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Group project: the Climate System

Christine Hussendörfer and Alexandra Werder

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
import cartopy.feature as cfeature
# Some defaults:
plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size

Part 1 - temperature climatology

Open the ERA5 temperature data:

ds_t2m = xr.open_dataset('ERA5_LowRes_Monthly_t2m.nc')
#ds.head()

Plot three global maps:

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

mean_temp = ds_t2m['t2m'].mean(dim='time')  #in Kelvin
mean_temp_celcius = mean_temp - 273.15

# Plot
fig, ax = plt.subplots(figsize=(12, 5), subplot_kw={'projection': ccrs.PlateCarree()})
mean_temp_celcius.plot(ax=ax, cmap='coolwarm', cbar_kwargs={'label': 'Temperature (°C)'})
ax.coastlines()
ax.set_title('1.1: Temporal mean Temperature $\overline{T}$ (°C)')
plt.show()

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

# Calculation zonal mean temperature
zonal_mean = ds_t2m['t2m'].mean(dim='longitude')

# Calculation of the zonal anomaly
t_anomaly = ds_t2m['t2m'] - zonal_mean

# Mean values of the anaomaly over time
t_anomaly_mean = t_anomaly.mean(dim='time')  

# Plot
fig, ax = plt.subplots(figsize=(12, 5), subplot_kw={'projection': ccrs.PlateCarree()})
t_anomaly_mean.plot(ax=ax, cmap='coolwarm', robust=True, add_colorbar=True)
ax.coastlines()
ax.set_title('1.2: Zonal anomaly of the average temperature $\overline{T^{*}}$', fontsize=16)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
plt.show()

• 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).

monthly_avg_temp = ds_t2m['t2m'].groupby('time.month').mean(dim='time')

monthly_temp_max = ds_t2m['t2m'].groupby('time.month').max(dim='time')
monthly_temp_min = ds_t2m['t2m'].groupby('time.month').min(dim='time')

temp_range = monthly_temp_max - monthly_temp_min
mean_temp_range = temp_range.mean(dim='month')

# Plot
fig, ax = plt.subplots(figsize=(12, 5), subplot_kw={'projection': ccrs.PlateCarree()})
mean_temp_range.plot.contourf(ax=ax, cmap = 'YlOrBr', add_colorbar=True, cbar_kwargs={'label': 'Temperature Range (°C)'})
ax.coastlines()
ax.set_title('1.3: Average monthly Temperature Range (°C)', fontsize=16)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
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.

Answer: The norhtern Europe and the North Atlantic regeion is significantly warmer because of the golf stream. The golf stream transports warm water form the tropes of the north Atlantic towards the nothern Europe. The ocean has a much higher heat capacity then land, it absorbs the heat during sommer and releases it during winter. North America and Russia do not have such a golf stream, they are more influenced by the Arctic land mass, which is very cold. That is the reason for the temperature differences.

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

Answer: The Northern Pacific Ocean has no warm water stream like the Golf Stream. It is more influenced by cooler air masses and eceanic upwelling, especially along the costlines of Alaska and the Pacific Northwest. The cold ocean in combination with the less warming leads to colder temperatures in this region.

2. Look at the average monthly temperature range map.

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

Answer: The temperature range in the tropics is much smaller than in higher altitudes because the sun is nearly directly overhead throughout the year. The angle of the sun in higher altitudes varies a lot through the year, the angle is much lower in winter than in summer. The smaller the angle the less energy is recieved. Another reason is the duration of day/night. In the tropics it stays almost the same over the year, but in higher latitudes it varies a lot, so it also has a big influence on the temperature range.

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

Answer: The ocean heats/cools slower than land.

• Where is the temperature range largest? Explain why.

