Group project: the Climate System

Group project: the Climate System#

Instructions#

Objectives

In this final project, you will apply the methods you learned over the past weeks to answer the questions below.

Deadline

Please submit your project via OLAT before Thursday January 09 at 00H (in the night from Wednesday to Thursday).

Formal requirements

You will work in groups of two. If we are an odd number of students, one group can have three participants. (Tip: We recommend that students who have not followed a programming class to team up with students who have).

Each group will submit one (executed) jupyter notebook containing the code, plots, and answers to the questions (text in the markdown format). Please also submit an HTML version of the notebook. Please ensure that your HTML file is smaller than 10 MB. This helps us provide you with more detailed and readable feedback.

Each group member must contribute to the notebook. The notebook should be self-contained and the answers must be well structured. The plots must be as understandable as possible (title, units, x and y axis labels, appropriate colors and levels…).

Please be concise in your answer. We expect a few sentences per answer at most - there is no need to write a new text book in this project! Use links and references to the literature or your class slides where appropriate.

Grading

We will give one grade per project, according to the following table (total 10 points):

  • correctness of the code and the plots: content, legends, colors, units, etc. (3 points)

  • quality of the answers: correctness, preciseness, appropriate use of links and references to literature or external resources (5 points)

  • originality and quality of the open research question (2 points)

# Import the tools we are going to need today:
import matplotlib.pyplot as plt  # plotting library
import numpy as np  # numerical library
import xarray as xr  # netCDF library
import pandas as pd  # tabular library
import cartopy  # Map projections libary
import cartopy.crs as ccrs  # Projections list
# Some defaults:
plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size

Part 1 - temperature climatology#

Open the ERA5 temperature data:

ds = xr.open_dataset('../data/ERA5_LowRes_Monthly_t2m.nc')

Plot three global maps:

  • Compute and plot the temporal mean temperature \(\overline{T}\) for the entire period (unit °C)

  • Compute and plot \(\overline{T^{*}}\) (see lesson), the zonal anomaly map of average temperature.

  • Compute and plot the monthly average temperature for each month \(\overline{T_M}\) (annual cycle). We expect a variable of dimensions (month: 12, latitude: 241, longitude: 480). Hint: remember the .groupby() command we learned in the lesson. Now plot the average monthly temperature range map, i.e. \(\max(\overline{T_{M}})\) - \(\min(\overline{T_{M}})\) (maximum and minimum over the month dimension).

Questions:

  1. Look at the zonal temperature anomaly map.

    • Explain why northern Europe and the North Atlantic region is significantly warmer than the same latitudes in North America or Russia.

    • Explain why the Northern Pacific Ocean does not have a similar pattern.

  2. Look at the average monthly temperature range map.

    • Explain why the temperature range is smaller in the tropics than at higher latitudes

    • Explain why the temperature range is smaller over oceans than over land

    • Where is the temperature range largest? Explain why.

# Your answers here

Part 2 - Precipitation climatology#

Open the precipitation file and explore it. The units of monthly precipitation are wrongly labeled (unfortunately). They should read: m per day.

ds = xr.open_dataset('../data/ERA5_LowRes_Monthly_tp.nc')

Using .groupby(), compute the average daily precipitation for each month of the year (We expect a variable of dimensions (month: 12, latitude: 241, longitude: 480)). Convert the units to mm per day. Plot a map of average daily precipitation in January and in August with the levels [0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40] and the colormap `YlGnBu’

Questions:

  1. Describe the location of the ITCZ in January and August. Without going into the details, explain (in one or two sentences)

  2. Describe the precipitation seasonality in West Africa and in India. Name the phenomenon at play.

# Your answers here

Part 3: sea-level pressure and surface winds#

Open the file containing the surface winds (u10 and v10) and sea-level pressure (msl).

ds = xr.open_dataset('../data/ERA5_LowRes_Monthly_uvslp.nc')

Compute \(\left[ \overline{SLP} \right]\) (the temporal and zonal average of sea-level pressure). Convert it to hPa, and plot it (line plot). With the help of plt.axhline, add the standard atmosphere pressure line to the plot to emphasize high and low pressure regions. Repeat with \(\left[ \overline{u_{10}} \right]\) and \(\left[ \overline{v_{10}} \right]\) (in m s\(^{-1}\)) and add the 0 horizontal line to the plot (to detect surface westerlies from easterlies for example).

