function parameters

  5. Click OK.

    Your raster function will now locate regions with winter temperatures within the acceptable range for the Mediterranean climate. Next, you'll save the raster function.

  6. On the Function Editor toolbar, click the Save button.

    Save button

    The Save window appears.

  7. Change the following parameters:
    • For Name, type Mediterranean Baseline Winter Temperature.
    • For Category, choose Project.
    • For Sub-Category, choose Mediterranean-Climate.
    • In the Description field, type Locations with baseline conditions that meet the winter temperature requirement of a Mediterranean climate, with the mean monthly temperature of the coldest winter month being between -3 and 18 degrees Celsius.
  8. Click OK.

    The raster function is saved. Next, you'll run it.

  9. If necessary, in the Raster Functions pane, clear the search text. On the Project tab, under Mediterranean-Climate, click Mediterranean Baseline Winter Temperature.

    Mediterranean Baseline Winter Temperature raster function

    The raster function opens.

  10. Click Create new layer. After the layer is created, switch to the Mediterranean map view.

    Areas within the Mediterranean climate winter temperature range appear gray.

    Map with Mediterranean winter temperature range

    You'll turn off the monthly mean temperature layers and change the symbology of the result layer to better visualize which areas around the world fall into the correct temperature range.

  11. In the Contents pane, press Ctrl and click any visibility check box to turn off all layers. Turn on the following layers:
    • Mediterranean Baseline Winter Temperature
    • Human Geography Dark Detail
    • World Terrain with Labels
    • World Hillshade
  12. Open the Symbology pane for the Mediterranean Baseline Winter Temperature layer.
  13. Change Color scheme to Yellow-Green-Blue (Continuous). Close the Symbology pane.

    You'll add transparency to the layer to see the continents below.

  14. In the Contents pane, make sure the Mediterranean Baseline Winter Temperature layer is selected. On the Appearance tab, in the Effects section, change Layer Transparency to 30.0 percent.

    Layer Transparency option

    The transparency is applied automatically.

    Map with symbolized Mediterranean winter temperature range

    Based on the layer, there are two latitudinal bands where winter temperatures are within the Mediterranean winter temperature range. One band is in the Northern Hemisphere, while one is in the Southern Hemisphere. Some mountainous regions outside of these bands also have the correct winter temperatures.

  15. Save the project.

Find areas with specific summer temperatures

According to the Köppen system, the second condition that defines the Mediterranean climate is a mean monthly temperature higher than 10°C (50°F) during the hottest month of summer.

You'll determine which areas meet this condition with a raster function. The process for identifying high temperatures during the summer months is similar to the one you used to identify low temperatures during the winter months. You'll save a copy of your Mediterranean Baseline Winter Temperature raster function and modify it.

  1. On the Function Editor toolbar, click the Save As button.

    Save As button

  2. Save the raster function with the following parameters:
    • For Name, type Mediterranean Baseline Summer Temperature.
    • For Category, choose Project.
    • For Sub-Category, choose Mediterranean-Climate.
    • In the Description field, type Locations with baseline conditions that meet the summer temperature requirement of a Mediterranean climate, with the mean monthly temperature of the hottest summer month being greater than 10 degrees Celsius.

    Once you save, Mediterranean Baseline Summer Temperature becomes the open raster function in the function editor. Any changes you make to the raster function will not affect your winter raster function.

    You won't need to change the inputs, because the winter months for one hemisphere are the summer months for the other. You'll edit the Cell Statistics raster function to look for maximum values instead of minimum.

  3. Double-click the Cell Statistics raster function. For Operation, choose Maximum.

    Cell Statistics raster function parameters for the summer months

  4. Click OK.

    You'll also change some of the parameters in the Remap raster function. In particular, you'll change the minimum value to 10 and the maximum value to an artificially high number of 200 to ensure no values above 10 are omitted.

  5. Double-click the Remap raster function. Change Minimum to 10 and Maximum to 200.

    Remap raster function parameters for the summer months

  6. Click OK.

    Your raster function will identify regions that meet the summer temperature condition of a Mediterranean climate.

