Evaluating Diurnal Rainfall Signal Performance from CMIP5 to CMIP6

Abstract

This study provides a comprehensive overview of diurnal rainfall signal performance within the current collection of models in Phase 6 of the Coupled Model Inter-comparison Project (CMIP6). The results serve as a reference for understanding model physics performance to represent precipitating processes and atmosphere–land–ocean interactions in response to the diurnal solar radiation cycle. Performance metrics are based on the phase, amplitude, and two empirical orthogonal function (EOF) modes of the climatological diurnal rainfall cycle derived from a Tropical Rainfall Measurement Mission observational dataset. We found that the ensemble model biases of diurnal phase and amplitude over lands improved from CMIP5 to CMIP6; however, those over oceans are still highly uncertain among CMIP6 models. Evaluation with observed EOF modes shows that the CMIP6 models are bifurcated based on the second EOF (EOF2), which represents diurnal rainfall contrast of coastal regimes where large biases of phase and amplitude reside. While the model ensemble suggests models are benefited from higher resolution in simulating phase and amplitude biases, the most distinct difference between the bifurcations is that one group successfully captures prevailing nighttime rainfall over tropical islands and coasts, especially over the Maritime Continent. Convective rainfall diagnosed by cumulus parameterization is found to be responsible for such biases. Our results suggest that CMIP6 models have generally been improved in their representation of diurnal rainfall cycles; however, for coastal diurnal regimes, more study is needed to improve the model parameterization of precipitation processes interacting with islands and coastal regions as current model resolution is still too coarse to resolve them.

Comparison of Coupled Model Intercomparison Project Phases 5 and 6 in Simulating Diurnal Cloud Cycle

“…For climate models, clouds peak at midnight in CMIP5 but at around 6 a.m. in the early morning in all CMIP6 models, resulting in larger DCC biases. This is consistent with the changes in precipitation, which show increased biases from CMIP5 to CMIP6, particularly near the west coast of the Amazon rainforest [32]. For the temperate zone near southeastern China, rainfall is associated with the South China Sea monsoon and local circulations, which shows a strong diurnal cycle with peak rainfall occurring at night [33].…”

“…However, not all models show similar changes in cloud and precipitation cycles. It is evaluated that the biases of the DCP over land are reduced from CMIP5 to CMIP6 [32], whereas most models show earlier cloud peaks and consequently increase DCC biases. This suggests that precipitation can only be used for diagnosing cloud cycle patterns (e.g., identification of morning or afternoon peaks) but cannot be used to accurately quantify the slight variations associated with model structures (e.g., comparisons between CMIP5 and CMIP6).…”

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

“…Well‐captured precipitation diurnal cycle is a big challenge for GCMs (Y. C. Lee & Wang, 2021; Tang et al., 2021). Observed maximum precipitation occurs in the late afternoon to early evening rather than early afternoon over tropical land except in some mountainous areas, such as the Andes and Himalayas (Figure 12).…”

Land‐Locked Convection as a Barrier to MJO Propagation Across the Maritime Continent

“…Numerical models often underestimate the precipitation in this region, in part due to a poor representation of the DC of precipitation around the islands (Neale & Slingo, 2003) (Baranowski, Waliser, et al., 2016). Increasing the model resolution reduces the dry bias over the MC and has been linked to better‐resolved surface conditions and land‐sea contrast (Schiemann et al., 2014), but models are still too quick to trigger precipitation over land and exaggerate the amplitude of the DC over land compared to what is simulated over water (Lee & Wang, 2021; Li et al., 2017; Love et al., 2011).…”