Working Group I: The Scientific Basis

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Considerable progress has been made since the SAR in the realism of the ocean component of climate models. Models now exist which simultaneously maintain realistic poleward heat transports, surface temperatures and thermocline structure, and this has been a vital contributor to the improvement in non-flux adjusted models. However, there are still a number of processes which are poorly resolved or represented, for example western boundary currents (see Chapter 7, Section 7.3.6), convection (Chapter 7, Section 7.3.2), overflows (Chapter 7, Section 7.3.5), Indonesian through flow, eddies (including Agulhas eddies which travel long distances and may be hard to treat by a local parametrization; Chapter 7, Section 7.3.4), Antarctic Bottom Water formation (Chapter 7, Section 7.3.2) and interior diapycnal mixing (Chapter 7, Section 7.3.3). In many cases, the importance of these processes in controlling transient climate change has not been evaluated. Over the next few years there is likely to be a further move to finer resolution models, and a wider range of model types; these developments are likely to reduce further some of these uncertainties. Finally, there is still only patchy understanding of the effects of sub-grid scale parametrizations in the context of coupled models. Valuable understanding can be gained from sensitivity studies using ocean or atmosphere models alone, but Figure 8.9 shows the inherently coupled nature of the climate system - changes in ocean parametrizations can have a significant impact throughout the depth of the atmosphere (the reverse is also true). Further sensitivity studies in the coupled model context will help to quantify and reduce uncertainty in this area.

Figure 8.9: Zonal mean air and sea temperature “errors” in °C (defined here as the difference from the initial model state, which was derived from observations), for three different coupled models. The models are all versions of the ARPEGE/OPA model, with T31 atmospheric resolution, and differ only in the parametrization of lateral mixing used in the ocean component ((a) lateral diffusion, (b) isopycnal diffusion, (c) the scheme of Gent and McWilliams (1990)). The different mixing schemes produce different rates of heat transport between middle and high latitudes, especially in the Southern Hemisphere. The atmosphere must adjust in order to radiate the correct amount of heat to space at high latitudes (Chapter 7, Section 7.6 and Section 8.4.1), and this adjustment results in temperature differences at all levels of the atmosphere. From Guilyardi (1997).

8.5.3 Sea Ice Component

While the important role of sea ice in projections of future climate has been widely recognised (Chapter 7, Section 7.5.2), results of systematic intercomparisons or sensitivity studies of AOGCM sea-ice components remain very limited. The sea-ice simulations of fifteen global coupled models contributed to CMIP1 are summarised in Table 8.3. (All these models are also presented in Table 8.1, where the last two columns indicate whether an ice dynamics scheme is included, and whether the model is flux adjusted.) Sea-ice thermodynamic formulations of the coupled models are mostly based on simplified schemes: few employ a multi-layer representation of heat transfer through the ice, while the rest assume a linear temperature profile. In addition, roughly half of the models ignore leads and polynyas in the ice although these account for principal thermodynamic coupling of the atmosphere and ocean. Some models also ignore the thermodynamic effects of snow on sea ice. Despite the rather mature status of sea-ice dynamics modelling (e.g., the Sea Ice Model Intercomparison Project (SIMIP), Lemke et al., 1997), only two of the fifteen models include a physically based ice dynamics component. Three of the fifteen models allow ice to be advected with the ocean currents (the so-called ‘free drift’ scheme), and the remainder assume a motionless ice cover. Overall, this highlights the slow adoption, within coupled climate models, of advances in stand-alone sea ice and coupled sea-ice/ocean models (Chapter 7, Section 7.5.2).