  7. Save the raster function. Return to the Mediterranean map view.
  8. In the Raster Functions pane, open the Mediterranean Baseline Summer Temperature function.
  9. Click Create new layer.

    Map with Mediterranean summer temperature range

    The layer is added to the map. It shows all areas that have a maximum summer temperature of over 10°C. Because there is no upper limit to the temperature, the layer covers much more area than the winter layer. You'll symbolize the layer to see it better in the context of the rest of the map.

  10. Open the Symbology pane for the Mediterranean Baseline Summer Temperature layer. For Color scheme, choose Yellow-Orange-Red (Continuous).
  11. Close the Symbology pane.
  12. In the Contents pane, make sure the Mediterranean Baseline Summer Temperature layer is selected. On the Appearance tab, in the Effects section, change Layer Transparency to 40.0 percent.

    Both the winter and summer layers are now visible.

    Map with symbolized Mediterranean summer temperature range

    The Mediterranean climate will be somewhere in the area where both layers overlap. However, you still need to identify areas that follow the third condition for the Mediterranean climate.

  13. Close the function editor and save the project.

Mosaic precipitation layers

The third condition of a Mediterranean climate is a little more complicated than the previous two. The mean monthly precipitation in the driest month of summer must be less than 30 millimeters (mm) and less than one-third the precipitation in the wettest month of winter.

This condition uses a different definition of summer and winter than the previous conditions. Here, summer and winter are considered to last six months each, instead of three. Because the comparison for this condition is between the minimum summer value and the maximum winter value, these conditions must be tested separately for each hemisphere.

The process to create layers that show the wettest and driest mean monthly precipitation is similar to the process to determine the coldest and warmest mean monthly temperature, but with input layers based on precipitation NetCDF files instead of temperature NetCDF files.

For the purposes of this exercise, you've been provided four raster layers that already contain information about the wettest and driest mean monthly precipitation for each hemisphere.

  1. On the Map tab, in the Layer group, click the Add Data button.

    Add Data button

    The Add Data window appears.

  2. Browse to your climate-data folder and open the mediterranean-precipitation-rasters folder.

    The folder contains four raster files: Driest_Summer_Prec_N.tif, Driest_Summer_Prec_S.tif, Wettest_Winter_Prec_N.tif, and Wettest_Winter_Prec_S.tif. The N and S in these file names refer to the Northern or Southern Hemisphere.

  3. Press Ctrl and click all four raster files and click OK.

    The rasters are added to the map. Based on the legends, the driest summer precipitation levels are about 540 mm for the Northern Hemisphere and 360 mm for the Southern Hemisphere. The wettest winter precipitation levels are about 700 mm for the Northern Hemisphere and 620 mm for the Southern Hemisphere.

    The legends of all four layers have 0 as the lowest value. This is because data for the hemisphere that isn't represented in the layer is replaced with 0.

  4. Turn off all four precipitation layers. Press Ctrl and click the button to collapse the layer legend for the Driest_Summer_Prec_N layer.

    All layer legends are collapsed.

    You'll mosaic the layers together to create two raster layers, one that shows the driest precipitation levels around the world and one that shows the wettest. To do so, you'll create another custom raster function.

  5. On the Analysis tab, in the Raster category, click the Function Editor button.
  6. Drag the four precipitation raster layers into the function editor.
  7. In the Raster Functions pane, click System. Search for Mosaic Rasters.

    You'll need to run the tool twice, once for the driest precipitation levels and once for the wettest.

  8. Drag the Mosaic Rasters function into the function editor two times.
  9. Connect the two raster layers with the driest summer precipitation values to the Raster position of one Mosaic Rasters function. Connect the two raster layers with the wettest winter precipitation values to the Raster position of the other Mosaic Rasters function.

    Mosaic Rasters raster functions

    You'll rename each function to avoid confusing them.

  10. Right-click the Mosaic Rasters function for the driest summer precipitation values and choose Rename.

    Rename option

  11. Type Driest Summer Precipitation and press Enter.
  12. Rename the other Mosaic Rasters function Wettest Winter Precipitation.

