Climate and Climate Change: Assignment 6

Assignment 6: (100 points)

This assignment will explore the learnings from Regional Climate (Module 05) and Water at Climate Systems (Module 06). Understanding a few regional patterns and how water can impact on it (Precipitation and availability) is an important step to understand how climate is changing over time.

Part 1: Match the Climate (25 points)

Download the paper “Assignment06_The Changing Character of Precipitation.pdf”, and at this time you already know what to do:

what methods are used, it’s a global precipitation paper, or a local (regional) climate?

This paper explores precipitation suggesting Climate Change?

Try to find a connection between atmospheric circulation, regional climate, and water resources at this paper. This will not be that hard! “Climate change is certainly very likely to locally change the intensity, frequency, duration, and amounts of precipitation. Trenberth et al, 2003”

Part 2: Match the Climate (25 points)
Here you are given 10 locations around the world, and 10 descriptions of general climate. Indicate which location you believe is being described after each location.

Santiago, Chile	Sao Luis, Brazil	Longreach, Australia	Kano, Nigeria
Al Jubail, Saudi Arabia	Honolulu, Hawaii	Kathmandu, Nepal
Kumul, China	Aberdeen, United Kingdom	Anchorage, Alaska

Descriptions:

1. Warm, humid climate with prevailing winds out of the east. Exceptionally little seasonality for temperatures, with record high and low temperatures of 95 °F and 52 °F respectively. Despite the humid climate precipitation is rare during the summer, and mostly occurs during the winter.

Location:

• Cold winters (5°F – 30 °F) with short days and mild summers (55 °F – 78 °F) with long daylight. with prevailing winds out of the north during the winter and south during the summer. Year-to-year variability can be extreme, especially during the winter, where feet of snow can fall one year, or dry conditions the next.  Summers can have frequent rainfall, with mostly snow during winter.

Location:

• Hot climate with average max temperatures in the mid- to upper-80s and average humidity over 80% year-round with an extreme rainy season from January-July. Prevailing winds are out of the east. The warmest time of the year occurs September through December.

Location:

• Semi-arid climate with warm, dry summers that can see temperatures reach up to 95 °F and cool, humid winters with temperatures that drop close to 0 °F. Rainfall is heavily influenced by the El Nino Southern Oscillation. Prevailing winds are from the southwest. Precipitation is mainly rain, but snow can occur in the nearby mountain range, which prevents maritime weather from reaching this city.

Location:

• Mild maritime climate with little seasonality throughout the year. Rainy throughout the year and no dry season. Prevailing winds from the west, and mid-latitude cyclones dominate winter conditions that can feature fierce storms. Average summer high temperatures reach 63 °F while winter high temperatures reach 43 °F.

Location:

• Subtropical, hot semi-arid climate with mean annual temperature of 80 °F and little seasonality in temperatures. Receives large amount of rainfall, but only during the rainy season, which occurs between June-September. Prevailing wind is from the east.

Location:

• Extremely hot climate with some of the greatest heat index values on the planet due to the extreme humidity. Average temperatures can reach 115 °F during July and August, 70 °F during January.

Location:

• Mild climate with extreme rainy monsoon season during the summer months. Temperatures can approach 0 °F during the winter and 85 °F during the summer. Prevailing winds have extreme seasonal variability, with offshore flow during the winter and onshore flow during the monsoon season.

Location:

• Desert climate featuring large seasonality in temperatures, with hot and dry summers, and bitter cold winters. Overall dry climate throughout the year, with rainfall primarily coming during July and August. Low humidity with mid-latitude cyclones bringing prevailing winds from the west.

Location:

1. Hot climate with dry winter during June-August and rainy summer seasons during December-February. Average summer temperatures close to 100 °F and winter temperatures around 73 °F Prevailing wind from the west. Thunderstorms can develop during December-February months.

Location:

Part 3: Utah Precipitation vs. Elevation (50 points)

This section will demonstrate the link between elevation and precipitation using monthly average precipitation data on a 4-km grid across the state of Utah.

1. Download the Assignment6_data file from Canvas and open in excel.
   
   
   1. In sheet 1 titled “Precipitation vs. Elevation”, you’ll see a column for elevation (meters) and twelve (12) columns of monthly precipitation (mm). These values represent average precipitation for a 30-year climatology spanning 1980-2009. To capture the state’s topography accurately, high-resolution data is necessary. For this dataset, a grid of points is overlaid across the state with 4 km between each grid point; referred to as a 4-km dataset. This creates over 13,000 data points (rows in excel) making this a very large dataset, indeed! Using this dataset, complete the following:
      
      
      1. In the blue highlighted column titled ‘Annual Average’, compute the 12-month average for each row of data by programing the “=AVERAGE()”equation to reference the twelve January-December columns. For a quick check, the annual average of the first grid point (elevation of 786 m) should come to 20.145 mm. Use the quick ‘fill handle’ technique to have the equation autofill the 13,000+ rows of this data.

• Next, create a scatterplot with the y-axis referencing the elevation column, and the x-axis, referencing precipitation. For the first chart, plot January precipitation. When the plot is created, you should see a jumbled cloud of markers for all 13,000+ rows (grid points). To get a better idea between the relationship between elevation and precipitation, right-click on the data in the chart and have Excel (1) add a trendline, (2) display the equation, and (3) display the R² value. Once you have a chart that displays all three of these, move to the next step.

• In sheet 2 titled “Slope and Correlation”, you’ll find 2 rows, one labeled “Slope Coefficient” highlighted in purple and one for “Correlation” highlighted in orange. From the chart in sheet 1, the trendline equation is given in the form y=mx+b, where the slope coefficient is represented by “m”. The correlation value is the R² value.

On this sheet, you will be adding the values for these variables you acquire from the chart you’ve created in sheet 1. To quickly do this for each of the 12 months and the annual average you’ve computed, right-click on the chart and choose “Select Data”. In the window that opens, edit the series, then select the x-axis reference data window and simply change the column reference letter. For example, the first chart you created should be plotting January precipitation, which references column B. To quickly plot February precipitation without creating a new chart from scratch, change the “B” reference to “C”. This will quickly and automatically update the markers, trendline, equation, and R² value. Repeat this step for each month and the annual average until you have all the data filled in for sheet 2.

• Using the data in sheet 2, create a line chart with two series; one line for the slope coefficient and one for the correlation coefficient.  Next, right-click the line that represents the correlation coefficient and select “Format Axis”. Find the option to display secondary axis and select it. This will create a secondary y-axis scale on the right hand side of the chart.

Make your chart match the design of the following example and copy and paste it over this when you are ready to turn the assignment in.

                             Using this chart, answer the following questions.

1. If a large correlation coefficient tells you the two datasets (elevation vs. precipitation) are closely related, what time of the year has a stronger relationship between elevation and precipitation? What time of year has the weakest relationship?

• Slope coefficient describes how great of a difference between the low- and high-elevations. A larger slope coefficient describes that lower elevations get much less precipitation than higher elevations, while smaller slope coefficients describes a more balanced amount of precipitation falls across all elevations. Knowing this, what time of year has the greatest differences between high- and low-elevation precipitation.

• Do your findings on the relationship between elevation and precipitation make intuitive sense to you? How about the seasonal relationship between the two? Are these results what you would expect to be the case? Why or why not?
  
  

• Copy and paste the chart from sheet 1 showing the data from the month with highest and lowest correlation values onto the next page. Make sure to include the specific month in the title of the chart and have the trendline, equation, and R² values displayed!