Questions:

  1. Based on your knowledge about the general circulation of the atmosphere, explain the latitude location of the climatological high and low pressure systems of Earth.

  2. Similarly, explain the direction and strength of the zonal and meridional winds on earth (tip: the sea-level pressure plot helps)

# Your answers here

Part 4: temperature change and CO\(_2\) concentrations#

Download the global average CO\(_2\) concentration timeseries data in the CSV format (source: NOAA). Let us help you read them using pandas:

df = pd.read_csv('../data/co2_mm_gl.csv', skiprows=38)

# Combine the first two columns into a datetime column and set as index
df['date'] = pd.to_datetime(df.iloc[:, 0].astype(str) + '-' + df.iloc[:, 1].astype(str), format='%Y-%m')
df.set_index('date', inplace=True)

Prepare three plots:

  • plot the monthly global CO\(_2\) concentration as a function of time.

  • plot the annual average timeseries of global CO\(_2\) concentration as a function of time.

  • plot the annual average timeseries of global CO\(_2\) concentration and of global 2m temperature from ERA5 on the same plot (using a secondary y axis for temperature).

Questions:

  1. Describe and explain the annual cycle of CO\(_2\) concentrations

  2. What was the CO\(_2\) concentration in the atmosphere in the pre-industrial era? Compute the annual increase in CO\(_2\) concentration (unit: ppm per year) between 1980 and 1985 and between 2016 and 2021.

  3. Describe the relationship between global temperatures and CO\(_2\) concentrations. Beside CO\(_2\), name three processes that can influence temperature variability and change at the global scale.

# Your answers here

Part 5: Climate Risk Dashboard (open research question)#

We will use the Climate Risk Dashboard climate-risk-dashboard.climateanalytics.org from the Horizon 2020 PROVIDE Project to create and analyze an open research question. To learn how to use the dashboard, visit the OGGM-Edu website, which includes examples using mean temperature and glacier volume.

  1. Select Indicators and Geography

    • Choose two to three Indicator’s from the dashboard. Ensure that you pick only one Indicator per Sector (e.g., one from Terrestrial Climate and one from Biodiversity).

    • Select a GEOGRAPHY for your Indicator’s (not all geographies are available for all indicators).

      • Try to pick related locations. For example, if you choose a city, also consider selecting its country or region for comparison.

      • Or, if it fits to your research question, you can also select an additional GEOGRAPHY for comparison (e.g. compare two countries), but you do not have to.

  2. Formulate a Research Question

    • Based on your selected Indicator’s and GEOGRAPHY, create a research question.

      • Please mention this research question clearly in your notebook.

    • Your analysis will focus on comparing two to three scenarios available in the dashboard.

  3. Conduct the Analysis

    • Visualizations:

      • Use at least one plot per Indicator by downloading plots directly from the dashboard. You can add them to your notebook with drag-and-drop.

      • Further, include at least two custom plots in your analysis. This means download the raw data from the dashboard and create your own plot. For an example see this section on OGGM-Edu. Please use the original filenames of the downloaded data in your notebook.

    • References:

      • Try to find at least one reference (reports, papers or articls) related to your research question and mention them in the notebook by providing a link. A good resource for many climate change related topics is the IPCC report.

  4. Answer Guiding Questions to your Research Question

    • Answer at least three of the questions below, or come up with your own creative questions! You’re encouraged to mix and match the provided questions with your own ideas or explore different angles that interest you.

      • How do the scenarios differ over time for each Indicator?

      • What are the spatial differences between scenarios for each Indicator?

      • If applicable: How much risk is avoided by staying below a certain threshold for your Indicator?

      • Are there any correlations between your selected Indicators?

# Your answers here
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