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REMO cloud microphysics

REMO cloud microphysics

Susanne Pfeifer

The stratiform cloud scheme in REMO is taken from the MPI Global Model ECHAM4. It is based on the approach from Sundqvist (Sundqvist, 1978). Subgrid cloud formation is included by incorporating fractional cloud cover (parameterised as a non-linear function of grid-mean relative humidity) for each grid box. Prognostic variables are water vapour and cloud liquid water. For these variables, budget equations are solved, taking into account sources and sinks due to phase changes, formation of precipitation by coalescence of cloud droplets and sedimentation of ice crystals. As cloud ice is not prognostic, the amount of ice in the cloud has to be diagnosed.

One basic shortcoming of the stratiform cloud scheme used in REMO was that " the precipitation formation in a mixed phase i.e. in a temperature range between about 0°C and -40°C , can be treated independently for the ice phase and the liquid phase respectively. A proper treatment of the interaction between both phases, such as the rapid condensational growth of ice crystals at the expense of cloud droplets (Bergeron-Findeisen process) will require a more elaborate scheme which should be based on the budget equation for each phase." (DKRZ, 1994).

Modified stratiform cloud scheme

The changes made in the stratiform cloud scheme have been adopted from the Global Circulation model ECHAM5 where among other things a prognostic equation for cloud ice is implemented. The changes are described in detail in the ECHAM5 manual (Roeckner et al., 2003). The simulation with the modified cloud scheme is called REMO ice in the following.

Applications of the modified cloud scheme

1. Baltic Sea area

Cloud Micro Fig1 01

Figure 1: Orography of the model domain (1/2° horizontal Resolution)

With the modified cloud parameterisation, a simulation of European climate for 1979 to 1993 has been conducted. For the analysis of the simulation, the Baltic Sea catchment, highlighted in the figure left was used. Comparing the simulated results with results from the standard version without the modifications in the cloud scheme shows that on the average, the total amount of precipitation does not change (Fig. 2). The simulation with the modified cloud scheme shows a slight increase in summer precipitation and a slight decrease in winter precipitation. Compared to Observations from the Global Precipitation Climatology Project (GPCP) both simulations agree rather well to the observed values.

Cloud Micro Fig2 01

Figure 2: Comparison of monthly precipitation sums for REMO standard, REMO ice and GPCP Observations

2. Elbe flooding in August 2002

Cloud Micro Fig3 01

Figure 3: Observed hourly precipitation for Dresden

The flooding of the Elbe river in August 2002 was initiated by extreme precipitation events occurring in very short time periods. With the modified cloud scheme, REMO is now better able to reproduce such extreme precipitation events.


Figure 4: Hourly precipitation for Dresden modelled with the standard REMO version (left panel) and with REMO ice (right panel)

The Elbe flooding in August 2002 has been simulated with REMO standard and with the modified version both in a horizontal resolution of 1/6 °. The changes in the cloud scheme reduce the number of precipitation events with low intensities ( precipitation < 3mm/hour) and lead to a visible increase of higher precipitation events (precipitation > 10mm/hour). This is illustrated in the comparison of hourly precipitation values for Dresden (Fig. 3 - 4) as well as in the histogram of precipitation intensities for the Elbe catchment area (Fig. 5), which shows a shift of the distribution towards higher hourly values and a decrease of the amount of low-intensity precipitation.


Figure 5: Hourly precipitation for Dresden modelled with the standard REMO version (left panel) and with REMO ice (right panel)

Cloud Micro Fig5 03

Figure 6: histogram of precipitation intensities for the Elbe catchment Area


  • Sundqvist, H., 1978: A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Met. Soc., 104, 677-690.
  • DKRZ, 1994: The ECHAM3 atmospheric general circulation model. Technical Report No.6. Hamburg, September 1993, 184pp.
  • E. Roeckner, G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, A. Tompkins, 2003: The atmospheric general circulation model ECHAM 5. PART I: Model description. Max-Planck-Institut für Meteorologie,Report No. 349.

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