Sunday, August 13, 2017

Climate Change Impact - Part 5 - Great Lakes of Africa (Lakes Victoria,Tanganyika and Malawi)

Climate Change Impact

Part 5: Example – Great Lakes of Africa


The Great Lakes of Africa are an important source of fish. The United Nations Food and Agriculture Organisation (FAO) wished to know to what extent climate change would influence the water temperature in the lakes. A study was carried out which found that lake temperature would increase by around 1°C by the middle of the 21st.century.


FAO initiated an activity to investigate the possible effects and impacts of climate change on fish and fisheries production on the African Great Lakes; Lakes Victoria, Tanganyika and Malawi.
Figure 1 Great Lakes of Africa

Figure 1 Great Lakes of Africa

Current climate

The Joint Research Centre (JRC) at Ispra, Italy, maintains databases of unpublished satellite data including water surface temperatures on the African Great Lakes on an 8-day basis. A time series of a number of decades was required to be analysed in order to present the temperature fluctuations on the Great Lakes
The satellite data from which temperatures are estimated are held in TIF (tagged image format) files, one for each year from 1985 to 2008. Each file holds data for 45 passes of the satellite.  For each pass a value is recorded for each cell on a 400 by 250 grid, provided there is no cloud cover and provided it is over water. Each cell is approximately 10 km by 10 km.

One major problem with the data is cloud cover.  For each of the lakes the approximate percentage of time for which temperature can be calculated is:

  • Lake Malawi – 70%
  • Lake Tanganyika – 60%
  • Lake Victoria – 30%
For all lakes, the problem is seasonal and is related to the rainy season.

The method adopted was as follows:
  •          Calculate the average lake temperature for each of the 45 passes and develop a temperature profile.
  •          Assume that although temperature in different parts of the lake would be higher or lower than the average, the distribution throughout the year would be the same.
  •          If data were missing, due to cloud cover, for one of the satellite passes find the value of the previous and of the following passes which had data for that cell.
  •          Base the temperature that satellite pass on the weighted average of the previous and following pass.

This enabled a complete grid of ‘observed’ surface temperature data to be prepared for all three lakes.

The following chart shows an example, for one cell of Lake Malawi.


Figure 2 Example of infilled lake temperature data

Climate change impact

As a first stage in assessing impact, an air temperature record was established for each of the lakes. The data from climate stations was of limited availability; few stations and long gaps in the data. As an alternative, the temperature data based on RSS (Remote Sensing Systems) estimates was used. This is one of two the ‘standard’ temperature records based on (Advanced) Microwave Sounding Unit data (AMSU/MSU). Comparing the limited observed data and the satellite derived data, showed similar trends but less variation. The difference in the temperature variation was due to the fact the satellite data were based on a 2.5 ° grid, not a single point, and therefore represented values over an area. In fact, this data was in that way more suitable than point data.

For each of the lakes a relationship between air temperature and water temperature was developed.

The climate change projections were based on the average of six climate models using the A1B (‘business as usual’) scenario. The models were those used in the IPCC “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”. This gave the projected change in air temperature
The final stage was to use the relationship between lake surface temperature and air temperature to estimate the change in surface temperature of the lakes.

This shows that, for all lakes, the increase in surface water temperature would be around 1°C in the middle of the century.

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