Find areas with specific precipitation levels

You'll use the mosaicked rasters as inputs to determine which areas fulfill the precipitation condition for the Mediterranean climate. Using the Less Than raster function, you'll determine areas where the driest summer month's precipitation is under 30 mm and less than a third of the wettest winter month's precipitation.

  1. In the Raster Functions pane, search for Less Than and drag the Less Than raster function into the function editor two times.

    You'll run this raster function twice, once for each component of the precipitation condition.

  2. Connect the Out position of the Driest Summer Precipitation element to the Raster position of one of the Less Than raster functions.

    Driest Summer Precipitation connected to Less Than raster function

  3. Double-click the connected Less Than function to open its properties. For Raster2, type 30.

    Parameters for the first Less Than raster function

  4. Click OK.

    A new element, named 30, is added to the function editor.

  5. Rename the connected Less Than raster function Driest Summer Month Is Less Than 30 mm.

    Raster function to determine locations with precipitation less than 30 mm

    The result of this Less Than function is a raster that shows locations where the driest summer month has less than 30 mm precipitation.

    The second part of the precipitation condition is that the driest summer month must have precipitation values less than one-third the precipitation values of the wettest winter month. Before you use the second Less Than function, you'll use the Divide function to calculate one-third of the wettest winter precipitation values.

  6. In the Raster Functions pane, search for Divide and drag a Divide function into the function editor.
  7. Connect the Out position of the Wettest Winter Precipitation function to the Raster position of the Divide function.
  8. Double-click the Divide function to open its properties. For Raster2, type 3 and click OK.

    Divide raster function

    You'll use the results of the Divide function and the Driest Summer Precipitation function as the inputs for the second Less Than function.

  9. Connect the Out position of the Driest Summer Precipitation function to the Raster position of the second Less Than function.
  10. Connect the Out position of the Divide function to the Raster2 position of the second Less Than function.
  11. Rename the second Less Than function Driest Summer Month Is Less Than One Third Wettest Winter Month.

    Less Than raster functions

Combine the precipitation results

You now have raster functions to determine whether areas fulfill each part of the precipitation condition. All that remains is to combine these results into a single raster layer. You'll use the Boolean And function to combine them. Then, you'll remap the values like you did for the other conditions.

  1. Search for and add a Boolean And raster function to the function editor.
  2. Connect the Out positions of the Driest Summer Month Is Less Than One Third Wettest Winter Month and Driest Summer Month is Less Than 30 mm functions to the Raster and Raster2 positions of the Boolean And raster function.

    You don't need to adjust any parameters of this function. By default, it'll create a layer that shows the intersection of its inputs.

  3. Add a Remap raster function to the function editor.
  4. Connect the Out position of the Boolean And function to the Raster position of the Remap function.
  5. Double-click the Remap function.

    The results of the Boolean And function will only contain values of 0 and 1, with 1 values representing areas where the inputs intersect. You'll set a minimum and maximum value that will include all 1 values and change all 0 values to NoData.

  6. Change Minimum to 1, Maximum to 2, and Output to 1.
  7. Check Change Missing Values to NoData.

    Remap raster function parameters for the precipitation condition

  8. Click OK.
  9. On the Function Editor toolbar, click the Auto Layout button.

    Raster function to determine precipitation condition

    Your custom raster function is complete.

  10. On the Function Editor toolbar, click the Save button.
  11. Save the function with the following parameters:
    • For Name, type Mediterranean Summer Precipitation.
    • For Category, choose Project.
    • For Sub-Category, choose Mediterranean-Climate.
    • In the Description field, type Locations with baseline conditions that meet the summer precipitation requirement of a Mediterranean climate, meaning precipitation for the driest summer month is under 30 mm and less than one third of the wettest winter month.
  12. Close the function editor and save the project.
  13. In the Raster Functions pane, click Project. Open the Mediterranean Summer Precipitation raster function and click Create new layer.

    A new layer is added to the map.

    Map of areas that fulfill the precipitation condition

    The layer primarily includes oceanic areas in the mid-latitudes, although it also includes many areas near the poles. Overall, there is a larger degree of latitudinal variance than in the temperature layers. You'll symbolize the layer in a way similar to the others.

