# Assignment: CERES data#

To solve the following exercises you can copy and paste code from the previous notebook. The code modifications required to solve the exercises are minimal (e.g. changing the name of a variable, add a small computation…): don’t think too “complicated”!

## Importing the modules#

This one is easy. I’ll do it for you:

```
# 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
```

## TOA fluxes#

Read the TOA dataset we used during the lesson. Do you remember all variables it contains?

```
# Your answer here
```

### Albedo#

Compute the climatological mean of clear-sky planetary albedo \(\overline{\alpha_{P_{clr}}}\) and plot it on a map. Analyse the plot.

```
# Your answer here
```

Repeat the operation with all-sky planetary albedo \(\overline{\alpha_{P_{all}}}\). Where are the largest differences? Can you plot the difference between the two on a map, too?

```
# Your answer here
```

Now plot the zonal, climatological means \(\left[ \overline{\alpha_{P_{all}}} \right]\) and \(\left[ \overline{\alpha_{P_{clr}}} \right]\) on the same plot. Add a legend to it!

```
# Your answer here
```

Compute the global average of \(\overline{\alpha_{P_{all}}}\) and \(\overline{\alpha_{P_{clr}}}\) (remember to weight according to latitude!). Compare the values you obtain with the ones we mentioned in the lecture.

```
# Your answer here
```

### Longwave outgoing radiation#

Repeat the operations above with \(LW_{all} \) and \(LW_{clr}\) (i.e.: maps of \(\overline{LW_{all}}\), \(\overline{LW_{clr}}\), line plots of \(\left[ \overline{LW_{all}} \right]\), \(\left[ \overline{LW_{clr}} \right]\)). What is the global effect of clouds on outgoing longwave radiation?

```
# Your answer here
```

## Surface fluxes#

Now open the EBAF-Surface dataset, available for download from the download page.

```
# Your answer here
```

### Surface albedo#

Compute the all-sky surface albedo \(\overline{\alpha_{S_{all}}}\). Plot it on a map.

```
# Your answer here
```

Compute the global average of \(\overline{\alpha_{s}}\).

```
# Your answer here
```

### Surface energy balance#

Now compute the net surface energy intake \(\overline{SEB} = \overline{SW_{in}} - \overline{SW_{out}} + \overline{LW_{in}} - \overline{LW_{out}}\). Plot it on a map and analyse your results. Where does the surface gain most energy? Is the net radiative energy a gain or a loss for the surface of the globe?

```
# Your answer here
```

Compute the global averages of each term and compare them to the values we discussed in the lecture (ref). For reference, here is the figure again:

```
# Your answer here
```

**Discuss the processes that will counterbalance this net radiative energy imbalance, in the oceans and on land! Also discuss the differences between your results and the plot above.** (Careful literature search might lead you to more recent estimates of the budget if you are interested).

```
# Your answer here
```