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2 changes: 1 addition & 1 deletion assignment/climate.Rmd
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Expand Up @@ -20,7 +20,7 @@ library(tidyverse)
```{r message=FALSE}
co2 <-
readr::read_table("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
read_table("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
comment="#",
col_names = c("year", "month", "decimal_date", "average",
"interpolated", "trend", "days"),
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241 changes: 132 additions & 109 deletions assignment/climate.md
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Expand Up @@ -2,187 +2,210 @@ Climate Exercise
================
Carl Boettiger & Dana Seidel

Unit I: Climate Change Module
=============================
# Unit I: Climate Change Module

Warm-up exercise: Examining CO2 trends in R
-------------------------------------------
## Warm-up exercise: Examining CO2 trends in R

- Example from <http://climate.nasa.gov/vital-signs/carbon-dioxide/>
- Raw data from <ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt>
- Example from <http://climate.nasa.gov/vital-signs/carbon-dioxide/>
- Raw data from
<ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt>

<!-- end list -->

``` r
library(tidyverse)
```

``` r
co2 <-
readr::read_table("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
read_table("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
comment="#",
col_names = c("year", "month", "decimal_date", "average",
"interpolated", "trend", "days"),
na = c("-1", "-99.99"))
co2
```

## # A tibble: 715 x 7
## year month decimal_date average interpolated trend days
## <int> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1958 3 1958.208 315.71 315.71 314.62 NA
## 2 1958 4 1958.292 317.45 317.45 315.29 NA
## 3 1958 5 1958.375 317.50 317.50 314.71 NA
## 4 1958 6 1958.458 NA 317.10 314.85 NA
## 5 1958 7 1958.542 315.86 315.86 314.98 NA
## 6 1958 8 1958.625 314.93 314.93 315.94 NA
## 7 1958 9 1958.708 313.20 313.20 315.91 NA
## 8 1958 10 1958.792 NA 312.66 315.61 NA
## 9 1958 11 1958.875 313.33 313.33 315.31 NA
## 10 1958 12 1958.958 314.67 314.67 315.61 NA
## # ... with 705 more rows
## # A tibble: 725 x 7
## year month decimal_date average interpolated trend days
## <int> <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1958 3 1958. 316. 316. 315. NA
## 2 1958 4 1958. 317. 317. 315. NA
## 3 1958 5 1958. 318. 318. 315. NA
## 4 1958 6 1958. NA 317. 315. NA
## 5 1958 7 1959. 316. 316. 315. NA
## 6 1958 8 1959. 315. 315. 316. NA
## 7 1958 9 1959. 313. 313. 316. NA
## 8 1958 10 1959. NA 313. 316. NA
## 9 1958 11 1959. 313. 313. 315. NA
## 10 1958 12 1959. 315. 315. 316. NA
## # ... with 715 more rows

