Influence of the Coupling South Atlantic Convergence Zone-El Niño-Southern Oscillation (SACZ-ENSO) on the Projected Precipitation Changes over the Central Andes

There are no studies related to the influence of the coupling between the South Atlantic Convergence Zone (SACZ) and El Niño-Southern Oscillation (ENSO) pattern variability on future changes in the austral summer (December-February, DJF) precipitation over the central Andes. Therefore, we evaluated the historical simulations (1980–2005) and projections (2070–2099) for the Representative Concentration Pathway 8.5 (RCP 8.5) scenario of 25 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5). Moreover, we also consider the Regional Climate Model version 4 (RegCM4) projections nested in three CMIP5 GCMs (GFDL-ESM2M, MPI-ESM-MR, and HadGEM2-ES) under RCP 8.5. We separate the CMIP5 GCMs according to their abilities to simulate the nonlinear characteristics of ENSO and the SACZ for the historical period. We found that only three out of 25 CMIP5 GCMs (hereafter group A) simulate the nonlinear characteristics of ENSO and the SACZ during the historical period. Although most CMIP5 GCM project DJF precipitation decreases over the central Andes, group A project precipitation increases related to the projected increase in deep convection over the central Peruvian Amazon. On the regional scale, only RegGFDL (nested in a group A CMIP5 GCM) projects a statistically significant increase in DJF precipitation (~5–15%) over the northern central Andes and the central Peruvian Amazon. Conversely, all RegCM4 simulations project a decrease in DJF precipitation (~−10%) over the southern central Andes.

Comparison of CMIP6 and CMIP5 Model Performance in Simulating Historical Precipitation and Temperature in Bangladesh: A Preliminary Study

The relative performance of global climate models (GCMs) of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. The multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skilful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, a significant improvement in CMIP6 MME compared to CMIP5 MME was noticed in simulating rainfall over Bangladesh at annual and seasonal scales. CMIP6 MME also showed significant reduction in maximum and minimum temperature biases over Bangladesh. However, systematic wet and cold biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlation with observed data compared to CMIP5 GCMs, but higher difference in terms of standard deviations and centered root mean square errors, indicating better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables for different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature compared to CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is incompatible with the climate models used in this research.