Radiative effects of observationally constrained tropical upper-level clouds in a radiative-convective equilibrium model

Abstract

Tropical upper-level clouds (TUCs) control the radiation budget in a climate system and strongly influence surface temperatures. This study examines global mean surface temperature changes due to the percent change in TUC cover, which is referred to as the tropical upper-level cloud radiative effect (TUCRE, in units of Kelvin per %). We use a radiative-convective equilibrium model that can control both upper- and lower-level cloud layers separately in three idealized regions (extratropics, tropical moist, and tropical dry regions) and two sub-regions (clear-moist and cloudy-moist regions) within the tropical moist regions. In the simulation, tropical reflectivity based on the TUC fraction assumes a primary role in determining the TUCRE. Accurate estimate of the TUCRE requires careful prescriptions according to actual satellite observations. We use the extent of TUC fraction and reflectivity obtained from 18 years (2003–2020) of satellite data on daily MODIS cloud properties. Our results show that the estimated net TUCRE ranges from 0.19 to 0.33 K/%, with a higher TUC fraction leading to higher temperatures (a warming effect) in the climate system. This means that a longwave TUCRE dominates over a shortwave TUCRE. When upper- and lower-level clouds interplay in the model, the range of the TUCRE was greater with a combination of two cloud layers, although all values were positive. The TUCRE is greater by 0.22 to 0.40 K/% when upper- and lower-level clouds are negatively coupled, because the Earth warms due to a decline in the reflectance of solar radiation. When upper- and lower-level clouds are positively coupled, the TUCRE is lower by 0.14 to 0.30 K/%, as less radiation reaches the Earth through combined cloud layers. Finally, we test the sensitivity of the TUCRE with five TUC fractions and 15 combinations of tropical reflectivity. Comparing our results with the TUCREs estimated from climate models will help us understand how TUC cover affects climate, and should greatly reduce uncertainty associated with cloud feedback. © 2023, The Author(s).