here is the kuroshio current, but the prevailing winds drive it further away from land/ the mainland US is blocked by the rocky mountains.

Siberia and North America are blocked by mountain ranges like the Rocky and Ural Mountains, which trap cold Arctic air and lead to harsher winters.

urope's geography, with few large mountain ranges, allows these winds to reach the region easily.

oincides with subtropical westerlies in both S and N hemispheres as well as with the location of most low pressure systems thus the wind is stronger there. around the itcz the wind speed is 0 in the S-N direction which makes sense as the trade winds meet there. and the eastern trade winds in the tropics NE/SE for S/N hemisphere respectively

coincides with the location of the circumpolar low, the southern oceanhighs the equatorial trough and the hawaian+bermuda high and the iceland/aleutian low. These climatological highs and lows are connected to the cell model of global circulation. with a large amount of advection at the equator due to solar radiation and a large amount of subsidence at the subtropical high level as well as advection between the ferrell and polar cell.

B

it coincides with the location of the circumpolar low, the southern oceanhighs the equatorial trough and the hawaian+bermuda high and the iceland/aleutian low.

# Your answers here slp=ds.msl.mean(dim=['time', 'longitude'])*0.01 u10=ds.u10.mean(dim=['time', 'longitude']) v10=ds.v10.mean(dim=['time', 'longitude']) atmp=1013.25 zero_line=0 #plots plt.figure(figsize=(12, 6)) plt.plot(slp['latitude'], slp, label="SLP (hPa)", color='blue') plt.axhline(y=1013.25, color='red', linestyle='--', label="Standard Atmosphere (1013.25 hPa)") plt.title("Zonal and Temporal Average of SLP") plt.xlabel("Latitude") plt.ylabel("Pressure (hPa)") plt.legend() plt.grid() plt.show() plt.figure(figsize=(12, 6)) plt.plot(u10['latitude'], u10, label="u10 (m/s)", color='green') plt.axhline(y=0, color='black', linestyle='--', label="Zero Line") plt.title("Zonal and Temporal Average of u10") plt.xlabel("Latitude") plt.ylabel("Wind Speed (m/s)") plt.legend() plt.grid() plt.show() plt.figure(figsize=(12, 6)) plt.plot(v10['latitude'], v10, label="v10 (m/s)", color='orange') plt.axhline(y=0, color='black', linestyle='--', label="Zero Line") plt.title("Zonal and Temporal Average of v10") plt.xlabel("Latitude") plt.ylabel("Wind Speed (m/s)") plt.legend() plt.grid() plt.show()

Describe the location of the ITCZ in January and August. Without going into the details, explain (in one or two sentences) in January the ictz is further south because that is the area with the highest solar flux and august it moves further north Describe the precipitation seasonality in West Africa and in India. Name the phenomenon at play. in summer the ictz moves over both west africa and india and brings a strong rainy season called a monsoon season, west-african/ indian monsoon

# Your answers here tp_month_mean=ds.tp.groupby('time.month').mean(dim='time') tp_month_mean=tp_month_mean*1000 #plot levels = [0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40] cmap = 'YlGnBu' # Plot the map for January (month = 1) and August (month = 8) fig, axes = plt.subplots(1, 2, figsize=(14, 6)) january_precip = tp_month_mean.sel(month=1) august_precip = tp_month_mean.sel(month=8) # January Plot january_precip.plot.contourf(ax=axes[0], levels=levels, cmap=cmap, extend='max') axes[0].set_title('Average Daily Precipitation - January') # August Plot august_precip.plot.contourf(ax=axes[1], levels=levels, cmap=cmap, extend='max') axes[1].set_title('Average Daily Precipitation - August') # Adjust layout and show the plots plt.tight_layout() plt.show()

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. N

Where is the temperature range largest? Explain why. Eastern siberia and Polar North-America surface albedo would probably play a large role in those environments since they are covered by snow during winter so would have a higher albedo, leading to cooler temperatiures, while on the other hand in summer, these landmasses are not covered by snow and can absorb much more solar radiation. both regions can be affected by polar jet streams as well, which could lead to very cold temperatures.

s the annual variability of energy balance due to earths tilt

hile at higher latitudes the difference is much larger.

es at lo

warm ocean currents

Prevailing westerly winds carry warm, moist air from the Atlantic, helping to maintain milder winters.

, moderating the climate.

t_month_mean=ds.t2m.groupby('time.month').mean() t_range=t_month_mean.max(dim="month")-t_month_mean.min(dim="month") t_range.plot()

t_avg_dep = t2_tavg - t2_tavg.mean(dim='longitude') ax = plt.axes(projection=ccrs.Robinson()) t_avg_dep.plot.imshow(ax=ax, transform=ccrs.PlateCarree()) ax.coastlines(); ax.gridlines();

#temporal mean t2_tavg = ds.t2m.mean(dim='time') ax = plt.axes(projection=ccrs.Robinson()) t2_tavg.plot(ax=ax, transform=ccrs.PlateCarree()) ax.coastlines(); ax.gridlines();

Cole_and_Constantine.ipynb

, bringing the water masses from the ocean to West Africa and leading to a rain season there. During rain season in India there is a dry season in West Africa and vice versa.

# calculating the monthly average temperature for each month

t more ocean cover, leading to higher wind speeds, because there are less barriers.

headly

The direction and strength of zonal and meridional winds are primarily determined by Earth's atmospheric circulation patterns, which are influenced by solar heating, the Coriolis effect, and the distribution of high and low-pressure systems.

Question: 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 diagram shows that the low pressure area of the southern hemisphere is much more pronounced than the one of the northern hemisphere.