Answer: The temperature range is the largest ober the pols. The seasonal variation in sunlight is there the most extreme and therefore also has the largest temperature 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.

ds_tp = xr.open_dataset('ERA5_LowRes_Monthly_tp.nc') #tp=total_precipitation
#ds_tp.head()

ds_tp['tp'].attrs['units'] = 'm/day'
#ds_tp['tp']

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'

monthly_avg_precip = ds_tp['tp'].groupby('time.month').mean(dim='time')

# Convert to mm per day
monthly_avg_precip = monthly_avg_precip * 1000
monthly_avg_precip.attrs['units'] = 'mm/day'

#monthly_avg_precip

# Plot
fig, axes = plt.subplots(2, 1, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})

# January
ax1 = axes[0]
monthly_avg_precip.sel(month=1).plot.contourf(ax=ax1, levels=[0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40], cmap='YlGnBu')

# August
ax2 = axes[1]
monthly_avg_precip.sel(month=8).plot.contourf(ax=ax2, levels=[0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40], cmap='YlGnBu')

#set title
ax1.set_title('2.1: Mean average precipitation per day in January (mm/day)')
ax1.coastlines()
ax2.set_title('2.2: Mean average precipitation per day in August (mm/day)')
ax2.coastlines()

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)

   Answer: The Intertropical Convergance Zone is located in January closer to the Southern Hemisphere and in August the ITCZ is shifted northwards into the Northern Hemisphere.

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

   Answer: During the summer months(e.g. August), the monsoon brings heavy rains to West Africa and to India. In the winter months (e.g. January), the West of Africa and India are more dry.

Part 3: sea-level pressure and surface winds

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

ds_uvslp = 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).

# Mean SLP and converted to hPa
mean_slp = ds_uvslp['msl'].mean(dim=['time', 'longitude']) / 100

# Mean u and v
mean_u10 = ds_uvslp['u10'].mean(dim=['time', 'longitude'])
mean_v10 = ds_uvslp['v10'].mean(dim=['time', 'longitude'])

# Plot
fig = plt.figure(figsize=(12, 15))

# Mean SLP Plot
ax0 = fig.add_subplot(3, 1, 1)
ax0.plot(mean_slp['latitude'], mean_slp, label="Mean SLP", color='blue')
ax0.axhline(y=1013, color='grey', linestyle='--', label="Standard atmospheric pressure (1013 hPa)")
ax0.set_title("3.1: Temporal and Zonal Average of Sea-Level Pressure")
ax0.set_xlabel("Latitude")
ax0.set_ylabel("SLP (hPa)")
ax0.legend()

# Mean u10 Plot
ax1 = fig.add_subplot(3, 1, 2)
ax1.plot(mean_u10['latitude'], mean_u10, label="Mean u10", color='green')
ax1.axhline(y=0, color='grey', linestyle='--', label="Zero line")
ax1.set_title("3.2.a: Temporal and Zonal Average of u10 (m/s)")
ax1.set_xlabel("Latitude")
ax1.set_ylabel("u10 (m/s)")
ax1.legend()

# Mean v10 Plot
ax2 = fig.add_subplot(3, 1, 3)
ax2.plot(mean_v10['latitude'], mean_v10, label="Mean v10", color='red')
ax2.axhline(y=0, color='grey', linestyle='--', label="Zero line")
ax2.set_title("3.2.b: Temporal and Zonal Average of v10 (m/s)")
ax2.set_xlabel("Latitude")
ax2.set_ylabel("v10 (m/s)")
ax2.legend()

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.

   Answer:

   • At the Equator is a low pressure system. The sun leads to intense heating, the warm air rises and creates a low-pressure area. (=Intertropical Convergence Zone)
   • At 30° north and south, the rising air from the Equator cools and sinks down, it crates a high-pressure area. (=suptropical high)
   • At 60° north and south, cold air from the pols meets waremer air from the subtropical regions. This causes the rise of the air and produces an low-pressure system. (=subpolar low)
   • At the Poles, the air is cold and dense, there is a high-pressure area.

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

   Answer:

   • The Zonal Winds (East-West) are parallel to the latitudes, they are strongest between the 30° and 60° latitude. The winds are caused by the pressure gradient force between the low and high-pressure systems together with the Coriolis effect.
   • The Meridional Winds (North-South) are perpendicular to the latitudes, they are weaker than the zonal winds. The winds are caused by temperauture and pressure differences between the pols and the tropics.

