Description and evaluation of the bergen climate model: ARPEGE coupled with MICOM

Abstract

A new coupled atmosphere–ocean–sea ice model has been developed, named the Bergen Climate Model (BCM). It consists of the atmospheric model ARPEGE/IFS, together with a global version of the ocean model MICOM including a dynamic–thermodynamic sea ice model. The coupling between the two models uses the OASIS software package. The new model concept is described, and results from a 300-year control integration is evaluated against observational data. In BCM, both the atmosphere and the ocean components use grids which can be irregular and have non-matching coastlines. Much effort has been put into the development of optimal interpolation schemes between the models, in particular the non-trivial problem of flux conservation in the coastal areas. A flux adjustment technique has been applied to the heat and fresh-water fluxes. There is, however, a weak drift in global mean sea-surface temperature (SST) and sea-surface salinity (SSS) of respectively 0.1 °C and 0.02 psu per century. The model gives a realistic simulation of the radiation balance at the top-of-the-atmosphere, and the net surface fluxes of longwave, shortwave, and turbulent heat fluxes are within observed values. Both global and total zonal means of cloud cover and precipitation are fairly close to observations, and errors are mainly related to the strength and positioning of the Hadley cell. The mean sea-level pressure (SLP) is well simulated, and both the mean state and the interannual standard deviation show realistic features. The SST field is several degrees too cold in the equatorial upwelling area in the Pacific, and about 1 °C too warm along the eastern margins of the oceans, and in the polar regions. The deviation from Levitus salinity is typically 0.1 psu – 0.4 psu, with a tendency for positive anomalies in the Northern Hemisphere, and negative in the Southern Hemisphere. The sea-ice distribution is realistic, but with too thin ice in the Arctic Ocean and too small ice coverage in the Southern Ocean. These model deficiencies have a strong influence on the surface air temperatures in these regions. Horizontal oceanic mass transports are in the lower range of those observed. The strength of the meridional overturning in the Atlantic is 18 Sv. An analysis of the large-scale variability in the model climate reveals realistic El Niño – Southern Oscillation (ENSO) and North Atlantic–Arctic Oscillation (NAO/AO) characteristics in the SLP and surface temperatures, including spatial patterns, frequencies, and strength. While the NAO/AO spectrum is white in SLP and red in temperature, the ENSO spectrum shows an energy maximum near 3 years.

Appendix 1

1.1 Computation of sea-ice surface temperature

The thermodynamic component of the sea-ice model has one ice layer and one snow layer. The temperature is determined at the ice-snow and the snow-air boundaries, assuming linear temperature profiles in the interior of the ice and snow, and with the freezing temperature of sea water as a boundary condition at the bottom of the ice. The temperatures are then diagnosed by balancing the heat flux budget at the snow-air boundary.

If only the situation with ice and no snow is considered, the heat balance at the top of ice can be stated as:

$$k\left. {{{\partial T} \over {\partial z}}} \right|_{h_{{\rm ice}}} + Q_{{\rm ns}} + Q_{{\rm sw}} = 0\enspace,$$

(3)

where k is ice conductivity, T is temperature in the ice, h _ice is ice thickness, Q _ns is non-solar heat flux, and Q _sw is solar heat flux. The non-solar heat flux depends on the surface temperature T _s and is updated in the atmosphere model using the temperature of the previous time step

$$Q_{{\rm ns}}^n = Q_{{\rm ns}} (T_s^{n - 1})\enspace.$$

(4)

Here n denotes the coupling time step. Assuming a linear temperature profile through the ice gives

$$k{{T_s - T_f} \over {h_{{\rm ice}}}} + Q_{{\rm ns}} + Q_{{\rm sw}} = 0\enspace,$$

(5)

where T _f is the freezing temperature of sea water. Solving for T _s ⁿ⁺¹ using heat fluxes from time step n leads to an unstable time step procedure. To remedy this, Q _ns can be linearised around T _s to approximate the non-solar flux at n + 1:

$$ \tilde Q_{{\rm ns}}^{n + 1} \approx Q_{{\rm ns}}^n + {{\partial _{{\rm ns}}^n} \over {\partial T_s}} (T_s^{n + 1} - T_s^n)\enspace. $$

(6)

A stable updating of T _s is obtained from

$$ k{{T_s^{n + 1} - T_f} \over {h_{{\rm ice}}}} + \tilde Q_{{\rm ns}}^{n + 1} + Q_{{\rm sw}}^n = 0\enspace, $$

(7)

The term ∂Q _ns ⁿ/∂T _s , delivered from the atmosphere model, is also used by the coupler to modify the distribution of non-solar heat fluxes received by ocean cells covered by the same atmosphere cell (see Sect. 3).