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Group project: the Climate System

Group members: Rainer Michelak, Christoph Killer

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)

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# 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:

1	ds = xr.open_dataset('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.

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# Setze die Dimensionen und Variablen temperature = ds['t2m'] - 273.15 # Umrechnung von K nach °C # Plot 1: Durchschnittstemperatur über den gesamten Zeitraum mean_temperature = temperature.mean(dim='time') # Erstelle die Karte mit Robinson-Projektion fig = plt.figure(figsize=(10, 6)) ax = plt.axes(projection=ccrs.Robinson()) # Verwende plt.axes für die Projektion mean_temperature.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', cbar_kwargs={'label': 'Mean Temperature (°C)'}) ax.coastlines() ax.gridlines() ax.set_title('Mean Temperature (°C) for the Entire Period') # Plot 2: Zonal Anomaly Map # Berechne die zonale Durchschnittstemperatur zonal_mean_temperature = temperature.mean(dim='longitude') zonal_anomaly = temperature - zonal_mean_temperature fig = plt.figure(figsize=(10, 6)) ax = plt.axes(projection=ccrs.Robinson()) zonal_anomaly.mean(dim='time').plot(ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', cbar_kwargs={'label': 'Zonal Anomaly (°C)'}) ax.coastlines() ax.gridlines() ax.set_title('Zonal Temperature Anomaly') # Plot 3: Monatliche Durchschnittstemperatur monthly_mean_temperature = temperature.groupby('time.month').mean(dim='time') # Berechne die Temperaturspanne (max - min) monthly_temperature_range = monthly_mean_temperature.max(dim='month') - monthly_mean_temperature.min(dim='month') fig = plt.figure(figsize=(10, 6)) ax = plt.axes(projection=ccrs.Robinson()) monthly_temperature_range.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', cbar_kwargs={'label': 'Temperature Range (°C)'}) ax.coastlines() ax.gridlines() ax.set_title('Monthly Temperature Range (Max - Min)') plt.show()

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.

Northern Europe and the North Atlantic region are warmer because of the Gulf Stream, which brings warm water from the tropics to this area. This warm ocean current helps keep the temperature higher than in other regions at similar latitudes, like parts of North America or Russia where there are no such warm ocean currents.

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

The Northern Pacific Ocean does not have the same warming effect as the North Atlantic because it is affected by cold currents, such as the California Current. These cold currents cool the surrounding regions, making them colder compared to the North Atlantic where warm currents prevail.

2. Look at the average monthly temperature range map.

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

In the tropics, the sun is more direct and consistent throughout the year, so the temperature does not vary much between day and night or across the months. In contrast, at higher latitudes, the sun’s angle changes more during the year, leading to larger temperature differences between summer and winter and between day and night.

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

Oceans have a higher heat capacity than land, meaning they can absorb and release more heat without a big change in temperature. This causes the temperature over oceans to remain more stable. On land, the temperature changes more quickly, leading to larger temperature ranges.

• Where is the temperature range largest? Explain why.

The temperature range is largest over land especially in deserts or continental areas, because there is less moisture to moderate the temperature. In places like deserts, the temperature can change drastically between day and night, and between summer and winter. Over oceans, the temperature changes more slowly, therefore this leading to a smaller range.

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.

1	ds = xr.open_dataset('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.

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# Your answers here # Konvertiere die Einheiten (m pro Tag -> mm pro Tag) precipitation = ds['tp'] * 1000 # Konvertiere von Meter auf Millimeter (1m = 1000mm) # Berechne den durchschnittlichen Niederschlag pro Monat monthly_precipitation = precipitation.groupby('time.month').mean(dim='time') # Monate Januar (1) und August (8) months = [1, 8] # Setze das Colormap für die Karte cmap = 'YlGnBu' # Levels für die Farbabstufung levels = [0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40] # Erstelle die Plots für Januar und August untereinander, mit der Robinson-Projektion fig, axs = plt.subplots(2, 1, figsize=(10, 12), subplot_kw={'projection': ccrs.Robinson()}) for i, month in enumerate(months): # Wähle die Achse für den jeweiligen Plot ax = axs[i] # Erstelle die Karte für den entsprechenden Monat data = monthly_precipitation.sel(month=month) # Plot der Niederschlagskarte data.plot(ax=ax, transform=ccrs.PlateCarree(), cmap=cmap, levels=levels, cbar_kwargs={'label': 'Precipitation (mm/day)'}) # Füge Küstenlinien und Gitterlinien hinzu ax.coastlines() ax.gridlines(draw_labels=True, linewidth=0.5, color='gray', linestyle='--') # Setze den Titel je nach Monat ax.set_title(f'Average Daily Precipitation in {["January", "August"][i]}') # Zeige die Plots plt.tight_layout() plt.show()

Questions:

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

The ITCZ (Intertropical Convergence Zone) is located near the equator, where the trade winds from the Northern and Southern Hemispheres meet. In January, the ITCZ is closer to the Southern Hemisphere, while in August, it shifts northward, following the sun's position and the seasonal changes.