Surface winds¶ In [13]: #computing the temporal and longitudinal average wind components u_wind= ds_uvslp.u10.mean(dim=['time', 'longitude']) v_wind= ds_uvslp.v10.mean(dim=['time', 'longitude']) #plotting on a line plot u_wind.plot(label='temporal and zonal average of u wind component'); v_wind.plot(label='temporal and zonal average of v wind component') plt.axhline(0, label= '0 horizontal wind line', color='red') plt.legend(title='Wind speed [ms-1]') plt.ylabel('ms-1') plt.xlabel('latitude [°N]') plt.title('temporal and zonal average wind speed at 10m, ERA5 1979-2018'); # easterlies: negative values # westerlies: positive values

u_wind.plot(label='temporal and zonal average of u wind component'); v_wind.plot(label='temporal and zonal average of v wind component')

ms-1

horizontal

#the temporal and zonal average of sea-level pressure SLP= ds_uvslp.msl.mean(dim=['time', 'longitude'])/100 SLP.plot(label='temporal and zonal average of sealevel pressure'); plt.axhline(1013.25, label= 'standard atmosphere pressure (1013.25)', color='red') plt.legend(title='Pressure [hPa]') plt.ylabel('Pressure [hPa]') plt.xlabel('latitude [°N]') plt.grid(False) plt.title('The temporal and zonal average of sea level pressure, ERA5 1979-2018');

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

n January, it moves south of the equator, extending over regions like South America, Southern Africa, and Northern Australia. By August, the ITCZ shifts northward, positioning itself between 5° and 15° north of the equator over the Atlantic and Pacific Oceans, and even further north over the landmasses of Africa and Asia (see blue band on maps)

# computing the average daily precipitation for each month of the year avg_pt = ds_tp.tp.groupby('time.month').mean(dim='time') #print(avg_pt) avg_pt_mm= avg_pt*1000 # converting the m/day scale to mm/day #print(avg_pt_mm) # January selection levels= np.array([0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40]) avg_pt_mm_Jan= avg_pt_mm.sel(month=1) # January plot ax= plt.axes(projection= ccrs.Robinson()) avg_pt_mm_Jan.plot.contourf(ax=ax, transform= ccrs.PlateCarree(), levels=levels, cmap= 'YlGnBu', extend='max',cbar_kwargs={'label':'Precipitation [mm/day]','ticks':levels}) ax.coastlines(); ax.gridlines(); ax.set_title('January average precipitation, ERA5 1979-2018');

On the US west coast for example, the California current is a cold water current flowing southwards towards the equator. Sea surface temperatures are thus colder(https://www.fleetscience.org/activities-resources/pacific-coast-colder-atlantic-coast). Due to cold ocean temperatures, the westerly winds are also colder in comparison, because they have less energy input from the sea

The largest temperature range is found in the north easterly parts of Russia in Sibiria (see on the map). Eurasia is the largest landmass, which makes this part of Russia the most continental area on earth. Additionally, the high latitude is responsible for much sunlight in summer and low sunlight in winter, which further intensifies the variability of temperatures (https://www.britannica.com/place/Russia/Climate, https://www.ces.fau.edu/nasa/module-3/why-does-temperature-vary/seasons.php). For instance, Verkhoyansk has recorded temperature ranges from -68°C in winter to over +38°C in summer, marking one of the most significant annual temperature variations worldwide (https://en.wikipedia.org/wiki/Verkhoyansk).

(4184 JKg-1K-1

(https://pubmed.ncbi.nlm.nih.gov/25560606/)

Nort

compute the mean temperature over time T_mean_K= ds_t2m.t2m.mean(dim='time') T_mean_C= T_mean_K-273.15 # plot the mean temperature on a map ax = plt.axes(projection=ccrs.Robinson()) T_mean_C.plot(ax=ax, transform = ccrs.PlateCarree(),cmap= 'RdBu_r' ,vmin= -50, vmax= 30,levels=9, cbar_kwargs={'label':'°C'}) ax.coastlines(); ax.gridlines(); ax.set_title('Annual mean global 2m Temperature, ERA5 1979-2018'); #T_mean_C.min().values

Anual

The direction and strength of winds are determined by the pressure gradient force, Coriolis effect, and friction. The sea-level pressure plot helps illustrate these dynamics: Zonal Winds (u10: East-West Component) Easterlies (Trade Winds) at 0°–30°N/S: Air moves from the subtropical highs (30°) to the equatorial low (0°). The Coriolis effect deflects winds to the west, creating northeasterly trades in the Northern Hemisphere and southeasterly trades in the Southern Hemisphere. These winds are strong due to the consistent pressure gradient toward the equator. Westerlies at 30°–60°N/S: Air moves poleward from the subtropical highs toward the subpolar lows (60°). The Coriolis effect deflects winds to the east, creating the mid-latitude westerlies. These winds are particularly strong due to the steep pressure gradient and the formation of the polar front jet stream. Polar Easterlies at 60°–90°N/S: Cold air flows from the polar highs toward the subpolar lows. The Coriolis effect deflects these winds to the west, producing polar easterlies. Meridional Winds (v10: North-South Component) Weak Poleward Flow at 30°N/S: Sinking air at the subtropical highs tends to flow poleward toward the subpolar lows. This is generally weak at the surface but contributes to the westerlies at higher latitudes. Equatorward Flow Near the Poles: Cold, dense air sinks at the polar highs and flows equatorward toward the subpolar lows.

# Extract variables msl = ds['msl'] # Sea-level pressure (Pa) u10 = ds['u10'] # Zonal wind component (m/s) v10 = ds['v10'] # Meridional wind component (m/s) # 1. Compute the temporal and zonal mean of sea-level pressure msl_hpa = msl.mean(dim='time') / 100.0 # Convert from Pa to hPa msl_zonal_mean = msl_hpa.mean(dim='longitude') # Zonal average # 2. Compute the temporal and zonal mean of u10 and v10 u10_zonal_mean = u10.mean(dim='time').mean(dim='longitude') # Zonal average of u10 v10_zonal_mean = v10.mean(dim='time').mean(dim='longitude') # Zonal average of v10 # Define latitudes for the x-axis latitudes = ds['latitude'] # Plotting fig, axes = plt.subplots(3, 1, figsize=(10, 12), sharex=True) # Plot sea-level pressure axes[0].plot(latitudes, msl_zonal_mean, color='b', label='Zonal Mean SLP') axes[0].axhline(1013.25, color='r', linestyle='--', label='Standard Atmospheric Pressure (1013.25 hPa)') axes[0].set_title('Zonal and Temporal Mean of Sea-Level Pressure') axes[0].set_ylabel('Pressure (hPa)') axes[0].legend() axes[0].grid() # Plot u10 (zonal wind component) axes[1].plot(latitudes, u10_zonal_mean, color='g', label='Zonal Mean u10') axes[1].axhline(0, color='k', linestyle='--', label='Zero Line') axes[1].set_title('Zonal and Temporal Mean of Zonal Wind (u10)') axes[1].set_ylabel('Wind Speed (m/s)') axes[1].legend() axes[1].grid() # Plot v10 (meridional wind component) axes[2].plot(latitudes, v10_zonal_mean, color='m', label='Zonal Mean v10') axes[2].axhline(0, color='k', linestyle='--', label='Zero Line') axes[2].set_title('Zonal and Temporal Mean of Meridional Wind (v10)') axes[2].set_ylabel('Wind Speed (m/s)') axes[2].set_xlabel('Latitude') axes[2].legend() axes[2].grid() # Adjust layout plt.tight_layout() plt.show()

The currents in the Pacific are less intense, and the Pacific Ocean's larger size leads to more thermal inertia, which prevents localized warming similar to the Atlantic.