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('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)

#df.head()

Prepare three plots:

• plot the monthly global CO$_2$ concentration as a function of time.

df['average'].plot(figsize = (12, 5))

plt.title('4.1: Monthly global CO2 Concentration (1979-2024)')
plt.ylabel('Co2 concentration')
plt.xlabel('Date')
plt.show()

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

df_annual = df.resample('YE').mean()
df_annual['average'].plot(figsize = (12, 5))

plt.title('4.2: Annual average global CO2 Concentration (1979-2024)')
plt.ylabel('Co2 concentration')
plt.xlabel('Year')
plt.show()

• 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).

global_mean_temp = ds_t2m['t2m'].mean(dim=('longitude','latitude'))
annual_global_mean_temp = global_mean_temp.groupby('time.year').mean()

#annual_global_mean_temp.plot()

fig, ax1 = plt.subplots(figsize=(12, 5))

#1: global CO2 concentration
ax1.plot(df_annual['year'], df_annual['average'], color='blue', label='CO2 Concentration')
ax1.set_xlabel('Year')
ax1.set_ylabel('CO2 Concentration', color='blue')

ax2 = ax1.twinx()   #second axis

#2: global 2m temperature
ax2.plot(annual_global_mean_temp['year'], annual_global_mean_temp, color='red', label='Global Mean Temperature')
ax2.set_ylabel('Global Mean Temperature (°C)', color='red')

plt.title('4.3: CO2 Concentration and Global Mean Temperature')
plt.show()

Questions:

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

df_monthly = df.groupby(df.index.month)['average'].mean()   #Yearly cycle averaged over all years

df_monthly.plot()

plt.title('4.4.a: Annual Cycle of CO2 Concentrations')
plt.xlabel('Month')
plt.ylabel('CO2 Concentration')
plt.show()

In this plot you can see, that the annual cycle of CO2 concentration has a special pattern. The concentration has got the highest peak between April and June (months: 4-6) and the CO2 concentration is lowest in August and September (months: 8-9). This might be the case because in the northern hemisphere summer, many plants are growing and performing photosynthesis. In summer is also less heating with fossil fuels needed due to warmer temperatures. Because there is more land in the NH then in the SH, we can see this seasonal changes. In winter, when lot of CO2 is emitted due to heating and when there is no CO2 transformed from plants, the CO2 concentration rises until it gets it's peak in spring.

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.

The pre-industrial era has been before the industrial revolution. According to an article on climate.gov (https://www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide#:~:text=Before%20the%20Industrial%20Revolution%20started,was%20280%20ppm%20or%20less., 9.4.2024), the CO2 concentration was at about 280 ppm: "Before the Industrial Revolution started in the mid-1700s, atmospheric carbon dioxide was 280 ppm or less."

#first available CO2 concentration in this dataset
co2_1979 = df_annual['average'].loc['1979'].item()

#CO2 increase 1980-1995
co2_19 = df_annual['average'].loc['1980':'1985']
co2_19_increase = (co2_19.iloc[-1] - co2_19.iloc[0]) / (len(co2_19) - 1)

# CO2 increase in 2016-2021
co2_20 = df_annual['average'].loc['2016':'2021']
co2_20_increase = (co2_20.iloc[-1] - co2_20.iloc[0]) / (len(co2_20) - 1) 

#print
print(f"First annual mean of the CO2 concentration in this dataset (1979): {co2_1979:.2f} ppm")
print(f"Annual CO2 increase (1980-1985): {co2_19_increase:.2f} ppm per year")
print(f"Annual CO2 increase (2016-2021): {co2_20_increase:.2f} ppm per year")

First annual mean of the CO2 concentration in this dataset (1979): 336.85 ppm
Annual CO2 increase (1980-1985): 1.33 ppm per year
Annual CO2 increase (2016-2021): 2.33 ppm per year

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.