  14. Open the Symbology pane for the Mediterranean Summer Precipitation layer. For Color scheme, choose Purple-Green (Continuous).
  15. Close the Symbology pane.
  16. Change the layer transparency to 70.0 percent.

    Symbolized map of areas that fulfill the precipitation condition

Combine the three conditions

Next, you'll combine the three condition rasters and produce a final output that shows the location of the Mediterranean climate around the world.

All of your rasters have a value of 1 where cells meet the condition and NoData where cells don't meet the condition. You'll only need to determine areas where the rasters intersect.

  1. In the Raster Functions pane, search for and open the Cell Statistics function.
  2. For Rasters, choose Mediterranean Summer Precipitation, Mediterranean Baseline Summer Temperature, and Mediterranean Baseline Winter Temperature.
  3. For Operation, choose Sum. Confirm that Extent Type is set to Intersection Of.

    Cell Statistics raster function parameters for final layer

    With these parameters, the result layer will only show areas where all three rasters intersect. You'll change additional parameters to give the result layer a name and description.

  4. At the top of the list of parameters, click General.
  5. For Name, type Köppen Mediterranean Climate.
  6. For Description, type Köppen Mediterranean Climate as calculated from mean monthly baseline temperature and precipitation data from 1986-2005.

    Cell Statistics raster function general parameters

  7. Click Create new layer.

    The layer is added to the map.

  8. Turn off the Mediterranean Summer Precipitation, Mediterranean Baseline Summer Temperature, and Mediterranean Baseline Winter Temperature layers.

    Map of the Mediterranean climate

    The Mediterranean climate covers many land areas, including northern and southern Africa, western North America, and parts of Australia and South America. It also covers a lot of sea areas.

    You'll finish the map by symbolizing the data.

  9. Open the Symbology pane for the Köppen Mediterranean Climate layer. Change Color scheme to Yellow-Orange-Red (Continuous).
  10. Close the Symbology pane. Change the layer transparency to 30.0 percent.

    Symbolized map of the Mediterranean climate

    With this symbology, it's apparent which parts of the climate are on land and which are on ocean. You'll also change the map's projection.

    In particular, you'll use the Mollweide projection, an equal area projected coordinate system. This means the raster cells will be shown with minimal distortion. You'll get a better idea of how much of the world meets the criteria for this definition of a Mediterranean climate.

  11. In the Contents pane, double-click Mediterranean.

    The Map Properties window appears.

  12. On the Coordinate Systems tab, search for Mollweide. Expand Projected coordinate system and World.
  13. Click Mollweide (world).

    Mollweide projection

  14. Click OK.

    Map with Mollweide projection

    Lastly, you'll compare your results with a layer that shows the projected location of the Mediterranean climate in 2080–2099 based on the RCP 8.5 scenario.

    For the purposes of this exercise, you've already been provided this layer. It was created by running the same raster functions you used on baseline data, but with combined baseline and anomaly data instead.

  15. On the Map tab, in the Layer group, click the Add Data button.
  16. In the Add Data window, browse to the climate-data folder and open the mediterranean-rcp-85 folder. Choose Koppen_Mediterranean_85_2.tif and click OK.

    The layer is added to the map. Before you compare it to your layer, you'll rename and symbolize it.

  17. Rename the layer Köppen Mediterranean Climate RCP 8.5 2080-2099. Open its Symbology pane and change Color scheme to Magma.
  18. Close the Symbology pane and change the layer transparency to 40.0 percent.

    Final map

    Based on the RCP 8.5 projection, the location of the Mediterranean climate will change by the end of the century. It will shrink particularly in the oceanic areas and North America, with other significant shifts on the Arabian peninsula and northern Africa.

  19. Save the project.

In this lesson, you learned about climate data and how to work with it in ArcGIS Pro. You created raster layers based on NetCDF files and learned about baseline and anomaly climate data. You compared projected climates for two different time periods. You also identified a specific type of climate and saw how its location might change based on a climate projection.

You can find more lessons in the Learn ArcGIS Lesson Gallery.