The Earth's general circulation of the atmosphere is responsible for the distinct latitudinal bands of high and low pressure: Equatorial Low (Near 0° latitude): The Intertropical Convergence Zone (ITCZ) lies near the equator, where intense solar heating causes air to rise. This creates a low-pressure zone due to the upward movement of air and its divergence aloft. Rising warm air leads to significant cloud formation and precipitation, characteristic of the tropical rain belts. Subtropical Highs (Around 30°N and 30°S): At around 30° latitude, descending air from the Hadley Cells causes regions of high pressure. This zone is known as the subtropical ridge and is associated with arid climates (e.g., the Sahara Desert, deserts of Australia) because the sinking air suppresses cloud formation. Subpolar Lows (Around 60°N and 60°S): Near 60° latitude, the cold polar air meets warmer mid-latitude air, creating the polar front. The warm air rises over the denser cold air, leading to low-pressure zones. This region is characterized by storm systems and significant precipitation, particularly in the polar front jet stream region. Polar Highs (Near 90°N and 90°S): At the poles, cold, dense air sinks, creating high-pressure zones. These regions are dry and stable due to the lack of vertical air motion and limited moisture.

In January, the Intertropical Convergence Zone (ITCZ) is located south of the equator, over the southern hemisphere, particularly over northern parts of South America, southern Africa, and northern Australia. In August, the ITCZ shifts north of the equator, moving over regions like West Africa, India, and Southeast Asia, driven by the seasonal heating of the land in the northern hemisphere.

West Africa: Precipitation peaks during the summer months (June–September) when the ITCZ shifts north, bringing monsoonal rains. This phenomenon is part of the West African Monsoon system. India: Precipitation seasonality is dominated by the Indian Summer Monsoon, where heavy rains occur from June to September due to the seasonal reversal of winds (southwesterly flow) that transport moisture from the Indian Ocean onto the Indian subcontinent. The ITCZ migration and the associated monsoon systems (West African Monsoon and Indian Summer Monsoon) are driven by seasonal changes in solar heating, which cause shifts in atmospheric circulation and the convergence of moist air.

# Compute the monthly climatology (average precipitation for each month) precip_monthly_mean = tp_mm.groupby('time.month').mean() # Define the color levels and colormap levels = [0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40] cmap = 'YlGnBu' # Create a figure with two subplots (January and August) fig, axes = plt.subplots(1, 2, subplot_kw={'projection': ccrs.Robinson()}, figsize=(14, 6)) # January precipitation map ax = axes[0] january_precip = precip_monthly_mean.sel(month=1) im1 = january_precip.plot.contourf( ax=ax, transform=ccrs.PlateCarree(), levels=levels, cmap=cmap, cbar_kwargs={'shrink': 0.6, 'label': 'Precipitation (mm/day)'} ) ax.coastlines() ax.set_title('Average Daily Precipitation - January') # August precipitation map ax = axes[1] august_precip = precip_monthly_mean.sel(month=8) im2 = august_precip.plot.contourf( ax=ax, transform=ccrs.PlateCarree(), levels=levels, cmap=cmap, cbar_kwargs={'shrink': 0.6, 'label': 'Precipitation (mm/day)'} ) ax.coastlines() ax.set_title('Average Daily Precipitation - August') # Add a title for the whole figure plt.suptitle('Monthly Average Daily Precipitation (mm/day)', fontsize=16) # Adjust spacing plt.tight_layout() plt.show()

Questions: 1. Look at the zonal temperature anomaly map.

the North Atlantic are significantly warmer due to the North Atlantic Drift, an extension of the Gulf Stream. This ocean current carries warm water from the tropics towards Europe, moderating the clim

continentality

In contrast, North America and Russia experience colder continental climates due to their distance from large water bodies, which provide thermal moderation.

The largest temperature ranges occur over continental interiors at high latitudes, such as Siberia, Canada, and parts of Central Asia.

# Compute the temporal mean temperature t2m = ds['t2m'] t2m_c = t2m - 273.15 # Convert to Celsius T_mean = t2m_c.mean(dim='time') # Plot the global mean temperature fig = plt.figure(figsize=(12, 5)) ax = plt.axes(projection=ccrs.Robinson()) T_mean.plot(ax=ax, transform=ccrs.PlateCarree(), cbar_kwargs={'label': 'Mean Temperature (°C)'}) ax.coastlines() ax.gridlines() plt.title('Temporal Mean Temperature (°C)') plt.show()

# Compute the monthly climatology (average temperature for each month) T_monthly_mean = t2m_c.groupby('time.month').mean() # Plot the monthly average temperature maps fig, axes = plt.subplots(3, 4, subplot_kw={'projection': ccrs.Robinson()}, figsize=(15, 10)) # Loop over the 12 months and plot each month's mean temperature for i, ax in enumerate(axes.flat): # Select the temperature for month i+1 month_data = T_monthly_mean.isel(month=i) # Plot the 2D data im = month_data.plot.pcolormesh( ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', add_colorbar=False ) ax.coastlines() ax.set_title(f'Month {i+1}') # Add a single vertical colorbar on the right-hand side cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7]) # [left, bottom, width, height] fig.colorbar(im, cax=cbar_ax, orientation='vertical', label='Temperature (°C)') # Adjust spacing between subplots plt.subplots_adjust(hspace=0.3, wspace=0.05, top=0.9, bottom=0.1, right=0.9) # Add a title and show the figure plt.suptitle('Monthly Average Temperature (°C)', fontsize=16) plt.show()

Now plot the average monthly temperature range map, i.e. max(¯¯¯¯¯¯¯TM)max(TM¯)\max(\overline{T_{M}}) - min(¯¯¯¯¯¯¯TM)min(TM¯)\min(\overline{T_{M}}) (maximum and minimum over the month dimension).