Let's have a look at plot 4.3. There is an obvious relationship between global temperatures and CO2 concentrations. Both, global temperature and CO2 concentrations have been increasing over the last one hundred years. The CO2 concentration is steadily increasing while temperature has more fluctuations. Temperature reacts faster to small changes and other components compared to the CO2 concentration, but in total, both are increasing. Examples for additional changes and variabilities in temperature are:

- melting ice and therefore less albedo

- variabilities in the sun's energy income due to solar cycle or changing solar activity

- changing ocean streams can also affect the global temperature

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?

Impact of mean temperature in glacier area loss

Christine Hussendörfer and Alexandra Werder

Indicators: Mean Temperature (Terrestrial Climate) and Glacier Area (Glaciers)

Geography: Countries: Canada, Argentina

Research Question: How does the mean temperature change influence glacier area loss under two different climate action scenarios in Canada and Argentina?

5.1: Change in mean temperature compared Canada and Argentina in 2100

The two graphics show the change in Mean temperature in Canada and Argentina in 2100. On the left side of each image, the map shows the 2020 cimate policies scenerio and the right hand side shows the scenerio of stabilisation with 1.5 °C. In both graphics you can see, that the increase of mean temperature change with the 2020 climate policies is much stronger. As you can see the nothern parts of the countries get a higher mean temperature change, no matter if southern or nothern hemisphere.

5.2: Risks of glacier area loss in Canada and Argentina

In the left plot, you can see that there is no risk of a 50% glacier area loss in Canada until 2100 no matter which scenario you choose. On the right hand side in Argentina it's similar to Canada until the year 2050, but then we have risk of 30% in the 2020 climate policies to lose the half of the galcier area. In this case both countries have similar risks in the next 50 years. But there is a huge difference if you choose another percentage of risk. For example, when choosing a impact level of 20% directly on the website. (It is only possible to download the 50% impact level plot, so you have to visit the website and look for it manually: https://climate-risk-dashboard.climateanalytics.org/impacts/explore). When choosing an impact level of 20% you can see that in Argentina the galcier area loss starts already in 2050 and in 2100 there is an unavoilable risk even in highest ambition scenario. Compared to Canada the glacier are loss with an impact level of 20% is zero until 2050 and in 2100 we get a risk between 15% and 82% depending on which scenario.

# Load the CSV files
temp_canada_15 = pd.read_csv('impact-time_CAN_ref-1p5_terclim-mean-temperature_0.5_present-day.csv')
temp_canada_curpol = pd.read_csv('impact-time_CAN_curpol_terclim-mean-temperature_0.5_present-day.csv')
temp_argentinia_15 = pd.read_csv('impact-time_ARG_ref-1p5_terclim-mean-temperature_0.5_present-day.csv')
temp_argentinia_curpol = pd.read_csv('impact-time_ARG_curpol_terclim-mean-temperature_0.5_present-day.csv')

# Plot
plt.figure(figsize=(12, 5))
plt.plot(temp_canada_15['year'], temp_canada_15['terclim-mean-temperature_mean'], label='Stabilisation at 1.5°C Canada', color='darkblue')
plt.plot(temp_canada_curpol['year'], temp_canada_curpol['terclim-mean-temperature_mean'], label='2020 climate policies Canada', color='cornflowerblue')
plt.plot(temp_argentinia_15['year'], temp_argentinia_15['terclim-mean-temperature_mean'], label='Stabilisation at 1.5°C Argentina', color='darkred')
plt.plot(temp_argentinia_curpol['year'], temp_argentinia_curpol['terclim-mean-temperature_mean'], label='2020 climate policies Argentina', color='lightcoral')
plt.title('5.3: Changes in Mean temperature in Canada and Argentina from 2020 to 2100', size = 15)
plt.xlabel('Year')
plt.ylabel('Temperature (°C)')
plt.legend()
plt.grid(color='lightgrey')
plt.show()

The plot shows the changes in mean temperature in Canada and Argentina from 2020 to 2100 in two different scenarios. The first scenario is the stabilisation at 1.5 °C and the second is the 2020 climate policies. Notable is that the change in mean temperature in Canada will be higher than in Argentina.