Table 8.3: Coupled model simulations (CMIP1) for December, January, February (DJF) and June, July, August (JJA) of sea-ice cover (columns 2 to 5) and snow cover (106 km2) columns 6 and 7). Model names (column 1) are supplemented with ordinal numbers (in brackets) which refers to the models listed in Table 8.1. The observed sea-ice extent is from Gloersen et al. (1992) and the climatological observed snow is from Foster and Davy (1988).
  Sea-ice cover (106 km2)
Snow cover (106 km2)
  Northern Hemisphere Southern Hemisphere Northern Hemisphere
Model name DJF
ARPEGE/OPA1 (1) 10.1 8.8 2.5 1.9 50.6 19.2
BMRCa (3) 13.7 12.0 0.0 0.0 42.4 2.2
CCSR/NIES (5) 13.0 9.3 16.7 8.6 46.2 12.0
CGCM1 (6) 8.6 7.0 12.3 8.2 47.5 13.9
COLA1 (8) 9.4 5.9 0.0 0.0 58.7 2.5
CSIRO Mk2 (10) 14.3 14.1 14.2 13.6 48.8 18.9
CSM 1.0 (11) 18.6 13.1 22.8 10.0 43.7 4.7
ECHAM3/LSG (14) 12.5 10.4 11.1 7.3 35.8 9.1
ECHAM4/OPYC3 (15) 10.5 9.1 21.0 13.4    
GFDL_R15_a (16) 10.6 8.8 13.2 6.5 56.9 2.4
GISS1 (19) 15.3 14.6 8.7 7.1    
GISS2 (20) 15.7 15.2 10.9 9.5 43.2 9.3
HadCM2 (22) 12.0 10.1 24.7 11.8 45.0 8.2
IPSL-CM1 (24)         44.2 11.2
MRI1 (26) 19.4 18.3 14.5 4.1 60.2 11.6
NCAR1 (28) 11.6 10.6 20.8 16.4 38.9 3.6
Observed 14.5 11.5 11.5 7.0 49.3 3.7

Table 8.3 provides a comparison of ice extent, defined as the area enclosed by the ice edge (which is in turn defined as the 0.1 m thickness contour or the 15% concentration contour, depending on the data provided), for winter and summer seasons in each hemisphere. The last row of the table provides an observed estimate based on satellite data (Gloersen et al., 1992) covering the period 1978 to 1987. It should be noted that assessment of sea-ice model performance continues to be hampered by observational problems. For the satellite period (1970s onward) the accuracy of observations of sea-ice concentration and extent is fair, however observational estimates of sea-ice thickness and velocity are far from satisfactory.

Figure 8.10 provides a visual presentation of the range in simulated ice extent, and was constructed as follows. For each model listed in Table 8.3, a 1/0 mask was produced to indicate presence or absence of ice. The fifteen masks were averaged for each hemisphere and season and the percentage of models that had sea ice at each grid point was calculated.

Figure 8.10: Illustration of the range of sea-ice extent in CMIP1 model simulations listed in Table 8.3: Northern Hemisphere, DJF (left) and Southern Hemisphere, JJA (right). For each model listed in Table 8.3, a 1/0 mask is produced to indicate presence or absence of ice. The fifteen masks were averaged for each hemisphere and season. The 0.5 contour therefore delineates the region for which at least half of the models produced sea ice. The 0.1 contour indicates the region outside of which only 10% of models produced ice, while the 0.9 contour indicates that region inside of which only 10% of models did not produce ice. The observed boundaries are based on GISST_2.2 (Rayner et al., 1996) averaged over 1961 to 1990.

There is a large range in the ability of models to simulate the position of the ice edge and its seasonal cycle, particularly in the Southern Hemisphere. Models that employ flux adjustment tend, on average, to produce smaller ice extent errors, but there is no obvious connection between fidelity of simulated ice extent and the inclusion of an ice dynamics scheme. The latter finding probably reflects the additional impact of errors in the simulated wind field and surface heat fluxes that offset, to a great extent, any improvements due to including more realistic parametrizations of the physics of ice motion. In turn this partially explains the relative slowness in the inclusion of sophisticated sea ice models with AOGCMs. However, even with quite simple formulations of sea ice, in transient simulations, some AOGCMs demonstrate ability to realistically reproduce observed annual trend in the Arctic sea ice extent during several past decades of the 20th century (see Chapter 2, Section, which adds some more confidence in the use of AOGCM for future climate projections (Vinnikov et al., 1999)

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