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

helps) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42# 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()

coolwarm

Meridional winds (v10) are generally weaker but show significant north-south flow at the equator (ITCZ) and around the subtropical highs.

he pressure gradients between high and low pressure systems.

westerly at higher latitudes and easterly near the equator. The

Questions:

monsoon

In West Africa, the rainy season occurs from May to October, driven by the movement of the ITCZ.

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.

# 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()

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.

he 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.

n places like deserts, the temperature can change drastically between day and night,

Questions: 1. Look at the zonal temperature anomaly map.

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.

# Setze die Dimensionen und Variablen

In [13]: v10_t_avg = ds.v10.mean(dim='time') v10_avg = v10_t_avg.mean(dim= 'longitude').plot(label = 'v10') u10_t_avg = ds.u10.mean(dim='time') u10_avg = u10_t_avg.mean(dim= 'longitude').plot( label = 'u10') plt.xlabel('Latitude') plt.ylabel('Windspeed in m/s)') plt.title('Average windspeed in u and v-component (Time-Longitude Mean)') plt.axhline(y=0, color = 'red') plt.legend() plt.grid() plt.show()

In [4]: t2 = ds.t2m t2m_avg_cel = t2.mean(dim='time') -273.15 ax = plt.axes(projection=ccrs.Robinson()) t2m_avg_cel.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='inferno', center=False, vmin=-40, vmax=20, levels=7, cbar_kwargs={'label': '°C'}) ax.set_title('Average annual 2m air temperature, ERA5 1979-2018') ax.coastlines(); ax.gridlines();

Questions:

west

The Coriolis force is quite strong at these latitudes since it increases toward the poles. Additionally, there is far less landmass in these latitudes compared to their counterparts in the Northern Hemisphere, meaning there is less friction from landmasses to slow down the winds.

The subpolar low is stronger in the southern hemisphere due to less land surface and stronger temperature gradients. It arises where cold air from the polar cell mixes with warm air from the Ferell cell

there is a polar high in both hemispheres. T

msl_t_avg = ds.msl.mean(dim='time')/100 msl_avg = msl_t_avg.mean(dim= 'longitude').plot( label='Zonal Average Pressure') plt.axhline(y=1013, linewidth=2, linestyle='--', color='black', label='Standard Atmosphere Pressure [1013 hPa]') plt.legend() plt.xlabel('Latitude') plt.ylabel('Sea-Level Pressure (hPa)') plt.title('Average Sea-Level Pressure (Time-Longitude Mean)') plt.grid() plt.show()

from IPython.display import Image Image(filename=r'C:..\climate\data\WhatsApp Image 2025-01-08 at 19.18.13.jpeg')

India is characterized by low average precipitation values (mostly below 2 mm per day) in January, whereas average precipitation values in summer can reach up to 15 mm per day. The reason for this significant difference is the monsoon. As the Inter-Tropical Convergence Zone (ITCZ) moves northward in summer, it changes the northeast monsoon, which brings dry air from Siberia, into the moist southwest monsoon. The southwest, or summer, monsoon brings warm and moist air from the ocean and is thus associated with heavy rainfall. West Africa experiences a pronounced precipitation seasonality, with dry Sahel conditions dominating in January (mostly below 2 mm per day) except for light rainfall along the southern coastal areas near the Gulf of Guinea. In August, the West African monsoon brings heavy rainfall, with the peak shifting northward into the Sahel while the Guinea coast also receives significant precipitation. This pattern is again driven by the seasonal migration of the ITCZ.

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

avg_daily_tp_mm = ds.tp.groupby('time.month').mean() * 1000 J_avg_daily_tp_mm = avg_daily_tp_mm.sel(month=1) A_avg_daily_tp_mm = avg_daily_tp_mm.sel(month=8) fig, (ax1, ax2) = plt.subplots(ncols=2, subplot_kw={'projection': ccrs.Robinson()}) # January J_avg_daily_tp_mm.plot(ax=ax1, transform=ccrs.PlateCarree(), cmap='YlGnBu', levels=[0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40], cbar_kwargs={'label': 'mm per day'}) # August A_avg_daily_tp_mm.plot(ax=ax2, transform=ccrs.PlateCarree(), cmap='YlGnBu', levels=[0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40], cbar_kwargs={'label': 'mm per day'}) ax1.set_title('Average Daily Precipitation in January, ERA5 1979-2018');ax1.coastlines();ax1.gridlines() ax2.set_title('Average Daily Precipitation in August, ERA5 1979-2018');ax2.coastlines();ax2.gridlines() plt.tight_layout() plt.show()

As shown in the plot, northeastern Siberia experiences the largest seasonal temperature variation. This can be explained by the factors mentioned in the two previous questions. Firstly, the region is located roughly at 52°N, meaning that incoming solar radiation exhibits a strong seasonal cycle. Additionally, Siberia is dominated by a continental climate, which minimizes the ocean's buffering effect on temperature. These factors make Siberia a prime region for large average monthly temperature variations. The region with the second-highest variability is located in the polar regions of northern Canada. This is caused by similar factors, although the influence of the continental climate might be slightly less pronounced.

Geosystems: An Introduction to Physical Geography

The Northern Pacific Gyre is an ocean current quite similar to the Gulf Stream at first glance. However, it causes a less pronounced rise in temperature for the North Pacific area. One reason for this is that the current has a longer east-west extension, meaning that the flow does not cover much change in latitude, which is important for heat exchange.

Gulfstream Westerly winds Russia continental climate(no ocean to the west) North america: ocean current strached zonaly, warm water travels longer in the north??? ROcky mountains

Since the golfstream transports water from low lattitiudes to high ones it transports a lot of heat to the north Atlantic region. Due to the pressence of the westerlys and eourpos location east of the Atlantic, this heat gets transportet to northern Europe. As a result northern Europe is significantlly warmer than the same latitudes in North America or Russia.

altitudes

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.

