Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) are unable to resolve the spatial and temporal characteristics of the South Asian Monsoon satisfactorily. A CGCM with the capability to reliably project the global as well as the regional climatic features would be a valuable tool for scientists and policymakers. Analysis of 28 CMIP5 models highlights varying degree of biases in precipitation and 2 m surface air temperature (T2m) over south Asia, and the Community Earth System Model (CESM) developed at the National Center for Atmospheric Research is found to be one of the best performing models. However, like all other CMIP5 models, CESM also has some inherent model biases. Using CESM, it is found that the precipitation and T2M biases reduce with increase in the model horizontal resolution from 2° to 0.5°. Further, a few deep convective parameters in the Zhang-McFarlane convection scheme are tuned for 2° and 1° model resolutions using both manual and semi-automatic model tuning methods. Comparing results from the two tuning methods we find that the performance of the manually tuned model is better than that of the semi-automated one.

The influence of surface topography resolution in Indian summer monsoon simulation is investigated. Three sets of six-member ensemble simulations with climatological sea surface temperature are conducted with the Community Atmospheric Model, version 5.1 (CAM5.1): COARSE simulation at 1.9° × 2.5° latitude–longitude resolution, FINE simulation at 0.47° × 0.63° resolution, and HYBRID simulation, that is, using COARSE surface topography imposed on the FINE configuration. With regard to the representation of the surface topography, substantial differences occur at the regional scales between the simulations, especially over the foothills and steep flanks of the mountains. In the COARSE and HYBRID simulations, the orographic height of the foothills is overestimated whereas that of the steep flanks adjacent to the foothills is underestimated. The biases are severe (up to 1 km) over the Himalayas and Tibet and have detrimental effects on regional climate through barrier effects on the low-level flow, and the lapse rate and elevated heat source effects. Overall, the simulations show remarkable improvement with an increase in resolution, mainly because of the improved representation of atmospheric and surface processes. However, local climate—surface air temperature, sea level pressure, precipitable water, and wind—of the orographic regions, particularly where large orographic biases exist in COARSE, is found to benefit substantially from increased resolution of surface topography. Local precipitation and evaporation are exceptions, although, as they are negligibly sensitive to topographic resolution, showing strong dependence on the resolution of surface and atmospheric processes. Moreover, resolution of surface topography generally does not have notable remote impacts.

This study examines the capability of the Coupled Model Intercomparison Project Phase 6 to replicate temperature and precipitation across eight African sub-regions, as well as their correlation with three large-scale climate indices. Qualitative estimations indicate that reference datasets (ERA5, CRU, CHIRPS) and the MMM exhibit similar patterns, albeit with slightly different intensities in many sub-regions and seasons. For precipitation and temperature, several models exhibit good performance across various sub-regions and seasons, with PCCs of 0.98. The MMM effectively captures the signs of the observed trend in many African sub-regions but fails to capture its magnitude. The MMM exhibits better performance than individual models across various sub-regions. Models like MMM and GFDL-ESM4 consistently outperform others, particularly in precipitation. However, significant regional disparities are observed, with SWAF, CAF, and WAF (for temperature during DJF) being the most challenging areas, where few models meet the performance thresholds. In MAM, models exhibit strong performance in NAF, WAF, CAF, NEAF, and SWAF but fail to meet the cutoff in SAH and CEAF. The models exhibit robust performance across many regions during JJA and SON. The MMM also captures the increasing (but with relatively lower value) temperature depicted by ERA5 and CRU. The MMM shows a limitation in reproducing the response to some large-scale climate indices.