## Climate Exercise

Attention: To get started on the exercise, visit the Piazza post to create your team repo.

# Exercise I: Temperature Data

Each of the last years has consecutively set new records on global climate. In this section we will analyze global mean temperature data.

## Question 1:

Describe the data set to the best of your ability given the documentation provided. Describe what kind of column each data contains and what units it is measured in. Then address our three key questions in understanding this data:

• How are the measurements made? What is the associated measurement uncertainty?
• What is the resolution of the data?
• Are their missing values? How should they be handled?

## Question 2:

Construct the necessary R code to import and prepare for manipulation the following data set: http://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt

## Question 3:

Plot the trend in global mean temperatures over time. Describe what you see in the plot and how you interpret the patterns you observe.

## Question 4: Evaluating the evidence for a “Pause” in warming?

The 2013 IPCC Report included a tentative observation of a “much smaller increasing trend” in global mean temperatures since 1998 than was observed previously. This led to much discussion in the media about the existence of a “Pause” or “Hiatus” in global warming rates, as well as much research looking into where the extra heat could have gone. (Examples discussing this question include articles in The Guardian, BBC News, and Wikipedia).

By examining the data here, what evidence do you find or not find for such a pause? Present an analysis of this data (using the tools & methods we have covered in Foundation course so far) to argue your case.

What additional analyses or data sources would better help you refine your arguments?

## Question 5: Rolling averages

• What is the meaning of “5 year average” vs “annual average”?
• Construct 5 year averages from the annual data. Construct 10 & 20-year averages.
• Plot the different averages and describe what differences you see and why.

# Exercise II: Melting Ice Sheets?

## Question 1:

• Describe the data set: what are the columns and units? Where do the numbers come from?
• What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?

## Question 2:

Construct the necessary R code to import this data set as a tidy Table object.

## Question 3:

Plot the data and describe the trends you observe.

# Exercise III: Rising Sea Levels?

## Question 1:

• Describe the data set: what are the columns and units?
• Where do these data come from?
• What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?

## Question 2:

Construct the necessary R code to import this data set as a tidy Table object.

## Question 3:

Plot the data and describe the trends you observe.

# Exercise IV: Arctic Sea Ice?

## Question 1:

• Describe the data set: what are the columns and units?
• Where do these data come from?
• What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?

## Question 2:

Construct the necessary R code to import this data set as a tidy Table object.

## Question 3:

Plot the data and describe the trends you observe.