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ADH Data Resource
This chart shows the average monthly temperature for Agadir, Morocco. The red and blue areas show average maximum and minimum temperatures for that month.
It's important to remember that a lot of the data is modelled rather than directly observed, which means the data for a specific location may be calculated based on measurements taken nearby, and other sources may differ in their estimations of temperature.
To make the temperature data a little easier to communicate, we've broken it down by months in this chart. Comparing the average monthly temperature for every August for the last 30 years, for example, is more visually revealing than looking at all the data for all seasons.
To assist you with your own analysis, we've also prepared the temperature record as a table. Here we are showing the "climate anomaly" in the fourth column. This tells you exactly how much hotter or colder than the historical average a month was.
| Month | Avg | 2024 | Anomaly |
|---|---|---|---|
| January | 13.56°C | 17.24°C | 3.68°C |
| February | 14.59°C | 17.61°C | 3.02°C |
| March | 15.90°C | 17.51°C | 1.61°C |
| April | 16.66°C | 20.40°C | 3.74°C |
| May | 18.61°C | 19.81°C | 1.20°C |
| June | 20.00°C | 20.73°C | 0.73°C |
| July | 22.15°C | 24.70°C | 2.55°C |
| August | 22.35°C | 24.69°C | 2.34°C |
| September | 21.27°C | 22.11°C | 0.84°C |
| October | 19.51°C | 20.65°C | 1.14°C |
| November | 16.75°C | 20.30°C | 3.55°C |
| December | 13.76°C | 15.64°C | 1.88°C |
This chart is created using the anomaly described above, and is based on the work of Ed Hawkins and #ShowYourStripes. Red bars show years in which the temperature has been higher than the historical average, blue bars years in which it has been colder.
Historical baseline rainfall data (1950 - 1980) comes from the Global Precipitation Climatology Centre (GPCC). Recent rainfall data comes from GloH2O. As with the temperature data above, this data is modelled at the global scale to estimate rainfall at any given moment in time. It is measured in millimetres of rain per month (mm).
This chart also shows the GPCC data for your location, but in this case we have illustrated it as a line chart showing a single month over time.
We've also included the rainfall data as an interactive table for you, which shows the difference between a particular month and the historical average.
| Month | Avg | 2024 |
|---|---|---|
| January | 32.03mm | 35.34mm |
| February | 30.24mm | 30.24mm |
| March | 37.45mm | 37.45mm |
| April | 20.08mm | 20.08mm |
| May | 5.00mm | 5.00mm |
| June | 0.37mm | 0.37mm |
| July | 0.16mm | 0.16mm |
| August | 1.34mm | 1.34mm |
| September | 6.44mm | 6.44mm |
| October | 20.26mm | 20.26mm |
| November | 15.91mm | 15.91mm |
| December | 33.12mm | 33.12mm |
Aerosol Optical Depth (AOD) data can be used as a proxy to suggest air quality, specifically to infer particulate matter concentrations (like PM2.5). AOD represents the degree to which particles in the atmosphere (dust, smoke, pollutants) prevent sunlight from passing through the atmosphere. Higher AOD values generally indicate higher levels of aerosols, which can correspond to poorer air quality.
AOD can be used as a proxy for air quality, with higher values indicating worse air quality. This chart shows the average AOD for the time period.
PM2.5 refers to particulate matter that is 2.5 micrometers (µm) or smaller in diameter. These tiny particles are about 30 times smaller than the width of a human hair and can be made up of various substances, including organic chemicals, metals, dust, soot, and liquid droplets.
Because of their small size, PM2.5 particles can be easily inhaled deep into the lungs and even enter the bloodstream, posing significant health risks, especially to the respiratory and cardiovascular systems. Long-term exposure to elevated PM2.5 levels has been linked to serious health issues like asthma, lung disease, heart attacks, and premature death.
An AOD value between 0 and 100 would be an indication of clean air, minimal aerosols, equivalent to PM2.5 levels under 12 µg/m³ (good air quality).
An AOD value bwteeen 601 and 1000 would indicate significant air pollution, equating to PM2.5 levels between 55–150 µg/m³, affecting most of the population.
An AOD value higher than 2000 would indicate extremely high levels of pollution, likely corresponding to events like wildfires or dust storms. PM2.5 values in this range could exceed 250 µg/m³, severely impacting health and visibility.
As discussed, this data is a proxy for air quality, but more accurate information about different gases and particles can be obtained from sensor networks, the largest of which is at www.aqicn.org. We will be including this data in future iterations of the Africa Climate Observer.
Embedded data resource goes here. Appearance will need to be fully controlled by the dev team
The Africa Data Hub Climate Observer is a unique resource that contains historical climate-related data for African towns and cities.
There are several well-established public datasets published by academic institutions which are used by researchers when investigating the climate crisis. These typically aggregate data from land, sea and air-based monitoring stations all around the world, sometimes also using satellite observations, to model temperature and precipitation (rainfall) changes over time.
While these datasets are comprehensive, they are often very hard to use for non-specialists. Not only are they very large, but they use complex file structures (such as NetCDF) to combine geospatial, time and observed datapoints in one entity.
While there have been successful projects to make the data useful to journalists and other researchers, these are generally limited for African users. Many easy-to-use tools limit investigations to national or provincial scale queries, or focus on regions in the Global North.
The Africa Data Hub Climate Observer, however, only contains data about Africa. You can enter any GPS location on the continent to see the best quality data there is about that area.
When dealing with a topic as broad, important and complex as the climate crisis, it is important to understand the limitations of data before you use it in your reporting.
The data is mapped to a grid in which each square is 1 degree latitude by 1 degree longitude in size, which is roughly 100x100km. What this means is that data is only accurate to regions of that size. Rainfall for a single town within that area may be different.
In addition, much of the data is not measured, but modelled. The planet is not covered in temperature and rainfall sensors to generate 100% accurate observations. If you see a measurement of 52mm for rainfall in a particular city, in other words, this is not an exact number and should be treated as an approximation. It’s important to bear this in mind when reporting numbers that you find here.
The data is distributed under Creative Commons licences that encourage sharing and reuse, but please be sure to check any data producer specific licensing requirements below. And please credit us and the original data source for anything you use and, if possible, share your work with us too by mailing us here. We'd love to see it!
Berkeley Earth’s data is licensed under Creative Commons BY-NC 4.0 International for non-commercial use only. Attribution under CC BY-NC terms should be given to Berkeley Earth, including reference to www.berkeleyearth.org when possible. For licensing in commercial applications, please contact admin@berkeleyearth.org.
Free or low cost access is also available for academics and journalists. For academic publications and other permissions requests please contact permissions@berkeleyearth.org.
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