Comparison of CMIP6 and CMIP5 Models in Simulating Mean and Extreme Precipitation over East Africa

• DOI: 10.20944/preprints202102.0111.v1
• Publication Date: 2021-02-04T09:03:53Z
• Authors: Brian AYUGI; Jiang Zhidong; Huanhuan Zhu; Hamida Ngoma; Hassen Babaousmail; Karim Rizwan; Victor Dike
• Keywords: Atmospheric Science; Climate Change and Variability Research; Precipitation; Coupled model intercomparison project; Oceanography; Climate model; 01 natural sciences; Environmental science; Meteorology; General Circulation Model; Climate change; 0105 earth and related environmental sciences; Climatology; Global and Planetary Change; Geography; Geology; FOS: Earth and related environmental sciences; Numerical Weather Prediction Models; atmospheric_science; Earth and Planetary Sciences; 13. Climate action; Environmental Science; Physical Sciences; Global Drought Monitoring and Assessment; Climate Modeling

This study examines the improvement in coupled intercomparison project phase six (CMIP6) models against predecessor CMIP5 simulating mean and extreme precipitation over East Africa region. The compares climatology of indices simulated by CMIP with CHIRPS dataset using robust statistical techniques for 1981 – 2005. results display varying performance general circulation (GCMs) simulation annual seasonal domain. CMIP6-MME shows improved local cycle a better representation two peaks, especially MAM rainfall relative to its predecessor. Moreover, is well captured CMIP6 performed than CMIP5-MME lesser biases SDII, CDD, R20mm Africa. Remarkably, most are unable simulate extremely wet days (R95p). A few (e.g., NorESM2-MM CNRM-CM6-1) depicts reproducing observed across all analyses. Conversely, OND season overestimation some (i.e., R95p, PRCPTOT), except R20mm. Consistent other studies, ensemble both CMIP5/6 due cancellation systematic errors individual models. Generally, akin However, new model generation still marred uncertainty, thereby depicting substandard calls further investigation attribution studies into sources persistent prerequisite identifying features that can accurately patterns future usage.