# Assignment: temperature and precipitation data

## Contents

# Assignment: temperature and precipitation data#

```
# 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 cartopy # Map projections libary
import cartopy.crs as ccrs # Projections list
# Some defaults:
plt.rcParams['figure.figsize'] = (12, 5) # Default plot size
```

## Temperature#

**Compute the mean temperature \(\overline{T}\) in °C. Plot it.**

```
# Your answer here
```

**Compute the monthly average temperature for each month \(\overline{T_M}\) (annual cycle). I 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, i.e. \(\overline{T_M}max\) - \(\overline{T_M}min\) on a map**.

**Where is the range of monthly temperature highest? Note the latitudinal differences. Is the variability higher over land or over oceans? Why?**

```
# Your answer here
```

**Compute the zonal mean temperature \(\overline{\left[ T \right]}\). Plot it. At what latitudes is the zonal average temperature equal to 0°C? Describe the differences between North and South. Can you explain them easily? Add \(\left[\overline{T_{January}}\right]\) and \(\left[\overline{T_{July}}\right]\) to the plot.**

```
# Your answer here
```

Similarly to the decomposition in time, geophysical fields can also be decomposed zonally:

\(A = \left[ A \right] + A^{*}\)

Where \(\left[ A \right]\) is the zonal average and \(A^{*}\) the departure from the zonal average.

**Verify that for any field A:**

\(\overline{A^{*}} = \overline{A} - \left[ \overline{A} \right]\)

\(A = \left[ \overline{A} \right] + \left[ A' \right] + \overline{A^{*}} + A'^{*}\)

**Compute \(\overline{T^{*}}\) (use eq. 1 above), and plot it on a map. Discuss**

```
# Your answer here
```

**Compute the zonal mean temperature over land \(\overline{\left[ T_{Land} \right]}\) and over oceans \(\overline{\left[ T_{Oceans} \right]}\) and plot them both on the same plot. Discuss.**

```
# Your answer here
```

## Precipitation#

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

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

<xarray.DataArray 'tp' (time: 480, latitude: 241, longitude: 480)> [55526400 values with dtype=float32] Coordinates: * longitude (longitude) float32 -179.6 -178.9 -178.1 ... 178.1 178.9 179.6 * latitude (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0 * time (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2018-12-01 Attributes: units: m long_name: Total precipitation

**Compute the average total annual precipitation (average precipitation over a year, in mm yr\(^{-1}\)) and store it in a variable called “annual_prcp”. Plot it.**

```
# Your answer here
```

**Draw a new plot of “annual_prcp”, this time with a new colormap (‘YlGnBu’) and with the following discrete levels specified: [50, 200, 500, 700, 1000, 1500, 2000, 3000, 5000]. Now have a look at the patterns again.**

**Using your knowledge from the lecture, try to answer questions such as:**

why are the oceans (mostly) dryer than land in the subtropics?

this was not covered in the lecture, but why is there only one large desert (in Africa) while other continents at the same latitude are rather wet?

why are the eastern subtropical oceans dryer than in their western part? Do all three oceans have similar patterns for precipitation?

where does it fall more than 3000 mm precipitation a year? Less than 50 mm precipitation a year? Note that it could be easy to use the “.where()” function to highlight these areas easily. Optional: can you come up with a plot showing them on the map?

```
# Your answer here
```

**Plot the average precipitation in January on a map. Do the same with precipitation in July, and choose the same levels for both maps in order to compare them. Discuss.**

```
# Your answer here
```