Ice clouds, often appearing at high altitudes, have a large impact on the Earth’s radiation budget. More reliable model simulation of ice cloud occurrence can greatly improve the quality of weather prediction and climate projection. The research team, including scientists from PNNL, investigated whether the global climate model CAM5 can realistically simulate the macro- and microphysical properties of ice clouds.
The team found that further improvements are needed to understand sub-grid relative humidity (RH) variability and ice nucleation and growth in the model.
The research team evaluated the cloud properties simulated by CAM5 using aircraft measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign. The model was nudged towards reanalysis data, so that the model data can be collocated with flight tracks and directly compared with the observations. The model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 °C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations.