Radiative forcing is a crucial metric in climate modeling that helps researchers understand the impact of different atmospheric factors on the Earth’s energy balance. While general circulation models (GCMs) have advanced our understanding of climate dynamics, there are still uncertainties associated with certain atmospheric components, such as clouds and precipitation. Clouds, for example, are known to introduce biases in GCMs, affecting radiative forcing calculations. However, a lesser-known source of uncertainty is the radiative effects of precipitation (REP). Precipitating particles can disrupt incoming shortwave and outgoing longwave radiations, influencing the energy budget of the Earth system.

Traditional GCMs, particularly those in the Coupled Model Intercomparison Project Phase 6 (CMIP6), often treat precipitation diagnostically and do not account for the radiative effects of precipitation. This approach limits our ability to accurately capture the impact of precipitating particles on the Earth’s radiation budgets and hydrological cycles. Extracting the magnitude of REP in climate models is challenging due to complex atmosphere-ocean feedback mechanisms and model variabilities. However, a recent study led by Associate Professor Takuro Michibata from Okayama University sought to address this gap by investigating the influence of REP on radiative forcing at different geographical scales.

Dr. Michibata’s study utilized three sub-versions of the Japanese GCM, MIROC6, each incorporating different treatments of precipitation and radiative calculations. By comparing diagnostic precipitation without REP (DIAG), prognostic precipitation without REP (PROG REP-OFF), and prognostic precipitation with REP (PROG REP-ON), the researchers were able to quantify the impact of precipitating particles on radiation budgets and hydrological cycles globally and regionally. The study revealed that REP not only affects local thermodynamic profiles but also influences remote precipitation rates and distributions by altering atmospheric circulation patterns.

The inclusion of REP in GCMs could lead to improved accuracy in simulating temperature and precipitation changes, particularly in regions like the Arctic where climate variability is more pronounced. By factoring in REP, researchers may be able to better predict future climate changes and the occurrence of extreme weather events. Additionally, the findings of this study highlight the importance of considering the radiative effects of precipitation in climate models to enhance the overall reliability of climate simulations against observational data. Dr. Michibata’s work underscores the need for continued research on REP and its implications for global and regional climate dynamics for more accurate climate predictions in the future.


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