IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis Simulation of Mean Temperature and Salinity Structure

Before discussing the oceanic variables directly involved in determining the climatic response, it is important to discuss the fluxes between the ocean and atmosphere. Modelling experience shows that the surface fluxes play a large part in determining the fidelity of the oceanic simulation. Since the atmosphere and ocean are coupled, the fidelity of the oceanic simulation feeds back to the atmospheric simulation, affecting the surface fluxes.

Unfortunately, the total surface heat and water fluxes (see Supplementary Material, Figure S8.14) are not well observed. Normally, they are inferred from observations of other fields, such as surface temperature and winds. Consequently, the uncertainty in the observational estimate is large – of the order of tens of watts per square metre for the heat flux, even in the zonal mean. An alternative way of assessing the surface fluxes is by looking at the horizontal transports in the ocean. In the long-term average, the heat and water storage in the ocean are small so that the horizontal transports have to balance the surface fluxes. Since the heat transport seems better constrained by the available observations, it is presented here.

North of 45°N, most model simulations transport too much heat northward when compared to the observational estimates used here (Figure 8.6), but there is uncertainty in the observations. At 45°N, for example, the model simulations lie much closer to the estimate of 0.6 x 1015 W obtained by Ganachaud and Wunsch (2003). From 45°N to the equator, most model estimates lie near or between the observational estimates shown. In the tropics and subtropical zone of the SH, most models underestimate the southward heat transport away from the equator. At middle and high latitudes of the SH, the observational estimates are more uncertain and the model-simulated heat transports tend to surround the observational estimates.

Figure 8.6

Figure 8.6. Annual mean, zonally averaged oceanic heat transport implied by net heat flux imbalances at the sea surface, under an assumption of negligible changes in oceanic heat content. The observationally based estimate, taken from Trenberth and Caron (2001) for the period February 1985 to April 1989, derives from reanalysis products from the National Centers for Environmental Prediction (NCEP)/NCAR (Kalnay et al., 1996) and European Centre for Medium Range Weather Forecasts 40-year reanalysis (ERA40; Uppala et al., 2005). The model climatologies are derived from the years 1980 to 1999 in the 20th-century simulations in the MMD at PCMDI. The legend identifying individual models appears in Figure 8.4.

The oceanic heat fluxes have large seasonal variations which lead to large variations in the seasonal storage of heat by the oceans, especially in mid-latitudes. The oceanic heat storage tends to damp and delay the seasonal cycle of surface temperature. The model simulations evaluated here agree well with the observations of seasonal heat storage by the oceans (Gleckler et al., 2006a). The most notable problem area for the models is in the tropics, where many models continue to have biases in representing the flow of heat from the tropics into middle and high latitudes.

The annually averaged zonal component of surface wind stress, zonally averaged over the oceans, is reasonably well simulated by the models (Figure 8.7). At most latitudes, the reanalysis estimates (based on atmospheric models constrained by observations) lie within the range of model results. At middle to low latitudes, the model spread is relatively small and all the model results lie fairly close to the reanalysis. At middle to high latitudes, the model-simulated wind stress maximum tends to lie equatorward of the reanalysis. This error is particularly large in the SH, a region where there is more uncertainty in the reanalysis. Almost all model simulations place the SH wind stress maximum north of the reanalysis estimate. The Southern Ocean wind stress errors in the control integrations may adversely affect other aspects of the simulation and possibly the oceanic heat uptake under climate change, as discussed below.

The largest individual model errors in the zonally averaged SST (Figure 8.8) are found at middle and high latitudes, particularly the mid-latitudes of the NH where the model-simulated temperatures are too cold. Almost every model has some tendency for this cold bias. This error seems to be associated with poor simulation of the path of the North Atlantic Current and seems to be due to an ocean component problem rather than a problem with the surface fluxes. In the zonal averages near 60°S, there is a warm bias in the multi-model mean results. Many models suffer from a too-warm bias in the Southern Ocean SSTs.

