Parameterization of mires in a numerical weather prediction model Alla YurovaHydrometeorological Centre of Russia

Mire (peatland): definition
Drainage of water is blocked. Precipitation is retained. Water table is close to the surface (max 70 cm)
Specific vegetation-Sphagnum moss, sedges
Decomposition of organic matter is slowed-peat is formed

The spatial distribution of mires in Russia from the GIS
"Peatlands of Russia" (Vompersky et al., 2005).
Mires have a specific:
Heat balance
Moisture exchange

Global semi-Lagrangian NWP model SL-AV (Tolstykh, 2001)
Operational NWP in Hydrometeorological Centre of Russia
Resolution 0.72о lat и 0.9о lon, 50 vertical levels
Dynamical core- semi-Lagrangian, semi-implicit, vorticity and divergence as prognostic variables, unstaggered horizontal grid
Physical parameterizations- ALADIN/ALARO, including ISBA LSS
In Siberia forecasts in summer are biased towards high air temperature and low relative humidity

Modifications done to the SL-AV model to simulate mire heat and
water balance
Multilayer soil heat transfer model with heat capacity and thermal conductivity from Wania et al. (2009)
Water balance with MMWH
Two schemes to simulate evapotranspiration(1-Lafleur et al., 2005;2-Weiss et al., 2006)
Prescribed roughness length and albedo
1
2
ET-evapotranspiration
РЕТ-potential ET

The Mixed Mire Water and Heat model MMWH (Granberg et al., 1999)
ΔW=P-E-q,
q=lq·i·K_h(zcat-zwt),
W-water content, P-precipitation, E-evapotranspiration, q-runoff, i-slope of the water table,·K_h-transmissivity coefficient, lq-lumped parameter
zcat
zwt

Sensible heat
Latent heat
Components of the heat balance from the eddy-flux measurements, standard model simulation (stand), and simulation with a new model (mire). Degero Srormyr mire, Sweden

Components of the radiation and heat balance from the standard model simulation (stand), and simulation with a new model (mire).
July-August 2008, “mire” grid cells only, Western Siberia
LW balance
Sensible heat
Latent heat
Soil heat flux

Mean bias error (MBE) for forecasted temperature °C, for the standard model (ref), for the saturated mire surface (satur) model, for the model with the Weiss et al. (2006) function for evapotranspiration (finevap), and for the model incorporating the Lafleur et al. (2005) function for evapotranspiration (canevap).
July-August 2008, “mire” stations only, Western Siberia

.
Mean absolute error (MAE) for forecasted temperature °C, for the standard model (ref), for the saturated mire surface (satur) model, for the model with the Weiss et al. (2006) function for evapotranspiration (finevap), and for the model incorporating the Lafleur et al. (2005) function for evapotranspiration (canevap).
July-August 2008, “mire” stations only, Western Siberia

Mean bias error (MBE) for forecasted relative humidity, for the standard model (ref), for the saturated mire surface (satur) model, for the model with the Weiss et al. (2006) function for evapotranspiration (finevap), and for the model incorporating the Lafleur et al. (2005) function for evapotranspiration (canevap).
July-August 2008, “mire” stations only, Western Siberia

RMSE for forecasted relative humidity, for the standard model (reef), for the saturated mire surface (satur) model, for the model with the Weiss et al. (2006) function for evapotranspiration (finevap), and for the model incorporating the Lafleur et al. (2005) function for evapotranspiration (canevap).
July-August 2008, “mire” stations only, Western Siberia

Conclusions:
It is important to incorporate mires when forcasting weather in Siberia
Heat balance partitioning has changed
The mire parameterization has helped to reduce a large warm temperature bias in Western Siberia for the forecast for lead times of 12, 36 and 60h, but did not eliminate forecast bias for lead times of 24, 48 and 72h.

Future plans:
Testing the model for winter conditions (freezing and thawing)
Investigating the effect of mire drainage on local and regional weather conditions