``` r
ggplot(co2, aes(x = decimal_date, y = average)) + geom_line()
```

![](climate_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-3-1.png)
![](climate_files/figure-gfm/unnamed-chunk-3-1.png)<!-- -->

Which months are the CO2 values at the maximum? Minimum? Why is this?

What rolling average is used in computing the "trend" line? How does the trend depend on the rolling average?
What rolling average is used in computing the “trend” line? How does the
trend depend on the rolling average?

------------------------------------------------------------------------
-----

Exercise I: Temperature Data
============================
# 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.
Each of the last years has consecutively set new records on global
climate. In this section we will analyze global mean temperature data.

Data from: <http://climate.nasa.gov/vital-signs/global-temperature>

Question 1:
-----------
## 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:
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?
- 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:
-----------
## 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>
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:
-----------
## 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.
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?
-------------------------------------------------------------
## Question 4: Evaluating the evidence for a “Pause” in warming?

The [2013 IPCC Report](https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_SummaryVolume_FINAL.pdf) 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](http://www.theguardian.com/environment/2015/jun/04/global-warming-hasnt-paused-study-finds), [BBC News](http://www.bbc.com/news/science-environment-28870988), and [Wikipedia](https://en.wikipedia.org/wiki/Global_warming_hiatus)).
The [2013 IPCC
Report](https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_SummaryVolume_FINAL.pdf)
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](http://www.theguardian.com/environment/2015/jun/04/global-warming-hasnt-paused-study-finds),
[BBC News](http://www.bbc.com/news/science-environment-28870988), and
[Wikipedia](https://en.wikipedia.org/wiki/Global_warming_hiatus)).

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

Question 5: Rolling averages
----------------------------
## 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.
- 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?
================================
# Exercise II: Melting Ice Sheets?

- Data description: <http://climate.nasa.gov/vital-signs/land-ice/>
- Raw data file: <http://climate.nasa.gov/system/internal_resources/details/original/499_GRN_ANT_mass_changes.csv>
- Data description: <http://climate.nasa.gov/vital-signs/land-ice/>
- Raw data file:
<http://climate.nasa.gov/system/internal_resources/details/original/499_GRN_ANT_mass_changes.csv>

Question 1:
-----------
## 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?
- 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:
-----------
## Question 2:

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

Question 3:
-----------
## Question 3:

Plot the data and describe the trends you observe.

Exercise III: Rising Sea Levels?
================================
# Exercise III: Rising Sea Levels?

- Data description: <http://climate.nasa.gov/vital-signs/sea-level/>
- Raw data file: <http://climate.nasa.gov/system/internal_resources/details/original/121_Global_Sea_Level_Data_File.txt>
- Data description: <http://climate.nasa.gov/vital-signs/sea-level/>
- Raw data file:
<http://climate.nasa.gov/system/internal_resources/details/original/121_Global_Sea_Level_Data_File.txt>

Question 1:
-----------
## 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?
- 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:
-----------
## Question 2:

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

Question 3:
-----------
## Question 3:

Plot the data and describe the trends you observe.
Plot the data and describe the trends you
observe.

Exercise IV: Arctic Sea Ice?
============================
# Exercise IV: Arctic Sea Ice?

- <http://nsidc.org/data/G02135>
- <ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/Sep/N_09_area.txt>
- <http://nsidc.org/data/G02135>
- <ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/N_seaice_extent_daily_v3.0.csv>

Question 1:
-----------
## 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?
- Describe the data set: what are the columns and units?
- Where do these data come from?
- What is the uncertainty in measurement? Resolution of the data?
Interpretation of missing values?

Question 2:
-----------
## Question 2:

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

Question 3:
-----------
## Question 3:

Plot the data and describe the trends you observe.

Exercise V: Longer term trends in CO2 Records
=============================================
# Exercise V: Longer term trends in CO2 Records

The data we analyzed in the unit introduction included CO2 records dating back only as far as the measurements at the Manua Loa observatory. To put these values into geological perspective requires looking back much farther than humans have been monitoring atmosopheric CO2 levels. To do this, we need another approach.
The data we analyzed in the unit introduction included CO2 records
dating back only as far as the measurements at the Manua Loa
observatory. To put these values into geological perspective requires
looking back much farther than humans have been monitoring atmosopheric
CO2 levels. To do this, we need another approach.

[Ice core data](http://cdiac.ornl.gov/trends/co2/ice_core_co2.html):

Vostok Core, back to 400,000 yrs before present day

- Description of data set: <http://cdiac.esd.ornl.gov/trends/co2/vostok.html>
- Data source: <http://cdiac.ornl.gov/ftp/trends/co2/vostok.icecore.co2>

Questions / Tasks:
------------------

- 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?
- Read in and prepare data for analysis.
- Reverse the ordering to create a chronological record.
- Plot data
- Consider various smoothing windowed averages of the data.
- Join this series to Mauna Loa data
- Plot joined data
- Describe your conclusions
- Description of data set:
<http://cdiac.esd.ornl.gov/trends/co2/vostok.html>
- Data source:
<http://cdiac.ornl.gov/ftp/trends/co2/vostok.icecore.co2>

## Questions / Tasks:

- 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?
- Read in and prepare data for analysis.
- Reverse the ordering to create a chronological record.
- Plot data
- Consider various smoothing windowed averages of the data.
- Join this series to Mauna Loa data
- Plot joined data
- Describe your conclusions
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