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

In West Africa, the rainy season occurs from May to October, driven by the movement of the ITCZ. In India, the monsoon season brings heavy rains from June to September, also due to the seasonal shift of the ITCZ and the influence of ocean currents.

Part 3: sea-level pressure and surface winds

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

1	ds = xr.open_dataset('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)

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# Your answers here # Berechne den temporalen und zonalen Durchschnitt des Meeresspiegeldrucks (msl) slp = ds['msl'] / 100 # Umrechnung von Pa auf hPa slp_avg = slp.mean(dim='time').mean(dim='longitude') # Berechne den temporalen und zonalen Durchschnitt der Oberflächenwinde (u10, v10) u10 = ds['u10'] v10 = ds['v10'] u10_avg = u10.mean(dim='time').mean(dim='longitude') v10_avg = v10.mean(dim='time').mean(dim='longitude') # Erstelle die Plots fig, axs = plt.subplots(3, 1, figsize=(10, 15)) # Plot für den durchschnittlichen Meeresspiegeldruck axs[0].plot(slp_avg, label='Mean Sea-Level Pressure', color='blue') axs[0].axhline(y=1013, color='black', linestyle='--', label='Standard Pressure (1013 hPa)') axs[0].set_title('Zonal and Temporal Average of Sea-Level Pressure') axs[0].set_xlabel('Latitude') axs[0].set_ylabel('Pressure (hPa)') axs[0].legend() # Plot für den durchschnittlichen Wind in u-Richtung (zonal) axs[1].plot(u10_avg, label='Mean u10 Wind', color='green') axs[1].axhline(y=0, color='black', linestyle='--', label='Zero Line') axs[1].set_title('Zonal and Temporal Average of u10 Wind') axs[1].set_xlabel('Latitude') axs[1].set_ylabel('Wind Speed') axs[1].legend() # Plot für den durchschnittlichen Wind in v-Richtung (meridional) axs[2].plot(v10_avg, label='Mean v10 Wind', color='red') axs[2].axhline(y=0, color='black', linestyle='--', label='Zero Line') axs[2].set_title('Zonal and Temporal Average of v10 Wind') axs[2].set_xlabel('Latitude') axs[2].set_ylabel('Wind Speed (m/s)') axs[2].legend() # Layout und Darstellung der Plots plt.tight_layout() plt.show()

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.

The major high-pressure systems are located around 30° N and S, which are the subtropical high-pressure belts. These areas are characterized by descending air that leads to dry and clear conditions. The major low-pressure systems are found around the equator (the Intertropical Convergence Zone, or ITCZ) and near 60° N and S, where warm air rises and cools, causing cloud formation and precipitation.

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

Zonal winds (u10) are generally westerly at higher latitudes and easterly near the equator. The westerlies are stronger at mid-latitudes due to the pressure gradients between high and low pressure systems. Meridional winds (v10) are generally weaker but show significant north-south flow at the equator (ITCZ) and around the subtropical highs.

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:

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df = pd.read_csv('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.

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# Your answers here plt.figure(figsize=(10, 6)) plt.plot(df.index, df['average'], label="Monthly CO₂ Concentration (ppm)") plt.xlabel('Year') plt.ylabel('CO₂ Concentration (ppm)') plt.title("Monthly Global CO₂ Concentration") plt.show() # Jährlicher Durchschnitt der CO2-Konzentrationen df_annual = df.resample('YE').mean() # Plot des jährlichen Durchschnitts der CO2-Konzentrationen plt.figure(figsize=(10, 6)) plt.plot(df_annual.index, df_annual['average'], label="Annual Average CO₂ Concentration (ppm)") plt.xlabel('Year') plt.ylabel('CO₂ Concentration (ppm)') plt.title("Annual Average Global CO₂ Concentration") plt.show() temperature = ds['t2m'] - 273.15 # Umrechnung von K nach °C # Plot 1: Durchschnittstemperatur über den gesamten Zeitraum mean_temperature = temperature.mean(dim='time') # Lade das ERA5-Dataset mit den Temperaturdaten ds_temp = xr.open_dataset('ERA5_LowRes_Monthly_t2m.nc') # Berechnung der jährlichen Mittelwerte, falls noch nicht geschehen ds_temp_annual = ds_temp.resample(time='YE').mean() # Umrechnung der Temperatur von Kelvin auf Celsius ds_temp_annual['t2m'] = ds_temp_annual['t2m'] - 273.15 # Erstelle ein Plot mit einer sekundären y-Achse für die Temperatur fig, ax1 = plt.subplots(figsize=(10, 6)) # Plot der jährlichen CO₂-Konzentrationen ax1.plot(df_annual.index, df_annual['average'], label='Annual Average CO₂ Concentration (ppm)') ax1.set_xlabel('Year') ax1.set_ylabel('CO₂ Concentration (ppm)') # Zweite y-Achse für Temperatur (in Celsius) ax2 = ax1.twinx() ax2.plot(ds_temp_annual.time.dt.year, ds_temp_annual['t2m'].mean(dim=['latitude', 'longitude']), label='Annual Average Temperature (°C)', color='red') ax2.set_ylabel('Temperature (°C)', color='red') ax2.tick_params(axis='y', labelcolor='red') # Titel und Layout plt.title('Annual CO₂ Concentration and Global Temperature') fig.tight_layout() plt.show()

