Oral or Poster

IAMAS

M08 - Dynamics and microphysics of moist convection

Process-Level insights into Climate Model Biases in Amazonian Convective Heating Profiles: A Single-Column Model Perspective

Atmospheric convection plays a crucial role in Earth��s climate system, regulating energy transport, hydrological cycles, and large-scale circulation. However, climate models persistently struggle to accurately represent critical features of deep convection, including precipitation characteristics (e.g., amount, frequency, intensity, diurnal timing) and vertical diabatic heating structure. In a previous single-column model (SCM) intercomparison work from the Global Atmospheric System Studies (GASS) Diurnal Cycle of Precipitation (DCP) project, all model results show a confined peak of afternoon convective heating at 700?800 hPa over the Amazonia, in contrast to observations from the 2014?2015 GOAmazon field campaign, which reveal heating maxima near 500 hPa. This persistent discrepancy underscores systemic shortcomings in representing convective processes within model parameterizations. Leveraging these simulations from the 11 SCMs participating in the GASS DCP project, this study diagnoses common biases in simulating the vertical heating structure of convection during the GOAmazon campaign. We first quantify systematic errors in heating profiles across all SCMs, documenting their magnitude, vertical structure, and variability, and link these biases to misrepresented processes across convective regimes. Furthermore, we employ targeted sensitivity experiments with the SCAM6 model to isolate key deficiencies in current parameterizations. By bridging multi-model diagnostics with process-level experimentation, this work identifies priority areas for refining convective parameterizations to improve the vertical structure of convection in climate models. These findings offer actionable insights for advancing the physical realism of climate models.