Country

Chad

Explore historical and projected climate data, climate data by sector, impacts, key vulnerabilities and what adaptation measures are being taken. Explore the overview for a general context of how climate change is affecting Chad.

Climate by Sector Energy

The energy sector is linked to climate variability and change in numerous ways. On one side, global energy production is a strong contributor to the drivers of climate change, namely through the emission of greenhouse gases. On the other side, it is also exposed to the diverse impacts of climate variability and change through changes in energy supply (e.g. disruption of operations and distribution) and demand (growing populations and evolving power needs). The consequences can be complex, yet they are often both positive and negative.

This section provides the visualization of four climate indices that are most relevant for energy sector.

Temperature: Spatial Variability

Implications

Annual temperature provides the broadest assessment of the climate of a region. It is important to recognize a change of this quantity over time because it reflects the expected pressure on the general need for heating or cooling, one of the driving factors for broad power needs. In many countries, the temperature changes are fairly uniformly distributed across the year and thus the annual mean changes are representative. Sometimes, however, changes are more pronounced in particular seasons, particularly in higher latitudes and the change over the seasonal cycle should be consulted. In addition, extremes might not linearly follow the mean temperature change. But overall, annual mean temperature change offers the first order impact from the changing climate with some regions changing faster than others.

Data

The map shows change in projected Annual Mean Temperature by 2050 compared to the reference period (1986-2005) under RCP 8.5 of CIMP5 ensemble modeling. Purple/Red areas are likely to experience annual temperature increase compared to baseline period. Meanwhile, Blue/Green areas are likely to experience annual temperature decrease.

Caveats

Global climate models are quite well suited to project temperature changes given different emission scenarios, and thus the field of change in temperature is probably the most robust quantity in climate projections. The only limitation in the global models is their limited spatial resolution which does not resolve small, local scale detail. Higher resolution outcomes could be achieved through downscaling, taking into account topography, aspect, ground coverage, moisture, etc.

Temperature: Seasonal Variability

Implications

The relationship of daily heat with the demand for electricity can be estimated through a quantity called the Cooling Degree Days. This quantity accumulates the temperatures above 18C threshold which broadly represents a comfortable living environment. The cooling degree days capture the amount of heat that society would like to get rid of by period through some form of active cooling, be it through air conditioning or through evaporative processes that generally require pumps for water. The monthly changes provide insight into potentially extended seasons of power demand for cooling, or highlighting when during the year likely power demand increases might occur. Low emissions connected to the RCP2.6 will likely lead to significantly smaller warming than high emissions of the RCP8.5, with the intermediate scenarios somewhere in-between.

Data

The graph shows projected change in Cooling Degree Days per month by 2050 compared to the reference period (1986-2005) under all RCPs of CIMP5 ensemble modeling. Positive values indicate that cooling degree days will likely to increase compared to the baseline, and vice versa. The shaded area represents the range between the 10th and 90th percentile of the model projections.

Caveats

The actual threshold chosen is less important than the changes. In most locations, the general rise in mean temperatures directly gets reflected in accumulated heat above a fixed threshold, which will be accumulated in the cooling degree days. The variations in the absolute quantity of cooling degree days across the different CMIP5 models are quite large because of systematic biases in the temperature fields. The relative changes throughout the annual cycle, however, are more robust and are well reflected in the ensemble median of change. Particularly in higher latitudes, the inter-annual variability can be substantial and by far exceed the climatological change.

Precipitation: Extreme Events

Implications

As warmer air has a higher capacity to carry moisture in form of water vapor, future climate raises the likelihood for strong rainfall events and particularly towards extremes. The 10-year return period rainfall episodes, such as the 5-day cumulative rainfall, is a good measure of these extremes. In many places around the world, the maximum expected amount of rainfall in a 10-year period is projected to increase, which can lead to flooding. As a result, power production can be largely affected. For example, the transportation lines for fuel can be interrupted by local flood, or distribution networks can be disturbed by excessive rainfall and flooding.

Data

The boxplot shows recorded 5-Day Cumulative Rainfall for 1986-2005 and projected 5-Day Cumulative Rainfall 10-yr Return Level by 2050 under all RCPs of CIMP5 ensemble modeling. This indicator focuses on the maximum 5-day cumulative rainfall amount that can be expected within a 10-yr period.

Caveats

Extremes are often behaving differently than the mean of a process. In many regions, the largest events are bigger than what would be expected when looking at more frequent events. Therefore, special treatment is necessary to capture the heavy tail of extreme rainfall. 10-yr return events are rare enough to warrant special analysis based on extreme value theory. Here, monthly maxima of the 5-day cumulative rainfall over any 20-year window were calculated and the values used to derive the extreme value parameters. Note, using just 20-year time series is somewhat short to best measure rare events, and thus the values might be somewhat noisy. For specific applications, a more targeted local study with the longest possible records should be performed and the local confidence interval should be consulted next the return levels.

Drought: Time Series

Implications

The direction of SPEI changes provide insight into increasing or decreasing pressure on water resources for direct power production or indirectly through cooling. At regional scales, the trends separate generally quite well between high and lower emission scenarios. Both power demand and production are also tied to water availability. Obviously, this is most directly the case in hydropower systems. But dry conditions might also come along with higher temperatures and thus heightened cooling needs and an increase in demand for water pumping, particularly in regions of intense agriculture. On the production side, water is required for cooling of power plants. If there is not enough water, then cooling is restricted and thus production might need to be slowed. In some places, there are regulations preventing power plants from causing an increasing the temperature of returned water above specific thresholds dangerous for local fish and plants. These thresholds are more quickly reached if stream flows are low during dry conditions. In a few regions, too much moisture can also be of an issue as water might need to be removed.

Data

The graph shows the recorded Mean Drought Index (or Standardized Precipitation Evapotranspiration Index, SPEI) per year for 1986-2005, and projected SPEI for 2020-2100 under all RCPs of CIMP5 ensemble modeling. Note, the shaded ranges illustrate the inter-model differences, here using the +/- one standard deviation. The reason for using a narrower metric compared to the 10th and 90th percentile is that the inter-model difference is large for precipitation, and in particular for the count of days with rainfall.