Questions:

1. Describe and explain the annual cycle of CO$_2$ concentrations

The CO2 concentration changes during the year. It is higher in late spring and early summer and lower in late summer and fall. This happens because of plants in the northern hemisphere. In spring and summer, they take CO2 from the air for photosynthesis, so the CO2 level goes down. In winter, plants stop growing and release CO2, so the level goes up again.

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.

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# Datumsspalte erstellen und als Index setzen df['date'] = pd.to_datetime(df.iloc[:, 0].astype(str) + '-' + df.iloc[:, 1].astype(str), format='%Y-%m') df.set_index('date', inplace=True) # Jährliche Mittelwerte df_annual = df.resample('YE').mean() # Zeitraum für 1980–1985 und 2016–2021 co2_1980_1985 = df_annual.loc['1980':'1985']['average'] co2_2016_2021 = df_annual.loc['2016':'2021']['average'] # Lineare Zunahme (ppm pro Jahr) berechnen annual_increase_1980_1985 = (co2_1980_1985.iloc[-1] - co2_1980_1985.iloc[0]) / (1985 - 1980) annual_increase_2016_2021 = (co2_2016_2021.iloc[-1] - co2_2016_2021.iloc[0]) / (2021 - 2016) print(f"Annual CO₂ increase (1980–1985): {annual_increase_1980_1985:.2f} ppm/year") print(f"Annual CO₂ increase (2016–2021): {annual_increase_2016_2021:.2f} ppm/year") # Mittelwerte der CO2-Konzentrationen für die 1980-1985 & 2016-2021 berechnen mean_co2_1980_1985 = co2_1980_1985.mean() mean_co2_2016_2021 = co2_2016_2021.mean() print(f"Mean CO₂ concentration (1980–1985): {mean_co2_1980_1985:.2f} ppm") print(f"Mean CO₂ concentration (2016–2021): {mean_co2_2016_2021:.2f} ppm")

Annual CO₂ increase (1980–1985): 1.32 ppm/year
Annual CO₂ increase (2016–2021): 2.33 ppm/year
Mean CO₂ concentration (1980–1985): 342.00 ppm
Mean CO₂ concentration (2016–2021): 408.85 ppm

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.

The relationship between global temperatures and CO2 concentrations is strongly positive and correlated. As CO2 concentrations increase, global temperatures tend to rise. This occurs because CO2 is a greenhouse gas that traps heat in the Earth's atmosphere, enhancing the natural greenhouse effect. This process is a major driver of global warming.

Apart from CO2, three other processes that can influence temperature variability and change globally are:

• Volcanic Eruptions: Large eruptions can inject significant amounts of aerosols (like sulfur dioxide) into the stratosphere, reflecting solar radiation and temporarily cooling the Earth.

• Solar Activity: Variations in solar radiation, such as changes in sunspot activity, can affect the amount of energy the Earth receives from the Sun, influencing temperature trends.

• El Niño and La Niña: Phenomena like El Niño and La Niña events influence global temperature patterns by redistributing heat in the ocean and atmosphere, causing periods of warming or cooling.

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?

1	# Your answers here

Research Question:

How will the projected changes in mean temperature in Austria from 2020 to 2100 impact glacier area loss in the region?

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We used the Indicator "Glacier area" and "Mean Temperature" which graphs are shown above.

Warmer temperatures are directly linked to glaciers shrinking, especially in places like Austria. When temperatures rise, glaciers melt faster than they can form new ice, causing them to lose mass and size. In Austria, temperatures have been increasing since the 20th century, which has led to glaciers in the Alps retreating.

From 2020 to 2100, temperatures are expected to rise even more, making glaciers melt even faster. This could affect water supply because glaciers provide a lot of meltwater, especially in summer. Less glacier water could impact agriculture, water availability, and local ecosystems.

In short, the expected rise in temperatures in Austria over the next decades will likely speed up glacier retreat, which could affect water resources and ecosystems, as well as contribute to rising sea level

For further understanding of the link between rising temperatures and glacier retreat, I referred to the IPCC's Sixth Assessment Report (AR6), which discusses the impacts of global warming on various regions, including the Alps. You can access the report here: https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-9/