Figure 8.7

Figure 8.7. Annual mean east-west component of wind stress zonally averaged over the oceans. The observationally constrained estimate is from the years 1980 to 1999 in the European Centre for Medium Range Weather Forecasts 40-year reanalysis (ERA40; Uppala et al., 2005), and the model climatologies are calculated for the same period in the 20th-century simulations in the MMD at PCMDI. The legend identifying individual models appears in Figure 8.4.

Figure 8.8

Figure 8.8. Annual mean, zonally averaged SST error, simulated minus observed climatology. The Hadley Centre Sea Ice and Sea Surface Temperature (HadISST; Rayner et al., 2003) observational climatology for 1980 to 1999 is the reference used here, and the model results are for the same period in the 20th-century simulations in the MMD at PCMDI. In the presence of sea ice, the SST is assumed to be at the freezing point of seawater. The legend identifying individual models appears in Figure 8.4.

In the individual model SST error maps (see Supplementary Material, Figure S8.1), it is apparent that most models have a large warm bias in the eastern parts of the tropical ocean basins, near the continental boundaries. This is also evident in the multi-model mean result (Figure 8.2a) and is associated with insufficient resolution, which leads to problems in the simulation of the local wind stress, oceanic upwelling and under-prediction of the low cloud amounts (see Sections 8.2 and 8.3.1). These are also regions where there is a relatively large spread among the model simulations, indicating a relatively wide range in the magnitude of these errors. Another area where the model error spread is relatively large is found in the North Atlantic Ocean. As noted above, this is an area where many models have problems properly locating the North Atlantic Current, a region of large SST gradients.

In spite of the errors, the model simulation of the SST field is fairly realistic overall. Over all latitudes, the multi-model mean zonally averaged SST error is less than 2°C, which is fairly small considering that most models do not use flux adjustments in these simulations. The model mean local SST errors are also less than 2°C over most regions, with only relatively small areas exceeding this value. Even relatively small SST errors, however, can adversely affect the simulation of variability and teleconnections (Section 8.4).

Over most latitudes, at depths ranging from 200 to 3,000 m, the multi-model mean zonally averaged ocean temperature is too warm (see Figure 8.9). The maximum warm bias (about 2°C) is located in the region of the North Atlantic Deep Water (NADW) formation. Above 200 m, however, the multi-model mean is too cold, with maximum cold bias (more than 1°C) near the surface at mid-latitudes of the NH, as discussed above. Most models generally have an error pattern similar to the multi-model mean (see Supplementary Material, Figure S8.12) except for CNRM-CM3 and MRI-CGCM2.3.2, which are too cold throughout most of the mid- and low-latitude ocean (see Supplementary Material, Figure S8.12). The GISS-EH model is much too cold throughout the subtropical thermocline and only the NH part of the FGOALS-g1.0 error pattern is similar to the model mean error described here. The magnitude of these errors, especially in the deeper parts of the ocean, depends on the AOGCM initialisation method (Section 8.2.7).

Figure 8.9

Figure 8.9. Time-mean observed potential temperature (°C), zonally averaged over all ocean basins (labelled contours) and multi-model mean error in this field, simulated minus observed (colour-filled contours). The observations are from the 2004 World Ocean Atlas compiled by Levitus et al. (2005) for the period 1957 to 1990, and the model results are for the same period in the 20th-century simulations in the MMD at PCMDI. Results for individual models can be seen in the Supplementary Material, Figure S8.12.

The error pattern, in which the upper 200 m of the ocean tend to be too cold while the layers below are too warm, indicates that the thermocline in the multi-model mean is too diffuse. This error, which was also present at the time of the TAR, seems partly related to the wind stress errors in the SH noted above and possibly to errors in formation and mixing of NADW. The multi-model mean errors in temperature (too warm) and salinity (too salty; see Supplementary Material, Figure S8.13) at middle and low latitudes near the base of the thermocline tend to cancel in terms of a density error and appear to be associated with the problems in the formation of Antarctic Intermediate Water (AAIW), as discussed below.