9.5 Understanding of Change in Other Variables during the Industrial Era

The objective of this section is to assess large-scale climate change in variables other than air temperature, including changes in ocean climate, atmospheric circulation, precipitation, the cryosphere and sea level. This section draws heavily on Chapters 3, 4, 5 and 8. Where possible, it attempts to identify links between changes in different variables, such as those that associate some aspects of SST change with precipitation change. It also discusses the role of external forcing, drawing where possible on formal detection studies.

9.5.1 Ocean Climate Change

9.5.1.1 Ocean Heat Content Changes

Since the TAR, evidence of climate change has accumulated within the ocean, both at regional and global scales (Chapter 5). The overall heat content in the World Ocean is estimated to have increased by 14.2 × 10²² J during the period 1961 to 2003 (Section 5.2.2). This overall increase has been superimposed on strong interannual and inter-decadal variations. The fact that the entire ocean, which is by far the system’s largest heat reservoir (Levitus et al., 2005; see also Figure 5.4) gained heat during the latter half of the 20th century is consistent with a net positive radiative forcing of the climate system. Late 20th-century ocean heat content changes were at least one order of magnitude larger than the increase in energy content of any other component of the Earth’s ocean-atmosphere-cryosphere system (Figure 5.4; Levitus et al., 2005).

All analyses indicate a large anthropogenic component of the positive trend in global ocean heat content. Levitus et al. (2001) and Gregory et al (2004) analyse simulations from the GFDL R30 and HadCM3 models respectively and show that climate simulations agree best with observed changes when the models include anthropogenic forcings from increasing greenhouse gas concentrations and sulphate aerosols. Gent and Danabasoglu (2004) show that the observed trend cannot be explained by natural internal variability as simulated by a long control run of the Community Climate System Model (CCSM2). Barnett et al. (2001) and Reichert et al. (2002b) use detection analyses similar to those described in Section 9.4 to detect model-simulated ocean climate change signals in the observed spatio-temporal patterns of ocean heat content across the ocean basins.

Barnett et al. (2005) extend previous detection and attribution analyses of ocean heat content changes to a basin by basin analysis of the temporal evolution of temperature changes in the upper 700 m of the ocean (see also Pierce et al., 2006). They report that whereas the observed change is not consistent with internal variability and the response to natural external forcing as simulated by two climate models (PCM and HadCM3), the simulated ocean warming due to anthropogenic factors (including well-mixed greenhouse gases and sulphate aerosols) is consistent with the observed changes and reproduces many of the different responses seen in the individual ocean basins (Figure 9.15), indicating a human-induced warming of the world’s oceans with a complex vertical and geographical structure that is simulated quite well by the two AOGCMs. Barnett et al. (2005) find that the earlier conclusions of Barnett et al. (2001) were not affected by the Levitus et al. (2005) revisions to the Levitus et al. (2000) ocean heat content data.

Figure 9.15. Strength of observed and model-simulated warming signal by depth for the World Ocean and for each ocean basin individually (in ^oC, see Barnett et al., 2005 and Pierce et al., 2006 for calculation of signal strength). For ocean basins, the signal is estimated from PCM (Table 8.1) while for the World Ocean it is estimated from both PCM and HadCM3 (Table 8.1). Red dots represent the projection of the observed temperature changes onto the normalised model-based pattern of warming. They show substantial basin-to-basin differences in how the oceans have warmed over the past 40 years, although all oceans have experienced net warming over that interval. The red bars represent the ±2 standard deviation limits associated with sampling uncertainty. The blue crosshatched swaths represent the 90% confidence limits of the natural internal variability strength. The green crosshatched swaths represent the range of the anthropogenically forced signal estimates from different realisations of identically forced simulations with the PCM model for each ocean basin (the smaller dots within the green swaths are the individual realisations) and the green shaded regions represent the range of anthropogenically forced signal estimates from different realisations of identically forced simulations with the PCM and HadCM3 models for the World Ocean (note that PCM and HadCM3 use different representations of anthropogenic forcing). The ensemble-averaged strength of the warming signal in four PCM simulations with solar and volcanic forcing is also shown (grey triangles). From Barnett et al. (2005) and Pierce et al. (2006).

In contrast, changes in solar forcing can potentially explain only a small fraction of the observationally based estimates of the increase in ocean heat content (Crowley et al., 2003), and the cooling influence of natural (volcanic) and anthropogenic aerosols would have slowed ocean warming over the last half century. Delworth et al. (2005) find a delay of several decades and a reduction in the magnitude of the warming of approximately two-thirds in simulations with the GFDL-CM2 model that included these forcings compared to the response to increasing greenhouse gases alone, consistent with results based on an upwelling diffusion EBM (Crowley et al., 2003). Reductions in ocean heat content are found following volcanic eruptions in climate simulations (Church et al., 2005), including a persistent centennial time-scale signal of ocean cooling at depth following the eruption of Krakatoa (Gleckler et al., 2006).

Although the heat uptake in the ocean cannot be explained without invoking anthropogenic forcing, there is some evidence that the models have overestimated how rapidly heat has penetrated below the ocean’s mixed layer (Forest et al., 2006; see also Figure 9.15). In simulations that include natural forcings in addition to anthropogenic forcings, eight coupled climate models simulate heat uptake of 0.26 ± 0.06 W m^–2 (±1 standard deviation) for 1961 to 2003, whereas observations of ocean temperature changes indicate a heat uptake of 0.21 ± 0.04 W m^–2 (Section 5.2.2.1). These could be consistent within their uncertainties but might indicate a tendency of climate models to overestimate ocean heat uptake.

In addition, the interannual to decadal variability seen in Levitus et al. (2000, 2005) (Section 5.2.2) is underestimated by models; Gregory et al. (2004) show significant differences between observed and modelled interannual deviations from a linear trend in five-year running means of world ocean heat content above 3,000 m for 1957 to 1994. While some studies note the potential importance of the choice of infilling method in poorly sampled regions (Gregory et al., 2004; AchutaRao et al., 2006), the consistency of the differently processed data from the Levitus et al. (2005), Ishii et al. (2006) and Willis et al. (2004) analyses adds confidence to their use for analysing trends in climate change studies (Chapter 5). Gregory et al. (2004) show that agreement between models and observations is better in the well-observed upper ocean (above 300 m) in the NH and that there is large sensitivity to the method of infilling the observational data set outside this well-observed region. They find a strong maximum in variability in the Levitus data set at around 500 m depth that is not seen in HadCM3 simulations, a possible indication of model deficiency or an artefact in the Levitus data. AchutaRao et al. (2006) also find that observational estimates of temperature variability over much of the oceans may be substantially affected by sparse observational coverage and the method of infilling.