Variations in Projections of Precipitations of CMIP6 Global Climate Models under SSP 2–45 and SSP 5

Variations in Projections of Precipitations of CMIP6 Global Climate Models under SSP 2–45 and SSP 5–85

Shiru, Mohammed Sanusi / Kim, Jin Hyuck / Chung, Eun-Sung

This study projects precipitation over East Asia comprising of East China, South Korea, and Japan using ten Global climate models (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and their Multi-Model Ensemble (MME). The historical GCMs were assessed using statistical metrics and compromise programming for their abilities in replicating the observed precipitation in order to assess their skills in projecting precipitation relative to the MME. Linear scaling method was used in bias correcting the GCMs using Global Precipitation Climatology Center (GPCC) as reference observed data. Probability density function and mean monthly precipitation of GCMs for the different months over the period 2021–2060 and 2061–2100 were compared with those of the observed under SSPs 2–45 and 5–85. The study revealed that NorESM2-MM, MRI-ESM2-0, MME and ACCESS-ESM1-5 have the best abilities to replicate the observed precipitation in the area of study. Under SSP 2–45, spatial projections show that the changes in precipitation for 2021–2060 are expected to range between −32.0–4.0% while those for 2061–2100 are −46.7–4.4%. Under SSP 5–85, the expected changes in precipitation range from −40 to 6.6% during 2021 to 2060 while the range is −87.3 to 6.6% with the highest decreases expected for CanESM5. The MME projects the changes of −12.0–6.6% during this period. The mean monthly precipitations for the different months over 2021–2060 and 2061–2100 show that while some GCMs project increases in mean monthly precipitation, others projected decreases depending on the months. Study show, no model exhibits the exact features of the observed precipitation. A model’s projection skill may be independent of its ability to replicate the properties of the observed climate. Therefore, there is a need for caution in choosing GCMs for the climate projection. This study suggests the use of the MME or the other models that have closest projections to it for climatic and hydrological studies in the region.