Tests of Monte Carlo Independent Column Approximation in the ECHAM5 Atmospheric GCM
| Raisanen, Petri | Finnish Meteoroligical Institute |
| Jarvenoja, Simo | Finnish Meteorological Institute |
| Jarvinen, Heikki | Finnish Meteorological Institute |
Category: Modeling
The Monte Carlo Independent Column Approximation (McICA) was recently introduced as a new approach for parametrizing broadband radiative fluxes in global climate models (GCMs). The McICA allows a flexible description of unresolved cloud structure, and it is unbiased with respect to the full ICA, but its results contain conditional random errors (i.e., noise). In this work, McICA and a stochastic cloud generator have been implemented to the Max Planck Institute for Meteorology's ECHAM5 atmospheric GCM. The impact of McICA noise on climate simulated by ECHAM5 has been tested in a series of multi-year ensemble simulations with different noise levels. It is found that McICA noise leads to a slight reduction in low cloud fraction and cloud liquid water path in ECHAM5, somewhat similarly to earlier results for the National Center for Atmospheric Research Community Atmosphere Model. Overall, the impact on climate simulated by ECHAM5 is small. A special feature of ECHAM5 is that it carries prognostic variables for the probability distribution of total water (water vapor + liquid + ice) within GCM grid cells; a beta distribution is assumed. This allows us to derive cloud subgrid-scale variability directly from the model's prognostic fields. A preliminary comparison with cloud-system resolving model data and ISCCP satellite data suggests that the ECHAM5 cloud fields derived from beta distributions of total water are, on average, slightly too homogeneous.
This poster will be displayed at the ARM Science Team Meeting.