Look at the zonal temperature anomaly map.

Answer:

Zonal Winds (East-West) are parallel to the latitudes, they are strongest between the 30° and 60° latitude.

Answer

# 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()

In [9]: # 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()

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

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.

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.

urope 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.

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.

Answer: The ocean heats/cools slower than land.

altitudes

onthly_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()

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()

Meridional

The largest ranges are on continents at high latitudes due to the lack of ocean moderation and extreme seasonal changes in solar radiation.

The Northern Pacific does not have such a strong stream like the golf stream and is significantly larger, so that it would take much stronger streams to heat up. [https://nicklutsko.github.io/blog/2020/01/07/What-Keeps-Europe-Warm-In-Winter]

At the equator solar heating causes air to rise and creates low pressure zones. At around 30° these airmasses descend and form subtropical highs. At 60° warm subtropic air meets cold polar air and creates subpolar lows. At the poles the dense, cold air sinks and creates polar highs.

Zonal winds:

The westerly jet streams at 30° and 60°.

u.plot(label = 'U-component of surface wind', color= 'orange'); v.plot(label = 'V-component of surface wind'); plt.axhline(y= 0, color= 'black'); plt.legend(loc='upper right'); plt.xlabel('Latitude'); plt.ylabel('Surface wind [m/s]'); plt.grid(); plt.title('Temporal and zonal averages of surface wind components');

slp.plot(label = 'mean sea level pressure', color= 'orange'); plt.axhline(y= 1013.25, xmin=0.045, xmax=0.955, label='standard atmosphere pressure'); plt.legend(loc='lower right'); plt.xlabel('Latitude'); plt.ylabel('Mean sealevel pressure [hPa]'); plt.grid(); plt.title('temporal and zonal average of sea-level pressure in comparison to standard pressure');

l

summer

induces

### plotting for January ax = plt.axes(projection=ccrs.Robinson()) p_m.sel(month=1).plot(ax=ax, transform=ccrs.PlateCarree(), cmap='YlGnBu', levels=[0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40], cbar_kwargs={'label': 'mm per day'}) ax.coastlines(); ax.gridlines(); ax.set_title('Average daily precipitation in January');

Heat transport by the Gulf Stream, which is then transferred to the atmosphere and advected over western Europe. Seasonal heat storage by the ocean: the ocean absorbs heat in summer and release it in winter, warming downwind regions. Atmospheric heat transport, and quasi-stationary waves in the atmosphere.

In the tropics is a consistant solar energy radiation, minimal season variation and therefore stable day and night lengths.

Question 1:

### plotting the temperal average temperature t2_tavg = ds.t2m.mean(dim='time') -273.15 ax = plt.axes(projection=ccrs.Robinson()) t2_tavg.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', center=False, vmin=-40, vmax=20, levels=10, cbar_kwargs={'label': '°C'}) ax.set_title('Average annual 2m air temperature, ERA5 1979-2018') ax.coastlines(); ax.gridlines();

at the southpole

equator

Answer

https://earthscience.stackexchange.com/questions/43/why-is-europe-warmer-than-north-america-at-similar-latitudes https://www.americanscientist.org/article/the-source-of-europes-mild-climate https://www.carbonbrief.org/guest-post-why-does-land-warm-up-faster-than-the-oceans/

#plt.rcParams['figure.figsize'] = (12, 5) # Default plot size

u_mean.plot(ax=axes[1], label='U-Wind(m/s)') axes[1].axhline(0, color='black', linestyle='--',label='No Wind') axes[1].legend() axes[1].set_title('Zonal Mean of U-Wind', fontsize=14, fontweight='bold') #V-Wind Plot v_mean.plot(ax=axes[2], label='V-Wind(m/s)') axes[2].axhline(0, color='black', linestyle='--',label='No Wind') axes[2].legend() axes[2].set_title('Zonal Mean of V-Wind', fontsize=14, fontweight='bold')

At 60°N/S especially S we have very high wind speed

s due to the jet stream

Answer

U

U

In general on the SH pressure is lower than on the NH because of the difference in sea:land ratio. When there is more ocean Low pressure systems can form and exist easier.

and

d(m

fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 10)) #MSL Plot SLP.plot(ax=axes[0], label='SLP (hPa)') axes[0].axhline(1013.25,color='r', linestyle='--', label='1 atm (1013.25)') axes[0].legend() axes[0].set_title('\nZonal Mean of Sea-Level Pressure', fontsize=14, fontweight='bold')

In west Africa there is the seasonal effect of the 'West African Monsoon'. Because in NH summer there is southwest wind which blows all the humidity onto land leading to precipitation in west Africa. In Winter there is a dry norhteast wind resulting in dry conditions and therefore the ITCZ shifts more to the south there. India experiences the 'southern Asia summer monsoon' which is caused by the different heat capacities and the resulting season shift of the wind. In summer the wind blows from the ocean to the land bringing in the precipitation because lands heat up faster creating a Low. In winter the wind comes from the Northeast bringing dry conditions from inland.

The ITCZ in January is a more south than in August. This is because the ITCZ is following the sun's zenith. There are perturbations of a straight line because of the differences of land masses and oceans. The straight line would go through the Tropics either north(Aug) or south(jan) of the equator.

levels= [0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40] fig, axes = plt.subplots(2, 1, figsize=(15, 10), subplot_kw={'projection': ccrs.Robinson()}) fig.suptitle('Average Daily Precipitation \n',fontsize=15, fontweight='bold') # January jan = pre_mthly_mm.sel(month=1) jan.plot(ax=axes[0], cmap='YlGnBu', transform=ccrs.PlateCarree(),cbar_kwargs={'label': 'Precipitation(mm)'}) axes[0].coastlines(); axes[0].gridlines() axes[0].set_title('January') #August aug= pre_mthly_mm.sel(month=8) aug.plot(ax=axes[1],cmap='YlGnBu', transform=ccrs.PlateCarree(), cbar_kwargs={'label':'Precipitation(mm)'}) axes[1].coastlines(); axes[1].gridlines() axes[1].set_title('August');

The largest Temp range is located in Siberia/North-East Russia. THis is because of the continentality, i.e. there is no significant ocean close by(especially not westwards) where warm marine winds could heat up the land. In winter there is very few sunlight and as we know land loses energy way faster than water so it can get up to -60°C. Whereas in summer there is way more sunlight and bc of the small heat capacity, only few energy is needed to heat up the land. Also in winter there is the "siberian high" which is inducing clear sky so there is a lot of radiative cooling due to the nights being way longer than the days

The winds over the Northern Pacific carry air from the pacific ocean westward onto the western coasts of North America. These winds, however, do not penetrate very far inland because of the Rocky Mountains, which block the warm maritime air and limit its influence on the interior of the continent. Although similar, the Kuroshio Current in the Pacific does not extend as far north and carries less heat to high latitudes. The result of this weaker northward transport of warm water is that the Northern Pacific does not moderate temperatures in the same way the North Atlantic does.

T2_m_dif= T2_mthly.max(dim='month')-T2_mthly.min(dim='month') ax = plt.axes(projection=ccrs.Robinson()) T2_m_dif.plot(ax=ax, transform=ccrs.PlateCarree(),cbar_kwargs={'label': 'Temperature Range (°C)'}) ax.coastlines(); ax.gridlines() plt.title('Annual Temperature Range\n',fontsize=15, fontweight='bold');

In [3]: T2_temp= t2.mean(dim='time')-273.15 ax = plt.axes(projection=ccrs.Robinson()) T2_temp.plot(ax=ax, transform=ccrs.PlateCarree(),cbar_kwargs={'label': '2m Temp(°C)'}) ax.coastlines(); ax.gridlines() plt.title('Average 2 Meter Temperature from 1979-2018 \n', fontsize=16, fontweight='bold');

Zonal winds are predominantly west-to-east in the mid-latitudes due to the Coriolis effect and strong pressure gradients between subtropical highs and subpolar lows. In the tropics, easterly trade winds dominate, driven by the pressure gradient between the subtropical highs and the equatorial low (ITCZ). Meridional winds are generally weaker, occurring where air moves poleward or equatorward, such as in the Hadley and Polar cells.

ph, it becomes clear that the areas with the strongest wind are the same areas, where the pressure-gradient is the largest (For Example at lat=-50).

In the extra tropical convergence zone (ETCZ) the high solar radiation causes a rise of moist, warm air, which leads to a low near the equator. A pronounced subtropical-high (around 30° latitude in both hemispheres) next to ETCZ is caused by sinking air of the Hadley cell. Furthermore, a subpolar-low (Around 60° latitude in both hemispheres) is caused when warm air from the mid-latitudes meets cold polar air, creating a region of strong temperature contrast (Ferrel-Cell is thermally indirect cell). The subpolar-low is a lot stronger on SH because of a stronger temperature gradient and less land surface. Finally, a polar-high near the poles is prevalent, because cold, dense air sinks over the poles, creating regions of high pressure (Polar-Cell).

E

extra

In [13]: ### Exercise 3.2: # Computing the temporal and zonal averages of windspeed for east/west and north/south direction: u_zonal_avg = ds3.u10.mean(dim=['time','longitude']) v_zonal_avg = ds3.v10.mean(dim=['time','longitude']) # Creating the plot including the line for 0 wind speed plt.plot(ds3.latitude, u_zonal_avg, linewidth=2, color='darkgreen', label='Zonal Average Wind u (West/East)') plt.plot(ds3.latitude, v_zonal_avg, linewidth=2, color='purple', label='Zonal Average Wind v (North/South)') plt.axhline(linewidth=2, color='black',linestyle='--', label = 'Reference Line at 0') # Adding Labels and a legend: plt.title('Temporal and Zonal Average Wind Speed 10m over Ground (ERA5 1979-2018)') plt.xlabel('Latitude') plt.ylabel('Average Wind [m/s]') plt.legend() plt.show()

In [12]: ### Exercise 3.1: # Calculating the temporal and zonal average of sea-level pressure: p_zonal_avg = ds3.msl.mean(dim=['time','longitude']) p_zonal_avg = p_zonal_avg / 100 # Converting from [Pa] to [hPa] # Creating the line-plot: plt.plot(ds3.latitude, p_zonal_avg, linewidth=2, label='Zonal Average Pressure') plt.axhline(y=1013, linewidth=2, linestyle='--', color='black', label='Standard Atmosphere Pressure [1013 hPa]') # Adding lables and a legend: plt.title('Temporal and Zonal Average Sea-level Pressure (ERA5 1979-2018)') plt.xlabel('latitude') plt.ylabel('Mean Sea-level Pressure [hPa]') plt.legend() plt.show()

25-27 fronm Chapter 2

Firstly, the seasonality of the precipitation is caused by the shift oft the ITCZ over the year. In August the ITCZ is futher north over India and West Africa, so there is a lot of Precipitation in this region. In January the ITCZ is further south and so India and West Africa have less precipitation. Additionally, as discussed in the lecture a phenomena called monsoon (Indian Summer Monsoon or West African Monsoon respectively) which is mainly caused by the different heat capacities between ocean and land and the resulting wind direction, is induced by this shift of the ITCZ. The result is a rainy season in Summer and a dry season in Winter for these two regions.

In General the ITCZ can always be found near the equator in the tropics. In August the ITCZ is quite a bit further north than in Jannuary. A really distinct difference in the position of the ITCZ can befound over south-east-asia, where the ITCZ is north of the equator in August and south of it in January. The ITCZ shifts seasonally due to the Earth's axial tilt, causing variations in solar heating, which, in turn influences the position of the zone of maximum convection.

### Exercise 2.2: # Defining levels and colormap: levels = [0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 40] colormap = "YlGnBu" # Creating a figure with 2 subplots: fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16,9), subplot_kw={'projection': ccrs.Robinson()}) im1 = monthly_avg_pre.sel(month=1).plot(ax=ax1, transform=ccrs.PlateCarree(), cmap=colormap, vmin=0, vmax=40, levels=levels, add_colorbar=False) ax1.coastlines() ax1.gridlines() ax1.set_title("January") im2 = monthly_avg_pre.sel(month=8).plot(ax=ax2, transform=ccrs.PlateCarree(), cmap=colormap, vmin=0, vmax=40, levels=levels, add_colorbar=False) ax2.coastlines() ax2.gridlines() ax2.set_title("August") # Adding a shared colorbar and a title to the plot: cbar = fig.colorbar( im1, ax=[ax1, ax2], orientation='vertical', fraction=0.02, # Adjusting width of the colorbar pad=0.03, # Adjusting the space between colorbar and plots label='Average Daily Precipitation [mm/d]') fig.suptitle("Monthly Average Precipitation (ERA5 1979-2018)", x=0.7, size=12) plt.show()

This is again related to the ocean currents in the respective region. We have already discussed the influence of the gulf-stream above, where really warm water from near the equator eventualy finds its way towards northern europe. In the northern Pacific ocean there are also some ocean currents, but none that are this pronounced and impactful, also because the Pacific is much bigger. Moreover, the prevalent current in this region transports the water just from east to west, without any major change in latitude and hence the water is not that warm.