argentina_glacier_ref = pd.read_csv('impact-time_ARG_ref-1p5_glacier-area_0.5_present-day-2020.csv')
argentina_glacier_curpol= pd.read_csv('impact-time_ARG_curpol_glacier-area_0.5_present-day-2020.csv')
canada_glacier_ref = pd.read_csv('impact-time_CAN_ref-1p5_glacier-area_0.5_present-day-2020.csv')
canada_glacier_curpol= pd.read_csv('impact-time_CAN_curpol_glacier-area_0.5_present-day-2020.csv')

# Plot
plt.figure(figsize=(12, 5))

plt.plot(canada_glacier_ref['year'], canada_glacier_ref['glacier-area_mean']*100, label='Stabilisation at 1.5°C Canada', color='darkblue')
plt.plot(canada_glacier_curpol['year'], canada_glacier_curpol['glacier-area_mean']*100, label='2020 climate policies Canada', color='cornflowerblue')
plt.plot(argentina_glacier_ref['year'], argentina_glacier_ref['glacier-area_mean']*100, label='Stabilisation at 1.5°C Argentina', color='darkred')
plt.plot(argentina_glacier_curpol['year'], argentina_glacier_curpol['glacier-area_mean']*100, label='2020 climate policies Argentina', color='lightcoral')

plt.title('5.4: Changes in Glacier area in Canada and Argentina from 2020 to 2100', size = 15)
plt.xlabel('Year')
plt.ylabel('Percentage (%)')
plt.legend()
plt.grid(color='lightgrey')
plt.show()

The plot represents the changes in glacier area in Canada and Argentina from 2020 to 2100 with the same scenarios chosen as before. Glacier area loss in Argentina is stronger than in Canada. Regarding to the 2020 climate policies the galcier area loss is clearly bigger.

5.5: Reference

IPCC report 9.5.1.1.3: Drivers for glacier change

https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-9/#9.5.1.1.3

This report discusses the different drivers for glacier change. One main sentence says: "The SROCC assessed that it is very likely that atmospheric warming is the primary driver for the global glacier recession." But not only temperature is responsible, also other things can have an impact, according to following statement from the article: "...other factors, such as precipitation changes or internal glacier dynamics, have modified the temperature-induced glacier response in some regions." Another responsible aspect are the varieties of albedo due to light-absorbing particles on the glaciers and also thickness of the ice plays a role in the melting process. The article therefore emphasizes that there are different drivers depending on each region. In the end of the article there is figure 9.21 which shows the global and regional glacier mass evolution between 1901 and 2100 relative to glacier mass in 2015. In the figure, it is visible that there are already significant differences in glacier loss only in Canada (Arctic Canada North, South and West). Comparing Arctic Canada North (3) to Southern Andes (17) you can see similarities to the data we got in our resarch.

5.6: Conclusion

How do the scenarios differ over time for each Indicator?

In the 2020-policies-scenarios, glaciers in Canada and Argentina melt faster and temperatures rise more. In the 1.5-scenario, the melting slows down and there is less warming. Initially, both scenarios follow similar trends, but they diverge increasingly over time. For mean temperature, the paths begin to separate around 2035, while for glacier melting, the divergence occurs between 2035 and 2045. (Part 5.3 and 5.4)

How much risk is avoided by staying below a certain threshold for your Indicator?

Staying below 1.5°C makes a huge difference in glacier loss and also the change in mean temperature. This is best visible in part 5.2 when you go to the website and choose the 20 % impact. There the (un)avoidable risk is clearly shown depending on the scenarios.

Are there any correlations between your selected Indicators?

There is a strong correlation between both selected indicators which is probably quite obvious. But the increase in mean temperature is not the only responsible factor for glacier area loss. You can realize, if you look on our plots, that although mean temperature increases more in Canada, the glacier area loss is not as high as in Argentina. (Part 5.3 and 5.4) Therefore other components also have to influence this process. In part 5.5 there is a link to an article which describes the different drivers and components.