Stochastic sub-grid scale parametrizations for coupled earth system models

Principal Investigators

Prof. H. Järvinen
Finnish Meteorological Institute (FMI)
P.O.Box 503
FIN-00101 Helsinki
Finland

heikki.jarvinen@fmi.fi

Project description

Climate and global change research unit of the Finnish Meteorological Institute (FMI) participates in the COSMOS Earth System Modelling consortium, led by the Max-Planck-Institute for Meteorology (MPI-M) in Hamburg, Germany. FMI contributes to the COSMOS cooperation by developing and testing of a stochastic approach to the sub-grid scale cloud-radiation interactions. The developments are implemented to the ECHAM5 atmospheric general circulation model (GCM). Later, the testing will also involve the coupled atmosphere-ocean model (ECHAM5­OASIS-coupler-MPI-OM). The scientific objectives of the project are to 1) test and demonstrate the viability of the stochastic approach and 2) use this approach to improve the presentation of cloud-radiation interaction in ECHAM5. The motivation behind the project, and the central tools used, are described below.

Interaction of radiation with clouds is strongly influenced by unresolved cloud features, that is, features which are smaller than the GCM grid cell. Within the current paradigm of modeling radiative transfer in GCMs, assumptions about the unresolved cloud structures are embedded deep in the radiative transfer codes. This makes the radiative transfer codes fairly complicated and difficult to modify. Furthermore, substantial biases in radiative fluxes and heating rates arise due to different choises on cloud overlap assumptions and lacking description of horizontal variability (e.g., Barker et al. 2003).

As a radical alternative to the current paradigm, Pincus et al. (2003) introduced the Monte Carlo Independent Column Approximation (McICA). The foundation of McICA is that it extricates the description of unresolved optical structure from the radiative transfer solver. This gives a total freedom for describing the unresolved cloud properties, and unresolved surface, aerosol and gas properties, too. Most importantly, McICA is unbiased with respect to the Independent Column Approximation (ICA), whose estimates, in turn, are usually in excellent agreement with full three-dimensional radiative transfer solutions, especially for large areas, such as GCM grid cells and diurnal averages. However, McICA's results feature sizable conditional random errors around the ICA solution from grid cell to grid cell and from time step to time step. Techniques to reduce these errors have been discussed by Räisänen and Barker (2004). Tests performed so far suggest that the "McICA noise" has either negligible (Pincus et al. 2003) or small impact on model simulations (Räisänen et al. 2005). However, the response to noise is expected to be model-dependent and therefore has to be tested for ECHAM5, and further studied with the coupled COSMOS modeling system.

In order to apply McICA in an ordinary GCMs, a set of sub-columns has to be produced for every GCM grid cell. This can be achieved by using a stochastic cloud generator (Räisänen et al., 2004). The generator is initialized with information from the GCM grid cell mean values (cloud fraction, liquid water and ice amounts) and with assumptions regarding horizontal variance of cloud water and vertical de-correlation lengths determining overlap rates for cloud fraction and condensate. It has been demonstrated that the cloud generator/McICA -union has the potential to essentially eliminate the biases associated with the maximum-random overlap (MRO) and plane-parallel horizontally homogeneous (PPH) assumptions, which are employed in most current GCMs.

References:

Barker, H W, G Stephens, P T Partain, J W Bergman, B Bonnel, K Campana, E E Clothiaux, S Clough, S Cusack, J Delamere, K F Evans, Y Fouquart, S Freidenreich, V Galin, Y Hou, S Kato, J Li, E Mlawer, J-J Morcrette, W O'Hirok, P Räisänen, V Ramaswamy, B Ritter, E Rozanov, M Schlesinger, K Shibata, P Sporyshev, Z Sun, M Wendisch, N Wood and F Yang, 2003: Assessing 1D atmospheric solar radiative transfer models: Interpretation and handling of unresolved clouds. J. Climate, 16, 2676-2699.

Pincus, R, H W Barker, and J-J Morcrette, 2003: A fast, flexible, approximate technique of computing radiative transfer for inhomogeneous clouds. J. Geophys. Res., 108, 4376, doi:10.1029/2002JD003322.

Räisänen, P and H W Barker, 2004: Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation. Quart. J. Roy. Meteor. Soc., 130, 2069-2085.

Räisänen, P, H W Barker and J N S Cole, 2005: The Monte Carlo Independent Column Approximation's conditional random noise: Impact on simulated climate. J. Climate, accepted.

Räisänen, P, H W Barker, M F Khairoutdinov, J Li and D Randall, 2004: Stochastic generation of subgrid-scale cloudy columns for large-scale models. Quart. J. Roy. Meteor. Soc., 130, 2047-2067.

For more details, please also refer to the latest Progress Report

Additional information

Project started in 2006