The largest temperature range can typically be found over areas in high latitudes with a lot of landsurface in it's surroundings (especially to the west). If this is the case, we have only small damping-effects of the oceans and the differences between the seasons are significant. To name a few, exemplary spots are siberia and north-east canada, where a continental climate is prevalent. Overall, temperatures on the northern hemisphere fluctuate more than on the southern hemisphere, since large parts are covered of landmass.

(especially to the west)

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:

Stream

# Calculating the average Temperature in °C: t2m = ds1.t2m.mean(dim='time') avg_t2m = t2m - 273.15 # Converting from [K] into [°C] # Defining the map projection (how does the earths sphere get depicted on a 2-dimensional map): ax = plt.axes(projection=ccrs.Robinson()) # Plotting the variable T onto ax: avg_t2m.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='plasma', vmin=-40, vmax=30, cbar_kwargs={'label': 'Temperature [°C]'}) #Note: The keyword "transform" tells the function in which projection the data is stored. # Adding gridlines and coastlines to the plot ax.coastlines(); ax.gridlines(); ax.set_title("Average annual 2m air temperature (ERA5 1979-2018)") plt.show()

### Exercise 1.3: # Computing the average temperature for each month: monthly_avg_T = ds1.t2m.groupby('time.month').mean(dim='time') monthly_avg_T = monthly_avg_T - 273.15 # Converting from [K] in [°C] # Calculating the temperature range between the coldest and the warmest month on average: T_range = monthly_avg_T.max(dim='month') - monthly_avg_T.min(dim='month') # Constructing the plot: ax = plt.axes(projection=ccrs.Robinson()) T_range.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='Oranges', vmin=0, vmax=60, levels=7, center=False, cbar_kwargs={'label': 'Temperature Range [°C]'}) ax.coastlines(); ax.gridlines(); ax.set_title("Temperature range of the average monthly Temperature (ERA5 1979-2018)") plt.show()

vmin=-20, vmax=20,

# Doing the same plot again with discrete levels for better readability: ax = plt.axes(projection=ccrs.Robinson()) avg_t2m.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='plasma', vmin=-40, vmax=30, levels=8, cbar_kwargs={'label': 'Temperature [°C]'}) ax.coastlines(); ax.gridlines(); ax.set_title("Average annual 2m air temperature (ERA5 1979-2018)") plt.show()

About the zonal winds: Starting from the equator until +30 and -30 degrees we know there are trade winds present (u-component below zero meaning going from east to west). Above 30 degrees (NH and SH) we have the westerlies, which are also reflected in the plot (u-component above zero meaning going from west to east). About the meridional winds: Close to the equator we can see that the v-component is close to zero. Starting there going northwards the v-component is negative because of the south component of the trade winds. The same occurs vice versa going from the equator towards south, where we can see positive values for the v-component, meaning the winds go from south to north, which is also because of the trade winds. Above 30 (NH and SH) we have the westerlies which are better seen in the v-component on the southern hemisphere showing negative values again meaning going from south to north. (For the NH the westerlies have positive values but here they are barely visible.)

What can be seen in the pressure is the low pressure system around the equator -> the ITCZ (intense solar heating -> warm air rises). What we also know is the polar lows the most intense low is at around latitude -60 until -70 degrees. At around 30 degrees north and south we can see a higher pressure as the average: here are the subtropical highs where air is descending.

Answers:

slp_mean=(dd.v10.mean(dim='time').mean(dim='longitude'))

slp_mean=(dd.u10.mean(dim='time').mean(dim='longitude'))

In [13]: slp_mean = (dd.msl.mean(dim='time').mean(dim='longitude'))/100 - 1013.25 slp_mean.plot(label='MSL') slp_mean = dd.u10.mean(dim='time').mean(dim='longitude') slp_mean.plot(label='U') slp_mean = dd.v10.mean(dim='time').mean(dim='longitude') slp_mean.plot(label='V') plt.legend() plt.axhline(y=0, color='black') plt.savefig('part3')

In [10]: slp_mean=(dd.msl.mean(dim='time').mean(dim='longitude'))/100 slp_mean.plot() plt.title('Temporal and zonal average of sea-level pressure') plt.axhline(y=1013.25)

Questions:

Zonal winds move along east west. They arise at the equator due to warm rising air at the ITCZ generating lows resulting into east west trade winds. In the subtropics descending air from the hadley cell forms subtropical highs contributing to trade winds. In mid-latitudes westerlies prevail due to ferrell cell circulation. Meridional winds move along north south. More significant flow occurs, especially in regions with strong pressure gradients, such as the polar front. In polar regions meridional flow is influenced by the polar easterlies and westerlies. These winds arise from diffrences in sea-level pressure, shaped by solar heating and influenced by the Coriolis effect induced by the Earth's rotation.

As we can see at -30° and 30° we have high pressure systems wich are known as the subtropical highs (between hadley cell and ferrell cell) At -60° and 60° we can see low pressure systems (the one at -60° is way stronger because there is not that much landmass in the south) wich are known as the subpolar lows (between ferrell cell and polar cell) At the poles we usually have high pressure systems wich are very weak

#temporal average for u10 and v10

#temporal average temporal_average = (ds_winds_and_msl['msl'].mean('time'))/100 #zonal average zonal_average = ( ds_winds_and_msl['msl'].mean(['longitude','time']))/100 y1 = temporal_average y2 = zonal_average x = ds_winds_and_msl['latitude'] plt.plot(x,y1,color = 'Blue') plt.plot(x,y2,color = 'Red') plt.title('Zonal average (Red) and Temporal average (Blue) of sea level pressure') plt.ylabel('Pressure in hPa') plt.xlabel('Latitude') plt.axhline(1013.25,color='Green', linestyle='--', label = 'Standard atmospher pressure (hPa)') # Add the value on the y-axis plt.text(x.min()-10, 1013.25, f'{1013.25}', va='center', ha='right', color='Green') plt.legend(loc = 'lower right') plt.show()

Answer:

l='sea-lvl-pressure')

ax2.set_ylabel('Speed (m/s)') # we already handled the x-label with ax1

ds = ds.rename({'u10':'Zonal Winds', 'v10':'Meridional Winds'}) # Compute the temporal and zonal average of sea-level pressure avg_slp = ds['msl'].mean(dim=['time', 'longitude']) # Convert it to hPa avg_slp_hpa = avg_slp / 100 fig, ax1 = plt.subplots(figsize=(10, 6)) color = 'tab:red' ax1.axhline(y=1013.25, color='r', linestyle='--', label='standard atmosphere pressure') # standard atmosphere pressure ax1.set_xlabel('Latitude') ax1.set_ylabel('Pressure (hPa)', color=color) ax1.plot(avg_slp_hpa['latitude'], avg_slp_hpa, color=color, label='sea-lvl-pressure') # specify latitude as x-axis ax1.tick_params(axis='y', labelcolor=color) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis colors = ['blue', 'lightblue'] variables = ['Zonal Winds', 'Meridional Winds'] ax2.set_ylabel('Speed (m/s)') # we already handled the x-label with ax1 ax2.axhline(y=0, color='b', linestyle='--', linewidth=1, label='0-line') #ax1.axvline(x=) for var, color in zip(variables, colors): avg_var = ds[var].mean(dim=['time', 'longitude']) ax2.plot(avg_var['latitude'], avg_var, color=color, label=var) # specify latitude as x-axis ax2.tick_params(axis='y', labelcolor=color) # Adjust the y-axis limits so that s_msl corresponds to 0 ax1.set_ylim(bottom=983, top=1043.5) ax2.set_ylim(bottom=-8, top=8) fig.tight_layout() # otherwise the right y-label is slightly clipped plt.title('Temporal and Zonal Average of Sea-Level Pressure and Wind Speeds') # Add legends ax1.legend(loc='upper left') ax2.legend(loc='upper right') plt.savefig('msl.png') plt.show()

The general circulation of the Earth's atmosphere includes the equatorial low-pressure zone near the equator, subtropical high-pressure zones around 20-30 degrees latitude, and polar low-pressure zones near the poles. These patterns are attributed to the distribution of solar radiation. Zonal winds (east-west) on Earth are influenced by pressure differences between the equator and mid-latitudes, creating Trade Winds near the equator and prevailing westerlies in mid-latitudes. Meridional winds (north-south) result from the Hadley and Ferrel Cell circulations, causing air to move poleward and equatorward. Near the poles, polar easterlies flow equatorward. These wind patterns are driven by the distribution of sea-level pressure, influenced by solar heating, and are affected by the Coriolis effect due to Earth's rotation.

ax2.set_ylabel('Speed (m/s)')

# Compute the temporal and zonal average of sea-level pressure a_slp = ds_wind['msl'].mean(dim=['time', 'longitude']) # Convert it to hPa a_slp_hpa = a_slp / 100 fig, ax1 = plt.subplots(figsize=(10, 6)) # Plot sea-level pressure color = 'tab:red' ax1.axhline(y=1013.25, color='r', linestyle='--', label='Standard Atmosphere Pressure') ax1.set_xlabel('Latitude') ax1.set_ylabel('Pressure (hPa)', color=color) ax1.plot(a_slp_hpa['latitude'], a_slp_hpa, color=color, label='Sea Level Pressure') ax1.tick_params(axis='y', labelcolor=color) ax2 = ax1.twinx() # Plot wind speeds colors = ['blue', 'lightblue'] variables = ['u10', 'v10'] ax2.set_ylabel('Speed (m/s)') ax2.axhline(y=0, color='b', linestyle='--', linewidth=1, label='0-line') for var, color in zip(variables, colors): a_var = ds_wind[var].mean(dim=['time', 'longitude']) ax2.plot(a_var['latitude'], a_var, color=color, label=f'{var} Wind Speed') ax1.set_ylim(bottom=a_slp_hpa.min(), top=a_slp_hpa.max()) ax2.set_ylim(bottom=-8, top=8) ax1.grid(True) ax2.grid(True) ax1.legend(loc='upper left') ax2.legend(loc='upper right') plt.title('temporal and zonal average of sea-level pressure and wind speeds') fig.tight_layout() plt.show()

Questions:

The strongest winds are the westerlies, which equals the strong low pressure around the Antartica and also the northerly wind coming from the subtropical high system zone. Little bit south of the equator the wind is coming from south and a bit north of the equator from north. The zonal winds are coming from the East in the tropics due to the Coriolis force. This are the trade winds.

From south to north: the first and deepest low pressure system is the sub-polar low, the subtropical high at around 30° follows and then at the equator is the low pressure system of the ITCZ. On the northern hemisphere it is the other way round with weaker intensities because of more landmass.

sterlies for example). In [11]: slp = dw.msl.mean(dim=['time', 'longitude']) / 100 slp.plot(label='slp') plt.axhline(y=1013.25, c='red', label='standard atmosphere pressure') plt.legend(loc='lower right') plt.ylabel('sea-level pressure [hPa]') plt.title('The temporal and zonal average of sea-level pressure (slp)');

'slp

avg_u10.plot(label='u-component') avg_v10.plot(label='v-component')

plt.ylabel('wind speed [ms**-1]')

summary, the sea-level pressure plot helps identify the positions of high and low-pressure systems, and the wind component plots provide insights into the direction and strength of zonal and meridional winds across latitudes.

Direction and strength of zonal and meridional winds:

Zonal Winds (East-West): The zonal winds (represented by u10u10u_{10}) exhibit a pattern from east to west and west to east at different latitudes. The plot helps in visualizing the dominant wind direction. Near the equator, easterly trade winds prevail, while at higher latitudes, westerlies dominate.

Answers:

v-component

u component of the wind