Mean Projections
This page presents Chad's projected climate. The Mean Projections page offers CCKP's complete suite of indicators for in-depth analysis into future climate scenarios and potential risks due to changing climates. Data can be investigated as either the projected mean or anomaly (change) and is presented spatially, as a seasonal cycle, time series, or heat plot, which shows seasonal change over long-term time horizons. Data can be analyzed as annual, seasonal, or monthly. You can further tailor your analysis by selecting different projected time periods and Shared Socioeconomic Pathways (SSPs). SSPs are meant to provide insight into future climates based on defined emissions, mitigation efforts, and development paths.
Indicators are presented as multi-model ensemble, which represent the range and distribution of the most plausible projected outcomes of change in the climate system for a selected SSP. Individual models will be made available soon.
Climate projection data is modeled data from the global climate model compilations of the Coupled Model Inter-comparison Projects (CMIPs), overseen by the World Climate Research Program. Data presented is CMIP6, derived from the Sixth phase of the CMIPs. The CMIPs form the data foundation of the IPCC Assessment Reports. CMIP6 supports the IPCC's Sixth Assessment Report. Data is presented at a 0.25º x 0.25º (25km x 25km) resolution.
CCKP continues to add indicators as they are produced to complete currently available selection list.
Section II Model Dispersion
In this figure, we present the dispersion across different CMIP6 models and scenarios. This dispersion illustrates how the projections can vary depending on the model used, reflecting the uncertainty and range of projections within the CMIP6 ensemble. Understanding this variability is crucial as it helps assess the robustness of the projected climate changes and highlights the differences in model behavior under various scenarios. By examining the spread of results, we can better understand the degree of consensus or divergence among models and make more informed decisions regarding climate projections and their potential impacts.
In this figure, we present the correlation between temperature-related variables and precipitation-related variables across various CMIP6 models and scenarios. Initially, we examine how these variables correlate during the historical period, incorporating ERA5 and CRU data where available, and then we display the correlation between anomalies. It’s important to note that this analysis is not intended as a skill assessment but rather to explore the broader patterns of these correlations across different models. Temperature and precipitation are inherently linked in the climate system, with changes in one often driving shifts in the other. For example, rising temperatures can influence evaporation rates and atmospheric moisture, thereby affecting precipitation patterns. Understanding the correlation between temperature and precipitation across CMIP6 models is essential for gaining insights into the variability of climate projections.